The Value Relevance of the Fair Value Hierarchy of FAS 157the FVH is value relevant for all industry sectors. However, although the degree of value relevance varies across industry

The Value Relevance of the Fair Value Hierarchy of FAS 157

A Dissertation

Submitted to the Graduate Division

of the

University of Hawai‘i at Mānoa

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

in

International Management

May 2012

by

Eric C. Wen

Dissertation Committee:

Hamid Pourjalali, Chairperson

Shirley J. Daniel

Boochun Jung

Qianqiu Liu

Sumner La Croix

ii

All Rights Reserved

iii

Acknowledgements

I owe deepest gratitude to my dissertation chair, Hamid Pourjalali, who encouraged me to

apply to the doctoral program, taught me everything I know about accounting theory,

introduced me to many of his friends, and provided me not only with countless cups of

coffee and quiet places to work, but also with encouragement and sage advice that

shepherded me through the entire process. I also thank the members of my committee,

Shirley Daniel, Boochun Jung, Qianqiu Liu, and Sumner La Croix for their comments,

encouragement, and support throughout the process. I especially thank Shirley for her

support, through the Center for International Business Education and Research (CIBER),

that allowed me to participate in the 2009 Annual Meeting of the American Accounting

Association (AAA) in New York where I heard Wayne Thomas present the work that

would become Song, Thomas, and Yi (2010); Boochun for his careful help with the

statement of some of my hypotheses and for hosting several doctoral candidates to the

most delicious and varied Korean barbecue dinner that I have ever had where we enjoyed

the evening under a huge tree; Qianqiu for leading the finance seminar that introduced

doctoral candidates to several seminal papers and for always welcoming me into his

office with a warm smile, and Sumner for his wealth of insights especially on research

methods and industrial organization, and for his very careful reading of earlier drafts of

this dissertation.

I thank my dear wife, Aida, for her constant support and encouragement with

everything, really. I could write another dissertation, longer than this one, on all of the

iv

adventures we have shared together. I also thank my parents and three daughters1 for

their support, encouragement, and prayers. In addition, I also thank the members of my

church, International Baptist Fellowship, for their prayers and support. Finally, I thank

the International Baptist Ministries for hosting the Thursday Lunch, right across the street

from the Shidler College of Business, which not only fed my body with a balanced hot

meal during the semester, but also provided an open venue for spiritual refreshment

through the community of international scholars and friends who gathered there.

1 At the time of this writing, our son is only saying a handful of recognizable words and wobbling around.

v

Table of Contents

Acknowledgements ............................................................................................................ iii

List of Tables ..................................................................................................................... vi

Table of Figures ................................................................................................................ vii

ABSTRACT ..................................................................................................................... viii

1. Introduction ................................................................................................................. 1

2. Review of Literature .................................................................................................... 4

2.1 Review of Literature on the FVH of FAS 157 ..................................................... 4

2.2 Overview of the Literature on Characteristics of Industries .............................. 10

3. Development of Hypotheses ...................................................................................... 16

H1. Book-to-Market Equity ......................................................................................... 18

H2. Industry Concentration .......................................................................................... 19

H3. Industry Status....................................................................................................... 20

H4. Liquidity ................................................................................................................. 21

H5. Relative Amount ................................................................................................... 21

3.6 Industry Classification ........................................................................................... 22

4. Sample Selection ....................................................................................................... 24

5. Results ....................................................................................................................... 27

5.1 The Value Relevance of the FVA across Industry Sectors ................................ 33

5.2 Results of Tests of H1 ........................................................................................ 36

5.3 Results of Tests of H2 ........................................................................................ 38

5.4 Results of Test of H3 .......................................................................................... 42

5.5 Results of Tests for H4 ....................................................................................... 45

5.6 Results for H5 ..................................................................................................... 48

6 Sensitivity Analysis ................................................................................................... 49

7 Conclusion ................................................................................................................. 52

References ................................................................................................................... 179

Appendix A ..................................................................................................................... 182

Appendix B. Computation of the Herfindahl of Sales ................................................... 187

vi

List of Tables

Table 1. Four (Known) Papers that Examine the Fair Value Hierarchy (FVH) ............... 61

Table 2. The Main Characteristics of the Fair Value Hierarchy (FVH) of FAS 157 ...... 62

Table 3. Datasets of the Four Early Papers ....................................................................... 63

Table 4. Summary of Hypothesis and Results ................................................................. 65

Table 5 Papers examining characteristics of industries in more depth ............................ 70

Table 6 Summary of Main Results of Selected Papers ..................................................... 72

Table 7. Steps Taken to Develop the Sample .................................................................. 76

Table 8. Summary of Selected Variables by GICS Sectors .............................................. 82

Table 9. Results of OLS Regression Using Winsorized Variables .................................. 86

Table 10. Results of Iteratively Reweighted Least Squares (IRLS) Regression ............. 91

Table 11. Results of OLS Regression on Trimmed Sample ............................................ 94

Table 12. Investigating the Effect of BE/ME .................................................................. 98

Table 13. Investigation of the Effect of Industry Concentration (HHI) ........................ 111

Table 14. Three-year Average Herfindahl of Net Sales ................................................ 123

Table 15. Entering, Incumbent, and Exiting Firms ........................................................ 134

Table 16. Investigation of the Effect of Liquidity, Using the Quick Ratio (QR) and

Operating Cash Flow Ratio (CR) .................................................................................... 142

Table 17. Investigating the Value Relevance of the FAS 157 Ratio ............................. 153

Table 18. Sensitivity Analysis ....................................................................................... 155

vii

Table of Figures

Figure 1: Timeline of the Issuance and Effective Date of FAS 157 ................................. 57

Figure 2. Weekly Close of the S&P 500 Index from Jan 1, 2007 to Jan 1, 2012 ............. 58

Figure 3 Sample Disclosures Required by FAS 157 ........................................................ 59

viii

ABSTRACT

Financial Accounting Standard (FAS) 157 requires disclosures of the fair value hierarchy (FVH)

of assets and liabilities for financial reports issued after November 15, 2007. This dissertation

examines the value relevance of the FVH across a set of industry sectors. The results suggest that

the FVH is value relevant for all industry sectors. However, although the degree of value

relevance varies across industry sectors, the relative amount of assets and liabilities measured at

fair value, as a ratio to total assets and liabilities, does not influence the value relevance of the

FVH. This study also examines the influence of several other characteristics of firms on the

value relevance of the FVH across industry sectors. Its main findings suggest that book-to-market

equity and a firm's status (as entering or incumbent) influence the value relevance of the FVH.

The dissertation also finds some evidence that a firm's size and firm's status are related and that

the firm's size may also influence the value relevance of the FVH. This dissertation is unable to

find a clear pattern of the influence of liquidity (as measured by both the quick ratio and cash

flow from operations) on the value relevance of the Level 3 fair value of assets across industry

sectors.

1. Introduction

The purpose of accounting is to provide useful information to decision-makers. The early users

of accounting deemed reporting net income useful information and therefore adopted an

“Income Statement Approach” to financial reporting. However modern standard-setters view

valuation as useful information and advocate a “Balance Sheet Approach.” One of the key

differences between the two approaches is the preference of methodology for measurement of

assets and liabilities. While the traditional “Income Statement Approach” prefers (depreciated)

historical cost, the “Balance Sheet Approach” advocates measurement by fair value (Dichev

2008). The debate over the merits of measurement by historical cost vs. fair value is not new to

accounting. Laux and Leuz (2009) and Penman (2007) provide recent contributions to this long-

standing conceptual debate. Essentially, the debate revolves around the concepts of relevance vs.

reliability: Proponents argue that fair value measurement (FVM) has greater relevance to users

of financial information, while opponents counter that FVM is less reliable, especially when no

market exists.2

Notwithstanding the philosophical debate for or against it, prior to 2008, generally

accepted accounting principles in the United States (U.S. GAAP) included multiple definitions

for FVM that were scattered across various pronouncements. The lack of consistency and

comparability resulting from the multiple definitions of FVM provided challenges to users of

financial statements with FVM. In response to requests from constituents on this issue, the

Financial Accounting Standards Board (FASB) began its standard-setting procedure by publish-

ing an exposure draft on June 23, 2004 that sought to improve guidance on measuring fair value

(FASB 2004). The FASB followed its standard-setting procedure which it completed in the fall

2 When applied to the balance sheet, the term “mixed attribute model” means that certain items are

measured at (depreciated) historical cost while others are measured at market value.

2

of 2006 with the issuance of Financial Accounting Standard (FAS) 1573 that provided a single

definition of fair value (FASB 2006). “This statement defines fair value, establishes a

framework for measuring fair value, and expands disclosures about fair value measurements”

(FAS 157 paragraph 1). Although the FASB subsequently delayed by one year its application to

nonfinancial assets and liabilities measured or disclosed on a non-recurring basis, for all other

assets and liabilities FAS 157 became a part of the U.S. GAAP for fiscal years beginning after

November 15, 2007 (FASB 2008). This timeline is illustrated in Figure 1.

[Insert Figure 1 about here]

[Insert Table 1 about here]

Four early papers, listed in Table 1, study the value relevance and information content of

the main disclosures of FAS 157. The papers, which report generally consistent results, share

two other common features as well: All used disclosures from unaudited interim quarterly

financial reports before the final accounting period of 2008 and all examined firms from a single

industry: financial services. While some of their results may generalize to firms in other

industries, because firms in the financial services have characteristics that differ from firms in

other industries, some results may be unique to firms in financial services. Therefore, which of

the findings of the early papers apply more broadly to firms in other industries and which are

unique to the financial services industries is an open, important, and interesting research

question.

This dissertation will extend the examination of the value relevance of the fair value

hierarchy (FVH) more broadly and beyond firms in financial services. This extension is

important since certain findings of the early papers may be unique to firms in the financial

services. Through FAS 157, the FASB has standardized the measurement and reporting of the

FVH for all firms across all industries. However the market assigns value based on discounted

3 Under the FASB’s new codification, FAS 157 has become Accounting Standard Codification (ASC)

820.

3

expected future cash flows and other characteristics that can be very different between firms and

industries. Therefore, the value relevance of the FVH could vary among industries.

On the other hand, the value relevance of the FVH may be influenced by similar

variables or common characteristics. For example, Goh, Ng, and Ow Yong (2009), one of the

early papers, found evidence that suggests that firms with higher liquidity (banks with higher

Tier 1 capital ratio) have higher estimated coefficients of Level 3 assets. They attributed this to

the fact that FAS 157 defines fair value measurement as an exit price in the asset’s or liability’s

most favorable market, and firms with higher liquidity are more likely to be able to realize it in

its most favorable market. While the early papers used (unaudited) interim reports, this

dissertation examines the value relevance of the (audited) fiscal year-end reports from the first

fiscal year in which FAS 157 became U.S. GAAP. Furthermore, the sensitivity analysis extends

the findings to the second year (2009) after FAS 157 became GAAP. Due to the volatility of

financial markets during this period, this dissertation addresses an interesting research question

by examining whether patterns of value relevance observed by the early papers persisted through

the reporting period of the (audited) year-end data.

It is important to note that, “In 2008 the financial markets froze” (Spiegel 2011). Figure

2 shows the weekly close of the S&P 500 Index, a well-known indicator of the equity markets in

the U.S. The figure shows that the impact of the crisis on the equity markets in the U.S. was

most severe at the end of 2008 and beginning of 2009. For the study of value relevance, this

study will use prices from March 31, 20094. Because prices are thought to be based on expecta-

tions of future performance, and because the equity markets may have begun to recover from the

crisis by that date, prices of equities on this date may have less influence from the crisis.

However, the fact that the accounting data from this study comes from the period of crisis should

be kept in mind, and this could have an influence on the results.

4 Studies using returns of equities that span this period, the end of 2008 and beginning of 2009, may need

to exercise caution.

4

2. Review of Literature

This section provides a review of the literature on the FVH of FAS 157 followed by a review of

the literature on characteristics of industries.

2.1 Review of Literature on the FVH of FAS 157


One of the expanded disclosures required by FAS 157 is the three-tiered fair value hierarchy

(FVH) based on the type of input to determine the FVM. “The fair value hierarchy gives the

highest priority to quoted prices (unadjusted) in active markets for identical assets or liabilities

(Level 1) and the lowest priority to unobservable inputs (Level 3)” (FAS 157 paragraph 22).

Table 2 displays the main characteristics of the FVH. Several recent papers have taken an early

look at the value relevance of and mitigation of information risk by the FVH5 of FAS 157. This

section describes the main results of four (known) early papers on the value relevance and

information content of the fair value hierarchy (FVH) of FAS 157. Table 1 lists the papers and

the abbreviations that will be used for each paper.


Panel A of Table 3 provides an overview of the data used in each paper. These early

studies use (unaudited) interim financial reports from 20086 and examine firms in the financial

services industries. Goh, Ng, and Ow Yong (2009), hereinafter GNO, restrict their investigation

to firms with Standard Industrial Classification (SIC) beginning with 60 (Depository institutions)

5 In addition to the FVH, Kolev also uses FAS 157’s Level 3 Reconciliation disclosure. 6 One paper, Riedl and Serafeim (2009), also includes data from 2007.

5

or 61 (Nondepository credit institutions)7. Kolev (2009) focuses on Industry 40 of the Global

Industry Classification Standard (GICS), which are the Financials, but removes GICS Industry

4040 which are real estate firms. Kolev’s study uses members of the “S&P 1500” that is

comprised of the firms in the following three constituent indices: S&P 500, S&P MidCap 400,

and S&P SmallCap 600. Therefore, Kolev’s study does not include the smaller firms that are

not included in the S&P SmallCap 600 but are included in GNO and Song, Thomas, and Yi

(2010), hereinafter STY. Riedl and Serafeim (2011), herein after RS, take a different approach

and concentrate on large firms (total assets in excess of $10 billion) that are in three particular

financial services industries (SIC 6020, 6035, and 6211). RS have the fewest firms in their

initial sample (56). Finally STY draw their sample from the Compustat Bank dataset.

Panel B of Table 3 also shows that all four papers include accounting data from the first

and second quarters of 2008. Kolev uses data from only these two quarters. Both GNO and

STY include data from the third quarter of 2008, while RS augments the two quarters in 2008

with data from all four quarters of 2007. None of the four papers includes data from the fourth

quarter of 2008 or from 2009. Although the main contribution of this project is to understand

the value relevance of the FVH across industries, the fact that the data used is audited year-end

data for the first year in which FAS 157 became GAAP will allow us to see whether the pattern

observed using the earlier interim reports is also present in the more recent year-end reports as

well.

Panel A of Table 3 also summarizes a few other characteristics of the different data sets,

including the number of distinct firms (N), the number of firm-quarters, the method of handling

outliers, and the method of handling the FVH disclosures. In particular, both GNO and Kolev

subtract Level 1(2)[3] liabilities from the corresponding assets and use net Level 1(2)[3] assets

in their estimations. RS and STY use the “raw” information as-is, while in one series of tests,

7 Website of the 1997 Economic Census, http://www.census.gov/epcd/ec97sic/E97SUSH.HTM, accessed

July 15, 2011

6

RS form the “sum ratio” as the sum of Level 1(2)[3] assets and Level 1(2)[3] liabilities divided

by the sum of assets and liabilities. A more detailed review of these papers follows.


GNO seek to understand whether investors use the information in the FVH, and whether

investors weigh the information in the FVH differently across banks. Panel A of Table 4

summarizes GNO’s hypotheses, main tests, and findings. As shown in the Panel, their first

hypothesis relates to their first goal of seeking to understand whether investors use information

provided in the FVH. Their findings, which support their hypothesis, suggest that the

information provided by the FVH is value relevant. More specifically, their regression analyses

provide monotonically decreasing values of their estimated FVH coefficients. However, GNO’s

results suggest that the coefficients of Level 2 and Level 3 net assets are not statistically

different. They note, however, that “the difference in the coefficients (0.626 versus 0.489)

appear [sic] to be economically significant” (GNO p. 19).

Their second and third hypothesis examine whether investors weigh the information

provided by the FVH differently across banks. They partition the sample with two indicator

variables. One, based on the bank’s Tier 1 Capital Ratio, which is a measure of its financial

strength, has the value 1 when the Capital Ratio is above the median, and 0 otherwise. The

value of the other indicator variable is based on whether the bank’s auditor is a Big Four auditor

or not. Their findings support their hypotheses and suggest that investors do weigh the

information provided by the FVH differently among banks. In particular, they found the

estimated coefficient of the interaction between Tier 1 Capital Ratio and Level 3 net assets is

statistically significant, suggesting that the market perceives banks with higher financial strength,

and liquidity, as more likely to realize its Level 3 net assets as measured and reported. Finally,

GNO find statistically significant coefficients on the interaction between Big Four auditor and

all three net assets, with the coefficients of the interaction between Level 2 and 3 net assets and

7

the Big Four indicator being highly significant. This suggests that the presence of a Big 4

auditor helps reduce information risk associated with Level 2 and Level 3 net assets. This is

summarized in Panel A of Table 4.

Kolev has two hypotheses. His first posits that investors find Level 2 and Level 3

measurements sufficiently reliable to be reflected in firm value while his second is that investors

find Level 2 and Level 3 estimates less reliable than Level 1. Kolev’s analysis is quite technical.

First, he develops a set of controls that include industry indicators within the GICS financials,

company-level credit ratings, and proxies for size, growth, and profitability. Kolev’s first test

suggests that investors find the quantitative FVH disclosures informative. Similar to GNO, the

magnitude of the coefficients of the FVH regressors are monotonically decreasing. Kolev

performs a Wald test to assess equality among the coefficients that suggests in almost every

specification, the coefficient of the Level 3 net assets is different from the coefficients of the

Level 1 and Level 2 net assets. In order to control for the possibility of correlated omitted

variables, Kolev differences the level model and incorporates information from the Level 3

Reconciliation, another new disclosure introduced by FAS 157. Panel B of Figure 3 provides an

example of the Level 3 Reconciliation disclosure. Where statistically significant, the results are

consistent with earlier findings. Panel B of Table 4 summarizes Kolev’s results.

While the papers above examine the value relevance of the FAS 157 FVH disclosures,

RS begin from a different point. They observe that finance theory suggests that information risk,

the uncertainty regarding valuation parameters of an asset, is reflected in a firm’s equity beta and

the information asymmetry component of bid-ask spreads. RS first estimate the firm’s equity

beta using the single-factor CAPM, and then regress this beta on FVH Level data and controls.

RS use the FVH disclosures as reported and in particular do not compute net assets for each

level of the FAS 157 FVH. The coefficients of the fair value of assets increase monotonically

and F-tests suggest that the coefficient of Level 3 assets is significantly higher, suggesting more

8

risk, than Levels 1 and 2. Although the coefficients of liabilities do not exhibit monotonicity,

nevertheless the coefficient of Level 3 liabilities has the largest magnitude, and F-tests suggest

the coefficient is different from the others. In their model for the cost-of-capital, RS include the

FAS 157 FVH disclosures in “absolute form.” The numerator is the sum of the absolute value of

assets and liabilities deflated by the sum of the absolute value of total assets and total liabilities.

Their results suggest that the coefficient of Level 3 fair values is positive and statistically

significantly while coefficients for Level 1 and Level 2 fair values are not statistically significant.

This is summarized in Panel C of Table 4.

STY extract a sample from the Compustat Bank file for the first three quarters of 2008.

They eliminate observations that have studentized residuals greater than two as outliers and

examine the value relevance of the FVH by regressing stock price onto accounting information

including the FAS 157 FVH. They find that the coefficients of FVH decrease monotonically. In

particular, the coefficient of Level 3 assets is positive, and small which suggests “that investtors

place less weight on Level 3 fair value assets relative to Levels 1 and 2” (STY p. 18), and the

coefficient of Level 3 liabilities is significantly less than minus one, which “is consistent with

investors perceiving Level 3 fair value liabilities to be understated” (STY p. 18). Furthermore,

FAS 157 requires disclosure of the type of asset or liability at each level. Vuong’s statistic

applied to the R2 of regressions with and without the additional type-of-asset and liability

information, “suggests that when the Level information is used together with the Type

information, FAS 157 Level information is incrementally value relevant” (STY p. 20).

Lastly, STY’s final tests suggest that corporate governance is value relevant, with the

greatest impact on the valuation of Level 3 fair value assets. They suggest that because

management has the greatest ability to bias the measurement of Level 3 assets, investors “trust”

firms with better corporate governance more than others. They carefully apply techniques

appropriate for scale effects, as described in the recent paper by Barth and Clinch (2009). As a

9

part of their sensitivity analysis, STY also include a dummy variable for high and low Tier 1

capital. Their results are robust with this characteristic of banks and are summarized in Panel D

of Table 4.

Thus, the results from these four early papers, which are largely consistent, suggest that

the disclosures of the FVH are value relevant and contain information, and in particular that the

coefficients of the Level 1 holdings8 are significantly different from the coefficients of the Level

3 holdings for firms in the financial services industries, and especially banks. However, firms in

the financial services industry, and especially banks, are subject to regulations that are not

applied to firms in other industries. For example, regulators from the Federal Deposit Insurance

Corporation (FDIC) routinely visit banks, and if a bank is not maintaining certain regulatory

ratios that are mandated by law, the regulators are required to revoke its charter to operate,

which effectively forces the bank to close. The firms studied by the early papers are regulated,

and some heavily so, suggesting that some of the findings could be unique to regulated firms.

Therefore it is important to examine the value relevance of the FVH of firms in other industries

that are less regulated (e.g., utilities), or unregulated (most other industries).

Accounting literature provides strong evidence that the level of regulation influences

earnings response coefficient (ERC), earnings relationship with stock prices and alignment

between the market value and balance sheet values. Teets (1992) compares the earnings

response coefficient (ERC) of electric utilities and a random sample of non-regulated firms. He

finds that the ERC of electric utilities are smaller on average than non-regulated firms. El-

Gazzar, et al. (2009) examine “the valuation effects of earnings and two non-earnings-based

measurements (book values and operating cash flow) on security prices of airline companies

under two different market structures: regulated and deregulated” (p. 88). Their results suggest

“that nonearnings measures have higher explanatory power of security prices in regulated times

8 Hereinafter the term “holdings” will be used for the collective expression “assets and liabilities.”

10

for the airline firms. In deregulated times, earnings have a stronger relationship with prices” (p.

88). Comparing electric utilities to comparable manufacturing firms, Nwaeze (1998) provides

evidence that the alignment between the market value and the balance sheet book value for

utilities is considerably high. In particular, Nwaeze’s result suggests that the ratio of BE/ME of

electric utilities is close to one.

2.2 Overview of the Literature on Characteristics of Industries

Researchers in accounting and finance have examined the relationship between firms, industries,

earnings, and market valuation. This section provides a brief overview of this rich literature.

The study by King (1966), one of the earliest that examined a firms’ industry effect on capital

markets, uses factor analysis to identify “a smaller set of clusters of security prices changes that

tend to move as homogenous groups” (King p. 139). Using price data from the Center for

Research in Security Prices (CRSP), King studies 63 firms in six two-digit industries9 from May

1927 – December 1960. Based on this sample, the study finds that over half of the variance in

price of a typical stock is explained by elements that affect the entire market and also that on

average over the period, industries influence over 10% of the variance in price. He concludes,

“…the data give remarkable support to the hypothesis stating that the movement of a group of

security price changes can be broken down into market and industry components” (King p. 163).

As a type of “sensitivity analysis,” King (1966) shows remarkable consistency between his

(principal) market factor and the S&P 500. However, in a subsequent methodological study,

Livingston (1977) suggests that the market factor estimated from factor analysis is sensitive to

the set of firms included in the sample, and therefore recommends using a broader index, such as

the S&P 500, in market-based research. Furthermore, he suggests that regression analysis could

be more robust than factor analysis for market-based research.

9 In the paper, King describes the codes as from the SEC. The SEC currently uses SIC codes.

11

In the year following King (1966)’s paper, Brown and Ball (1967) published research

that examines accounting earnings in a manner similar to the way that King had studied security

prices. Using annual earnings information from Compustat, Brown and Ball identify 316 firms

in ten industries based on two-digit industry classification codes10 spanning 1947–1965. Based

on this sample, they report that on average approximately 35-40% of a the variability of a firm’s

annual earnings is due to the variability of all firms while on average 10–15% can be associated

with the industry average. They note, “On the whole, our results parallel those reported by King”

(Brown and Ball 1967, p. 67). In their suggestions for future research, they note that the

classification scheme for industries may be somewhat arbitrary and discuss some possible

alternate methods on which to base different strategies of distinguishing and classifying

industries.

In their review of marked-based accounting research, Lev and Ohlson (1982) note that,

“Existing empirical literature suggests that, at minimum, firms can be usefully grouped into

industries in the construction of valuation models” (p. 308). Reviewing capital markets research

in accounting in the subsequent decade, the 1980s, Bernard (1989) describes several papers that

have concentrated on particular industries, such as oil & gas and financial services, and

encourages more research to understand unique characteristics of specific industries.

In a more recent review article covering the 1990s, Kothari (2001) mentions one paper,

Biddle and Seow (1991), which specifically includes industries in their methodology to estimate

earnings response coefficients. Biddle and Seow group firms into industries because firms

within an industry generally share similar economic, financial characteristics and generally

would make similar choices of accounting methods. They base their 40 industries on the

traditional Standard Industry Classification (SIC) system that will be discussed in more depth

10 In their paper, Brown and Ball describe the codes as Compustat codes. These could be SIC codes.

12

below. Their results suggest that earnings response coefficients vary significantly across

industries.

Kothari (2001) also reviews important papers from finance that impact capital markets

research in accounting. One such paper is Moskowitz and Grinblatt (1999) whose results

suggest that industry is a significant factor in the momentum anomaly. Another such paper is

Fama and French (1997) who, similarly to Biddle and Seow (1991), created their own industry

categories based on the SIC and whose results suggest risk factor loadings on the ratio of book

equity-to-market equity (BE/ME) factor vary substantially across industries. Lang and

Lundholm (1996) results suggest that the earnings of firms within the same industry are

informative to a particular firm.


Traditionally, many researchers who included industries in their studies have simply

added “industry fixed-effects” to their models. However, recently some researchers have begun

to investigate the properties or characteristics of industries that have measurable economic or

financial effects. Three such papers whose findings could be relevant to this study are shown in

Panel A of Table 5. Panel B of the same table shows a brief comparison of some of the selection

criteria of the samples of each paper. The remainder of this section will discuss some of the

highlights of these papers.

Banko, Conover, and Jensen (2006), hereinafter BCJ, find evidence that suggests the

value effect varies by industry. The value effect refers to the empirical finding that the BE/ME

is significant for returns (Fama and French 1992). BCJ sought to understand the strength of the

value effect both at the firm- and industry-level. They use data from Compustat and CRSP from

1968–2000 and use SIC from CRSP because these SIC reflect the historical classification, while

the SIC from Compustat reflect the most recent classification.


13

From this sample, BCJ first form 21 industry groups that each have 15 or more firms.

Dropping firms with negative book equity (BE), they compute BE/ME for each firm, rank them

by BE/ME, and form (five) equally-weighted quintiles based on the BE/ME ranking, following

Fama and French (1992). Panel A of Table 6 highlights their main findings. Their table of

summary statistics of BE/ME by industry displays a rather wide variation in average BE/ME

with two industries (apparel and primary metals) in the extreme “value” range ( > 0.9) and three

industries (communications, services, and chemicals) in the extreme “growth” range ( < 0.5). To

examine the relationship between industry and the value effect, they use a generalized least-

squares approach. The results from this preliminary series of models on the entire sample

suggest that the quintile BE/ME is statistically significant. To further examine the prevalence of

the value effect, they separately run regressions for each industry and find that for over half of

the industries (11 of 21) the coefficient of BE/ME is significant, and for an additional fifth of the

industries (4 of 21) is moderately significant. They conclude that the intra-industry variation in

BE/ME is relevant in explaining stock returns. This intra-industry variation could also be

significant in value relevance studies.

By examining the risky cash flows that firms generate by their activities in product

markets and the market’s assignment of value based on those risky cash flows, Hou and

Robinson (2006), hereinafter HR, seek to develop a link between the theories of industrial

organization, e.g. industry concentration, and asset pricing. They develop their main hypothesis

from two different theoretical models. One model is the creative destruction hypothesis of

Joseph Schumpeter (1912) which states that innovation begins in firms outside of an established

industry and can become so successful that the new firms overtake the established ones. One of

the possible consequences of this hypothesis is that concentrated industries have lower returns

because concentrated industries contain more established firms, and with less innovation, and

lower returns. A second theoretical starting-point is the Structure/Conduct/Performance (S/C/P)

14

theory of Joe Bain (1954) which states that a Structure that is particular to an industry, like a

barrier to entry, affect a firm’s Conduct, and a firm’s conduct and decisions affect its

Performance. The presence of a barrier to entry would lead to a concentrated industry.

Protected firms would have less default risk, and since these firms are less risky, their market

return would be lower. From these two starting-points, HR begin to examine the average returns

of industries by concentration.

They develop their sample ordinary common shares11 from NYSE-, AMEX-, and

NASDAQ-listed companies in Compustat and CRSP. They remove regulated industries from

their sample and use the Herfindahl Index as a measure of industry concentration. The

Herfindahl Index is based on a firm’s market share, and they examine three separate ways to

estimate market share: Net Sales, Total Assets, and Book Equity. They observe that these

measures exhibit high correlation and noting that all of the measures are imperfectly correlated

with “true market share,” they use Net Sales as the basis for their Herfindahl Index. Following

Fama and MacBeth (1973), they create quintile portfolios based on their Herfindahl Index and

estimate pooled regressions. The coefficients from their regressions support their theoretical

predictions.

To establish their main results, they simply tabulate summary statistics for their quintile

portfolios. Their results show that in competitive industries (where the Herfindahl Index is low)

the average return is higher, while in concentrated industries (where the Herfindahl Index is high)

the average return is lower. HR suggest that

“It is well understood from industrial organization that the structure of product

markets affects managers' equilibrium operating decisions. If these operating

decisions affect the risk of a firm's cash flows, then these decisions should

impact stock returns. The main finding in this paper is that firms in highly

concentrated industries earn lower returns, even after controlling for size, book-

to-market, momentum, and other known return predictors. Moreover, the

economic magnitude of these effects is large.” (p. 1928).

11 CRSP assigns ordinary common shares a Share Code of 10 or 11.

15

These results suggest that the relationship between a firm’s holdings and its market

value may be influenced by the industry to which it belongs. In particular the coefficients of

Level 1, Level 2, and Level 3 holdings in a value relevance regression may be influenced by the

industry to which the firm belongs, providing a stronger argument that the results of the early

papers on the value relevance of the FVH could be unique to the financial services industries.

MacKay and Phillips (2005), hereinafter MP, begin with a review of theoretical models

of firms in equilibrium and partial-equilibrium. Instead of developing specific tests for the

different models, they identify and explore the common theme of these models: firms within an

industry behave differently. In particular, they investigate three types of decisions that firms

make: financial structure (debt structure), technology, and risk decisions. To examine a firm’s

technology within its industry, MP use Maksimovic and Zechner (1991)’s concept of a “natural

hedge” which measures how a firm’s technology compares to the rest of its industry. Then,

following Williams (1995) and Fries, Miller, and Perraudin (1997), MP develop a second

measure of a firm’s position within its industry that is based on its status as an entering,

incumbent, or exiting firm.

They develop their sample from active and inactive firms in Compustat and CRSP. In

order to compute diversification Herfindahls, they merge in Compustat’s business segment files,

for the years 1981–2000. Because the theoretical methods they use were developed for firms

with classical notions of capital and labor, they limit their sample to manufacturers with SIC

from 2000–3990. Furthermore, since the theories were developed for firms in competitive

industries, they use the Herfindahl-Hirshman Index (HHI)12 which provides a measure of the

level of competition within an industry, and they select unconcentrated firms with HHI < 1000.

For comparison, they also separately study concentrated firms, with HHI > 1800.

12 The HHI is a part of the Census of Manufactures that is conducted every five years that is published by

the U.S. Census Bureau’s Economic Census.

16

Their first analysis, “a table of subsample means” reported in Panel C of Table 6, clearly

shows that within an industry, firms exhibit characteristics based on their position as an entering,

incumbent or exiting firm. For example, while incumbents carry the lowest amount of relative

debt, entering firms typically have slightly higher amounts of debt, and firms exiting have the

highest debt. Their second analysis consists of a series of regression models with the firm

characteristics (financial structure, technology, and risk decisions) as the dependent variable.

They rely on differences in adjusted R2 to establish their result. In their first set of regressions

they include only industry fixed effects, while in their second they add firm fixed effects. They

find that for each dependent variable, the adjusted R2 of the second set of regressions is higher

than the first set. This clearly suggests that industry fixed-effects alone do not explain firm

characteristics.

They report the results of ordinary least squares (OLS) regressions, but to control for firm

fixed effects and address endogeneity bias, following Whited (1992), they estimate a system of

simultaneous equations using the generalized method of moments (GMM) with first-differences

as the instrument variables. The results from these more rigorous econometric techniques

support their main findings. Their finding suggests that a firm’s position within its industry

differentially affect their coefficients in a value relevance model.

3. Development of Hypotheses

The Efficient Market Hypothesis (EMH) states that security prices fully reflect available

information (Fama 1970, Kothari 2001). This simple assertion has been qualified into several

different forms, e.g. the weak, semi-strong, and the strong, and has been the subject of numerous

studies (Fama 1991, Kothari 2001). Because the purpose of accounting is to provide

information that is useful and the EMH asserts that security prices fully reflect information

(including disclosures of the FVH), therefore studies of value relevance build upon these

foundations by examining whether participants in the equity markets, one of the largest types of

17

users, respond to accounting information, using the change in price of an equity issue as a

measure of their response (Barth, Beaver, and Landsman 2001, STY).

Furthermore, the Capital Asset Pricing Model (CAPM) attributes variation in the

expected return of securities to differences due to the riskiness of individual firms (Kothari

2001). Empirical tests of the static-CAPM have shown difficulty finding evidence to support it

(Fama and French 1992). However a modified conditional-form of CAPM has received

empirical support, and a framework for associating risk with returns, the CAPM is very widely

used (Jagannathan and Wang 1996). For the purpose of developing our hypotheses (below), we

will restate the CAPM’s risk-return principle as investors dislike risk and therefore will demand

a premium to assume risk.

Prior to 2008 the generally accepted accounting principles in the United States (U.S.

GAAP) contained multiple definitions of fair value that were scattered across various

pronouncements. In September 2006, after working with constituents over the course of several

years, the Financial Accounting Standards Board (FASB) issued Financial Accounting Standard

(FAS) 157 that addressed this issue. FAS 157, which became effective for fiscal 2008, defines

fair value as an exit price in an orderly transaction between independent (unrelated) and

knowledgeable market participants in the principal or most advantageous market for the asset or

liability, and introduces several new disclosures, one of which is the fair value hierarchy (FVH)

that classifies fair value measurements (FVM) into three categories based on inputs: Level 1

inputs are from quoted prices for identical assets or liabilities that are traded in an active market;

Level 2 inputs are other-than Level 1 inputs but are observable, either directly or indirectly, and

for example could be observable prices for similar assets or liabilities; and Level 3 inputs are

unobservable for the asset or liability.

Prior research examined the value relevance and information content of the FVH of

firms in the financial services industries. One of the main findings of these efforts is that in

18

value relevance models of firms in the financial services industries, the coefficients of the FVH

are monotonically decreasing with the coefficient of Level 1 (3) assets largest (smallest), and

furthermore the coefficient of Level 3 assets is statistically different from the coefficients of

Level 1 and 2 assets. Another significant result is that the coefficient of Level 3 assets is higher

(lower) for banks with higher (lower) Tier 1 capital ratios, which suggests that investors

incorporate either liquidity or financial strength in their assignment of value to banks.

Although FAS 157 requires firms to disclose the information of the FVH in a consistent

manner and the EMH asserts that security prices fully reflect all information, nevertheless the

CAPM predicts that the investors assign values to firms differently based on their perception of

the risks associated with a particular firm. Thus, a tension may exist between the consistent

information firms provide through the disclosures of the FVH vs. the differing valuation the

market assigns them based on their individual riskiness. Because valuation can be different due

to a firm’s unique risk factors, this dissertation investigates the influence of certain risk factors

on the coefficients of the FVH in value relevance models.

The literature review provided theoretical and empirical evidence that market

participants consider the industry to which the firm belongs as well as other risk factors when

setting a value for a firm. In the following paragraphs, I will expand the possible effect of

industry and other risk factors on the value relevance of assets reported using the FVH. A priori,

I expect that the market will penalize (reward) value (growth) firms and more (less) risky

industries with lower (higher) valuation of the assets reported at fair value and expect to observe

the effects of this lower (higher) valuation in the relative magnitudes of the coefficients of Level

1, Level 2, and Level 3 assets.

H1. Book-to-Market Equity

My first hypothesis posits that BE/ME contains significant explanatory power in the

value of the firm. Banko, Conover, and Jensen (2006) examine how stocks are valued and find

19

evidence that valuation varies by industry. They form 21 industry groups for which BE/ME

displays a wide variation with two industries (apparel and primary metals) in the extreme “value”

range (BE/ME is large) and three industries (communications, services, and chemicals) in the

extreme “growth” range (BE/ME is small). To further examine the prevalence of the value effect,

they separately run regressions for each industry and find that for over half of the industries (11

of 21) the coefficient of BE/ME is significant, and for an additional fifth of the industries (4 of

21) the coefficient is moderately significant. They conclude that the intra-industry variation in

BE/ME is relevant in explaining stock returns. Furthermore, they provide empirical evidence

that suggests “value stocks” may exhibit higher default risk which supports the conjecture of

Fama and French (1992). Studies in asset pricing have consistently shown that BE/ME can

significant explain firm value (Banko, et al.), and therefore should be included when examining

value relevance.

H1a: The BE/ME explanatory variable will be significant in a value relevance model.

Although firms report assets at fair value based exit prices, the market could price them

differently based on the riskiness of the firm’s industry. The following hypotheses will test this

proposition.

H1b: The magnitude of the BE/ME coefficient of the FVH in a value relevance

models will be lower (higher) for firms in value (growth) industries.

The next four hypothesis (H2 to H5) concentrate on the coefficient of the FVH for assets

and liabilities and how they are influenced by industry concentration, firm’s position within an

industry, firm’s liquidity, and the magnitude of FMH.

H2. Industry Concentration

Industries can also be different with respect to level of concentration. In an attempt to

link the theories of industrial organization and asset pricing, Hou and Robinson (2006) develop

their propositions from two different models; one that states that innovation begins in firms

20

outside of an established industry and can become so successful that the new firms overtake the

established ones. A second theoretical model states that a structure that is particular to an

industry, like a barrier to entry, affects a firm’s conduct, and a firm’s conduct and decisions

affect its performance. One of the possible consequences of the first model is that concentrated

industries have lower returns because concentrated industries, having more established firms,

have less innovation, and therefore lower returns. The second model also suggests that the

presence a barrier to entry would lead to protected firms and concentrated industry; hence less

default risk and less expected market return. Hou and Robinson (2006) remove regulated

industries from their sample and use the Herfindahl Index as a measure of industry concentration.

The coefficients from their regressions support their theoretical predictions. In competitive

industries (where the Herfindahl Index is low) risk, including default risk, is higher and therefore

coefficients in a value relevance study should be discounted. In concentrated industries (where

the Herfindahl Index is high) risk is lower, and therefore coefficients in a value relevance study

should be higher.

H2: In industries with lower (higher) concentration, the coefficients of the

FVH of Assets will be lower (higher).

H3. Industry Status

MacKay and Phillips (2005) identify and explore why and how firms within an industry

behave differently. In particular, they investigate three types of decisions that firms make:

financial structure (debt structure), technology, and risk decisions. MacKay and Phillips (2005)

develop a second measure of a firm’s position within its industry that is based on its status as an

entering, incumbent, or exiting firm. Their results show that within an industry firms exhibit

characteristics based on their position as an entering, incumbent or exiting firm. For example,

from the perspective of debt, incumbents carry the lowest relative amount of debt. Entering

firms, typically, have higher amounts of debt, and firms exiting have the highest debt. They find

that industry fixed-effects alone do not explain firm characteristics. Since the location of the firm

21

within an industry can also provide clues to its future cash flow, it will affect how a firm is

valued in the capital market. Fries, Miller and Perraudin (1997) also suggest that a firm’s

position within its industry differentially affect their coefficients in a value relevance model. Of

these three categories, incumbents have the least risk while entering and exiting firms have the

most risk. Therefore, incumbents should be subject to less discounting while entering and

exiting firms will be subject to the more discounting.

H3: In a value relevance regression, the coefficients of the FVH of Assets will be

lower (higher) for entering and exiting (incumbent) firms.

H4. Liquidity

Goh, Ng, and Ow Yong (2009) found that the magnitude of the coefficient of Level 3

Assets, the most risky of the FVH, varied with whether the bank’s Tier 1 capital ratio was high

or low. The Tier 1 capital ratio represents the bank’s financial strength, which is a measure of

risk. FAS 157 states that fair value measurements are based on, “The most advantageous market

is the market in which the reporting entity would sell the asset or transfer the liability with the

price that maximizes the amount that would be received for the asset or minimizes the amount

that would be paid to transfer the liability, considering transaction costs in the respective

market(s)” (FASB 2006).

Clearly, firms with the liquidity would be most able to hold any of their assets, including

Level 2 and Level 3 assets, in order to realize them in the “most advantageous market.” I will

use the quick ratio (QR) and operating cash flow ratio (CR) as measures of liquidity that can be

observed across industries and test the following to determine the effect of firms’ liquidity on

FVH valuation:

H4: The interaction between liquidity and FVA3 will be positive.

H5. Relative Amount

Finally, in a survey of perceptions of CFOs toward the adoption of the Fair Value

Option, Daniel, et al. (2010) created the “FAS 157 Ratio” as a measure of the proportion of FAS

22

157 assets and liabilities to the firm’s total assets and liabilities, and show that this measure has

explanatory power. The rationale for the “FAS 157 Ratio” is straightforward. If the value of the

assets measured at fair value is small (large) compared to the total assets of the firm, then we

expect investors to be less worried about the measurements, and therefore we expect the

coefficients of the FVH in a value relevance model to be higher (lower).

H5: The relative amount of assets measured at fair value to total assets

will be value relevant.

3.6 Industry Classification

Early researchers used industry classification systems developed by the U.S government and

noted some caveats. More recently, several researchers have compared those systems with

another commercially-available system. This section discusses some of this work on using and

understanding industry classification systems. Traditionally, papers that have studied industries

in the U.S. have used the Standard Industrial Classification (SIC) which was first published in

1939 by economists and statisticians in the former Bureau of the Budget (now the Office of

Management and Budget, OMB) in order to provide the departments and agencies of the US

government a common definition of industries (Saunders 1999). Although the SIC was initially

developed to classify individual manufacturing facilities (plants), it has subsequently been

applied to classify entire firms (enterprises) (Clarke 1989). The SIC consists of a hierarchical

four-digit code where the left-most digit indicates a broad industry category and subsequent

digits provides more granularity in the classification.

Due to changes in the domestic and global economy since its original publication, the

SIC has undergone a major revision in each decade through 1987, with several minor revisions

occurring between the major ones. Unfortunately, Guenther and Rosman (1994) have noted that

the differences between the SIC in Compustat and the SIC in CRSP could be significant.

23

Around that time, in the early 1990s, the OMB created the Economic Classification

Policy Committee with representatives from all major Federal statistical agencies to examine

various issues with the SIC. In 1993, the ratification of the North American Free Trade

Agreement (NAFTA) created a need for the participating countries (Canada, Mexico, and the

United States) to have a coordinated industry classification system to meet the monitoring

requirements set-forth in the Agreement. At the time, Canada’s classification system dated to

1980, Mexico did not have one, and the US’s SIC was based on the major revision of 1987.

However, the existing industry classification systems were inconsistent with each other.

Therefore, statistical agencies from the three NAFTA countries cooperatively developed the

North American Industry Classification System (NAICS), whose first version was published in

1998 (Saunders 1999).

Since then, the major statistical agencies in all three countries, including the US, have

transitioned to the NAICS, and the US has no further planned updates to the SIC. Krishnan and

Press (2003) provide a thorough comparison between the SIC and the NAICS. Using techniques

from prior accounting researchers, they document that the newer NAICS provides an

improvement over the older SIC in most cases. Therefore, their results suggest the NAICS is

more suited to modern research in accounting. However, Bhoraj, Lee, and Oler (2003)

conducted a more thorough study that compared not only the SIC and NAICS, but also the

industry classification system of Fama and French (1997) and the Global Industry Classification

Standard (GICS)®. The results of their comparative study show that of the four classification

schemes, the GICS is the most suited to financial research. Kile and Phillips (2009) confirmed

the earlier conclusion of Bhoraj, et al. (2003). Werner (2005) performed a detailed study of

industry classification systems and also found that the GICS leads to lower valuation errors. This

24

dissertation will use the GICS® of Standard and Poor (S&P) and Morgan Stanley Capital

International (MSCI) Barra13.

4. Sample Selection


This section and Panel A of Table 7 describe the steps taken to obtain the sample. The sample

originated as an extract from the Compustat Fundamentals Annual data service, Step 0 in Panel

A, that yielded 6,380 observations of year-end accounting information for fiscal 2008 of active

issues traded on exchanges in the U.S. Panel B of Table 7 shows the Stock Exchange Code from

Compustat. Fiscal 2008 was selected because this is the first year after FAS 157 became

effective. The sensitivity analysis includes data from fiscal 2009. In order to concentrate on

actively-traded firms, the first step removed 753 issues that were not on a major exchange (e.g.

New York Stock Exchange (NYSE), American Stock Exchange (AmEx), or National

Association of Securities Dealers Association Quotations (NASDAQ)). The second step

removed 744 issues whose information Compustat marked as not final data. Step 3 removed

393 firms whose missing information consisted of general accounting information, such as total

assets, total liabilities, net income, minority interest, or common shares outstanding. Step 4

removed 1,892 firms that did not have a complete set of observations of the fair value hierarchy

(FVH), and Step 5 removed four firms without a code from the Global Industry Classification

Standard (GICS®), which is used in this study for industry classifications.

In order to minimize the effect of fluctuations in the equity markets in this study, this

study concentrates on firms whose fiscal year-end is December because these firms release their

(audited) year-end reports towards the end of the first quarter of the subsequent year. Step 6

removed all firms whose fiscal year-end was not December, while Step 7 extracted stock prices

13 Standard & Poor provides information on GICS at the following website

http://www.gics.standardandpoors.com, accessed July 15, 2011.

25

as of March 31, 2009 of active firms in the monthly database of the Center for Research in

Security Prices (CRSP) and merged this extract with the resulting dataset of Step 6. Because

175 firms could not be merged, the resulting sample consisted of 2,108 firms at the end of Step 7.

The next step examined the book equity, BE, computed as the difference of total liabilities, LT,

and minority interest, MIB, from total assets, AT, i.e. BE = AT – LT – MIB. Because investors

may treat firms with negative book equity differently from firms with positive book equity, Step

8 removed 84 firms with negative book equity.

In addition to the six observations of the levels of the FVH, Compustat also provides

two additional data items: the Total Fair Value Assets, TFVA, and Total Fair Value Liabilities,

TFVL. Therefore, one would expect the computed sum of the three disclosures of the levels of

the FVH of assets or liabilities to be equal to their respective total, e.g. letting SA represent the

computed sum of the Level 1, Level 2, and Level 3 disclosures of assets, one would expect this

sum, SA, to be equal to the reported total, TFVA, i.e. SA = TFVA. Similarly letting SL represent

the computed sum of the Level 1, Level 2, and Level 3 disclosures of liabilities, one would

expect SL = TFVL. Step 9 involved computing the two sums for each firm and comparing the

result to its related reported total. The two comparisons each have two possible results: equal or

not equal. Panel C of Table 7 shows the four possible results of the comparisons. Although in

most cases the computed sum did equal its respective reported total as expected, in 290 cases at

least one of the computed sums did not equal its reported total.14 Panel D of Table 7 shows the

distribution of the firms in each of the four cases by two-digit GICS Sector. The column with

the heading “Less” shows the sum of the three cases with at least one inequality by GICS sector.

This column also represents the number of firms dropped in a particular GICS sector. Step 9,

then, which removed the 290 firms where at least one of the computed sums did not equal its

reported total, yielded a sample with 1,734 firms. Within this sample, Financials (GICS Sector

14 One case, that of JP Morgan Chase, was investigated by downloading the firm’s 2008 Annual Report.

Their TFVA and TFVL was the sum of the respective FVH observations less “FIN 39 Netting” (p. 146).

26

40), with 515 firms, has the largest number of firms, followed by Information Technology,

Health Care, and Industrials with 284, 246, and 202 firms respectively. Two sectors, Consumer

Staples and Telecommunications Services, have fewer than 50 firms (39 and 28 respectively).

Lastly, as explained further below, the final step identified and removed154 outliers, that

resulted in a sample size of 1,580 firms.


Panels A and B of Table 8 briefly examine some characteristics of the data by GICS sector.

Panel A of Table 8 shows the mean and standard deviation of the ratios of Book Equity-to-

Market Equity (BE/ME), Fair Value of Assets to Total Assets (FVA), and Fair Value of

Liabilities to Total Liabilities (FVL). Because the computation of BE/ME requires the market

equity, ME, and Compustat did not report this item for 159 firms in this sample, this column

summarizes observations of 1,575 firms, instead of 1,734. While the mean of BE/ME for the

overall sample is 1.28, the Health Care, Utilities, and Consumer Staples GICS Sectors have the

lowest means of 0.71, 0.82, and 0.87 respectively and the Consumer Discretionary and

Financials GICS Sectors have highest means of 1.77 and 1.74 respectively. The overall mean of

the FVA is 0.18 for the sample. The Health Care, Information Technology, and Financials

Sectors have the highest means of 0.35, 0.25, and 0.22 respectively while the Energy, Materials,

and Utilities Sectors have the lowest mean of 0.05. The FVL ratio is quite low with an overall

mean of 0.03 with the Telecommunication Services and Materials Sectors having the highest

means of 0.07 and 0.06 respectively. Liabilities reported at fair value compose a very small

proportion of total liabilities. Panel B of Table 8 provides more details by showing the mean

and standard deviation of the ratio of the six individual level categories of the FVH scaled by its

appropriate aggregated total, e.g. the first column shows the mean and standard deviation of

Level 1 Assets scaled by Total Assets (FVA1 / AT). The ratio of Level 3 Assets to Total Assets

is extremely low, as are all of the ratios of liabilities. Statistical models involving these

27

explanatory variables could be very sensitive to measurement errors, and therefore the

discussion of results will concentrate on the coefficients of the fair value of assets. Following

STY, who combined the reported Level 1 and Level 2 liabilities into a single variable, FVL12,

this dissertation will also use this sum.

5. Results

This section begins with a brief overview of the results followed by a more detailed

discussion of the specific tests. In summary, the results suggest that the FVH has maintained its

value relevance in this sample and furthermore that the value relevance does in fact vary by

industry. Results from the tests of hypotheses H1 (BE/ME) suggest that the BE/ME is value

relevant. Furthermore, two industry sectors, Financials and Information Technology, strongly

display the predicted pattern of lower (higher) estimated coefficients for value (growth) firms for

all three estimated coefficients of the fair value of assets. However, as summarized in Panel G

of Table 12, three industries, Energy, Industrials, and Health Care, show the opposite pattern,

and therefore these results vary by industry sector. The results of tests of the second hypothesis,

the effect of industry concentration on the value relevance of the FVH, are mixed. One

possibility is the lack of a broad and accurate measure of industry concentration. Although the

Herfindahl-Hirschman Index is an accurate measure of industry concentration, it is not broadly

available for all industries. Following Hou and Robinson (2006), I computed the Herfindahl of

Sales for all firms in a sample, however this measure does not include data for private firms

across each industry, and the lack of results could be attributed to inadequacy of this scale. The

tests of hypothesis H3, on the effect of a firm’s status as an entering or incumbent on the value

relevance of the FVH, contain results for entering and incumbent firms. As the focus of this

dissertation is on the fiscal year 2008 and it is not yet possible to identify exiting firms, my tests

do not contain results for exiting firms. Panel E of Table 15 shows that four industry sectors,

28

Energy, Industrials, Health Care, and Financials, display the expected pattern with the

coefficient of entering firms lower than incumbents. Only one industry sector, Consumer

Discretionary, displays the opposite pattern, while Information Technology shows mixed results.

Therefore, the results suggest that hypothesis H3 is partially supported. Tests of hypothesis H4,

the effect of liquidity on the coefficient of FVA3, uses two different measures of liquidity: the

quick ratio (QR) and operating cash flow (CR). Results using the quick ratio, which is available

for only about one-third of the firms in the sample, are mixed while results using the operating

cash flow, which is available for about two-thirds of the firms in the sample, are significant and

as expected for three industry sectors. These results provide limited support for H3. Finally,

results of hypothesis H5, on the value relevance of the FAS Ratio, the ratio of FAS 157 assets

and liabilities to the firm’s total assets and liabilities, are mixed as shown in Panel A1 of Table

17. The following paragraphs provide a more detailed discussion of the two methods

investigated to mitigate the effect of outliers and to validate the approach selected by

highlighting the general consistency of its results with those of prior literature. Then, sections

5.1 to 5.5 discuss the results of tests of hypothesis.

In order to maintain compatibility with prior literature, the modified Ohlson regression

model employed by STY is also used in this dissertation, and is as follows:

(1)

where each of the explanatory variables is deflated by the number of common shares outstanding.

Following STY, Level 1 and Level 2 liabilities have been combined into a single explanatory

variable because they have similar characteristics. In order to examine the value relevance of

the FVH within an industry sector, separate regression studies are performed for each GICS

Sector. Therefore, for each model, the results present a set of coefficients for each industry

sector. To fully reflect this, each coefficient in Equation (1) could have an additional index to

29

indicate its GICS Sector. However, for clarity in Equation 1, those subscripts have been

dropped.

Empirical researchers have developed several techniques that identify and mitigate the

effect of outliers on the estimated coefficients of regression. Winsorization, one of the simplest

of such techniques, identifies outliers as values above or below a certain percentile, and

mitigates their effect by assigning the value of the low or high percentile to the respective

outliers. This study begins with a conservative 1%, and therefore the variables are Winsorized at

the first- and 99th-percentiles by GICS Sector.15


Panel A of Table 9 shows the estimated coefficients for the first set of regressions with

Winsorized variables16. The estimated coefficients, including the intercept, are displayed in the

first set of paired columns, while the number of observations in the regression, N, and the

Adjusted R2 are on the far right. The estimated values and their associated significance statistics

are presented in a pair of columns and rows. The header of the first row of a pair contains the

name of the GICS Sector and will show the estimated value followed by a number of stars

indicating the statistical significance as measured by the p-value. Following convention, if the

p-value is greater than 0.1, no star will be present. For p-values between 0.1 and 0.05, one star

will be displayed. For p-values between 0.01 and 0.05, two stars will be displayed, and for p-

values less than 0.01, three stars will be displayed. In the second row of each pair, the numerical

15 Jie (Jay) Cao provides a SAS macro that Winsorizes data, but it writes the Winsorized values into the

original column: http://ihome.cuhk.edu.hk/~b121456/tools/Winsorize_Macro.txt For this dissertation, his

macro was modified to create a new column within the SAS data set that contains the Winsorized values. 16 In order to present the estimated regression coefficients for each study by GICS Sector consistently and

efficiently, a series of SAS macros were developed to collect the coefficients and relevant parameters

from each regression model and present the results.

http://ihome.cuhk.edu.hk/~b121456/tools/Winsorize_Macro.txt

30

code of the GICS Sector is displayed followed by the letter “t,” which indicates the value is from

the t-test, as shown in Panel A of Table 9.17 The number in parenthesis is the p-value.

One can see that in general, none of the coefficients of FVA3 exhibit significance except

for the regression of Consumer Staples. The magnitude of the coefficient is extremely large and

negative for the 39 observations in this sector. There are four other industries in which the

coefficients of FVA1 and FVA2 exhibit statistical significance: Consumer Discretionary, Health

Care, Financials, and Information Technology. The first two, Consumer Discretionary and

Health Care, exhibit a similar pattern as reported in STY showing a higher coefficient for FVA1

than FVA2. The other two, Financial and Information Technology, exhibit the opposite pattern.

As discussed earlier in the literature review, the early papers, including STY, collected

FVH disclosures exclusively for banks. Therefore, the estimated coefficients from STY’s

regression studies are most comparable to those in the rows labeled “Financials” throughout this

dissertation. However, the GICS Financials in this dissertation is broader than the banking sub-

sector used by STY. Furthermore, this dissertation includes all financial institutions (both banks

and non-bank financial institutions). Also, while STY develop a pooled sample of unaudited

quarterly disclosures from an earlier time period, this study uses audited year-end disclosures

from the subsequent year. The coefficients of the fair value of assets are much smaller than

those reported by STY and the coefficient of Level 3 Assets is not statistically significant (Panel

A of Table 9). Panel B of Table 9 shows the results of a series of F-tests that examine whether

the coefficients for fair value items are significantly different from one and whether two

coefficients of FVH are different from one another. Prior research, e.g. STY, reports that the

coefficients of FVA1 and FVA2 are not significantly different from one and the coefficient of

FVA3 is different from one. As reported, results in Table 9 do not follow these patterns. These

17 Later, some models will be estimated using iteratively reweighted least squares (IRLS). In panels

showing the results of these studies, the letter “c” will follow the numerical code of the GICS sector,

indicating that the values in the row are from a Chi-square distribution.

31

differences may suggest that the Winsorization have not mitigated the effect of outliers on the

coefficients.

In order to help assess the effectiveness of the Winsorization in reducing the influence

of outliers, a comparison was made between the standard deviation and the standardized median

absolute deviation (MAD).18 The comparisons are in the Appendix as Table A1. The

Untreated column compares the standard deviation and MAD of the original sample before

Winsorization and the Winsorized column contains the same statistics, but after Winsorization.

If outliers are not present, the standard deviation and MAD should be similar. By inspection,

one can see that the Winsorization improves the correspondence somewhat. However, even the

Winsorized values could still be influenced by outliers.

This suggests that outliers are present even in the Winsorized dataset. One possible

solution could be to simply increase the percentiles and re-Winsorize at the third- and 97th-

percentiles and investigate the effectiveness of this treatment. However, Winsorization is

sometimes regarded as an arbitrary method and could be the reason that STY did not employ it.

Another technique is M-estimation, a type of iteratively reweighted least squares (IRLS)

algorithm (Huber 1981). IRLS examines the residuals of a regression, computes weights that

minimize the influence of observations with high residuals, and runs another regression. The

algorithm continues to improve weights based on residuals and to, iteratively, run regressions

until a convergence criterion is met. In this case, the convergence criterion is that the

coefficients of the regression converge. The results of the IRLS regression are shown in Panel A

of Table 10.


M-estimation identifies outliers as observations whose residuals are three times larger

than the scale of the regression, where the median method is used as a robust estimator for the

18 In his discussion of measures of deviation, Huber (1981) provides an illuminating discussion of the

historical development of the median absolute deviation (MAD) and the standard deviation.

32

scale. The tests of significance of the coefficients of M-estimation are conducted using the Chi-

square distribution, unlike OLS which uses the t-distribution. Another difference between OLS

and M-estimation is that while the former allows a much richer set of F-tests, the later only

allows basic tests of coefficients. The results of Panel A of Table 10 show that the magnitudes

of estimated coefficients of the FVH of the financials more closely resemble those of STY.

Furthermore, as one can see, many more coefficients in other industries have become significant.


STY eliminate outliers identified as observations having a studentized residual larger

than two and run OLS regressions on the (trimmed) sample. For comparison purposes, the

observations that M-estimation identified as outliers19 are deleted from the sample and OLS is

then run on the trimmed sample. Panel A of Table 11 shows that the estimated coefficients

resemble those of the IRLS studies (Panel A of Table 10). The Adjusted R2 of the OLS

regressions on the trimmed sample is much higher than the IRLS regressions on the full sample.

This suggests that its estimated coefficients explain more of the variation of the smaller sample,

which is consistent with the fact that this trimmed sample has had outliers removed.

In spite of the differences between the sample of STY and the one used in Table 11, the

coefficients of the explanatory variables exhibit several noteworthy similarities. For example,

the intercepts and coefficients exhibit strong statistical significance, and the signs of all the

coefficients are as expected. Moreover, the coefficients are similar in magnitude; e.g. the

coefficients related to assets (NFVA, FVA1, FVA2, & FVA3) all range between 0.5 and 1; and

the coefficients related to liabilities (NFVL, FVL12, & FVL3) all range between –2.2 and –0.7.

There are also differences between the sample of STY and the one used in Table 11.

The results of F-tests do not have the same correspondence. In particular, STY observed that

while the coefficients of FVA1 and FVA2 were not statistically different from one, the

19 Panel B of Table 10 shows the distribution by GICS Sector of the 154 outliers that M-estimation

identifies as outliers.

33

coefficient of FVA3 was statistically different from one. As shown in Panel B of Table 11, for

the GICS Financials, all of the coefficients of the FVH of assets, FVA1–FVA3, are statistically

different from one. Furthermore, STY observed that FVA1 was not statistically different from

FVA2 while FVA3 was statistically different from FVA1 and FVA2. The results in Panel B of

Table 11 suggest that none of the coefficients of the FVH of assets are statistically similar.

Overall while not exactly the same, the results of this study using the GICS Financials

are similar to those of STY. This suggests that the general pattern that STY reported for the

financials in the early quarters of 2007 also exists in this sample at the end of FY2008.

5.1 The Value Relevance of the FVA across Industry Sectors

The previous section describes the approach taken to mitigate the effects of outliers and

validates the approach by highlighting the general consistency of the results with those of prior

literature. This section reports the results of regressions testing the modified Ohlson model

across industry sectors.

Panel A of Table 11 reports the statistical significance of all five of the FVH coefficients

(FVA1, FVA2, FVA3, FVL12, and FVL3). As expected, the coefficients of the fair value of

assets, FVA1, FVA2, and FVA3 are nearly all positive while the coefficients of the fair value of

liabilities, FVL12 and FVL3, are nearly all negative. Variation in the significance of the

coefficients across industry sectors supports the notion that the value relevance varies by

industry sector. For example, one industry sector (Telecom Services—with only 26 firms)

exhibits one significant coefficient of the FVH, two sectors (Materials and Utilities) exhibit two

significant coefficients, four (Energy, Industrials, Consumer Discretionary, and Information

Technology) exhibit three significant coefficients, and the remaining three (Consumer Staples,

Health Care, and Financials) exhibit significance in all five coefficients of the FVH.

The coefficients of Consumer Staples are unexpectedly large in magnitude. This

abnormality could be due to the small sample size of this sector (only 36 observations). When

34

statistically significant, the coefficients of FVA1 range from 1.02 (Consumer Discretionary) to

1.42 (Information Technology). One would expect the coefficient of FVA2 to be lower than that

of FVA1 because investors to place highest credibility on Level 1 assets and discount Level 2

(and Level 3) measurements. Where both coefficients of FVA1 and FVA2 exhibit statistical

significance, of the five pairs where both coefficients are statistically significant (ignoring

Consumer Discretionary), only one sector, Industrials, shows this pattern. The other four

industry sectors, Consumer Discretionary, Health Care, Financials, and Information Technology,

show the opposite pattern. There are four sectors with significant coefficients of FVA3:

Financials (0.56***), Energy (0.73**), Information Technology (1.73***), and Health Care

(4.30***). Except for Health Care, the three other coefficients of FVA3 are the lowest of the

significant coefficients of the fair value of assets within their sector. This suggests that of the

Level 1, 2 and 3 disclosures of the fair value of assets, investors discount Level 3 the most. The

significant coefficients of FVL12 ranged from –1.98*** (Utilities) to –0.76** (Materials) as

expected, except for the Health Care sector, where the coefficient of FVL12 was 1.93***. The

sign of this coefficient is completely unexpected, and together with the surprisingly large

coefficient of FVA3 suggests that in this sector, investors may view Level 3 assets and Level 1

and Level 2 liabilities differently. Further study is necessary to address this anomaly.

The coefficient FVA1 (FVL12) is expected to be closer to one (negative 1). This

expectation was tested with F-tests, whose results are reported in Panel B of Table 11. While

many results are significant, they are not consistent. The results indicate that:

the Energy sector has three coefficients of the FVH that exhibit significance: FVA2,

FVA3, and FVL12. The F-tests suggest that the coefficients of FVA2 and FVA3 are

not significantly different from one and from each other, and the coefficient of FVL12

is not significantly different from –1;

35

the Materials sector has two coefficients of the FVH that exhibit significance: FVA2

and FVL12. The coefficient of FVA2 is significantly different from one, and the

coefficient of FVL12 is not significantly different from –1;

the Industrials sector has three coefficients of the FVH that exhibit significance: FVA1,

FVA2, and FVL12. Both coefficients of FVA1 and FVA2 are not significantly

different from one and from each other. The coefficient of FVL12 is not significantly

different from –1.

the Consumer Discretionary sector, the same three coefficients of the FVH are

significant, however the coefficient of FVA1 is not significantly different from one,

while the coefficient of FVA2 is significantly different from one, and the coefficients

are not similar to each other. The coefficient of FVL12 is not significantly different

from –1.

the Health Care sector, all five coefficients of the FVH exhibit significance. The three

coefficients of fair value of assets are significantly different from one. However, the

coefficients of FVA1 and FVA2 are not significantly different from each other, but

they are statistically significantly different from FVA3. The coefficients of FVL12 are

not significantly similar to –1 while the coefficient of FVL3 is similar to –1, and both

coefficients are significantly different from each other.

the Financials has all five coefficients of the FVH exhibiting statistical significance and

different from each other. None of the coefficients of the fair value of assets are similar

to one. The coefficient of FVL12 is statistically similar to –1 while the coefficient of

FVL3 is not.

the Information Technology sector, the three coefficients of the fair value of assets are

statistically significant. The F-tests suggest that the coefficients do not exhibit

statistical differences. However the coefficients of FVA1 and FVA2 are not

36

statistically similar to one, while the coefficient of FVA3 is. In the Telecommunication

Services sector, the only coefficient of the FVH that exhibits significance is that of

FVA2, and F-tests suggest that it is not statistically similar to one.

the Utilities sector has two coefficients of the FVH that exhibit significance. The

coefficient of FVA1 is not statistically different from one while the coefficient of

FVL12 is not significantly different from –1.

We expect to find that the estimated coefficients of FVA1 (and perhaps FVA2) to not be

different from (positive) one. While we find evidence of such a pattern, the results are not

consistent. In addition, we expect the estimated coefficient of FVL12 to not be different from

negative one. We observed this general pattern except for Health Care. The above discussion

suggests that the coefficients of the FVH are value relevant, and the value relevance is different

across industries, and that the results observed for the GICS financials are generally consistent

with prior literature. Next is the examination of the tests of the hypotheses.

5.2 Results of Tests of H1

H1 investigates the additional value relevance of BE/ME in two ways. The first

approach includes the BE/ME ratio into the modified Ohlson model as follows:

PRCit = α0 + α1NFVAit + α2FVA1it + α3FVA2it + α4FVA3it

+ α5NVFLit + α6FVL12it + α7FVL3it + α8 BE/MEit + β1NIit + εit (2)

Hypothesis H1a predicts that the coefficient of BE/ME, a8, will be significant and negative. The

second approach partitions the sample into two 45th–percentiles based on whether the value of

the BE/ME ratio is low or high. The hypothesis predicts that the coefficients of the fair value of

assets, FVA1 through FVA3, of the lower (higher) 45th–percentile, the growth (value) firms, will

be higher (lower) than those in the value (growth) firms. The results of the regression model in

Equation (2) are shown in the two Panels A1 and A2 of Table 12.

37


The comparison of Panel A1 of Table 12 with Panel A of Table 11 shows that all

coefficients that were statistically significant in model 1 continue to be significant in model 2

with minor shifts in the value of the coefficients. In Panel A1 of Table 12 one can see that the

coefficient of BE/ME is negative for all industries, and when N>40, the coefficient is also

significant. This suggests that BE/ME is value relevant, as hypothesized.

Panel B of Table 12 shows the composition of the two 45th-percentiles based on low or

high BE/ME. Forty-fifth-percentiles were selected in order to have some separation between the

higher and lower subsamples. Some studies partition the sample into quantiles, however, lower

percentiles were not possible due to the small size of the sample. The 45th-percentile, where

BE/ME is low, contains the growth firms in each GICS Sector, while the 45th-percentile where

BE/ME is high holds the value firms. The estimated coefficients for the growth (value) firms,

with BE/ME low (high), are presented in Panel C (Panel E) of Table 12.

H1b predicts the coefficients of the fair value of assets of the growth firms in Panel C

will be higher than those of the value firms in Panel E. To more easily compare the estimated

coefficients of the fair value of assets, Panel G of Table 12 contains corresponding pairs of

coefficients from Panels C and E where both are significant. For the two largest GICS Sectors,

Financials and Information Technology, all three pairs of coefficients of fair value of assets

exhibit the hypothesized pattern, while for three sectors, Energy, Industrials, and Health Care,

some pairs of coefficients exhibit the opposite pattern. While this suggests that H1b is supported

by only two sectors, an inspection of the relative sizes of the FVA and the number of firms in the

sample (see Panel B of Table 8) indicates that the hypothesis has the hypothesis of both largest

industries where the fair value of assets are the largest.

Panels D and F of Table 12 show the F-tests of coefficients for the corresponding

regression. The new results of F-tests for liabilities are even stronger than what was reported in

38

Panel B of Table 11. For example, the coefficient of FVL12 of Industrials was not significantly

different from –1 in the earlier regression, yet in both growth and value regressions, this

coefficient in the corresponding regressions is significant and not similar to negative one. This

suggests that investors value assets of growth and value firms differently in each industry.

Overall, these results suggest that hypothesis H1b may hold for two sectors (Financials

and Information Technology). Furthermore, these results suggest that in some cases the value

relevance of similar information from the disclosures of the FVH can be different for firms in

different industries and growth vs. value firms.

5.3 Results of Tests of H2

The second hypothesis investigates the effect of industry concentration on the value

relevance of the FVH, and will be approached by partitioning the sample into two groups of low

vs. high concentration. H2 states that in industries with higher (lower) concentration, we expect

the coefficient of the FVH Assets to be lower (higher).

Although the Herfindahl-Hirschman Index (HHI) is generally regarded as the most

accurate measure of industry concentration, it is limited both in issuance and coverage. The US

Census Bureau publishes the HHI once every five years as a part of its Economic Census. At the

time the analysis was performed, the most recently available HHI was from the Economic

Census of 2002. In addition, the Census Bureau computes the HHI only for a limited number of

industries. Panel A of Table 13 shows descriptive statistics of the HHI for the 510 firms in this

sample whose industries were included the computation of HHI in Economic Census of 2002.


Firms in two GICS Sectors, Financials and Utilities, did not have HHI scores, while four

other sectors, Energy, Materials, Consumer Discretionary, and Consumer Staples, had fewer

than 60 firms each. HHI scores for firms in four of the seven GICS sectors show a wide range of

39

values from a low HHI of under twenty-five to a high HHI of over 2,500. In order to increase

the number of firms that can be included in tests of this hypothesis, an alternate measure of

industry concentration, the three-year average Herfindahl of Sales, was subsequently computed

and studied. The results of the studies that used the HHI are presented in the panels of Table 13

while the results based on the alternate measure are presented in panels of Table 14. Lastly, as

an additional investigation, IRLS regression studies with an indicator variable with 0 for firms in

unconcentrated industries and 1 for concentrated industries are reported.


Panel B of Table 13 shows the low and high 45th percentiles of HHI. The number of

firms in each GICS Sector is quite small. Furthermore, the Health Care sector has an imbalance

with 46 firms with HHI Low and 110 firms with HHI High. Due to the small number of

observations, the regression tests may lack sufficient power.20 The results of OLS regressions on

the subsample with HHI low (high) are presented in Panel C (E) of Table 13 and are followed by

results of corresponding F-tests in Panel D (F). The significant coefficients of fair value of

liabilities in Panel C of Table 13 are positive, as are two of the three significant coefficients in

Panel E of Table 13. The only pair of significant coefficients of the fair value of liabilities is

that of FVL12 of Health Care where both coefficients are positive. These results of the fair

value of liabilities are unexpected and perhaps are a consequence of the small sample size.

According to H2, the coefficients in Panel C with HHI Low are expected to be higher than those

in Panel E with HHI high. Panel G of Table 13 shows the significant pairs of coefficients of fair

value of assets from Panels C and E. Clearly, the results are mixed.

Because the sample was reduced from its original size due to lack of HHI, in order to

explore the influence of outliers, as a robustness check, IRLS regressions were run on both 45th-

percentiles. The results, presented in Panels H and I of Table 13 and summarized in Panel J, are

20 Subsequently, an alternate measure of industry concentration with broader coverage will be computed

and discussed later.

40

consistent with the OLS regressions summarized in Panel G. In particular all pairs that were

significant in OLS Panel G were also significant in the IRLS Panel J. The coefficients between

the OLS and IRLS regressions generally maintained similar magnitude and, more importantly,

maintained the same relationship with each other.21 Thus, because the five pairs of OLS

regressions continue to exhibit the same pattern under the IRLS regressions, we conclude that

those results are not sensitive to outliers. The results are mixed and do not generally support the

hypothesis.22

As mentioned earlier, although the HHI is considered a very accurate measure of

industry concentration, its issuance and coverage are limited. The Herfindahl of Sales is a

measure of industry concentration that has broader coverage than the HHI, however because it is

computed only for public firms, it is not as reliable as the HHI. Appendix B describes the

computation of the three-year average of the Herfindahl of Sales, and Panel A of Table 14 shows

descriptive statistics of the measure which ranged from 0.03 to 0.41, and the standard deviation

across industries ranged from 0.01 to 0.09. Thus, this measure may not provide sufficient

variation.

In an attempt to follow the methodology in Table 13, the sample was split into two 45th

percentiles. However because the Herfindahl of Sales produces insufficient variation, the 45th

percentiles did not achieve sufficient separation, i.e. for seven of the 10 industry sectors, the high

values of the lower 45th percentile were the same as the low values of the higher 45th percentile.

Therefore, smaller percentiles were formed.

Using 40th percentiles reduced the sectors with insufficient variation to half of the total,

while with 35th percentiles, as shown in Panel B of Table 14, only three sectors exhibited

21 In the IRLS regression, a new pair of significant coefficients, FVA3 of Health Care, emerged. 22 The signs of the coefficients of the fair value of liabilities are also mixed. Two additional pairs of

significant coefficients are now present in Panels H and I. However, the coefficients of FVL12 of

Materials have opposite signs. The signs of the coefficients of the fair value of liabilities are also mixed.

Two additional pairs of significant coefficients are now present in Panels H and I. However, the

coefficients of FVL12 of Materials have opposite signs.

41

insufficient variation23: Energy, Materials and Telecommunication Services. Furthermore,

Consumer Staples, with 16 firms in the low 35th percentile, has insufficient data to perform a

regression study. These four industry sectors have been removed from the panels that follow.

Panel C of Table 14 shows the number of firms in each percentile group by industry sector.

Panels D and E of Table 14 show the results of OLS regression studies, while Panel F contains

the pairs of significant coefficients of assets. Similar to Panels G and J of Table 13, the results

are mixed.

As a final series of studies investigating industry concentration, the modified Ohlson

model with H_Ind as an indicator variable with 0 for unconcentrated industries and 1 for

concentrated industries as reported on the three-year average Herfindahl of Sales as shown in

Equation (3) was run:


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 H_Indit + β1NIit + εit (3)

This hypothesis suggests that firms with higher concentration should have smaller coefficients

for FVA1 – FVA3 than firms with lower concentration. Consequently, we expect a negative

coefficient for H_Ind. The first regression was for the entire sample. The results are shown in

Panel G of Table 14. The coefficient of H_Ind is not statistically significant. The same

regression model was run with the sample divided according to GICS Sector. The results are

shown in Panels H1 and H2 of Table 14. The results remain mixed with the coefficient of

H_Ind significant and positive in the following sectors: Energy, Financials, and Telecom

Services, but significant and negative in the Industrials and Consumer Staples. Based on the

results of this study, the effect of industry concentration on the value relevance of the FVH is

unclear. The results are mixed which suggests that the influence of concentration does not

23 The maximum value of the low 35th percentile is equal to the minimum value of the high 35th percentile.

42

clearly affect the value relevance of the disclosures of FVH. Lack of support, however, could be

due to insufficiency of the Herfindahl of Sales as an accurate measure of industry concentration.

5.4 Results of Test of H3

Hypothesis H3 investigates how firm’s status (entering, incumbent, exiting) influences

the value relevance of the FVH. As discussed in more details later, because this study uses

financial information for fiscal year ending December of 2008, we are limited to entering and

incumbent firms and we are unable to evaluate the effect of status of exiting firms on value

relevance of the FVH. Obviously, future exiting firms will be some of the current incumbent

firms, and thus it is possible to compare the value relevance of the disclosures of the FVH of

entering vs. the combined incumbent and exiting firms.

This study follows MacKay and Phillips (2005) (MP) and begins by classifying firms as

entering, incumbent, or exiting by examining the presence or absence of firms from the sample

in the decade preceding and year following 2008. A firm is classified as entering if it appears

for the first time in the decade prior to 2008, exiting if it disappears in the year after 2008, and

incumbent if it persists throughout the entire period. Clearly, exiting firms should be identified

using data from the entire decade following 2008 rather than simply the subsequent year.

However, at the time of this writing the data on the decade after 2008 is not yet available, and

therefore this means that this study is unable to distinguish between incumbent and exiting firms.

In what follows, the term “incumbent firms” will be used to refer to the group that includes both

incumbents and exiting firms that are unidentifiable at the time of this writing. We expect that

the coefficients of FVH of assets will be lower (higher) for entering (exiting and incumbents)

firms.

Table 15 provides yearly number of firms in the 2008 sample that entered during the

1990-2009 period. To obtain information in this table, I first identify the “2008 cohort,” and

then query Compustat for the presence this “cohort” from 1999 through 2009. Panel A of Table

43

15 follows this “2008 cohort” from 1999 through 2009. In order to more clearly identify the

entering and exiting firms, Panel B of Table 15 indicates the result of the subtraction of each

column of Panel A from the column of the “2008 cohort.” Thus, the columns of Panel B show

how many firms must be added to the corresponding column of Panel A in order to recover the

2008 cohort. Stated slightly differently, for the decade preceding 2008, the columns of Panel B

show the number of firms that entered a particular GICS Sector of the 2008 cohort in that year,

while the column after 2008 shows the number of firms that exited the cohort in that year.


Several industries had less than 30 firms entering, and with so few firms in that

subsample, statistical tests have less power. Consequently, Materials, Consumer Staples,

Utilities, and Information Technology were removed from analysis entering firms whose results

are shown in Panel C of Table 15.

In order to minimize the influence of outliers, IRLS regressions are run, and the

estimated coefficients of the entering firms (incumbents) are reported in Panel C (Panel D) of

Table 15. Comparing significant coefficients between entering firms and incumbents, one can

identify eleven pairs of significant coefficients of the FVH of assets that are shown in Panel E of

Table 15. By inspection, one can easily see that eight pairs support the hypothesis. It is

important to note that the Consumer Discretionary sector has only 31 entering firms, and perhaps

due to a small number of firms, this sector could also have been dropped. If this is done, then in

every case, the corresponding coefficient of the incumbents is higher than that of the entering

firms as hypothesized. This suggests that in almost all cases hypothesis H3 is supported and the

value relevance of FMV is higher (lower) for incumbent (entering) firms.24

These results could be biased by the size of the firms. Entering firms are naturally

smaller than incumbents. Panel F of Table 15 shows the results of a one-tailed t-test that

24 Panel F of Table 15 shows the four pairs of coefficients of FVH of liabilities that are significant. It is

interesting to note that in all four cases, the coefficients of incumbents are more negative.

44

compares the means of the logarithm of total assets of the entering to the established firms. The

one-tailed Scatterthwaite t-value for unequal variances, 6.68, is highly significant, which

suggests that the means of the two groups are different. The 95% confidence interval for the

mean of the entering firms is (6.18, 6.48), while the corresponding interval for the established

firms is (6.85, 7.08). The clear separation between the confidence intervals suggests that on

average the entering firms are smaller than the more established firms (incumbent) as expected.

Researchers in finance have consistently identified size as a significant risk factor in the

cross-section of returns of equities (Banz 1981 and Fama and French 1992), and therefore, the

influence of size on the value relevance of the FVH should also be investigated. The modified

Ohlson model was used with two additional variables. One was an indicator variable whose

value, Entri, was set to one for entering firms and zero for established firms, and the second was

the logarithm of total assets log(Ai):


+ α5NVFLit + α6FVL12it + α7FVL3it

(4)

+ α8 Entrit + α9 log(Ait) + α10 Entrit ×log(Ait) + β1NIit + εit

The estimated coefficients of four OLS regressions that use these two variables and their

interaction to investigate the influence of the firm’s status, as entering or incumbent, size, and

their interaction are reported in Panels G1 and G2 of Table 15.

The regressions were estimated on the entire sample which consisted of 1,580

observations. All coefficients are highly significant. The first model, which includes the

indicator variable, entering, by itself, shows that the coefficient is highly significant and negative,

which suggests that investors generally discount entering firms compared to incumbents. The

second model, which includes the logarithm of assets by itself, shows that size is significant and

positive. This indicates that size is value relevant, and furthermore that investors view larger

firms more favorably than smaller ones.

45

The third model includes both terms together, and both maintain both their significance

and sign. This suggests that both status and size are value relevant. Finally, the fourth model

includes the interaction of status and size. In this model, the coefficient of the entering indicator

variable changes sign and continues to be significant, while the coefficient of size maintains its

sign and high significance. Interestingly, the coefficient of the interaction term is highly

significant and negative. Overall, these results clearly suggest that size has explanatory power in

addition to a firm’s status as entering or incumbent in a value relevance regression of the FVH.

5.5 Results of Tests for H4

Hypothesis H4 investigates the effect of liquidity on the value relevance of the FVH. H4 states:

The magnitude of the coefficients of FVH will be higher (lower) for firms with higher (lower)

liquidity. The motivation for this hypothesis is GNO, who found a positive interaction between

high and low Tier 1 Capital Ratio and the coefficient of Level 3 fair value assets (FVA3). The

Tier 1 Captial Ratio is only available for banks, and therefore a different measure of liquidity

must be used for non-bank institutions. Two common measures of liquidity are the quick ratio

(QR)

and the operating cash flow ratio (CR)

where ACT = Total Current Assets, INVT = Total Inventories, XPP = Prepaid Expenses, LCT =

Total Current Liabilities, and OANCF = “Operating Activities—Net Cash Flow.” This

dissertation will investigate both measures of liquidity. As summarized in Panel A of Table 16,

of the trimmed sample, 1,061 firms were missing the QR, while only 480 were missing the CR.

46

As anticipated,25 there are almost no firms in the Financials with QR or CR and therefore the

discussion of this hypothesis will not include results from this sector. However, it is important

to recall that GNO, using the Tier 1 Capital Ratio, already have shown liquidity influences the

value relevance of FVA3 for banks. Thus, although results from financials will not be a part of

this dissertation, their effect has already been examined and supported by prior literature.


Panel B of Table 16, shows some summary statistics for the QR. For all GICS Sectors,

the mean is higher than the median, which indicates that the distribution of QR is skewed

towards higher values. In order to minimize the effect of outliers, IRLS regressions is used on

the modified Ohlson model that includes QR as shown below:


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 QRit + β1NIit + εit

(5a)

Panels C1 and C2 show the coefficients estimated by IRLS. Unfortunately, the

estimated coefficient of QR is marginally significant in only two industries, Health Care and

Information Technology. Moreover, for both industries, its sign is negative, which is the

opposite of what is expected. This suggests that investors are penalizing firms for maintaining

liquidity. One possible explanation for this could be that because interest rates are low during

this period, investors believe that firms in these industries should be borrowing to finance capital

investment.

To test H4, the interaction between QR and FVA3 was investigated as shown below:

PRCit = α0 + α1NFVAit + α2FVA1it + α3FVA2it + α4FVA3it + α5NVFLit

+ α6FVL12it + α7FVL3it + α8 QRit + α9QRit ×FVA3it + β1NIit + εit (5b)

25 Thanks to Prof. Roger Debreceny during the defense of the proposal.

47

Panels D1 and D2 of Table 16 show the estimated coefficients using an IRLS regression.

Only two industries, Industrials and Health Care, have significant coefficients, and unfortunately,

the signs are opposite. For the Industrials, the estimated coefficient of the interaction is

significant, has the anticipated sign, and is very large in magnitude. For Health Care, however,

the same estimated coefficient while significant and reasonably large, has the opposite of the

anticipated sign. These conflicting findings necessitate further study of the effect of liquidity as

measured by the quick ratio, QR, on the value relevance of the FVH.

Panel E of Table 16 shows summary statistics for CR. All GICS Sectors have firms

with negative CR, and in one, Health Care, even the median CR is negative. This negative

operating cash flow could be from the influence of the financial crisis on firms in this sample.

Panels F1 and F2 show the IRLS coefficient estimates in the modified Ohlson model

that includes the CR


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 CRit + β1NIit + εit .

(5c)

Although the CR is significant in only two GICS Sectors, Consumer Discretionary and

Consumer Staples, it is positive in both cases, which suggests that liquidity itself is value

relevant in those sectors. This result for the CR is in contrast to that of the QR.

The next pair of half-panels, G1 and G2 of Table 16 show the IRLS estimated

coefficients for the modified Ohlson model that includes the interaction term between CR and

FVA3


+ α6FVL12it + α7FVL3it + α8 CRit + α9 CRit ×FVA3it + β1NIit + εit .

(5d)

48

The coefficients of CR that were significant in the prior model, Consumer Discretionary and

Consumer Staples, remain significant and shift only slightly in magnitude. However the

coefficient of the interaction term in these models are not significant. This suggests that in these

GICS sectors, Consumer Discretionary and Consumer Staples, the value relevance of liquidity

has only a main effect on the equity price.

It is extremely interesting to note that in three GICS Sectors: Health Care, Telecom-

munication Services, and Utilities, the interaction term is significant and positive, as expected.

This suggests that there is empirical support in this study for some industries that liquidity, as

measured by CR, may indeed be value relevant for Level 3 assets. It is interesting to note that

these industries, as is the Financials, are regulated.

It is also important to note that one possible explanation for the null result of H4 is that

the accounting data is from the period of the financial crisis of 2008 (Spiegel 2011). During this

period, many firms faced external liquidity constraints which may weaken the influence of

liquidity on the value relevance of the disclosure of Level 3 assets.

5.6 Results for H5

The final hypothesis investigates whether the relative degree of assets reported at fair

value (to the total assets, FVArat) influences the value relevance of the FVH. A direct

relationship between the magnitude of fair value of reported assets for the test FVH is expected.

Stated differently, H5 hypothesizes that when the “FAS 157 ratio” is lower (higher), we expect

lower (higher) coefficients of the FVH. Summary statistics of the FVArat (assets reported at fair

value divided by total assets) are shown in Panel A of Table 8. To test this hypothesis, FVArat

was included in the modified Ohlson regression model as shown in Equation (5):


+ α5NVFLit + α6FVL12it + α7FVL3it + α8FVAratit + β1NIit + εit . (6)

49

Panels A1 and A2 of Table 17 contain the results of the IRLS regression studies, which show

that none of the estimated coefficients of the FVA ratio are significant. Therefore, in this sample,

the FVArat does not contain explanatory power, which suggests that hypothesis H5 is not

supported.


6 Sensitivity Analysis

To insure that the results are not specific to a single year, this sensitivity analysis tests the value

relevance of the FVH by pooling data from 2009 with 2008. Because of the lack of data on

industry concentration, H2 will not be included in the sensitivity analysis. Because the focus of

H3 is on the status of firms based on the year 2008, it does not make sense to pool the results

with those of the year 2009. Therefore H3 will also not be included in the sensitivity analysis.

Furthermore, the results suggests that H5 is clearly rejected. Therefore, this sensitivity analysis

concentrates on the general value relevance of the FVH across industry sectors and tests of

hypotheses H1 and H4. The new data is pooled year-end annual accounting data from 2008 and

2009, with stock market prices from March 31, 2009 and 2010 respectively.26 Panel A of Table

18 shows the steps taken to develop this sample for this analysis. These steps follow those

presented earlier in Panel A of Table 7, except that firms with negative book equity are not

dropped yet. Part of the sensitivity analysis is to determine whether the IRLS regression will be

able to minimize the effects of this type of outlier. Panel B of Table 18 shows the composition

of this sample by GICS Sector. Panel C1 (C2) provides descriptive statistics of the ratio of fair

26 The main analyses of the dissertation presented in the previous section used the variable, “Common

Shares Outstanding,” CSHO, to deflate items from the consolidated statement of financial position.

However, Compustat no longer provides this variable, and therefore this sensitivity analysis uses

Compustat’s variable “Common Shares Used to Calculate Earnings Per Share – Basic,” CSHPRI, to

deflate appropriate items. Also, while SAS was used for analyses in the previous section, R is used in this

section.

50

value of assets (liabilities) to total assets (liabilities) expressed as a percentage. Panel C2 also

shows the sum of the Level 1 and Level 2 fair value liabilities scaled by total liabilities reported

as FVL12.

Panel D of Table 18 shows the distribution of outliers that are identified as observations

with Studentized Residuals greater than two. Following STY, these are removed. Panel E of

Table 18 shows the sensitivity studies that were performed and the sample sizes for each test.

The sensitivity studies concentrated on the modified Ohlson model across industry sectors,

hypothesis H1 on the value relevance of the BE/ME ratio, and hypothesis H4 on the effect of

liquidity on the value relevance of the fair value of Level 3 assets, FVA3.

Panel F of Table 18 shows the results of the overall test of value relevance on the

trimmed sample across all industry sectors. As before, the FVH continues to be value relevant,

and the value relevance varies by industry sector. The estimated coefficients of the FVH of

Financials and Information Technology are all significant, and the estimated coefficients of the

FVH of the Financials continue to display the pattern described by STY. The estimated

coefficients of the FVH of the Information Technology sector display an unusual pattern,

however. Although the estimated coefficients of fair value assets are all positive, they do not

display a monotonically decreasing pattern, and although the estimated coefficient of FVL12 is

negative, the estimated coefficient of FVL3 is positive and large.

Because the variables of interest, such as the book-to-market equity, quick ratio, and

operating cash flow ratio, are not available for each firm, the regression models that investigate

their effects are run on subsets of the sample. In order to minimize the effects of outliers, IRLS

regressions are run on the subsamples. Panel G of Table 18, the results of H1 on the value

relevance of the BE/ME ratio, are very similar to those of Panel A1 of Table 12 in that all

estimated coefficients of BE/ME are negative, and in all but two industry sectors (Consumer

Discretionary and Staples) are highly significant, as expected.

51

Panel H of Table 18 investigates effect of liquidity on the value relevance of FVA3.

Similar to Panel C1 of Table 16, the estimated coefficients of QR are nearly all not statistically

significant. Furthermore, for one industry sector, Information Technology, the estimated

coefficient is statistically significant and negative. This is the opposite of the expected sign.

Panel I of Table 18 shows the results of the model that includes the interaction between

QR and FVA3. Similar to Panel D1 of Table 16, only one interaction is significant, Consumer

Staples, and large in magnitude. As with the main study, this suggests that the results of this

test, using QR, are not generally conclusive.

Panel J of Table 18 continues the examination of the value relevance of liquidity using

the operating cash flow ratio, CR. All estimated coefficients, except for one are positive as

expected. Furthermore, in five industries the coefficients exhibit statistical significance. This

result is stronger than that of Panel F1 of Table 16 where estimated coefficients of only two

industry sectors, Consumer Discretionary and Staples, are significant.

Panel K of Table 18 shows the results of an IRLS regression including the interaction

between CR and FVA3. No interaction terms are significant. The next two panels, Panels L and

M, continue this study using a trimmed sample where the stockholder’s equity, including

BE/ME, is not negative. Panel L of Table 18 shows the results of an IRLS regression of the

model that includes CR alone with no interaction. The estimated coefficients are similar to those

of Panel J, as expected. Panel M of Table 18 shows the results of an IRLS regression on a

model that includes the interaction of CR and FVA3. Interestingly, the estimated coefficient of

the interaction term for Utilities exhibits strong statistical significance, and its magnitude is

nearly identical to that of the in Panel G1 of Table 16. However, the interaction term in no other

industry sector exhibits statistical significance.

The next series of tests introduces a dummy variable for the year, with the value zero for

2008 and one for 2009, and investigates its interaction with CR and FVA3.

52

PRCit = α0 + α1NFVAit + α2FVA1it + α3FVA2it + α4FVA3it + α5NVFLit+ α6FVL12it

+ α7FVL3it + α8 CRit + α9 Yr09it + α10 CRit ×FVA3it+ α11 CRit ×Yr09it (7)

+ α12 FVA3it × Yrit + α13 CRit ×FVA3it ×Yr09it + β1NIit + εit .

Panel N introduces the year dummy. When the coefficient of this dummy variable is statistically

significant, its estimated value is larger than three, and exhibits strong statistical significance in

six industry sectors. As one can easily expect, this result suggests that prices in the second year,

from March 31, 2010, are higher than prices from the first year, from March 31, 2009. Panel O

investigates the interaction between the year dummy and CR, and shows the interaction exhibits

statistical significance for only one industry sector, Industrials. Panel P investigates interaction

between the year dummy and FVA3, and shows its interaction exhibits statistical significance

for one industry sector, Consumer Staples. Panel Q. investigates the interaction between FVA3

and CR when the year dummy variable is included as a regressor. Again, the estimated

coefficient of the interaction is positive, as expected, and exhibits statistical significance only for

the Utilities sector. Finally, for completeness, Panel R adds the triple interaction between the

year dummy, CR, and FVA3, but the result for the interaction between CR and FVA3 remains

the same.

7 Conclusion

The influence of the crisis of 2008 on this dissertation may have most heavily affected

the null result of H4, which sought to examine the impact of a firm’s liquidity on the value

relevance of the fair value disclosure of its Level 3 assets.

Although not specifically expressed in the form of hypothesis, this dissertation expected

the value relevance of the FVH to be different across different industries. Based on the findings

of prior literature, this dissertation used the Global Industry Classification Standard (GICS®) to

classify firms into industry sectors. To minimize the effect of outliers, the data was Windsorized

at the 1%/99% levels. However, the coefficients of the fair value hierarchy, FVH, of the GICS

53

Financial sector estimated by ordinary least squares (OLS) on the Winsorized sample were

different from what had been reported in the literature. Song, Thomas, and Yi (2010), STY,

observed a monotonically decreasing pattern of coefficients of Level 1 through 3 fair value

assets, FVA1—FVA3. Consequently, I applied iteratively reweighted least squares (IRLS), a

technique from robust statistics, to the sample and found that coefficients of FVA1—FVA3 of

the GICS Financials estimated using this approach more closely displayed the pattern observed

by STY. Finally, in a method similar to STY, I dropped observations that IRLS identified as

outliers due to their large residuals and also found that coefficients of FVA1—FVA3 of the

GICS Financials estimated by OLS on the trimmed sample were again more consistent with

prior literature.

In each of the ten GICS industry sectors, at least one of the five coefficients of the FVH

(FVA1, FVA2, FVA3, FVL12, and FVL3) exhibited statistical significance. In three industry

sectors, Consumer Staples, Heath Care, and Financials, all five coefficients of the FVH exhibited

statistical significance. In four industry sectors, Energy, Industrials, Consumer Discretionary,

and Information Technology, three of the five coefficients exhibited statistical significance. In

two industry sectors, Materials and Utilities, two of the five coefficients exhibited statistical

significance, and in only one industry sector, Telecommunication Services, only one of the five

coefficients exhibited statistical significant. These results strongly suggest that the FVH is value

relevant, and moreover that the value relevance varies by industry sector.

This dissertation also sought to investigate the additional effect of other characteristics on

the value relevance of the FVH. This dissertation’s first hypothesis posits that BE/ME contains

significant explanatory power in the value of the firm. Banko, Conover, and Jensen (2006)

conclude that the intra-industry variation in BE/ME is relevant in explaining stock returns. This

dissertation extends their work by examining the value relevance of BE/ME (vs. return). Using

the modified Ohlson model with an additional term for the BE/ME ratio, I found that the

54

estimated coefficient of the book-to-market equity ratio (BE/ME) exhibits statistical significance

with the expected sign. However, a comparison of estimated coefficients in regressions on value

vs. growth subsets, the results are mixed. In two industries, Financial Services and Information

Technology, all three pairs of coefficients of the fair value of assets, FVA1 through FVA3,

exhibit statistical significance and follow the expected pattern. Three other industries, however,

have one or two pairs of estimated coefficients that show the opposite pattern.

This dissertation’s second hypothesis examines the influence of industry concentration

on the value relevance of the FVH. Based on the work of Hou and Robinson (2006) who find

evidence that suggests that firms in competitive (concentrated) industries have higher (lower)

average returns, this dissertation’s second hypothesis posits that the coefficients of the FVH of

assets will be lower (higher) in industries with higher (lower) concentration. The results are

mixed, possibly because the two available measures of industry concentration each contain

separate inadequacies. The Herfindahl-Hirshman Index (HHI), computed every five years by

the Census Bureau of the US federal government, is considered to be one of the most accurate

measures of industry concentration. However its coverage is not broad. When published, it is

only available for a subset of manufacturing firms. Researchers can use data items such as net

sales to compute their own Herfindahl index of concentration. To do so, they need to have

access to all data items for all firms in an industry. However, there are often firms that are not in

a dataset that are in an industry whose data items should be included in the Herfindahl index.

Therefore, although a Herfindahl index could be computed for each firm in a dataset, it may not

accurately reflect the concentration in a particular industry.

The third hypothesis discusses the influence of a firm’s status as entering or incumbent

on value relevance of the FVH. Mackay and Phillips (2005) compare a firm’s status as entering,

incumbent, and exiting and find that within an industry, a firm’s status affects its valuation.

Mackay and Phillips identify entering (exiting) firms by examining the decade before (after) the

55

year of interest. Because the year studied, 2008, is less than a decade away from the current year,

exiting firms cannot be identified at this time, and therefore the study only compares entering to

incumbent firms. The results are generally consistently and exhibit the expected pattern. As the

literature in finance has shown that size is a significant factor in the cross sectional analysis of

expected stock returns, and there is a possibility that the effect observed could be due to size and

not the firm’s status as entering vs. incumbent, I briefly investigated this, and found evidence

that suggests that size has explanatory power. Furthermore, the firm’s status as entering or

incumbent remains significant even after the introduction of size in the model.

This dissertation’s fourth hypothesis examines the effect of liquidity on the Level 3

assets, FVA3. Goh, Ng, and Ow Yong (2009) report results that suggest that banks with higher

Tier 1 capital ratios have higher estimated coefficients of Level 3 assets. Because the Tier 1

capital ratio is only available for banks, this dissertation uses two alternate measure of liquidity:

the quick ratio (QR) and operating cash flow ratio (CR). The results do not provide strong

evidence suggesting that either measure of liquidity influences value relevance of Level 3 fair

value assets.

The final hypothesis examines whether the relative amount of assets measured at fair

value to total assets influences the value relevance of the FVH. The results consistently reject

this proposition. This suggests that the relative size of the assets measured at fair value does not

influence the value relevance of the FVH.

Prior studies examined the value relevance of the FVH for banks. This study provides

evidence that the value relevance of the FVH can be extended to other industry sectors. The

strong statistical significance of the BE/ME ratio suggests that it could be considered as a control

variable in future studies that use the modified Ohlson model to examine value relevance.

56

Further studies could examine the effect of size27 directly on the value relevance of the

FVH across industry sectors. The hypothesis of the influence of liquidity on the value relevance

of FVA3 was motivated by GNO’s result on the effect of the Tier 1 capital ratio on Level 3

assets of banks. With the lack of results from this study, it is possible that perhaps the analog of

the Tier 1 capital ratio in non-bank firms is not liquidity. Therefore, perhaps some other

measure of the riskiness of firms is more appropriate. Some possible alternatives are the

auditor’s going concern opinion, credit rating, or default probability. In further studies of the

value relevance of the FVH, the influence of these other measures on the value relevance of the

Level 3 assets could be of interest.

27 Thanks to Prof. Qianqiu Liu for this suggestion.

57

Figure 1: Timeline of the Issuance and Effective Date of FAS 157

The timeline shows that the FASB published an exposure draft seeking comments on

measurement of fair value in the summer of 2004. In the fall of 2006, the FASB issued FAS

157 which was to have become effective for fiscal years beginning after November 15, 2007.

For nonfinancial assets and liabilities recognized or disclosed on a nonrecurring basis, however,

in early 2008, the FASB postponed the effective date of FAS 157 to fiscal years beginning after

November 15, 2008. For all other assets and liabilities, FAS 157 became U.S. GAAP when

initially stated.

58

Figure 2. Weekly Close of the S&P 500 Index from Jan 1, 2007 to Jan 1, 2012

Source: MSN Money

This chart shows the weekly close of the S&P 500 Index from January 1, 2007 to January 1,

2012. The prices used in this dissertation are from the CRSP Monthly database on 3/31/2009.

3/30/2009

59

Figure 3 Sample Disclosures Required by FAS 157

Panel A. Example of the Fair Value Hierarchy (FVH) in tabular form

This sample shows a typical disclosure of the Fair Value Hierarchy (FVH), one of several new

disclosures that are required by Financial Accounting Standard (FAS) 157. The table clearly

identifies particular types of assets and liabilities, and also clearly shows the amounts of each

type that the filing entity measured using inputs that FAS 157 defines as Level 1, 2, and 3.

Table 2 summarizes the main characteristics of Levels 1 to 3. This disclosure was made by

Caterpillar, Inc. in its Form 10-K filing for 2008.

60

Figure 3 (cont’d)

Panel B. Example of the Reconciliation of Level 3 Assets and Liabilities.

This sample shows a typical reconciliation of Level 3 Assets and Liabilities, one of several

new disclosures that are required by Financial Accounting Standard (FAS) 157. Beginning

with the ending balance of the prior year of assets and liabilities measured with Level 3 inputs,

this reconciliation summarizes the main changes during the year that resulted in the ending

balance for the period. This disclosure was made by Caterpillar, Inc. in its Form 10-K filing

for 2008.

61

Table 1. Four (Known) Papers that Examine the Fair Value Hierarchy (FVH)

Abbr Author(s) Title SSRN

GNO Goh, Beng Wee

Ng, Jeffrey

Ow Yong, Kevin

Market Pricing of Banks’ Fair Value Assets Reported

Under SFAS 157 during the 2008 Economic Crisis

1335848

Kolev Kolev, Kalin Do Investors Perceive Marking-to-Model as Marking-

to-Myth? Early Evidence from FAS 157 Disclosure

1336368

RS Riedl, Edward J.

Serafeim, George

Information Risk and Fair Value: An Examination of

Equity Betas and Bid-Ask Spreads

(Journal of Accounting Research 49:4,

pp. 1083–1122)

1439851

& JAR 49:4,

pp. 1083–

1122

STY Song, Chang Joon

Thomas, Wayne

Yi, Han

Value Relevance of 157 Fair Value Hierarchy

Information and the Impact of Corporate Governance

Mechanisms

(The Accounting Review 85:4, pp. 1375–1401)

1198143

& Acc. Rev

85:4, pp.

1375-1401.

This table shows the four (known) early papers that studied the value relevance and content of

information of the fair value hierarchy (FVH) and the abbreviations used in this (proposed)

dissertation for them.

62

Table 2. The Main Characteristics of the Fair Value Hierarchy (FVH) of FAS 157

Level 1 Level 2 Level 3

Approach to Measurement of Fair

Value Identical

Instrument

Similar

Instrument Other*

Prices used in Measurement of Fair

Value Observed** Observed** Estimated

* Market (e.g. Broker’s Quotations), Income (e.g. Discounted Cash Flow) or Cost (e.g. to

replicate the service capacity of an asset)

**Quoted prices in active markets.

This table highlights the main characteristics of the fair value hierarchy (FVH) of Financial

Accounting Standard (FAS) 157.

63

Table 3. Datasets of the Four Early Papers

Panel A: Characteristics of Data used

GNO Kolev RS STY

Industry SIC 60 & 61 GICS40,

removed 4040

SIC 6020, 6035,

& 6211

(Compustat

Bank dataset)

Selection NYSE, AMEX,

NASDAQ

S&P 500, (Mar 08)

S&P Mid-Cap 400,

S&P SmallCap 600

12/31/2006:

TA > $10 B

Banks

(implicit)

Time Period 2008 Q1 – Q3 2008 Q1 & Q2 2007 Q1 –

2008 Q2

2008 Q1 – Q3

N 516 (Q1:177, Q2:172) 56 431

Firm-

quarters 1,462 349 148 1,260

Outliers Winsorized IRLS n/a (sensitiv.) Studentized

Levels

1, 2, 3 Net Assets Net Assets As-is &

“Sum Ratio” As-is

Sources Compustat

Quarterly;

CRSP; 10–Q

Compustat Quarterly;

CRSP; 10–Q

10–Q/10–K

(*Compustat N

= 1,045)

Compustat

Bank Qtrly;

CRSP; 10–Q

*In sensitivity analysis.

This table provides a summary the data sets that the early papers used. The papers and

abbreviations follow those defined in Table 1.

64

Table 3 (cont’d)

Panel B: Reporting Periods from which the papers examined the FVH

2007 (all) 2008 Q1 2008 Q2 2008 Q3 2008

Q4**

2009 Q1

GNO

Kolev

RS

STY

This Dissertation

Italics indicate portions related to this proposed dissertation.

** FAS 157 became US GAAP in this quarter (fiscal years ending after November 15, 2008).

This table provides a summary of the reporting periods that the (known) early papers used. The

papers and abbreviations follow those of Table 1.

65

Table 4. Summary of Hypothesis and Results

Panel A: GNO (Goh, Ng, and Ow Yong 2009)

Statement Findings

H1 Lower level FV assets are priced less than higher level FV assets. Supported

H2 The market pricing of FV assets is higher for banks with higher Tier 1 capital.

(liquidity)

γ13 >0*

H3 The market pricing of FV assets is higher for banks that are audited by Big 4 auditors. γ15 >0*

γ16, γ17 >0***

This table shows the main hypotheses and findings of GNO. The explanatory variables relating to the FVH are underlined because GNO use

net assets, or assets minus liabilities, in their models. The coefficients of their first model are monotonically decreasing and are statistically

different from one. However, β6 is not statistically different from β7. They use the model at the bottom of the table to address their next two

hypotheses. They test their second hypothesis using a dummy variable for whether the bank’s Tier 1 capital ratio is higher or lower than the

median, and find a significant interaction with the Level 3 net asset explanatory variable. Lastly the interaction of their Big4 dummy variable

with the FVH net assets suggests that the presence of a Big4 auditor increases investors confidence in the Level 2 and Level 3 net assets.

itit

ititititit

eFVAFVAFVA

NETBEEPSAMEXNYSEPRICE

321 765

43210

4342414

_13_12_11

_14_1]H1[

17161514

131211

1098

BIGFVABIGFVABIGFVABIGNETBE

INDCAPFVAINDCAPFVAINDCAPFVA

INDCAPNETBEBIGINDCAPPRICE it

***

66

Table 4 (cont’d)

Panel B: Kolev (2009)

Statement Findings

H1 Investors find mark-to-model [Levels 2 & 3] FV estimates sufficiently reliable to

be reflected in firm value.

Supported

H2 Investors perceive mark-to-model [Levels 2 & 3] estimates as less reliable than

mark-to-market FV.

Supported

Level

Difference

This table shows the main hypotheses and results of Kolev. As with GNO, in the previous panel, the explanatory variables are underlined to

indicate that Kolev also used net assets, or assets minus liabilities, in his model. To test his hypotheses, which is supported, he forms a

regression model with a complete set of controls. In order to control for the possibility of correlated omitted variables, he forms a differences

model and uses the Level 3 reconciliation table, an additional disclosure required by FAS 157, in the differences model. The differences model

supports his hypotheses.

ityprofitabil & growth, size,for proxies rating, credit ,indicatorsindustry Controls

ControlsNetBVELevelLevelLevelPrice

~

321 1321

ControlsNetBVEAddLGainL

LevelLevelLevelPrice

i154

321

33

321

67

Table 4 (cont’d)

Panel C: RS (Riedl and Serafeim 2011)

Statement Result

H1 The association between a bank’s equity beta and its financial assets is increasing

in the uncertainty about the parameters of the payoff distribution of those assets,

as measured by the Level 1, 2, or 3 designations.

Supported

H2 The information asymmetry component of the bid-ask spread is increasing in the

illiquidity of the bank’s financial assets, as measured by the Level 1, 2, or 3

designations.

Some supp:

γ7 significant

Sensitivity analysis (A. smaller banks): consistent with H1; not consistent with H2.

RS concentrate on the information content of the FVH of FAS 157. The dependent variable in their first model is the CAPM equity beta while

in their second they choose the information asymmetry component of bid-ask spreads. The results of their first test strongly support their

hypothesis, while their results of their second test generally support their hypothesis. Because their main sample is small, in their sensitivity

analyses, they use a larger and broader set of firms, and find evidence consistent with their first hypothesis, but not their second.

ididid

ititititititit

RETVWRET

LeverageOAFVAFVAFVAadjBeta

_

321_

10

54321

itit

itititit

FVFVFV

LogFollLogRiskLogTurnPriceLogLogSpread

321 765

43210

68

Table 4 (cont’d)

Panel D: STY (Song, Thomas, and Yi 2010 The Accounting Review)

Statement Result

H1 FV measurements under FAS 157’s hierarchy are value relevant. Supported

H2 FV measurements under FAS 157’s hierarchy are incrementally value relevant to

asset/liability type (Type information).

Supported

(Vuong 2&3)

H3 The value relevance of FV measurements under FAS 157’s hierarchy is less

evident for firms with weaker corporate governance. (Managers may “abuse”

private information.)

Generally

Supported

itititit

ititititit

NIFVLFVLNFVL

FVAFVAFVANFVAPRC

312

321

765

43210

1;1~

1;1~, 43

3

1

3

1

3

1

10

543210

(69.6%) ...

%)8.67( ...

C

CC

B

BB

A

AAtit

itititittit

ADEVLOANINVESCNFVAPRC

AOTHERADEVLOANINVSECNFVAPRC

*****

432

0.76 0.14 0.20

...321]1H[ GOVGOVFVAGOVFVAGOVFVAPRC itGitGitGit

69

Table 4 Panel D (cont’d)

STY’s first results very clearly suggest that the FVH exhibits value relevance. The numerical values of their coefficients exhibit a monotonic

pattern, and the coefficient of the Level 3 assets is statistically different from the coefficients of the Level 1 and Level 2 assets. Their second

hypothesis involves the value relevance of the expanded disclosures for the type of asset or liability reported at fair value. Their test involves a

pair of models: one with aggregated explanatory variables and one with detailed explanatory variables for several types of assets or liabilities.

They perform Vuong’s test which suggested that the models are different, and therefore support this hypothesis. Their final hypothesis

involves the effect of corporate governance on the value relevance of the FVH. Using a principal-component factor analysis, they compute an

index of corporate governance. The test of their hypothesis suggests that strong corporate governance, as measured by their index, has the most

effect on the coefficient of Level 3 assets.

70

Table 5 Papers examining characteristics of industries in more depth

Panel A: Identification of the papers

Abv Authors Title Year Journal

BCJ Banko, John C.

Conover, C. Mitchell

Jensen, Gerald R.

The Relationship between the Value Effect and

Industry Affiliation

2006 JoB

HR Hou, Kewei

Robinson, David T.

Industry Concentration and Average Stock Returns 2006 JF

MP Mackay, Peter

Phillips, Gordon M.

How Does Industry Affect Firm Financial

Structure?

2005 RFS

This table shows three papers that studied characteristics of industries or characteristics within industries, and the abbreviations used in this

dissertation for them.

JoB stands for Journal of Business, JF stands for Journal of Finance, and RFS stands for Review of Financial studies.

71

Table 5 (cont’d)

Panel B: Highlights of Samples Used (all draw from Compustat & CRSP)

Abbr Period Industry SIC Concentration

BCJ 1968 – 2000 15 or more Two-digit No

HR 1963 – 2001 No Regulated Three-digit Yes

MP 1981 – 2000 Manufacturing 2000 – 3990 Yes

This table provides a summary the data sets that the early papers used. The papers and abbreviations follow those in Panel A.

72

Table 6 Summary of Main Results of Selected Papers

Panel A: BCJ (Banko, Conover, and Jensen 2006 Journal of Business)

Statement Findings

H0 First look: Examine distribution of BE/ME Value (> 0.9): 2

Table with summary statistics of BE/ME (# Industries = 21) Growth (< 0.5): 3

H1 Understand the relationship between VE & Industry B1** (all models)

Rpt = α0 + B1 (Quintile BE/MEpt) + B2 (Industry BE/MEpt)

+ B3 MEpt+ B4 βp + εpt (whole sample)

B2 (self**; w/ >0.1)

B3** & B4**

H2 Understand the prevalence of VE by industry VE varies by Ind.

Rpt = α0 + B1 (BE/MEpt) + B2 MEpt+ B3 βp + εpt (by industry)

[ Also examined temporal variation of VE – exhibited ]

B1: 11**, 4*, (6)

H3 Understand risk (distress, earn. uncert., leverage) & VE Exhibit correlation

25 ports: two-step sort (1. industry BE/ME, 2. firm BE/ME)

73

Table 6 Panel A (cont’d)

BCJ examine the variation of BE/ME by industry. Their study includes 21 industries with 15 or more firms (in each industry). Industries (and

firms) are considered “value” industries when BE/ME is large (approaches 1), while the same are considered “growth” industries when BE/ME

is small (closer to 0). Their table of summary statistics of BE/ME by industry exhibits variation.

For each year in their study, they rank firms by BE/ME and form quintiles. Their pooled regressions suggest that quintile BE/ME is

significant. To further examine the prevalence of the value effect (VE), they separately run regressions for each industry and find that for over

half of the industries, the coefficient of BE/ME is significant, and for an additional fifth of industries is moderately significant. They conclude

that intra-industry variation in BE/ME is relevant in explaining stock returns.

74

Table 6 (cont’d)

Panel B: HR (Hou and Robinson 2006 The Journal of Finance)

Statement Findings

H0 Table: Quintiles of H(Sales) + size, R&D, profitability, risk; &

Fama-Macbeth (1973) regressions on firm (portfolio) characteristics

Support

Concentrated: protected, profit-rich, low R&D.

H1 (Table) Sort portfolios by H(Sales), and examine average return

Competitive Industries (H ~ 0): Average Return is HIGHER

Concentrated Industries (H ~ 1): Average Return is lower

HR seek to link theories of industrial organization and asset pricing. They examined that concentrated industries have less innovation and other

lower risk characteristics, and therefore have lower returns. They discuss the Herfindahl Index that provides a measure industry concentration,

and after computing and comparing several types of Herfindahl Indices, decide to base their Index on sales, H(Sales). They perform Fama-

MacBeth (1973) regressions and results support their hypothesis. To establish their main result, they form and sort portfolios by H(Sales) and

find that in competitive industries the average return is higher, while in concentrated industries, the average return is lower. These findings

validate their interpretation of theories from industrial organization.

75

Table 6 (cont’d)

Panel C: MP (MacKay and Phillips 2005 Review of Financial Studies)

Statement Findings

H1 {Entering, Incumbent, Exiters} make different choices Supported

Table of subsample means (based on MP Table 2):

Entering Incumbents Exiters

Debt Mid low HIGH

Tech Mid HIGH low

Risk n/a low HIGH

H2 {Debt, Tech, Risk} vary within an industry. Supported

{Debt, Tech, Risk} = {Industry Effects} + 2:{Firm Fixed Effects} R2 < 2:{R

2}

H3 Investigate with robust econometric methods Supported

OLS and GMM regressions

MP review several theoretical models of firms in equilibrium and partial equilibrium. Instead of developing tests for particular theories, they

attempt to explore the common theme of the models; that firms within industries behave differently. They identify firms as entering,

incumbents, and exiting firms and find that their characteristics differ, as predicted. Next, they perform pairs of regression models, the first

with industry effects alone and the second with both industry effects and firm fixed effects. They note that the R2 of the second (full) model is

higher than the first (industry only) and conclude that the industry information contains additional explanatory power, which supports their

hypothesis. Finally, they perform a more robust econometric analysis based on GMM regressions which supports their hypothesis.

76

Table 7. Steps Taken to Develop the Sample

Panel A. Description of Action Taken and Number of Listings at Each Step

Step Description Change Firms

Remaining

0 Extract from Compustat for fiscal year 2008 with:

11 <= Stock Exchange Code (EXCHG) <= 14

6,380 6,380

1 Less: Not Major Market (NYSE, AMEX, or NASDAQ) (753) 5,627

2 Less: Not Final Data (keep UPD = 3 only) (744) 4,883

3 Less: Missing AT, LT, NI, MIB, or CSHO (393) 4,490

4 Less: Missing complete set of FVH observations (1,892) 2,598

5 Less: Firms missing a GICS code (SIC & NAICS present) (4) 2,594

6 Less: Firms with fiscal year-end not December (311) 2,283

7 Extract from CRSP Monthly 3/31/2009 with PRC > 0.0 and

EXCHG <=3 returned 5,792 items. Merge by (TIC=Ticker; or

CUSIP8=CUSIP or CUSIP8=NCUSIP) yielded 2,105 matches.

(175) 2,108

8 Less: Firms with negative book equity, BE < 0, where

BE = AT – LT – MIB

(84) 2,024

9 Less: Firms where the computed sum of Level 1, Level 2, and Level

3 Assets or Liabilities did not equal the respective observation of

the Total from Compustat.

(290) 1,734

10 Less: Outliers (154) 1,580

This panel shows the steps taken to assemble the dataset, the number gained (lost) at each step, and the total number of firms at each step. UPD

is Compustat’s field that indicates the status of the data. UPD = 3 means the data is final. The next panel shows Stock Exchange Codes

(EXCHG). Other abbreviations are expanded below:

77

Table 7. Panel A (cont’d)

AT = Assets, Total

LT = Liabilities, Total

NI = Net Income

MIB = Minority Interest, from the Balance Sheet

CSHO = Common Shares Outstanding

BE = Book Equity

PRC = the price of one share of the firm’s stock

FVH = fair value hierarchy

CUSIP = Committee on Uniform Securities Identification Procedures

SIC = Standard Industrial Classification

NAICS = North American Industry Classification System

GICS® = Global Industry Classification Standard

78

Table 7. (cont’d)

Panel B. Stock Exchange Codes

Stock Exchange Code Name of Exchange

11 New York Stock Exchange

12 Amex

13 OTC Bulletin Board

14 NASDAQ-NMS Stock Market

This panel shows the numeric value of Compustat’s Stock Exchange Code (EXCHG) variable and the name of the stock exchange.

79

Table 7. (cont’d)

Panel C. (Step 9) Description of the Four Cases based on Equality or Inequality

SA, Sum of assets reported at fair value, computed as: SA = FVA1 + FVA2 + FVA3

TFVA = Observed Total, Fair Value of Assets (as reported by Compustat from the financial report)

SL, Sum of liabilities reported at fair value, computed as: SL = FVL1 + FVL2 + FVL3

TFVL = Observed Total, Fair Value of Liabilities (as reported by Compustat from the financial report)

In addition to reporting the disclosures of six FVH levels as individual data items, e.g. FVA1, FVA2, FVA3, FVL1, FVL2, and FVL3,

Compustat also provides the Total Fair Value Assets (TFVA) and Total Fair Value Liabilities (TFVL) as separate data items. Provided that

additional accounting standards do not apply, we expect the Total Fair Value Assets to be the sum of FVA1, FVA2, and FVA3, and similarly

for the Total Fair Value Liabilities. However, there were several cases where the sum of the individual FVH items did not equal the respective

total. This Panel shows the four possible cases. The next panel provides the number of firms in each case by Global Industry Classification

Standard (GICS®) sector.

Case SA TFVA SL TFVL

Assets Not Equal ≠ =

Liabilities Not Equal = ≠

Both Not Equal ≠ ≠

Both Equal = =

80

Table 7 (cont’d)

Panel D. Detail by GICS Sector of the Number of firms with Equality or Inequality of the Computed Sum vs. Observed Total

GICS Sectors Step 8

Assets

Not Equal

Liabilities

Not Equal

Both

Not Equal Less

Step 9

(Both Equal)

10 – Energy 157 6 5 5 (16) 141

15 – Materials 84 4 5 2 (11) 73

20 – Industrials 218 13 2 1 (16) 202

25 - Consumer Discretionary 171 13 4 1 (18) 153

30 - Consumer Staples 44 4 0 1 (5) 39

35 - Health Care 286 40 0 0 (40) 246

40 – Financials 644 100 11 18 (129) 515

45 - Information Technology 320 32 2 2 (36) 284

50 - Telecommunication Services 30 2 0 0 (2) 28

55 – Utilities 70 1 3 13 (17) 53

Total 2,024 215 32 43 (290) 1,734

81

Table 7 Panel D. (cont’d)

This panel shows the number of firms, by GICS Sector, where the sum of the individual FVH items, such as FVA1, FVA2, and FVA3, did not

equal the respective data item of its total, e.g. Total Fair Value Assets, and respectively for liabilities. It shows the number of firms trimmed in

Step 9 of Panel A by GICS Sector. The previous panel provides additional clarification of the meaning of the column headings.

82

Table 8. Summary of Selected Variables by GICS Sectors

Panel A. Selected Ratios

BE-to-ME Ratio FVA Ratio FVL Ratio

GICS Sectors N Mean Std N Mean Std N Mean Std

10 - Energy 140 1.40 1.36 141 0.05 0.07 141 0.05 0.12

15 - Materials 69 1.19 1.96 73 0.05 0.13 73 0.06 0.13

20 - Industrials 199 1.17 1.22 202 0.06 0.10 202 0.04 0.09

25 - Consumer Discretionary 147 1.77 3.01 153 0.07 0.13 153 0.02 0.05

30 - Consumer Staples 38 0.87 0.97 39 0.07 0.13 39 0.03 0.08

35 - Health Care 231 0.71 0.67 246 0.35 0.33 246 0.03 0.11

40 - Financials 412 1.74 1.80 515 0.22 0.24 515 0.03 0.11

45 - Information Technology 262 1.01 0.87 284 0.25 0.23 284 0.02 0.09

50 - Telecommunication Services 25 0.96 1.37 28 0.13 0.24 28 0.07 0.20

55 - Utilities 52 0.82 0.42 53 0.05 0.08 53 0.04 0.08

Sample 1575 1.28 1.62 1734 0.18 0.24 1734 0.03 0.10

BE-to-ME is Book Equity to Market Equity. Book Equity is computed as BE = AT – LT – MIB, where AT = Assets-Total, LT = Liabilities-

Total, and MIB = Minority Interest-Balance Sheet.

83


This table provides summary statistics by GICS Sector of three ratios at Step 9 of the data selection process. Not every firm had a Book

Equity-to-Market Equity (BE-to-ME) Ratio available. The BE-to-ME is important in hypothesis H1, and the table shows that there appears to

be sufficient variation in the sample to test my first hypothesis. Similarly, the summary statistics of the FVA and FVL Ratios suggest that there

appears to be sufficient variation in the sample for regression studies.

84

Table 8 (cont’d)

Panel B. Mean and Standard Deviation of the Fair Value Hierarchy Observations scaled by Respective Total

FVA1

AT

FVA2

AT

FVA3

AT

FVL1

LT

FVL2

LT

FVL12

LT

FVL3

LT

GIC Sectors N mean std mean std mean std mean std mean std mean std mean std

Energy 141 0.01 0.03 0.03 0.05 0.01 0.04 0.01 0.07 0.03 0.09 0.04 0.12 0.01 0.02

Materials 73 0.03 0.09 0.01 0.02 0.02 0.08 0.01 0.06 0.04 0.12 0.05 0.13 0.00 0.01

Industrials 202 0.04 0.08 0.01 0.03 0.01 0.03 0.00 0.03 0.02 0.07 0.03 0.07 0.01 0.06

Cnsmr Discret. 153 0.04 0.11 0.02 0.05 0.01 0.04 0.00 0.03 0.01 0.04 0.02 0.05 0.00 0.01

Cnsmr Staples 39 0.06 0.13 0.01 0.03 0.00 0.01 0.00 0.02 0.02 0.06 0.03 0.08 0.00 0.01

Health Care 246 0.21 0.27 0.12 0.21 0.02 0.06 0.01 0.06 0.02 0.09 0.02 0.10 0.01 0.04

Financials 515 0.03 0.09 0.16 0.19 0.03 0.13 0.00 0.04 0.01 0.08 0.02 0.09 0.01 0.07

Info Tech 284 0.16 0.19 0.07 0.13 0.02 0.06 0.01 0.04 0.01 0.06 0.02 0.08 0.00 0.04

Telecom Svcs. 28 0.12 0.24 0.01 0.02 0.01 0.02 0.00 0.00 0.03 0.12 0.03 0.12 0.03 0.16

Utilities 53 0.03 0.05 0.02 0.04 0.00 0.01 0.01 0.04 0.03 0.05 0.04 0.08 0.00 0.01

Sample 1734 0.08 0.16 0.08 0.15 0.02 0.08 0.01 0.04 0.02 0.08 0.02 0.09 0.01 0.05

85

Table 8 Panel B (cont’d)

This table provides summary statistics by GICS Sector of each of the levels of the FVH, and the sum of Level 1 and Level 2 liabilities scaled by

total assets or liabilities. FVAn (FVLn) represents Level n fair value assets (liabilities) while FVL12 represents the sum of Level 1 and Level 2

liabilities. AT (LT) means total assets (liabilities). The variable created by the sum of Level 1 and Level 2 Liabilities has no GICS Sector with

a mean of zero. For this statistical reason, and to maintain consistency with prior literature, this combined variable will be used in regression

studies. The Level 3 Liabilities has several GICS Sectors where the mean is zero. However, Level 3 is deemed to be quite different from Level

1 and Level 2. For this theoretical reason, and to maintain consistency with prior literature, this variable will be used by itself and not combined

with another variable.

86

Table 9. Results of OLS Regression Using Winsorized Variables

Panel A. Estimated Coefficients

GICS Sector Int NFVA FVA1 FVA2 FVA3 NFVL FVL12 FVL3 NI N

Adj

R2

Energy 5.27 *** 0.44 *** 2.66 ** 0.46 0.03 -0.36 ** -0.65 * 2.14 1.05 *** 141 0.48

3.39 (<.01) 4.55 (<.01) 2.42 (0.01) 0.69 (0.49) 0.03 (0.97) -2.42 (0.01) -1.71 (0.09) 1.07 (0.28) 4.86 (<.01)

Materials 10.29 *** 0.37 0.49 1.65 -0.53 -0.50 0.55 -17.42 3.68 *** 73 0.48

3.23 (<.01) 1.35 (0.18) 0.26 (0.79) 0.43 (0.66) -0.14 (0.88) -1.45 (0.15) 0.71 (0.48) -0.50 (0.61) 6.93 (<.01)

Industrials 4.38 *** 0.51 *** 1.04 *** 0.73 -1.10 -0.41 *** -1.19 ** -2.39 1.75 *** 202 0.48

3.67 (<.01) 5.68 (<.01) 3.12 (<.01) 1.09 (0.27) -0.84 (0.40) -3.71 (<.01) -2.37 (0.01) -1.12 (0.26) 6.91 (<.01)

Cnsmr

Discret.

5.14 *** 0.70 *** 6.27 *** 1.17 * -3.09 -0.64 *** -4.81 *** -18.03 0.82 *** 153 0.46

2.90 (<.01) 4.37 (<.01) 6.45 (<.01) 1.84 (0.06) -1.14 (0.25) -3.00 (<.01) -7.20 (<.01) -0.28 (0.77) 3.80 (<.01)

Cnsmr

Staples

1.22 0.65 ** 4.20 *** 48.31 *** -223.55 *** -0.37 -1.40 -113.63 ** 3.84 *** 39 0.82

0.44 (0.66) 2.68 (0.01) 2.75 (<.01) 5.07 (<.01) -4.71 (<.01) -1.34 (0.18) -1.44 (0.15) -2.18 (0.03) 3.32 (<.01)

Health Care 6.05 *** 1.12 *** 1.41 *** 0.92 *** 2.71 -1.26 *** 0.53 -7.86 *** 2.31 *** 246 0.52

5.89 (<.01) 7.17 (<.01) 2.97 (<.01) 3.87 (<.01) 1.47 (0.14) -5.76 (<.01) 0.65 (0.51) -3.18 (<.01) 5.19 (<.01)

Financials 7.46 *** 0.17 *** 0.20 *** 0.25 *** 0.04 -0.16 *** -0.75 *** -0.68 *** 1.40 *** 515 0.40

10.80 (<.01) 7.45 (<.01) 6.07 (<.01) 10.61 (<.01) 0.30 (0.76) -7.16 (<.01) -4.13 (<.01) -3.03 (<.01) 9.14 (<.01)

Info Tech 2.15 * 0.78 *** 1.45 *** 2.88 *** 1.72 -0.50 * -0.50 9.85 2.24 *** 284 0.33

1.72 (0.08) 4.22 (<.01) 3.16 (<.01) 4.94 (<.01) 1.15 (0.25) -1.96 (0.05) -0.28 (0.78) 0.24 (0.80) 5.18 (<.01)

87



Adj

R2

Telecom

Svcs.

5.73 * 0.09 0.88 3.80 2.14 0.15 -0.45 -0.33 3.23 ** 28 0.45

1.83 (0.08) 0.21 (0.83) 0.85 (0.40) 1.55 (0.13) 0.33 (0.74) 0.29 (0.77) -0.23 (0.82) -0.10 (0.92) 2.22 (0.03)

Utilities 5.35 * 0.20 0.75 -0.28 0.89 0.04 -1.27 * -2.37 2.76 ** 53 0.58

1.79 (0.08) 0.84 (0.40) 1.23 (0.22) -0.37 (0.71) 0.31 (0.75) 0.15 (0.88) -1.91 (0.06) -1.28 (0.20) 2.44 (0.01)

Total 1734

88

Table 9 (cont’d)

Panel B. Results of F-Tests of the Coefficients

GICS Sector NFVA=1 FVA1=1 FVA2=1 FVA3=1 NFVL=-1 FVL12=-1

Energy 32.79 <.01*** 2.28 0.13 0.67 0.41 1.11 0.29 17.84 <.01*** 0.88 0.35

Materials 5.31 0.02** 0.07 0.79 0.03 0.86 0.17 0.68 2.08 0.15 4.02 0.04**

Industrials 30.03 <.01*** 0.02 0.89 0.17 0.68 2.58 0.11 28.66 <.01*** 0.14 0.70

Cnsmr Discret. 3.36 0.06* 29.39 <.01*** 0.07 0.79 2.27 0.13 2.74 0.10 32.53 <.01***

Cnsmr Staples 2.01 0.16 4.39 0.04** 24.61 <.01*** 22.41 <.01*** 5.27 0.02** 0.17 0.68

Health Care 0.57 0.45 0.73 0.39 0.10 0.75 0.86 0.35 1.41 0.23 3.48 0.06*

Financials 1,340.26 <.01*** 574.59 <.01*** 1,015.34 <.01*** 66.73 <.01*** 1,324.59 <.01*** 1.82 0.17

Info Tech 1.37 0.24 0.96 0.32 10.40 <.01*** 0.23 0.63 3.93 0.04** 0.08 0.77

Telecom Svcs. 4.13 0.05* 0.01 0.90 1.30 0.26 0.03 0.86 4.84 0.04** 0.08 0.78

Utilities 11.83 <.01*** 0.17 0.68 2.88 0.09* 0.00 0.96 12.43 <.01*** 0.17 0.68

89


GICS Sector FVL3=-1 FVA1=FVA2 FVA1=FVA3 FVA2=FVA3 FVL12=FVL3

Energy 2.48 0.11 3.16 0.07* 3.56 0.06* 0.15 0.69 1.80 0.18

Materials 0.23 0.63 0.07 0.79 0.06 0.81 0.18 0.67 0.27 0.60

Industrials 0.42 0.51 0.15 0.70 2.31 0.12 1.86 0.17 0.30 0.58

Cnsmr Discret. 0.07 0.78 15.68 <.01*** 10.56 <.01*** 2.32 0.13 0.04 0.83

Cnsmr Staples 4.67 0.03** 23.37 <.01*** 22.07 <.01*** 24.90 <.01*** 4.61 0.04**

Health Care 7.72 <.01*** 0.88 0.34 0.41 0.52 0.85 0.35 11.36 <.01***

Financials 1.97 0.16 1.28 0.25 1.74 0.18 3.31 0.06* 0.05 0.81

Info Tech 0.07 0.79 3.61 0.05* 0.03 0.86 0.52 0.47 0.06 0.80

Telecom Svcs. 0.04 0.84 0.99 0.33 0.03 0.85 0.08 0.77 0.00 0.97

Utilities 0.55 0.46 1.20 0.28 0.00 0.96 0.14 0.70 0.28 0.59

90

Table 9 (cont’d)

Panel A

This table presents the coefficients of a modified Ohlson model estimated by OLS regression with

Winsorization at the 1- and 99-percentiles. The modified Ohlson model, Equation (1), us repeated

here:


+ α5NVFLit + α6FVL12it + α7FVL3it + β1NIit + εit

PRC is the price of one share of the firm’s stock. NFVA (NFVL) means non-Fair Value Assets

(Liabilities) and is computed as the total assets (liabilities) less the sum of Level 1, 2, and 3 assets

(liabilities). FVA1, FVA2, and FVA3 are the disclosures of Level 1, Level 2, and Level 3 fair value

assets respectively. FVL12 is the sum of the disclosures of Level 1 and Level 2 liabilities, while

FVL3 is the disclosures of Level 3 fair value liabilities. NI is net income. The explanatory

variables have been deflated by the number of common shares outstanding. Panel A shows the

estimated coefficients of the explanatory variables, the number of observations in the regression,

and its adjusted R2. Each coefficient and its related measures of statistical significance are

presented in a pair of columns and a pair of rows. The estimates of the coefficients of each

explanatory variable are in columns headed by the name of the explanatory variable and in rows

labeled with the name of the GICS® sector. To the immediate right of a coefficient, under a column

with no heading, are a number of stars that visually indicate its statistical significance. Following

convention, *** indicates a p-value < 0.01, ** indicates a p-value between 0.01 and 0.05, *

indicates a p-value between 0.05 and 0.1, and no star indicates a p-value greater than 0.1. Rows

directly beneath those with the name of a GICS Sector are blank. The number is the t-value. The

number in parenthesis to the immediate right of t-value is the p-value. In cases where the p-value is

less than 0.01, “<.01” is displayed instead of the actual p-value. The right-most columns, headed

by N and Adj R2, respectively show the number of observations in each sample for a particular

GICS sector and the adjusted R2 for that study.

Panel B

This panel shows the results of F-tests that examine various properties of and relationships between

coefficients within a regression model.

91

91

Table 10. Results of Iteratively Reweighted Least Squares (IRLS) Regression


GICS Sector Icept NFVA FVA1 FVA2 FVA3 NFVL FVL12 FVL3 NI N

Adj

R2

Energy 0.18 1.00 *** -0.35 1.06 *** 0.72 * -1.04 *** -1.01 *** -0.22 0.60 *** 141 0.31

0.03 (0.86) 174.02 (<.01) 0.68 (0.40) 12.91 (<.01) 2.87 (0.09) 76.99 (<.01) 41.44 (<.01) 0.03 (0.86) 17.98 (<.01)

Materials -0.07 1.26 *** 0.21 6.46 *** 2.30 -1.27 *** -0.82 ** -3.43 2.14 *** 73 0.46

0.00 (0.96) 104.66 (<.01) 0.06 (0.80) 13.87 (<.01) 1.83 (0.17) 66.08 (<.01) 5.61 (0.01) 0.05 (0.82) 79.81 (<.01)

Industrials 2.32 *** 0.69 *** 1.10 *** 0.55 ** 0.40 -0.63 *** -1.21 *** -2.06 1.24 *** 202 0.33

6.96 (<.01) 157.85 (<.01) 17.51 (<.01) 4.70 (0.03) 0.64 (0.42) 81.75 (<.01) 9.51 (<.01) 1.58 (0.20) 31.88 (<.01)

Cnsmr Discret. 3.95 *** 0.57 *** 1.17 *** 2.25 *** 0.17 -0.56 *** -1.09 *** -1.72 0.41 *** 153 0.24

29.63 (<.01) 321.64 (<.01) 8.06 (<.01) 46.20 (<.01) 0.10 (0.75) 120.45 (<.01) 10.51 (<.01) 0.11 (0.74) 18.89 (<.01)

Cnsmr Staples 0.74 0.78 *** 4.85 *** 53.54 *** -

249.68

*** -0.52 * -1.54 -

122.64

** 3.27 *** 39 0.54

0.06 (0.80) 9.19 (<.01) 8.97 (<.01) 28.05 (<.01) 24.65 (<.01) 3.14 (0.07) 2.25 (0.13) 4.93 (0.02) 7.11 (<.01)

Health Care 1.37 *** 1.28 *** 1.42 *** 1.37 *** 4.25 *** -1.47 *** 2.01 *** -1.18 ** 0.00 246 0.37

7.18 (<.01) 340.25 (<.01) 52.35 (<.01) 132.64 (<.01) 32.34 (<.01) 270.10 (<.01) 30.66 (<.01) 6.18 (0.01) 0.00 (0.98)

92

92



Adj

R2

Financials 1.49 *** 0.73 *** 0.73 *** 0.81 *** 0.58 *** -0.76 *** -1.11 *** -0.80 *** 0.75 *** 515 0.29

14.13 (<.01) 3,173.68 (<.01) 2,083.66 (<.01) 4,067.69 (<.01) 77.52 (<.01) 3,081.00 (<.01) 3,560.26 (<.01) 48.18 (<.01) 111.29 (<.01)

Info Tech 1.70 *** 0.60 *** 1.38 *** 1.79 *** 1.23 *** -0.45 *** -0.07 2.82 1.14 *** 284 0.37

16.94 (<.01) 109.75 (<.01) 101.47 (<.01) 127.57 (<.01) 20.68 (<.01) 38.60 (<.01) 0.24 (0.62) 0.66 (0.41) 68.72 (<.01)

Telecom Svcs. 4.77 0.04 0.84 4.94 ** 4.05 0.14 0.01 0.86 3.66 *** 28 0.42

2.46 (0.11) 0.01 (0.93) 0.72 (0.39) 4.31 (0.03) 0.40 (0.52) 0.07 (0.78) 0.00 (0.99) 0.07 (0.79) 6.69 (<.01)

Utilities 6.28 * 0.29 1.18 * 0.18 -0.10 -0.12 -1.94 *** -1.54 2.65 ** 53 0.42

3.84 (0.05) 1.36 (0.24) 3.26 (0.07) 0.05 (0.82) 0.00 (0.97) 0.14 (0.71) 7.41 (<.01) 0.60 (0.43) 4.81 (0.02)

Total 1734

This panel shows the coefficients of the same modified Ohlson model from the previous table estimated by an Iteratively Reweighted

Least Squares (IRLS) regression, a technique from robust statistics. IRLS is a type of weighted least squares regression where the

observations with large residuals are weighted less in subsequent regressions. The weights are readjusted at each step until a convergence

criterion is met. IRLS can identify outliers by observations with high residuals. In the rows under the name of GICS Sector, the number

is the value of the Chi-square statistic and the number in parenthesis is its p-value.

93

Table 10 (cont’d)

Panel B. Distribution of Outliers Identified by IRLS

GICS Sector Step 9 Outliers Trimmed

Energy 141 16 125

Materials 73 11 62

Industrials 202 8 194

Cnsmr Discret. 153 14 139

Cnsmr Staples 39 3 36

Health Care 246 36 210

Financials 515 35 480

Info Tech 284 28 256

Telecom Svcs. 28 2 26

Utilities 53 1 52

Total 1734 154 1580

This panel shows the distribution by GICS Sector of outliers identified by IRLS. These outliers

were subsequently dropped in Step 10 of the sample selection process described in Panel A of

Table 7.

94

Table 11. Results of OLS Regression on Trimmed Sample



Adj

R2

Energy 0.00 1.06 *** -0.37 1.22 *** 0.73 ** -1.12 *** -1.12 *** -0.65 0.58 *** 125 0.76

0.00 (0.99) 12.84 (<.01) -0.96 (0.33) 4.58 (<.01) 2.04 (0.04) -8.85 (<.01) -7.74 (<.01) -0.58 (0.56) 4.66 (<.01)

Materials -

0.20

1.21 *** 0.15 6.26 *** 2.25 -1.19 *** -0.76 ** -3.03 2.10 *** 62 0.83

-

0.16

(0.87) 9.29 (<.01) 0.19 (0.84) 3.61 (<.01) 1.46 (0.14) -7.32 (<.01) -2.24 (0.02) -0.24 (0.81) 6.10 (<.01)

Industrials 2.67 *** 0.69 *** 1.14 *** 0.64 *** 0.33 -0.65 *** -1.28 *** -2.03 1.13 *** 194 0.98

3.26 (<.01) 13.52 (<.01) 4.68 (<.01) 2.69 (<.01) 0.70 (0.48) -9.97 (<.01) -3.54 (<.01) -1.34 (0.18) 5.38 (<.01)

Cnsmr

Discret.

4.61 *** 0.50 *** 1.02 * 2.40 *** 0.08 -0.49 *** -0.96 *** -2.44 0.34 *** 139 0.53

5.38 (<.01) 6.19 (<.01) 1.93 (0.05) 6.80 (<.01) 0.16 (0.87) -5.00 (<.01) -2.63 (<.01) -0.50 (0.61) 3.79 (<.01)

Cnsmr Staples 1.06 0.77 *** 4.84 *** 56.04 *** -

254.42

*** -0.51 ** -1.47 * -

135.86

*** 3.09 *** 36 0.90

0.49 (0.62) 3.90 (<.01) 4.08 (<.01) 7.38 (<.01) -6.85 (<.01) -2.28 (0.03) -1.91 (0.06) -3.34 (<.01) 3.37 (<.01)

Health Care 1.53 *** 1.30 *** 1.31 *** 1.42 *** 4.30 *** -1.51 *** 1.93 *** -1.22 *** -

0.08

210 0.77

3.06 (<.01) 17.53 (<.01) 6.98 (<.01) 11.99 (<.01) 5.73 (<.01) -15.86 (<.01) 5.37 (<.01) -2.74 (<.01) -

0.39

(0.69)

95



Adj

R2

Financials 1.56 *** 0.73 *** 0.73 *** 0.82 *** 0.56 *** -0.77 *** -1.07 *** -0.78 *** 0.68 *** 480 0.87

3.70 (<.01) 40.05 (<.01) 29.49 (<.01) 47.47 (<.01) 8.29 (<.01) -41.02 (<.01) -10.17 (<.01) -7.07 (<.01) 7.39 (<.01)

Info Tech 1.80 *** 0.61 *** 1.42 *** 1.73 *** 1.23 *** -0.47 *** -0.04 2.55 1.12 *** 256 0.72

4.76 (<.01) 10.70 (<.01) 10.66 (<.01) 9.75 (<.01) 4.76 (<.01) -6.00 (<.01) -0.29 (0.77) 0.83 (0.40) 8.23 (<.01)

Telecom

Svcs.

5.63 ** -0.13 0.77 6.43 *** 7.93 0.19 0.14 2.77 4.34 *** 26 0.69

2.48 (0.02) -0.39 (0.69) 1.03 (0.31) 3.40 (<.01) 1.62 (0.12) 0.51 (0.62) 0.10 (0.92) 1.09 (0.29) 3.97 (<.01)

Utilities 6.33 ** 0.27 1.22 ** 0.26 0.10 -0.09 -1.98 *** -1.67 2.54 ** 52 0.62

2.24 (0.03) 1.23 (0.22) 2.03 (0.04) 0.35 (0.72) 0.04 (0.97) -0.32 (0.74) -2.92 (<.01) -0.95 (0.34) 2.39 (0.02)

Total 1580

Observations with large residuals were identified by a process described in Appendix A and removed to produce the trimmed sample. This

panel shows the estimated coefficients of the same modified Ohlson model as used in Table 9, however in this table the OLS regressions were

run on the trimmed sample. The format of presentation is the same in previous tables.

96

Table 11 (cont’d)

Panel B. Results of F-Tests

GICS Sector NFVA=1 FVA1=1 FVA2=1 FVA3=1 NFVL= –1 FVL12= –1

Energy 0.56 0.45 12.50 <.01*** 0.69 0.40 0.58 0.44 0.90 0.34 0.72 0.39

Materials 2.61 0.11 1.23 0.27 9.21 <.01*** 0.66 0.42 1.40 0.24 0.52 0.47

Industrials 35.49 <.01*** 0.32 0.57 2.34 0.12 2.06 0.15 29.07 <.01*** 0.60 0.43

Cnsmr Discret. 38.57 <.01*** 0.00 0.97 15.77 <.01*** 3.26 0.07* 27.20 <.01*** 0.01 0.92

Cnsmr Staples 1.33 0.25 10.49 <.01*** 52.49 <.01*** 47.32 <.01*** 4.92 0.03** 0.37 0.54

Health Care 16.49 <.01*** 2.77 0.09* 12.45 <.01*** 19.35 <.01*** 28.32 <.01*** 66.58 <.01***

Financials 221.35 <.01*** 116.14 <.01*** 112.57 <.01*** 41.10 <.01*** 157.90 <.01*** 0.42 0.51

Info Tech 46.90 <.01*** 9.91 <.01*** 16.91 <.01*** 0.77 0.37 45.21 <.01*** 44.14 <.01***

Telecom Svcs. 12.07 <.01*** 0.10 0.75 8.25 0.01** 2.00 0.17 9.81 <.01*** 0.65 0.43

Utilities 10.84 <.01*** 0.13 0.71 1.02 0.31 0.11 0.74 10.39 <.01*** 2.09 0.15

97


GICS Sector FVL3= –1 FVA1=FVA2 FVA1=FVA3 FVA2=FVA3 FVL12=FVL3

Energy 0.10 0.75 12.27 <.01*** 4.69 0.03** 1.46 0.22 0.18 0.67

Materials 0.03 0.87 10.46 <.01*** 1.59 0.21 3.24 0.07* 0.03 0.85

Industrials 0.46 0.49 1.41 0.23 2.20 0.13 0.37 0.54 0.23 0.62

Cnsmr Discret. 0.09 0.76 3.99 0.04** 1.67 0.19 13.90 <.01*** 0.09 0.76

Cnsmr Staples 11.02 <.01*** 49.92 <.01*** 46.68 <.01*** 52.54 <.01*** 10.86 <.01***

Health Care 0.25 0.61 0.22 0.64 14.56 <.01*** 14.13 <.01*** 34.42 <.01***

Financials 3.76 0.05* 13.20 <.01*** 5.64 0.01** 14.57 <.01*** 3.48 0.06*

Info Tech 1.34 0.24 1.79 0.18 0.48 0.49 2.40 0.12 0.71 0.39

Telecom Svcs. 2.18 0.15 6.40 0.02** 1.91 0.18 0.13 0.72 0.86 0.36

Utilities 0.15 0.70 1.18 0.28 0.14 0.70 0.00 0.95 0.02 0.87

This panel shows the results of F-tests that examine various properties of and relationships between coefficients within a regression model.

98

Table 12. Investigating the Effect of BE/ME

Panel A1. Several of the Estimated Coefficients of an OLS Regression

GICS Sector FVA1 FVA2 FVA3 BE/ME N Adj R2

Energy -0.18 1.16 *** 0.69 ** -1.90 *** 124 0.81

-0.52 (0.60) 4.82 (<.01) 2.15 (0.03) -4.82 (<.01)

Materials 0.15 5.97 *** 2.22 -0.85 ** 59 0.86

0.20 (0.84) 3.72 (<.01) 1.55 (0.12) -2.35 (0.02)

Industrials 1.07 *** 0.67 *** 0.66 -3.65 *** 191 0.99

5.03 (<.01) 3.23 (<.01) 1.61 (0.10) -7.86 (<.01)

Cnsmr Discret. 1.08 ** 2.10 *** 0.18 -0.93 *** 133 0.64

2.36 (0.01) 6.73 (<.01) 0.41 (0.68) -6.20 (<.01)

Cnsmr Staples 5.44 *** 57.45 *** -270.82 *** -1.43 35 0.90

4.55 (<.01) 7.73 (<.01) -7.28 (<.01) -1.03 (0.31)

Health Care 1.63 *** 1.78 *** 2.99 *** -3.08 *** 198 0.84

8.21 (<.01) 9.94 (<.01) 3.47 (<.01) -7.39 (<.01)

Financials 0.67 *** 0.71 *** 0.49 *** -1.57 *** 387 0.69

12.49 (<.01) 20.92 (<.01) 5.58 (<.01) -10.85 (<.01)

Info Tech 1.32 *** 1.56 *** 1.25 *** -2.10 *** 238 0.79

11.21 (<.01) 9.55 (<.01) 5.49 (<.01) -8.62 (<.01)

Telecom Svcs. 3.27 * 4.00 4.48 -1.08 23 0.67

1.78 (0.09) 1.62 (0.12) 0.82 (0.42) -1.03 (0.32)

Utilities 1.22 ** 0.54 0.01 -10.97 *** 51 0.78

2.58 (0.01) 0.98 (0.33) 0.01 (0.99) -4.95 (<.01)

Total 1439

The remaining coefficients are presented in the next half-panel.

99

Table 12 (cont’d)

Panel A2. Remaining Estimated Coefficients

GICS Sector Intercept NFVA NFVL FVL12 FVL3 NI

Energy 3.04 *** 1.02 *** -1.08 *** -1.09 *** -0.59 0.53 ***

2.65 (<.01) 13.68 (<.01) -9.46 (<.01) -8.35 (<.01) -0.59 (0.55) 4.66 (<.01)

Materials 1.31 1.20 *** -1.17 *** -0.80 ** -4.57 2.01 ***

0.97 (0.33) 9.69 (<.01) -7.59 (<.01) -2.53 (0.01) -0.39 (0.70) 6.11 (<.01)

Industrials 6.75 *** 0.77 *** -0.75 *** -1.26 *** -2.08 0.65 ***

7.59 (<.01) 16.83 (<.01) -12.82 (<.01) -3.98 (<.01) -1.58 (0.11) 3.35 (<.01)

Cnsmr Discret. 5.89 *** 0.57 *** -0.57 *** -1.01 *** -1.88 0.29 ***

7.30 (<.01) 7.96 (<.01) -6.58 (<.01) -3.15 (<.01) -0.44 (0.65) 3.75 (<.01)

Cnsmr Staples 1.95 0.92 *** -0.68 *** -1.68 ** -127.14 *** 2.66 ***

0.75 (0.45) 4.46 (<.01) -2.94 (<.01) -2.24 (0.03) -3.22 (<.01) 2.93 (<.01)

Health Care 3.45 *** 1.35 *** -1.53 *** 1.88 *** -1.38 *** -0.25

6.66 (<.01) 18.68 (<.01) -16.10 (<.01) 5.73 (<.01) -3.35 (<.01) -1.36 (0.17)

Financials 4.95 *** 0.63 *** -0.66 *** -1.20 *** -0.89 *** 0.45 ***

10.18 (<.01) 18.25 (<.01) -18.14 (<.01) -5.71 (<.01) -5.11 (<.01) 4.87 (<.01)

Info Tech 4.30 *** 0.59 *** -0.46 *** -0.00 1.23 0.89 ***

9.92 (<.01) 11.17 (<.01) -6.50 (<.01) -0.01 (0.99) 0.46 (0.64) 7.12 (<.01)

Telecom Svcs. 3.96 0.03 0.23 0.73 -1.32 2.39

1.31 (0.21) 0.08 (0.93) 0.55 (0.59) 0.47 (0.64) -0.37 (0.72) 1.30 (0.21)

Utilities 14.08 *** 0.53 *** -0.40 * -1.69 *** -2.06 1.76 **

5.34 (<.01) 3.02 (<.01) -1.78 (0.08) -3.08 (<.01) -1.54 (0.13) 2.06 (0.04)

Total

Half-panels A1 and A2 together show the coefficients of a modified Ohlson model estimated by

OLS regressions on the trimmed sample. The model is that of Panel A of Table 9 with the

BE/ME ratio as an additional regressor:

100

Table 12 Panel A2 (cont’d)


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 BE/MEit + β1NIit + εit

Additional columns, for the coefficient of BE/ME and its significance, would make a single panel

too wide to fit on a single page, and therefore the estimated coefficients are split into two half-

panels.

101

Table 12 (cont’d)

Panel B. Composition of the Low and High 45th

-percentile of BE/ME

In order to further examine the effect of BE/ME on the value relevance of the FVH, the sample

was split into two 45th percentiles. This panel shows the number of firms in each GICS sector

and the minimum and maximum values of the BE/ME in each 45th percentile.

BE/ME Low BE/ME High

BE/ME BE/ME

GICS Sector N Min Max N Min Max

Energy 56 0.13 0.97 56 1.14 9.90

Materials 29 0.06 0.59 27 0.73 13.11

Industrials 88 0.05 0.75 86 0.93 7.06

Cnsmr Discret. 65 0.09 0.91 60 1.07 28.63

Cnsmr Staples 16 0.03 0.53 16 0.67 4.54

Health Care 101 0.00 0.55 90 0.63 4.46

Financials 267 0.29 1.12 175 1.33 16.18

Info Tech 125 0.11 0.78 108 0.96 7.14

Telecom Svcs. 13 0.06 0.59 11 0.71 7.05

Utilities 23 0.08 0.67 23 0.83 2.66

783 0.00 1.12 652 0.63 28.63

102

Table 12 (cont’d)

Panel C. Estimated Coefficients from OLS Regression with BE/ME Low (Growth)


Adj

R2

Energy 2.35 * 1.21 *** 0.39 1.16 *** 0.79 ** -1.25 *** -1.32 *** -0.68 0.52 *** 56 0.89

1.95 (0.05) 13.39 (<.01) 0.24 (0.81) 4.67 (<.01) 2.28 (0.02) -8.91 (<.01) -9.70 (<.01) -0.43 (0.67) 2.73 (<.01)

Materials 3.11 1.29 *** 0.40 0.02 2.32 -1.28 *** 3.93 -52.98 1.30 ** 29 0.88

1.59 (0.12) 7.96 (<.01) 0.48 (0.63) 0.00 (0.99) 1.26 (0.22) -6.42 (<.01) 1.13 (0.27) -0.47 (0.64) 2.37 (0.02)

Industrials 5.59 *** 1.01 *** 1.02 *** 0.95 ** 0.79 * -1.01 *** -2.33 *** 2.00 0.94 *** 88 0.74

5.17 (<.01) 10.05 (<.01) 3.51 (<.01) 2.41 (0.01) 1.88 (0.06) -8.26 (<.01) -4.23 (<.01) 0.72 (0.47) 2.95 (<.01)

Cnsmr Discret. 7.18 *** 0.64 *** 0.82 2.07 *** 0.56 -0.65 *** 1.54 -4.25 0.39 *** 65 0.62

5.17 (<.01) 4.24 (<.01) 1.19 (0.24) 5.49 (<.01) 0.24 (0.80) -3.53 (<.01) 0.79 (0.43) -0.10 (0.92) 2.74 (<.01)

Cnsmr Staples 1.90 0.17 6.55 *** 73.93 *** 0.00 . 0.14 4.49 0.00 . 1.58 16 0.93

0.61 (0.55) 0.38 (0.71) 4.60 (<.01) 6.82 (<.01) . . 0.29 (0.77) 0.73 (0.48) . . 1.34 (0.21)

Health Care 2.93 *** 1.44 *** 1.51 *** 1.31 *** 5.21 *** -1.60 *** 1.38 *** -1.68 *** -0.53 101 0.80

3.92 (<.01) 9.64 (<.01) 6.93 (<.01) 7.16 (<.01) 5.15 (<.01) -9.61 (<.01) 2.86 (<.01) -3.12 (<.01) -1.41 (0.16)

Financials 2.58 *** 0.77 *** 0.77 *** 0.82 *** 0.50 *** -0.79 *** -1.12 *** -0.67 *** 0.56 *** 267 0.91

4.51 (<.01) 36.03 (<.01) 28.24 (<.01) 40.71 (<.01) 5.53 (<.01) -37.54 (<.01) -9.59 (<.01) -4.67 (<.01) 3.57 (<.01)

Info Tech 2.84 *** 0.76 *** 2.02 *** 1.77 *** 1.59 *** -0.72 *** -0.27 0.69 0.58 ** 125 0.78

4.89 (<.01) 8.70 (<.01) 9.54 (<.01) 6.73 (<.01) 4.69 (<.01) -6.62 (<.01) -1.44 (0.15) 0.21 (0.83) 2.07 (0.04)

103

Table 12 Panel C (cont’d)


Adj

R2

Telecom Svcs. 7.90 0.54 0.56 -197.24 13.16 -0.73 -1.15 4.73 3.62 13 0.16

1.18 (0.30) 0.57 (0.60) 0.45 (0.67) -0.77 (0.48) 0.96 (0.39) -0.75 (0.49) -0.46 (0.66) 0.67 (0.53) 1.29 (0.26)

Utilities 9.42 ** 0.26 0.30 0.36 1.99 -0.02 -0.32 -3.17 1.69 23 0.68

2.47 (0.02) 0.62 (0.54) 0.19 (0.85) 0.24 (0.81) 0.43 (0.67) -0.04 (0.97) -0.26 (0.79) -1.37 (0.19) 0.76 (0.46)

Total 783

This panel shows the estimated coefficients by GICS Sector of an OLS regression using the modified Ohlson model on the 35th-percentile with

BE/ME low, i.e. the growth firms. The model and variables are those given in Panel A of Table 9.

104

Table 12 (cont’d)

Panel D. Results of F-tests of Coefficients from OLS Regression with BE/ME Low


Energy 5.58 0.02** 0.14 0.71 0.42 0.52 0.36 0.55 3.19 0.08* 5.50 0.02**

Materials 3.21 0.08* 0.52 0.47 0.05 0.82 0.51 0.48 2.00 0.17 2.02 0.17

Industrials 0.01 0.93 0.00 0.94 0.02 0.88 0.26 0.61 0.01 0.93 5.82 0.01**

Cnsmr Discret. 5.55 0.02** 0.06 0.79 8.09 <.01*** 0.04 0.84 3.49 0.06* 1.69 0.19

Cnsmr Staples 3.69 0.08* 15.19 <.01*** 45.27 <.01*** . . 5.81 0.03** 0.80 0.39

Health Care 8.54 <.01*** 5.42 0.02** 2.86 0.09* 17.32 <.01*** 13.05 <.01*** 24.40 <.01***

Financials 115.93 <.01*** 69.40 <.01*** 84.24 <.01*** 29.46 <.01*** 100.17 <.01*** 1.09 0.29

Info Tech 7.53 <.01*** 23.17 <.01*** 8.58 <.01*** 3.04 0.08* 6.80 0.01** 15.57 <.01***

Telecom Svcs. 0.23 0.65 0.13 0.73 0.61 0.47 0.79 0.42 0.08 0.79 0.00 0.95

Utilities 3.12 0.09* 0.20 0.66 0.19 0.66 0.05 0.83 3.64 0.07* 0.30 0.59

105

Table 12 Panel D. (cont’d)


Energy 0.04 0.84 0.23 0.63 0.07 0.79 1.03 0.31 0.16 0.68

Materials 0.21 0.64 0.01 0.93 1.04 0.31 0.30 0.59 0.25 0.62

Industrials 1.15 0.28 0.02 0.89 0.21 0.64 0.09 0.76 2.32 0.13

Cnsmr Discret. 0.01 0.93 2.07 0.15 0.01 0.91 0.43 0.51 0.02 0.89

Cnsmr Staples . . 41.41 <.01*** . . . . . .

Health Care 1.60 0.20 0.50 0.48 13.11 <.01*** 16.27 <.01*** 30.17 <.01***

Financials 5.38 0.02** 2.68 0.10 7.99 <.01*** 11.70 <.01*** 5.99 0.01**

Info Tech 0.26 0.61 0.61 0.43 1.32 0.25 0.16 0.69 0.08 0.77

Telecom Svcs. 0.66 0.46 0.60 0.48 0.84 0.41 0.69 0.45 0.70 0.44

Utilities 0.88 0.36 0.00 0.98 0.13 0.72 0.11 0.75 1.26 0.28


106

Table 12 (cont’d)

Panel E. Estimated Coefficients from OLS Regression with BE/ME High (Value)

GICS Sector Int NFVA FVA1 FVA2 FVA3 NFVL FVL12 FVL3 NI N Adj R2

Energy -1.92 ** 0.77 *** 0.57 ** 1.89 *** 0.80 ** -0.81 *** -1.07 *** -0.40 0.33 *** 56 0.89

-2.36 (0.02) 10.62 (<.01) 2.48 (0.01) 5.36 (<.01) 2.11 (0.04) -7.68 (<.01) -5.49 (<.01) -0.52 (0.60) 3.82 (<.01)

Materials -0.50 0.75 *** 1.28 0.55 -3.07 -0.78 *** -0.14 6.43 0.56 * 27 0.89

-0.72 (0.48) 6.78 (<.01) 1.54 (0.14) 0.14 (0.89) -1.07 (0.30) -6.18 (<.01) -0.66 (0.51) 1.30 (0.20) 2.03 (0.05)

Industrials -2.39 *** 0.81 *** 0.26 1.46 *** 0.30 -0.82 *** -0.99 *** -0.90 0.02 86 0.99

-2.78 (<.01) 12.03 (<.01) 0.79 (0.43) 5.52 (<.01) 0.45 (0.65) -9.22 (<.01) -3.23 (<.01) -0.62 (0.53) 0.11 (0.91)

Cnsmr Discret. -0.89 0.54 *** 0.74 -1.44 0.35 -0.51 *** -0.76 ** -2.03 0.04 60 0.64

-1.13 (0.26) 8.71 (<.01) 1.29 (0.20) -0.88 (0.38) 1.12 (0.26) -7.01 (<.01) -2.07 (0.04) -0.68 (0.49) 0.40 (0.68)

Cnsmr Staples 0.45 1.07 ** 0.56 26.77 -137.03 -1.28 * 7.33 -62.66 2.81 16 0.92

0.14 (0.89) 3.14 (0.01) 0.31 (0.76) 0.96 (0.37) -0.61 (0.55) -2.14 (0.06) 0.43 (0.67) -1.10 (0.30) 1.38 (0.21)

Health Care -0.42 1.33 *** 0.31 1.49 *** 0.62 -1.45 *** 0.97 -17.13 * -0.04 90 0.88

-0.73 (0.46) 14.33 (<.01) 0.88 (0.38) 7.56 (<.01) 0.52 (0.60) -10.13 (<.01) 0.73 (0.46) -1.98 (0.05) -0.17 (0.86)

Financials 1.05 ** 0.28 *** 0.28 *** 0.36 *** 0.34 *** -0.28 *** -0.55 *** -0.36 *** 0.35 *** 175 0.55

2.58 (0.01) 7.87 (<.01) 4.84 (<.01) 9.27 (<.01) 5.66 (<.01) -7.55 (<.01) -3.38 (<.01) -3.45 (<.01) 6.29 (<.01)

107

Table 12 Panel E. (cont’d)


Adj

R2

Info Tech -0.25 0.74 *** 0.99 *** 1.10 *** 1.14 *** -0.72 *** -0.24 -0.96 0.33 ** 108 0.79

-0.76 (0.44) 12.11 (<.01) 8.85 (<.01) 6.38 (<.01) 5.06 (<.01) -6.80 (<.01) -0.28 (0.77) -0.19 (0.84) 2.58 (0.01)

Telecom Svcs. 0.50 0.69 4.28 6.75 ** 12.19 * -1.12 46.74 -9.49 2.32 11 0.97

0.30 (0.79) 1.06 (0.40) 2.13 (0.16) 4.49 (0.04) 3.94 (0.05) -1.03 (0.41) 1.76 (0.22) -2.48 (0.13) 2.41 (0.13)

Utilities -0.06 1.11 *** 1.42 ** 0.13 -0.74 -1.23 *** -1.50 -2.21 1.20 23 0.79

-0.02 (0.98) 6.26 (<.01) 2.26 (0.03) 0.16 (0.87) -0.31 (0.76) -4.93 (<.01) -1.55 (0.14) -1.03 (0.32) 1.75 (0.10)

Total 652

This panel shows the estimated coefficients by GICS Sector of an OLS regression using the modified Ohlson model on the 35th-percentile with

BE/ME high, i.e. the value firms. The model and variables are those given in Panel A of Table 9.

108

Table 12 (cont’d)

Panel F. Results of F-tests of Coefficients from OLS Regression with BE/ME High


Energy 10.06 <.01*** 3.59 0.06* 6.40 0.01** 0.27 0.60 3.26 0.07* 0.15 0.70

Materials 4.90 0.04** 0.11 0.74 0.01 0.91 2.00 0.17 2.96 0.10 15.26 <.01***

Industrials 8.09 <.01*** 5.18 0.02** 3.00 0.08* 1.04 0.31 3.99 0.04** 0.00 0.98

Cnsmr Discret. 57.20 <.01*** 0.20 0.65 2.20 0.14 4.29 0.04** 47.08 <.01*** 0.42 0.51

Cnsmr Staples 0.05 0.83 0.06 0.81 0.85 0.38 0.38 0.55 0.23 0.64 0.24 0.63

Health Care 12.81 <.01*** 3.87 0.05* 6.21 0.01** 0.10 0.75 9.97 <.01*** 2.22 0.14

Financials 419.58 <.01*** 160.93 <.01*** 270.78 <.01*** 124.50 <.01*** 362.48 <.01*** 7.58 <.01***

Info Tech 17.73 <.01*** 0.01 0.91 0.31 0.57 0.37 0.54 7.24 <.01*** 0.80 0.37

Telecom Svcs. 0.23 0.67 2.67 0.24 14.62 0.06* 13.07 0.06* 0.01 0.92 3.23 0.21

Utilities 0.40 0.53 0.45 0.51 1.19 0.29 0.53 0.47 0.83 0.37 0.27 0.61

109

Table 12 Panel F. (cont’d)


Energy 0.63 0.43 10.76 <.01*** 0.31 0.57 4.40 0.04** 0.75 0.39

Materials 2.26 0.14 0.03 0.85 1.67 0.21 0.60 0.44 1.76 0.20

Industrials 0.00 0.94 5.56 0.02** 0.00 0.95 2.60 0.11 0.00 0.94

Cnsmr Discret. 0.12 0.72 1.69 0.19 0.40 0.53 1.15 0.28 0.18 0.67

Cnsmr Staples 1.17 0.31 0.89 0.37 0.38 0.55 0.43 0.53 1.83 0.21

Health Care 3.47 0.06* 9.92 <.01*** 0.05 0.81 0.52 0.47 3.88 0.05*

Financials 37.94 <.01*** 3.95 0.04** 0.77 0.38 0.18 0.67 1.19 0.27

Info Tech 0.00 0.99 0.24 0.62 0.37 0.54 0.02 0.89 0.02 0.88

Telecom Svcs. 4.93 0.15 0.64 0.50 3.43 0.20 6.68 0.12 3.85 0.18

Utilities 0.32 0.58 5.22 0.03** 0.58 0.45 0.10 0.76 0.06 0.80


110

Table 12 (cont’d)

Panel G. Significant Pairs of Coefficients of Fair Value Assets from Growth and Value

45th

Percentiles

Industry FVA1

Growth, Value

FVA2

Growth, Value

FVA3

Growth, Value

Expected

Financials 0.77***, 0.28*** 0.82***, 0.36*** 0.50***, 0.34***

Info Tech 2.02***, 0.99*** 1.77***, 1.10*** 1.59***, 1.14***

Not Expected

Energy

1.16***, 1.89*** 0.79**, 0.80**

Industrials

0.95**, 1.46***

Health Care

1.31***, 1.49***

This table summarizes findings from OLS regressions presented in Panels C and E. The first

coefficient in each pair is the growth coefficient from Panel C, while the second in each pair

is the corresponding value coefficient from Panel E. The upper part of the table shows the

coefficients in industry sectors whose pattern of coefficients follows the hypothesized pattern,

while the lower part shows the coefficients of industry sectors whose pattern of coefficients

does not follow the hypothesized pattern. The number of stars after each coefficient

represents its statistical significance, following the convention described in Panel A of Table

9.

111

Table 13. Investigation of the Effect of Industry Concentration (HHI)

Panel A. Descriptive Statistics of the Herfindahl-Hirschman Index (HHI)

GICS Sector N MIN Q1 MEDIAN Q3 MAX MEAN STD

Energy 20 286.10 462.90 639.70 639.70 802.20 575.68 180.99

Materials 41 23.50 238.30 578.50 976.60 2,661.60 753.79 678.42

Industrials 85 23.50 233.10 497.90 988.70 2,527.70 654.50 527.02

Cnsmr Discret. 44 19.00 177.95 650.50 1,198.45 2,815.50 781.76 692.72

Cnsmr Staples 25 221.30 529.90 806.50 895.70 1,524.20 766.40 311.56

Health Care 156 187.20 476.30 529.90 634.70 2,703.60 569.51 287.36

Info Tech 139 23.50 583.60 818.70 1,417.10 2,662.40 994.31 558.50

Summary 510 19.00 460.70 634.70 929.70 2,815.50 742.47 516.82

This panel shows descriptive statistics of the Herfindahl-Hirschman Index (HHI) by GICS Sector. The Census Bureau publishes the HHI

for selected industries. Q1 is the first quartile and Q3 is the third quartile. STD is the standard deviation.

112

Table 13 (cont’d)

Panel B. Composition of the Low and High 45th

-percentiles of HHI

This panel shows the number of firms in each 45th-percentile, and the minimum and

maximum value of the HHI within each 45th-percentile.

HHI Low HHI High

HHI HHI


Energy 5 286.10 286.10 15 639.70 802.20

Materials 17 23.50 497.90 19 656.70 2,661.60

Industrials 38 23.50 442.50 39 583.60 2,527.70

Cnsmr Discret. 19 19.00 558.80 20 703.40 2,815.50

Cnsmr Staples 11 221.30 763.10 12 810.20 1,524.20

Health Care 46 187.20 476.30 110 529.90 2,703.60

Info Tech 41 23.50 624.50 67 1,398.50 2,662.40

177 19.00 763.10 282 529.90 2,815.50

113

Table 13 (cont’d)

Panel C. Estimated Coefficients from OLS Regression on the 45th

-percentile with HHI Low


Adj

R2

Energy 14.89 . -1.01 . 11.23 . 414.64 . 0.00 . 1.66 . 0.00 . 0.00 . 0.00 . 5

. . . . . . . . . . . . . . . . . .

Materials 1.14 0.82 ** 5.40 -8.45 1.70 -0.46 -3.28 -31.11 1.84 * 17 0.73

0.25 (0.81) 2.42 (0.04) 0.78 (0.45) -0.87 (0.40) 0.32 (0.76) -1.30 (0.22) -0.61 (0.55) -0.30 (0.77) 2.26 (0.05)

Industrials 4.02 * 0.44 ** 1.91 -2.88 -2.92 -0.36 -0.29 -3.18 2.37 *** 38 0.64

1.79 (0.08) 2.36 (0.02) 0.82 (0.41) -0.67 (0.50) -0.23 (0.81) -1.08 (0.28) -0.68 (0.50) -1.17 (0.24) 4.03 (<.01)

Cnsmr Discret. 1.88 0.17 2.43 ** 17.13 -12.01 -0.11 3.98 0.00 . 0.26 19 0.78

1.32 (0.21) 0.72 (0.48) 2.33 (0.04) 1.77 (0.10) -1.40 (0.18) -0.33 (0.74) 0.40 (0.69) . . 0.87 (0.40)

Cnsmr Staples 0.22 2.53 ** 2.93 -0.16 0.00 . -3.76 ** 44.32 * 0.00 . -3.49 11 0.81

0.08 (0.93) 3.99 (0.01) 0.97 (0.38) -0.01 (0.98) . . -3.18 (0.03) 2.14 (0.09) . . -1.68 (0.16)

Health Care 0.99 1.21 *** 2.90 *** 0.84 * 6.43 *** -1.34 *** 1.96 ** 170.54 1.15 ** 46 0.88

1.08 (0.28) 7.71 (<.01) 5.83 (<.01) 1.72 (0.09) 3.60 (<.01) -6.06 (<.01) 2.16 (0.03) 0.34 (0.73) 2.41 (0.02)

Info Tech 1.62 0.73 *** 1.06 ** 1.07 *** 0.99 -0.61 * -0.12 4.07 0.75 *** 41 0.86

1.61 (0.11) 5.15 (<.01) 2.56 (0.01) 3.15 (<.01) 1.39 (0.17) -1.96 (0.05) -0.59 (0.55) 0.05 (0.95) 3.29 (<.01)

177

114

Table 13 Panel C (cont’d)

This panel shows the estimated coefficients by GICS Sector of an OLS regression using the modified Ohlson model on the 45th-percentile

where the Herfindahl-Hirschman Index (HHI) is low, i.e. the firms in unconcentrated industries. The model and variables are those given in

Panel A of Table 9.

Panel D


115

Table 13 (cont’d)

Panel D. Results of F-Tests of Coefficients from the Previous Panel


Energy . . . . . . . . . . . .

Materials 0.29 0.60 0.40 0.54 0.96 0.35 0.02 0.90 2.33 0.16 0.18 0.68

Industrials 8.66 <.01*** 0.15 0.69 0.82 0.37 0.10 0.75 3.64 0.06* 2.71 0.11

Cnsmr Discret. 12.84 <.01*** 1.88 0.19 2.78 0.12 2.31 0.15 7.02 0.02** 0.25 0.62

Cnsmr Staples 5.84 0.07* 0.41 0.55 0.01 0.92 . . 5.44 0.08* 4.79 0.09*

Health Care 1.77 0.19 14.58 <.01*** 0.11 0.74 9.25 <.01*** 2.37 0.13 10.66 <.01***

Info Tech 3.79 0.06* 0.02 0.88 0.04 0.84 0.00 0.98 1.61 0.21 17.33 <.01***


Energy . . . . . . . . . .

Materials 0.09 0.77 1.99 0.19 0.14 0.72 0.76 0.40 0.07 0.79

Industrials 0.65 0.42 0.55 0.46 0.14 0.70 0.00 0.99 1.12 0.29

Cnsmr Discret. . . 2.18 0.16 2.99 0.11 4.41 0.05* . .

Cnsmr Staples . . 0.06 0.82 . . . . . .

Health Care 0.11 0.73 10.59 <.01*** 3.63 0.06* 7.86 <.01*** 0.11 0.74

Info Tech 0.00 0.94 0.00 0.99 0.01 0.93 0.01 0.92 0.00 0.95

116

Table 13 (cont’d)

Panel E. Estimated Coefficients from OLS Regression with 45th

-percentile with HHI High


Adj

R2

Energy 1.99 0.94 ** -5.37 -2.93 33.21 -1.05 * 9.42 * -46.83 1.63 * 15 0.74

0.40 (0.70) 3.29 (0.01) -1.55 (0.17) -0.69 (0.51) 0.54 (0.61) -2.29 (0.06) 1.95 (0.09) -0.54 (0.60) 2.24 (0.06)

Materials -0.17 0.71 1.84 8.14 *** 5.07 ** -0.78 -0.07 1.02 2.20 *** 19 0.91

-0.07 (0.94) 1.63 (0.13) 1.61 (0.13) 4.32 (<.01) 2.45 (0.03) -1.56 (0.15) -0.06 (0.95) 0.09 (0.93) 3.52 (<.01)

Industrials -2.53 1.10 *** -0.68 5.47 -6.51 ** -0.99 *** -2.06 -384.94 0.95 ** 39 0.76

-1.10 (0.28) 5.60 (<.01) -0.68 (0.50) 1.52 (0.13) -2.59 (0.01) -3.56 (<.01) -1.00 (0.32) -0.59 (0.56) 2.33 (0.02)

Cnsmr Discret. -1.11 1.24 *** 1.74 * 5.87 -2.21 -1.12 *** -1.67 ** -0.49 1.60 ** 20 0.57

-0.30 (0.76) 3.96 (<.01) 2.05 (0.06) 0.40 (0.69) -1.04 (0.32) -3.79 (<.01) -2.95 (0.01) -0.07 (0.94) 2.62 (0.02)

Cnsmr Staples 2.40 0.75 * 0.12 58.35 -318.87 -0.44 -0.93 -75.99 0.83 12 0.97

1.25 (0.29) 2.70 (0.07) 0.10 (0.93) 1.87 (0.15) -1.39 (0.25) -1.46 (0.23) -0.77 (0.49) -1.64 (0.20) 0.35 (0.75)

Health Care 0.29 1.01 *** 1.79 *** 1.99 *** 0.93 -0.75 *** 2.78 *** -6.20 -0.34 110 0.87

0.59 (0.55) 7.11 (<.01) 6.78 (<.01) 7.89 (<.01) 1.10 (0.27) -3.61 (<.01) 6.09 (<.01) -0.59 (0.55) -1.20 (0.23)

117

Table 13 Panel E (cont’d)


Adj

R2

Info Tech 1.59 ** 0.51 *** 1.78 *** 1.74 *** 1.09 ** -0.50 ** 9.85 6.42 1.62 *** 67 0.69

2.13 (0.03) 3.05 (<.01) 5.80 (<.01) 5.02 (<.01) 2.48 (0.01) -2.02 (0.04) 1.62 (0.10) 0.36 (0.72) 4.05 (<.01)

Total 282


where the value of the Herfindahl-Hirschman Index (HHI) is high, i.e. the firms in concentrated industries. The model and variables are those

given in Panel A of Table 9.

118

Table 13 (cont’d)

Panel F. Results of F-Tests on Coefficients of the Previous Panel.

GICS Sector NFVA=1 FVA1=1 FVA2=1 FVA3=1 NFVL=_1 FVL12=_1

Energy 0.04 0.84 3.38 0.11 0.87 0.38 0.27 0.62 0.01 0.91 4.65 0.07*

Materials 0.42 0.52 0.55 0.47 14.34 <.01*** 3.87 0.07* 0.20 0.66 0.61 0.45

Industrials 0.26 0.61 2.81 0.10 1.55 0.22 8.93 <.01*** 0.00 0.96 0.26 0.61

Cnsmr Discret. 0.60 0.45 0.76 0.40 0.11 0.74 2.28 0.15 0.17 0.68 1.40 0.26

Cnsmr Staples 0.85 0.42 0.49 0.53 3.39 0.16 1.95 0.25 3.33 0.16 0.00 0.95

Health Care 0.00 0.97 8.91 <.01*** 15.45 <.01*** 0.01 0.93 1.42 0.23 68.60 <.01***

Info Tech 8.35 <.01*** 6.46 0.01** 4.57 0.03** 0.04 0.84 4.08 0.04** 3.20 0.07*

GICS Sector FVL3=–1 FVA1=FVA2 FVA1=FVA3 FVA2=FVA3 FVL12=FVL3

Energy 0.28 0.61 0.23 0.64 0.39 0.55 0.33 0.58 0.42 0.54

Materials 0.03 0.86 7.61 0.02** 3.18 0.10 1.31 0.27 0.01 0.92

Industrials 0.35 0.56 2.35 0.13 4.57 0.04** 11.26 <.01*** 0.34 0.56

Cnsmr Discret. 0.01 0.94 0.08 0.78 3.12 0.10 0.27 0.61 0.03 0.86

Cnsmr Staples 2.61 0.20 3.61 0.15 1.93 0.25 2.10 0.24 2.70 0.19

Health Care 0.24 0.62 0.32 0.57 0.81 0.37 1.46 0.22 0.72 0.39

Info Tech 0.17 0.68 0.01 0.94 1.96 0.16 1.27 0.26 0.03 0.85


119

Table 13 (cont’d)

Panel G. Significant Pairs of Coefficients from OLS Regressions on HHI Low and High

45th

Percentiles

GICS Sector FVA1

Competitive, Conc.

FVA2

Competitive, Conc.

FVA3

Competitive, Conc.

Expected

Info. Tech. 1.06**, 1.78*** 1.07***, 1.74***

Not Expected

Cons. Disc. 2.43**, 1.74*

Mixed

Health Care 2.90***, 1.79*** [N] 0.84*, 1.99*** [E]

This table summarizes findings from OLS regressions presented in Panels C and E. The first

coefficient in each pair is the coefficient from Panel C the subsample with HHI low, while the

second in each pair is the corresponding coefficient from Panel E, the subsample with HHI

high. The upper part of the table shows the coefficients in the industry sector whose pattern of

coefficients followed the hypothesized pattern, while the middle part shows the coefficients of

the industry sector whose pattern of coefficients does not follow the hypothesized pattern. The

lower part shows the coefficients of the industry sector with mixed results. One pair of

significant coefficients exhibits (does not exhibit) the hypothesized pattern, and is followed by

an [E] ([N]) to indicate this. The number of stars after each coefficient represents its statistical

significance, following the convention described in Panel A of Table 9.

120

Table 13 (cont’d)

Panel H. Results of IRLS Regression on 45th

-percentile with HHI Low


Adj

R2

Materials 1.07 0.81 *** 5.82 *** -12.35 *** 1.41 -0.41 *** -2.47 *** -30.38 * 1.54 *** 17 0.30

1.88 (0.17) 198.42 (<.01) 24.57 (<.01) 57.11 (<.01) 2.41 (0.12) 47.08 (<.01) 7.38 (<.01) 3.03 (0.08) 125.32 (<.01)

Industrials 1.03 0.77 *** 1.80 -0.68 1.45 -0.79 *** -0.70 ** -2.40 1.51 *** 38 0.58

0.46 (0.49) 36.61 (<.01) 1.29 (0.25) 0.05 (0.81) 0.03 (0.86) 12.08 (<.01) 5.74 (0.01) 1.71 (0.19) 14.41 (<.01)

Cnsmr Discret. 0.79 ** 0.34 *** 2.64 *** 15.00 *** 0.53 -0.21 ** -9.26 *** 0.00 . 0.29 *** 19 0.43

4.35 (0.03) 31.08 (<.01) 90.69 (<.01) 34.13 (<.01) 0.05 (0.81) 5.38 (0.02) 12.33 (<.01) . . 13.00 (<.01)

Health Care 0.86 1.17 *** 2.95 *** 1.47 *** 6.24 *** -1.28 *** 1.69 * 200.52 1.30 *** 46

0.78 (0.37) 49.12 (<.01) 31.17 (<.01) 8.10 (<.01) 10.81 (<.01) 29.68 (<.01) 3.08 (0.07) 0.14 (0.71) 6.65 (<.01) 0.60

Info Tech 1.74 * 0.67 *** 0.86 ** 1.18 *** 1.07 -0.52 * -0.08 8.80 0.78 *** 41

3.03 (0.08) 23.12 (<.01) 4.31 (0.03) 12.31 (<.01) 2.29 (0.13) 2.88 (0.08) 0.13 (0.71) 0.01 (0.91) 11.96 (<.01) 0.63

Total 161

This panel shows the estimated coefficients by GICS Sector of an IRLS regression using the modified Ohlson model on the 45th-percentile

where the Herfindahl-Hirschman Index (HHI) is low, i.e. the firms in unconcentrated industries. The model and variables are those given in

Panel A of Table 9.

121

Table 13 (cont’d)

Panel I. Results of IRLS Regression on 45th

-percentile with HHI High


Adj

R2

Energy -1.03 1.06 *** -5.39 *** -2.51 -13.65 -1.14 *** 8.76 *** 18.99 1.52 *** 15 0.42

0.29 (0.59) 92.40 (<.01) 16.26 (<.01) 2.38 (0.12) 0.33 (0.56) 41.09 (<.01) 22.06 (<.01) 0.32 (0.57) 29.45 (<.01)

Materials 0.41 1.04 *** 0.90 8.24 *** 3.79 *** -1.15 *** -0.62 1.14 2.33 *** 19 0.21

0.07 (0.78) 13.58 (<.01) 1.51 (0.21) 46.57 (<.01) 8.19 (<.01) 13.08 (<.01) 0.67 (0.41) 0.02 (0.88) 33.87 (<.01)

Industrials -2.07 1.10 *** -0.63 5.01 -6.76 ** -0.99 *** -2.01 -329.06 0.95 ** 39 0.55

0.73 (0.39) 28.07 (<.01) 0.36 (0.54) 1.75 (0.18) 6.52 (0.01) 11.42 (<.01) 0.86 (0.35) 0.23 (0.63) 4.83 (0.02)

Cnsmr Discret. -5.80 1.50 *** 1.85 ** 5.63 -4.46 ** -1.16 *** -1.89 *** -6.39 2.37 *** 20 0.46

2.60 (0.10) 24.03 (<.01) 4.99 (0.02) 0.15 (0.69) 4.61 (0.03) 16.14 (<.01) 11.75 (<.01) 0.91 (0.33) 16.00 (<.01)

Health Care 0.62 * 0.57 *** 1.35 *** 1.55 *** 1.77 *** -0.11 3.89 *** 2.45 0.29 110

2.85 (0.09) 28.88 (<.01) 47.64 (<.01) 67.96 (<.01) 7.91 (<.01) 0.47 (0.49) 131.47 (<.01) 0.10 (0.75) 1.89 (0.16) 0.33

Info Tech 1.08 * 0.54 *** 1.25 *** 2.01 *** 1.02 *** -0.50 ** 6.70 5.96 1.36 *** 67

2.75 (0.09) 13.66 (<.01) 21.74 (<.01) 43.62 (<.01) 7.02 (<.01) 5.26 (0.02) 1.60 (0.20) 0.14 (0.70) 15.18 (<.01) 0.55

Total 270


where the Herfindahl-Hirschman Index (HHI) is high, i.e. the firms in concentrated industries. The model and variables are those given in

Panel A of Table 9.

122

Table 13 (cont’d)

Panel J. Significant Pairs of Coefficients from IRLS Regressions on HHI Low and High

45th

Percentiles

GICS Sector FVA1

Competitive, Conc.

FVA2

Competitive, Conc.

FVA3

Competitive, Conc.

Expected

Info. Tech. 0.86**, 1.25*** 1.18***, 2.01***

Not Expected

Cons. Disc. 2.64***, 1.85**

Mixed

Health Care 2.95***, 1.35*** [N] 1.47***, 1.55*** [E] 6.24***, 1.77*** [N]

This table summarizes findings from IRLS regressions presented in Panels H and I. The first

coefficient in each pair is the coefficient from Panel H the subsample with HHI low, while the

second in each pair is the corresponding coefficient from Panel I, the subsample with HHI

high. The upper part of the table shows the coefficients in industry sector whose pattern of

coefficients followed the hypothesized pattern, while the middle part shows the coefficients of

industry sector whose pattern of coefficients did not follow the hypothesized pattern. The

lower part shows the coefficients of the industry sector with mixed results. The pairs (pair) of

significant coefficients that exhibit (does not exhibit) the hypothesized pattern are (is)

followed by an [E] ([N]) to indicate this. The number of stars after each coefficient represents

its statistical significance, following the convention described in Panel A of Table 9.

123

Table 14. Three-year Average Herfindahl of Net Sales

Panel A. Descriptive Statistics

Three-year Average H


Energy 125 0.04 0.05 0.05 0.05 0.05 0.05 0.01

Materials 62 0.05 0.05 0.05 0.05 0.24 0.06 0.03

Industrials 194 0.03 0.03 0.07 0.11 0.26 0.08 0.05

Cnsmr Discret. 139 0.03 0.03 0.04 0.11 0.20 0.07 0.05

Cnsmr Staples 36 0.06 0.06 0.08 0.22 0.41 0.12 0.09

Health Care 210 0.05 0.06 0.07 0.15 0.15 0.09 0.05

Financials 480 0.03 0.03 0.03 0.04 0.19 0.06 0.05

Info Tech 256 0.05 0.05 0.11 0.12 0.33 0.10 0.04

Telecom Svcs. 26 0.06 0.06 0.06 0.10 0.10 0.08 0.02

Utilities 52 0.04 0.04 0.05 0.08 0.25 0.08 0.07

Summaries 1580 0.03 0.03 0.05 0.11 0.41 0.08 0.05

This panel shows descriptive statistics of the average of three-year Herfindahl of net sales by

GICS Sector. The details of the computation of the average of three-year Herfindahl of net sales

are provided in Appendix B. Q1 is the first quartile and Q3 is the third quartile. STD is the

standard deviation.

124

Table 14 (cont’d)

Panel B. The Low and High 35th

-percentiles (the mezzanine 30-percentile is not shown)

Havg Low Havg High

Havg Havg


Energy 125 0.04 0.05 106 0.05 0.05

Materials 38 0.05 0.05 24 0.05 0.24

Industrials 71 0.03 0.06 82 0.08 0.26

Cnsmr Discret. 63 0.03 0.03 53 0.08 0.20

Cnsmr Staples 16 0.06 0.06 20 0.08 0.41

Health Care 76 0.05 0.06 75 0.15 0.15

Financials 219 0.03 0.03 187 0.04 0.19

Info Tech 100 0.05 0.08 117 0.11 0.33

Telecom Svcs. 17 0.06 0.06 26 0.06 0.10

Utilities 22 0.04 0.04 24 0.07 0.25

Total 747 0.03 0.08 714 0.04 0.41

This table shows the minimum and maximum values of the three year average Herfindahl

of net sales within the high and low 35th-percentiles by GICS sector. Thirty-fifth-

percentiles produces insufficient separation for the following three industries: Energy,

Materials, and Telecommunication Services. This can be seen by observing that the

maximum value of the low 35th-percentile equals the minimum value of the high 35th-

percentile. Furthermore, Consumer Staples, with 16 (20) observations in the low (high)

35th-percentile has insufficient observations for regression studies. Therefore, these four

industries will be dropped in the regression studies that follow.

125

Table 14 (cont’d)

Panel C. Number of Firms in the Low 35th

-percentile, Between 30th

-percentile, and

High 35th

-percentile of Three-year Average Herfindahl of Net Sales by Industry

Sector

GICS Sector Sample Low 35th

Between High 35th

Industrials 194 71 41 82

Cnsmr Discret. 139 63 23 53

Health Care 210 76 59 75

Financials 480 219 74 187

Info Tech 256 100 39 117

Utilities 52 22 6 24

Total 1331 551 242 538

This panel shows the number of firms in the low and high 35th percentiles of the three-year

average Herfindahl by GICS Sector. Due to lack of separation, as shown in the previous

panel, three sectors have been removed from this portion of the study: Energy, Materials,

and Telecommunication Services. The column entitled “Between” is the number of firms

in the 30th-percenthile that lies between the low and high 35th-percentiles. The value in the

column entitled “Sample” is the subtotal of the three columns to the right: “Low 35th”-

percentile, between, and “High 35th”-percentile.

126

Table 14. (cont’d)

Panel D. Estimates of Coefficients by IRLS of the Subsample with Average Herfindahl of Net Sales Low (Low 35th

-percentile)


Adj

R2

Industrials 3.02 * 0.55 *** 0.54 0.24 0.05 -0.44 *** -0.64 0.77 1.37 *** 71 0.34

2.95 (0.08) 15.49 (<.01) 1.55 (0.21) 0.24 (0.62) 0.01 (0.92) 6.64 (<.01) 1.59 (0.20) 0.06 (0.81) 14.6 (<.01)

Cnsmr Discret. 1.93 0.56 *** 1.70 7.75 *** 1.51 -0.52 *** -0.84 67.99 * 0.39 ** 63 0.28

1.83 (0.17) 17.40 (<.01) 1.13 (0.28) 7.97 (<.01) 0.31 (0.57) 11.94 (<.01) 0.87 (0.35) 3.03 (0.08) 5.93 (0.01)

Health Care 2.12 ** 0.94 *** 1.84 *** 0.64 * 0.35 -0.98 *** 4.35 *** 102.5 1.65 *** 76

6.44 (0.01) 54.28 (<.01) 19.03 (<.01) 2.75 (0.09) 0.09 (0.75) 29.76 (<.01) 47.73 (<.01) 1.01 (0.31) 15.7 (<.01) 0.48

Financials 3.88 *** 0.37 *** 0.36 *** 0.50 *** 0.59 *** -0.40 *** -0.82 *** -3.58 1.23 *** 219

30.51 (<.01) 28.85 (<.01) 12.24 (<.01) 45.84 (<.01) 9.47 (<.01) 27.89 (<.01) 8.43 (<.01) 1.98 (0.15) 93.8 (<.01) 0.40

Info Tech 1.87 *** 0.61 *** 1.57 *** 1.71 *** 1.13 *** -0.61 *** 0.06 1.01 0.73 *** 100

8.06 (<.01) 34.59 (<.01) 31.65 (<.01) 36.32 (<.01) 9.07 (<.01) 13.23 (<.01) 0.10 (0.75) 0.00 (0.95) 13.4 (<.01) 0.56

Utilities 4.65 -0.26 -1.73 0.85 1.29 0.56 0.65 -2.00 5.07 22

0.34 (0.55) 0.24 (0.62) 0.42 (0.51) 0.11 (0.74) 0.05 (0.81) 0.73 (0.39) 0.16 (0.69) 0.67 (0.41) 2.61 (0.10) 0.38

Total 731

127



where the average Herfindahl of net sales is low, i.e. the firms in unconcentrated industries. The model and variables are those given in Panel

A of Table 9.

128

Table 14 (cont’d)

Panel E. Estimates of Coefficients by IRLS of the Subsample with Average Herfindahl of Net Sales High (High 35th

-percentile)


Adj

R2

Industrials 0.71 0.90 *** 1.96 *** -0.23 0.35 -0.89 *** -1.88 *** -4.47 ** 0.53 82 0.39

0.28 (0.59) 45.52 (<.01) 14.42 (<.01) 0.19 (0.66) 0.01 (0.93) 25.71 (<.01) 8.46 (<.01) 3.99 (0.04) 1.67 (0.19)

Cnsmr Discret. 2.86 ** 0.83 *** 1.71 ** 1.85 *** 0.18 -0.98 *** 3.58 -21.53 0.39 *** 53 0.53

5.69 (0.01) 27.47 (<.01) 5.86 (0.01) 24.24 (<.01) 0.13 (0.72) 17.06 (<.01) 2.52 (0.11) 0.16 (0.68) 7.73 (<.01)

Health Care 0.47 * 0.94 *** 0.77 *** 1.37 *** 0.53 0.50 *** 3.41 *** 3.24 0.39 *** 75 0.45

3.55 (0.05) 36.35 (<.01) 26.30 (<.01) 85.37 (<.01) 1.12 (0.29) 9.75 (<.01) 71.93 (<.01) 0.54 (0.46) 7.67 (<.01)

Financials 1.38 ** 0.79 *** 0.80 *** 0.85 *** 0.57 *** -0.82 *** -0.97 ** -0.80 *** 0.38 *** 187 0.51

5.43 (0.01) 1,253.80 (<.01) 717.42 (<.01) 1,906.74 (<.01) 50.11 (<.01) 1,334.51 (<.01) 5.00 (0.02) 27.29 (<.01) 9.88 (<.01)

Info Tech 0.66 0.65 *** 1.25 *** 1.64 *** 1.19 *** -0.41 *** -0.60 4.95 * 1.25 *** 117 0.48

1.78 (0.18) 85.50 (<.01) 75.36 (<.01) 45.02 (<.01) 14.47 (<.01) 18.80 (<.01) 0.32 (0.57) 2.87 (0.09) 37.86 (<.01)

Utilities 5.21 * 1.12 *** 3.30 *** 1.94 * -25.49 * -1.24 *** -4.38 *** 9.78 2.40 ** 24 0.60

3.81 (0.05) 19.75 (<.01) 13.25 (<.01) 2.92 (0.08) 3.45 (0.06) 12.81 (<.01) 10.66 (<.01) 0.43 (0.50) 5.66 (0.01)

714

129

Table 14 Panel E (cont’d)


where the average Herfindahl of net sales is high, i.e. the firms in concentrated industries. The model and variables are those given in Panel A

of Table 9

130

Table 14 (cont’d)

Panel F. Pairs of Significant Coefficients of Fair Value of Assets for Low and High

Herfindahl

GICS Sector FVA1

Competitive, Conc.

FVA2

Competitive, Conc.

FVA3

Competitive, Conc.

Not Expected

Cons. Disc. 7.75***, 1.85***

Mixed

Health Care 1.84***, 0.77*** [N] 0.64*, 1.37*** [E]

Financials 0.36***, 0.80*** [E] 0.50***, 0.85*** [E] 0.59***, 0.57*** [N]

Info. Tech. 1.57***, 1.25*** [N] 1.71***, 1.64*** [N] 1.13***, 1.19*** [E]

This table summarizes findings from IRLS regressions presented in Panels D and E. The first

coefficient in each pair is the coefficient from Panel D the subsample where the average

Herfindahl of net sales is low, while the second in each pair is the corresponding coefficient from

Panel E, the subsample where the average Herfindahl of net sales is high. The upper part of the

table shows the coefficients in industry sectors whose pattern of coefficients follows the

hypothesized pattern, while the middle part shows the coefficients of industry sectors whose

pattern of coefficients does not follow the hypothesized pattern. The lower part shows the

coefficients of the industry sector with mixed results. One pair of significant coefficients exhibits

(does not exhibit) the hypothesized pattern, and is followed by an [E] ([N]) to indicate this. The

number of stars after each coefficient represents its statistical significance, following the

convention described in Panel A of Table 9.

131

Table 14 (cont’d)

Panel G. Whole Sample with Herfindahl Indicator

Intercept NFVA FVA1 FVA2 FVA3 NFVL FVL12 FVL3 H_Ind N Adj R2

Coefficient 2.76*** 0.79*** 0.82*** 0.85*** 0.64*** –0.83*** –1.11*** –0.64*** 0.036 1,580 0.39

Chi-Square 157.86 13135 2099 105 105 10647 408 33.2 0.01

p-value (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) 0.09

This table shows the estimated coefficients of an IRLS regression of the entire sample with an indicator variable, H_Ind, which takes the

value 0 for unconcentrated industries and 1 for concentrated industries. The model is a modified Ohlson model with the indicator variable

included as an additional term:


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 H_Indit + β1NIit + εit (3)

Other variables are those given in Panel A of Table 9.

132

Table 14 (cont’d)

Panel H1. First half of Coefficients of IRLS regression with an Indicator Variable for

Herfindahl above Median by GICS Sector

GICS Sector FVA1 FVA2 FVA3 H_Ind N Adj R2

Energy -0.32 1.19 *** 0.79 ** 3.78 ** 125 0.50

0.67 (0.41) 19.95 (<.01) 4.94 (0.02) 4.97 (0.02)

Materials 0.21 6.90 *** 2.47 -0.30 62 0.58

0.07 (0.78) 15.38 (<.01) 2.40 (0.12) 0.01 (0.93)

Industrials 1.05 *** 0.63 *** 0.36 -2.15 ** 194 0.38

18.15 (<.01) 6.82 (<.01) 0.58 (0.44) 3.94 (0.04)

Cnsmr Discret. 1.16 ** 2.25 *** 0.20 0.14 139 0.35

5.02 (0.02) 42.03 (<.01) 0.16 (0.68) 0.02 (0.89)

Cnsmr Staples 7.93 *** 65.59 *** -329.57 *** -6.17 *** 36 0.64

89.73 (<.01) 150.08 (<.01) 159.13 (<.01) 6.92 (<.01)

Health Care 1.53 *** 1.37 *** 4.95 *** 0.33 210 0.44

76.11 (<.01) 150.73 (<.01) 49.81 (<.01) 0.22 (0.63)

Financials 0.74 *** 0.82 *** 0.57 *** 1.15 * 480 0.41

866.00 (<.01) 2,246.50 (<.01) 66.44 (<.01) 3.51 (0.06)

Info Tech 1.36 *** 1.82 *** 1.16 *** -0.38 256 0.50

106.65 (<.01) 107.38 (<.01) 20.51 (<.01) 0.66 (0.41)

Telecom Svcs. 1.04 ** 0.26 -6.29 ** 4.46 *** 26 0.56

5.08 (0.02) 0.05 (0.82) 4.04 (0.04) 7.12 (<.01)

Utilities 1.32 ** 0.34 -0.42 -0.38 52 0.42

3.96 (0.04) 0.17 (0.67) 0.02 (0.88) 0.02 (0.88)

1580

The remaining coefficients of the regression are shown in the next half-panel.

133

Table 14 (cont’d)

Panel H2. Remaining half of Coefficients of IRLS regression with an Indicator Variable

for Herfindahl above Median by GICS Sector

GICS Sector Icept NFVA NFVL FVL12 FVL3 NI

Energy -3.47 ** 1.10 *** -1.18 *** -1.11 *** -1.19 0.51 ***

4.00 (0.04) 174.48 (<.01) 84.74 (<.01) 57.13 (<.01) 1.12 (0.29) 16.77 (<.01)

Materials -0.21 1.35 *** -1.38 *** -0.95 *** -2.94 2.10 ***

0.02 (0.87) 78.30 (<.01) 58.86 (<.01) 7.52 (<.01) 0.05 (0.82) 26.76 (<.01)

Industrials 3.44 *** 0.70 *** -0.65 *** -1.18 *** -1.72 1.18 ***

12.16 (<.01) 179.36 (<.01) 96.24 (<.01) 10.36 (<.01) 1.26 (0.26) 30.76 (<.01)

Cnsmr Discret. 3.59 *** 0.57 *** -0.55 *** -1.08 *** -1.69 0.44 ***

14.90 (<.01) 49.59 (<.01) 31.79 (<.01) 8.95 (<.01) 0.12 (0.72) 25.25 (<.01)

Cnsmr Staples 1.61 1.19 *** -0.97 *** -2.20 *** -

121.08

*** 1.85 ***

0.85 (0.35) 69.53 (<.01) 37.38 (<.01) 16.04 (<.01) 17.81 (<.01) 7.40 (<.01)

Health Care 0.76 1.34 *** -1.54 *** 1.96 *** -1.28 *** -0.08

1.62 (0.20) 341.52 (<.01) 289.08 (<.01) 33.78 (<.01) 9.44 (<.01) 0.20 (0.65)

Financials 0.96 ** 0.74 *** -0.77 *** -1.06 *** -0.78 *** 0.71 ***

4.17 (0.04) 1,602.19 (<.01) 1,672.55 (<.01) 99.37 (<.01) 48.64 (<.01) 59.08 (<.01)

Info Tech 1.76 *** 0.59 *** -0.42 *** -0.03 3.16 1.15 ***

13.42 (<.01) 107.31 (<.01) 28.59 (<.01) 0.03 (0.85) 1.09 (0.29) 71.57 (<.01)

Telecom Svcs. 0.00 0.80 *** -0.44 * -0.18 -3.71 ** 1.48 **

0.00 (0.99) 15.48 (<.01) 3.31 (0.06) 0.04 (0.84) 5.16 (0.02) 4.63 (0.03)

Utilities 6.77 ** 0.32 -0.16 -2.16 *** -1.38 2.59 **

4.18 (0.04) 1.68 (0.19) 0.27 (0.60) 8.26 (<.01) 0.50 (0.48) 4.76 (0.02)

These two half-panels together show the estimated coefficients by GICS Sector of an IRLS

regression on the entire sample using the modified Ohlson model with an indicator variable for

high or low average Herfindahl of net sales. The model is shown in Panel G.

134

Table 15. Entering, Incumbent, and Exiting Firms

Panel A. The 2008 Cohort from 2001–2009

Data Year - Fiscal

GICS Sector 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Energy 67 75 83 95 103 113 120 125 125 125 125

Materials 45 48 49 53 57 59 59 60 61 61 61

Industrials 146 161 165 173 180 187 194 195 195 195 195

Cnsmr Discret. 107 119 125 129 134 136 138 138 138 138 137

Cnsmr Staples 28 30 31 33 33 34 35 35 36 36 36

Health Care 138 153 170 187 198 203 207 208 210 210 210

Financials 318 345 375 399 423 450 471 476 481 481 481

Info Tech 186 203 211 234 243 249 254 256 256 256 255

Telecom Svcs. 13 17 18 21 22 22 24 24 26 26 26

Utilities 47 47 47 48 50 51 51 52 52 52 52

Total 1095 1198 1274 1372 1443 1504 1553 1569 1580 1580 1578

Panel B. Change in the Cohort between Each Fiscal Year and 2008

Data Year - Fiscal

GICS Sector 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Energy 58 50 42 30 22 12 5 0 0 0 0

Materials 16 13 12 8 4 2 2 1 0 0 0

Industrials 49 34 30 22 15 8 1 0 0 0 0

Cnsmr Discret. 31 19 13 9 4 2 0 0 0 0 1

Cnsmr Staples 8 6 5 3 3 2 1 1 0 0 0

Health Care 72 57 40 23 12 7 3 2 0 0 0

Financials 163 136 106 82 58 31 10 5 0 0 0

Info Tech 70 53 45 22 13 7 2 0 0 0 1

Telecom Svcs. 13 9 8 5 4 4 2 2 0 0 0

Utilities 5 5 5 4 2 1 1 0 0 0 0

Total Change 485 382 306 208 137 76 27 11 0 0 2

135

Table 15 (cont’d)

Panel C. Results of OLS Regression on Subsample of Entering Firms

GICS

Sector Icept NFVA FVA1 FVA2 FVA3 NFVL FVL12 FVL3 NI N

Adj

R2

Energy 0.13 0.86 *** -0.51 0.84 *** 0.72 ** -0.82 *** -0.82 *** -1.18 0.48 ** 58 0.40

0.01 (0.92) 29.21 (<.01) 0.24 (0.62) 8.79 (<.01) 5.70 (0.01) 15.28 (<.01) 18.71 (<.01) 1.08 (0.29) 3.98 (0.04)

Industrials 2.74 ** 0.25 ** 0.90 * -1.31 -0.30 -0.23 -0.80 * 0.10 0.43 49 0.17

6.45 (0.01) 5.84 (0.01) 3.41 (0.06) 1.09 (0.29) 0.14 (0.70) 2.37 (0.12) 2.88 (0.08) 0.01 (0.93) 2.28 (0.13)

Cnsmr

Discret.

1.20 0.76 *** 2.41 ** 5.39 *** -0.34 -0.73 ** 6.65 ** 24.79 0.58 *** 31 0.38

0.61 (0.43) 6.76 (<.01) 4.61 (0.03) 8.31 (<.01) 0.63 (0.42) 4.43 (0.03) 5.31 (0.02) 0.52 (0.47) 7.75 (<.01)

Health

Care

1.28 ** 0.57 *** 0.68 *** 0.74 *** 0.37 -0.62 *** 6.13 1.46 -0.19 72 0.23

5.96 (0.01) 15.83 (<.01) 6.97 (<.01) 17.50 (<.01) 0.15 (0.70) 9.74 (<.01) 0.29 (0.58) 0.03 (0.85) 1.07 (0.30)

Financials 2.83 *** 0.45 *** 0.67 *** 0.54 *** 0.42 *** -0.48 *** -0.31 -0.59 *** 0.42 *** 163 0.31

21.63 (<.01) 96.63 (<.01) 55.49 (<.01) 152.80 (<.01) 30.24 (<.01) 101.90 (<.01) 0.78 (0.37) 26.39 (<.01) 12.31 (<.01)

Info Tech 1.49 * 0.43 *** 2.10 *** 1.49 *** 2.52 -0.24 1.10 2.76 1.03 *** 70 0.43

3.63 (0.05) 7.51 (<.01) 26.70 (<.01) 16.64 (<.01) 0.98 (0.32) 0.78 (0.37) 0.30 (0.58) 0.66 (0.41) 9.38 (<.01)

Total 443

This panel shows the estimated coefficients by GICS Sector of an OLS regression using the modified Ohlson model on the entering firms.

The model and variables are those given in Panel A of Table 9.

136

Table 15 (cont’d)

Panel D. Results of OLS Regression on Incumbents (Including Unidentifiable Exiting Firms)

GICS

Sector Icept NFVA FVA1 FVA2 FVA3 NFVL FVL12 FVL3 NI N

Adj

R2

Energy -1.57 1.08 *** -0.51 4.02 *** -0.41 -1.14 *** -1.68 *** 0.86 0.64 *** 67 0.67

0.75 (0.38) 88.89 (<.01) 1.15 (0.28) 12.37 (<.01) 0.11 (0.74) 41.84 (<.01) 23.26 (<.01) 0.21 (0.64) 16.23 (<.01)

Materials -0.51 1.52 *** -0.09 7.14 *** 3.18 -1.60 *** -1.17 *** -22.6 2.25 *** 46 0.66

0.12 (0.72) 118.39 (<.01) 0.01 (0.90) 21.02 (<.01) 2.41 (0.12) 80.72 (<.01) 13.53 (<.01) 0.20 (0.65) 27.64 (<.01)

Industrials 3.16 *** 0.67 *** 0.97 *** 0.48 * 0.39 -0.59 *** -0.92 ** -1.84 1.41 *** 145 0.43

10.41 (<.01) 143.53 (<.01) 12.97 (<.01) 3.42 (0.06) 0.57 (0.45) 66.41 (<.01) 4.54 (0.03) 0.85 (0.35) 36.14 (<.01)

Cnsmr

Discret.

3.16 *** 0.57 *** 1.29 ** 2.22 *** 1.63 -0.56 *** -1.15 *** -1.78 0.41 *** 108 0.38

9.34 (<.01) 44.27 (<.01) 4.92 (0.02) 35.98 (<.01) 1.07 (0.30) 29.02 (<.01) 8.32 (<.01) 0.12 (0.72) 12.42 (<.01)

Cnsmr

Staples

-0.36 1.15 *** 6.70 *** 58.89 *** -300 *** -0.91 *** -2.09 ** -105 ** 2.40 * 28 0.64

0.02 (0.88) 28.93 (<.01) 21.22 (<.01) 40.88 (<.01) 47.04 (<.01) 14.81 (<.01) 6.49 (0.01) 5.72 (0.01) 2.96 (0.08)

Health Care 1.30 ** 1.36 *** 1.43 *** 1.42 *** 5.26 *** -1.58 *** 1.74 *** -1.49 *** 0.12 138 0.58

5.12 (0.02) 299 (<.01) 51.53 (<.01) 125.74 (<.01) 42.40 (<.01) 264 (<.01) 24.98 (<.01) 12.05 (<.01) 0.20 (0.65)

Financials 1.94 *** 0.73 *** 0.74 *** 0.82 *** 0.56 *** -0.77 *** -1.10 *** -0.96 *** 0.83 *** 317 0.44

10.87 (<.01) 1,117 (<.01) 646 (<.01) 1,619 (<.01) 25.03 (<.01) 1,175 (<.01) 82.72 (<.01) 28.23 (<.01) 45.69 (<.01)

Info Tech 1.12 ** 0.64 *** 1.19 *** 2.06 *** 1.08 *** -0.42 *** -0.17 -2.31 1.10 *** 186 0.50

6.38 (0.01) 106 (<.01) 71.62 (<.01) 99.78 (<.01) 17.79 (<.01) 26.53 (<.01) 1.34 (0.24) 0.07 (0.79) 55.36 (<.01)

137



Adj

R2

Telecom

Svcs.

1.59 *** -0.28 *** 3.39 *** 8.37 *** 9.53 *** 0.31 *** 1.40 *** 10.00 *** 6.03 *** 13 0.29

623 (<.01) 981 (<.01) 1,894 (<.01) 45565 (<.01) 10595 (<.01) 418 (<.01) 2,365 (<.01) 17145 (<.01) 91687 (<.01)

Utilities 7.85 *** -0.11 1.28 ** -1.03 -1.18 0.29 -1.74 ** -0.56 5.60 *** 47 0.48

7.34 (<.01) 0.15 (0.69) 4.42 (0.03) 1.18 (0.27) 0.19 (0.65) 0.70 (0.40) 5.83 (0.01) 0.11 (0.74) 11.05 (<.01)

Total 1095

This panel shows the estimated coefficients by GICS Sector of an OLS regression using the Modified Ohlson model on the incumbent firms.

Although this group is called “incumbent firms,” it also includes unidentifiable exiting firms. The model and variables are those given in

Panel A of Table 9.

138

Table 15 (cont’d)

Panel E. Significant Pairs of Fair Value of Assets of Entering and Incumbent Firms

GICS Sector FVA1

Entering, Incumb.

FVA2

Entering, Incumb.

FVA3

Entering,

Incumb.

Expected Pattern

Energy 0.84***, 4.02***

Industrials 0.90*, 0.97***

Health Care 0.68***, 1.43*** 0.74***, 1.42***

Financials 0.67***, 0.74*** 0.54***, 0.82*** 0.42***, 0.56***

Not Expected Pattern

Cons. Discret. 2.41**, 1.29** 5.39***, 2.22***

Mixed

Info. Tech. 2.10***, 1.19*** [N] 1.49***, 2.06*** [E]

This table summarizes findings from OLS regressions presented in Panels C and D. The

first coefficient in each pair is the coefficient from Panel C the subsample of entering firms,

while the second in each pair is the corresponding coefficient from Panel D, the subsample

of incumbents and unidentifiable exiting firms. The upper part of the table shows the

coefficients in industry sectors whose pattern of coefficients follows the hypothesized

pattern, while the middle part shows the coefficients of the industry sector whose pattern of

coefficients did not follow the hypothesized pattern. The lower part shows the coefficients

of the industry sector with mixed results. One pair of significant coefficients exhibits (does

not exhibit) the hypothesized pattern, and is followed by an [E] ([N]) to indicate this. The

number of stars after each coefficient represents its statistical significance, following the

convention described in Panel A of Table 9.

139

Table 15 (cont’d)

Panel F. Results of One-sided t-Test of log(AT) by Status as Entering or Incumbent

Firms

Group Method

N

Mean 95% CL Mean Std Dev

95% CL Std

Dev

Entr = 0 1,095 6.97 6.85 7.08 1.94 1.86 2.03

Entr = 1 485 6.33 6.18 6.48 1.65 1.55 1.76

Diff (Entr=0 less

Entr=1)

Satterthwaite 0.64 0.48 Infty

This panel shows the results of a one-sided t-Test for the logarithm of total assets, log(AT),

by the firm’s status as an entering or incumbent.

Method Variances DF t-value p-value

Satterthwaite Unequal 1078.9 6.68 <.01

140

Table 15 (cont’d)

Panel G1. Certain Coefficients of OLS Regressions with an Indicator for Status, Size, and Their Interaction

Model FVA1 FVA2 FVA3 Entr

log(A)

Entr ×

logA

Adj R2

Status Indicator 0.84 *** 0.83 *** 0.67 *** -2.07 *** 0.93

t-value (p-value) 36.45 (<.01) 56.00 (<.01) 8.48 (<.01) -5.15 (<.01)

With Size 0.85 *** 0.82 *** 0.61 *** 0.69 *** 0.93

t-value (p-value) 37.05 (<.01) 56.12 (<.01) 7.77 (<.01) 6.37 (<.01)

With Status & Size 0.85 *** 0.83 *** 0.63 *** -1.77 *** 0.63 *** 0.93

t-value (p-value) 37.09 (<.01) 56.50 (<.01) 8.06 (<.01) -4.41 (<.01) 5.78 (<.01)

With Interaction 0.84 *** 0.83 *** 0.63 *** 3.68 ** 0.83 *** -0.84 *** 0.93

t-value (p-value) 37.02 (<.01) 56.77 (<.01) 8.08 (<.01) 2.40 (0.02) 6.85 (<.01) -3.68 (<.01)

N 1,580


141

Table 15 (cont’d)

Panel G2. Other Coefficients of OLS Regressions with an Indicator for Status, Size, and Their Interaction

Model Icept NFVA NFVL FVL12 FVL3 NI

Status Indicator 4.39 *** 0.79 *** -0.83 *** -0.83 *** -0.79 *** 0.81 ***

t-value (p-value) 16.61 (<.01) 88.94 (<.01) -80.40 (<.01) -11.81 (<.01) -5.57 (<.01) 14.12 (<.01)

With Size -0.60 0.78 *** -0.83 *** -0.88 *** -0.84 *** 0.82 ***

t-value (p-value) -0.85 (0.40) 88.94 (<.01) -80.88 (<.01) -12.47 (<.01) -5.95 (<.01) 14.32 (<.01)

With Status & Size 0.37 0.78 *** -0.83 *** -0.88 *** -0.83 *** 0.82 ***

t-value (p-value) 0.50 (0.61) 89.46 (<.01) -81.42 (<.01) -12.60 (<.01) -5.90 (0.72) 14.32 (<.01)

With Interaction -1.07 0.78 *** -0.83 *** -0.89 *** -0.80 *** 0.82 ***

t-value (p-value) -1.27 (0.20) 89.86 (<.01) -81.81 (<.01) -12.73 (<.01) -5.70 (<.01) 14.36 (<.01)

These two half-panels together show the estimated coefficients by GICS Sector of an OLS regression using the modified Ohlson model

on the entering firms:


+ α5NVFLit + α6FVL12it + α7FVL3it

+ α8 Entrit + α9 log(Ait) + α10 Entrit ×log(Ait) + β1NIit + εit

Entri is an indicator variable that takes the value 1 for entering firms and zero for established. The variable log(Ait) is the natural

logarithm of the firm’s total assets. Other variables are those given in Panel A of Table 9.

142

Table 16. Investigation of the Effect of Liquidity, Using the Quick Ratio (QR) and

Operating Cash Flow Ratio (CR)

Panel A. Presence and Absence of Measures of Liquidity (QR and CR)

GICS Sector Trimmed QR Missing QR Present CR Missing CR Present

Energy 125 49 76 0 125

Materials 62 25 37 2 60

Industrials 194 103 91 12 182

Cnsmr Discret. 139 79 60 7 132

Cnsmr Staples 36 21 15 0 36

Health Care 210 136 74 3 207

Financials 480 471 9 455 25

Info Tech 256 130 126 1 255

Telecom Svcs. 26 13 13 0 26

Utilities 52 34 18 0 52

Total 1580 1061 519 480 1100

This panel shows the number of firms in each GICS Sector, the number of firms for which

the Quick Ratio (QR) is missing and present, and number of firms for which the Operating

Cash Flow Ratio (CR) is missing or present. The QR and CR were computed as follows:

LCT

XPPINVTACTQR

and

LCT

OANCFCR

where ACT = Current Assets – Total, INVT = Inventories – Total, XPP = Prepaid Expenses,

LCT = Liabilities Current – Total, and OANCF = Operating Activities – Net Cash Flow.

143

Table 16 (cont’d)

Panel B. Descriptive Statistics of the Quick Ratio (QR) by GICS Sector


Energy 76 0.13 0.68 1.08 1.46 7.85 1.35 1.26

Materials 37 0.21 0.87 1.10 1.91 48.66 2.71 7.82

Industrials 91 0.39 1.01 1.29 1.85 11.74 1.67 1.40

Cnsmr Discret. 60 0.21 0.65 1.06 1.57 11.50 1.45 1.60

Cnsmr Staples 15 0.56 0.64 0.87 1.34 22.27 2.37 5.52

Health Care 74 0.58 1.43 2.37 4.69 13.82 3.35 2.67

Financials 9 0.69 1.00 2.52 3.79 12.27 3.67 3.63

Info Tech 126 0.36 1.41 2.19 4.00 25.08 3.28 3.28

Telecom Svcs. 13 0.08 0.59 1.06 1.18 12.04 1.80 3.12

Utilities 18 0.11 0.58 0.73 1.00 1.42 0.78 0.33

Total 519 0.08 0.91 1.39 2.52 48.66 2.33 3.28

This panel shows descriptive statistics of Quick Ratio (QR) by GICS Sector. Q1 is the first

quartile and Q3 is the third quartile. STD is the standard deviation.

144

Table 16 (cont’d)

Panel C1. First Half of the Estimated Coefficients from an IRLS Regression with QR

Alone

GICS Sector FVA1 FVA2 FVA3 QR N Adj R2

Energy -0.83 1.47 * 0.94 ** 0.14 76 0.49

0.59 (0.44) 3.80 (0.05) 6.62 (0.01) 0.05 (0.81)

Materials 0.05 5.44 2.02 0.01 37 0.63

0.00 (0.95) 2.49 (0.11) 0.87 (0.35) 0.01 (0.92)

Industrials 0.95 ** 0.35 0.47 -0.37 91 0.35

5.38 (0.02) 0.96 (0.32) 0.58 (0.44) 0.32 (0.57)

Cnsmr Discret. 2.76 2.18 *** 2.57 -0.61 60 0.32

2.69 (0.10) 20.36 (<.01) 0.80 (0.37) 0.78 (0.37)

Health Care 0.83 *** 0.75 *** 5.61 *** -0.30 * 74 0.49

9.15 (<.01) 24.17 (<.01) 10.59 (<.01) 2.90 (0.08)

Info Tech 1.33 *** 2.20 *** 1.08 *** -0.19 * 126 0.51

32.06 (<.01) 79.66 (<.01) 9.32 (<.01) 3.34 (0.06)

Total 464

The remaining estimated coefficients are in the next half-panel.

145

Table 16 (cont’d)

Panel C2. Remaining Estimated Coefficients from an IRLS Regression with QR Alone

GICS

Sector Icept NFVA NFVL FVL12 FVL3 NI

Energy -0.37 1.12 *** -1.21 *** -1.09 *** -0.81 0.41 ***

0.05 (0.82) 114.73 (<.01) 59.82 (<.01) 47.11 (<.01) 0.26 (0.61) 7.70 (<.01)

Materials 0.16 1.42 *** -1.45 *** -0.05 -4.32 1.99 ***

0.01 (0.92) 82.48 (<.01) 52.73 (<.01) 0.00 (0.98) 0.13 (0.71) 19.60 (<.01)

Industrials 2.32 0.66 *** -0.54 *** -1.96 ** -0.95 1.63 ***

1.39 (0.23) 68.83 (<.01) 22.51 (<.01) 4.79 (0.02) 0.08 (0.77) 18.91 (<.01)

Cnsmr

Discret.

3.42 ** 0.49 *** -0.47 *** 1.15 -2.74 0.23

3.99 (0.04) 17.07 (<.01) 11.56 (<.01) 0.38 (0.53) 0.22 (0.63) 2.57 (0.10)

Health Care 3.13 *** 0.84 *** -0.81 *** 4.02 *** -0.26 0.34

11.41 (<.01) 75.28 (<.01) 34.23 (<.01) 90.77 (<.01) 0.39 (0.53) 1.75 (0.18)

Info Tech 2.88 *** 0.46 *** -0.31 *** 0.13 29.51 1.33 ***

19.32 (<.01) 33.51 (<.01) 7.71 (<.01) 0.61 (0.43) 0.29 (0.59) 53.10 (<.01)


regression on the entire sample using the modified Ohlson model with an additional

explanatory variable, the Quick Ratio (QR).


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 QRit + β1NIit + εit

The other variables are those given in Panel A of Table 9.

146

Table 16 (cont’d)

Panel D1. Several Estimated Coefficients from an IRLS Regression with FVA3 x QR

GICS

Sector FVA1 FVA2 FVA3 QR FVA3xQR N

Adj

R2

Energy -0.81 1.43 * 1.88 * 0.43 -0.59 76 0.49

0.57 (0.45) 3.58 (0.05) 3.43 (0.06) 0.42 (0.51) 1.00 (0.31)

Materials 0.01 5.36 1.43 0.01 0.36 37 0.61

0.00 (0.98) 2.10 (0.14) 0.08 (0.78) 0.00 (0.95) 0.02 (0.90)

Industrials 1.09 *** 0.29 -21.11 ** -

0.67

17.50 ** 91 0.39

7.64 (<.01) 0.72 (0.39) 5.53 (0.01) 1.12 (0.29) 5.78 (0.01)

Cnsmr

Discret.

2.67 2.14 *** 3.51 -

0.30

-1.09 60 0.31

2.38 (0.12) 16.96 (<.01) 0.50 (0.48) 0.05 (0.83) 0.05 (0.82)

Health Care 1.10 *** 0.83 *** 11.34 *** -

0.15

-2.67 ** 74 0.49

18.23 (<.01) 30.34 (<.01) 14.02 (<.01) 0.71 (0.39) 5.68 (0.01)

Info Tech 1.31 *** 2.21 *** 0.96 -

0.20

* 0.09 126 0.51

29.70 (<.01) 78.67 (<.01) 2.30 (0.12) 3.08 (0.07) 0.05 (0.81)

Total 464


147

Table 16 (cont’d)

Panel D2. Remaining Estimated Coefficients from an IRLS Regression with FVA3 x

QR

GICS


Energy -0.63 1.13 *** -1.23 *** -1.10 *** -0.85 0.43 ***

0.15 (0.70) 118.17 (<.01) 62.42 (<.01) 48.42 (<.01) 0.28 (0.59) 8.48 (<.01)

Materials 0.12 1.42 *** -1.45 *** 0.01 -3.79 2.03 ***

0.00 (0.94) 73.60 (<.01) 47.01 (<.01) 0.00 (0.99) 0.08 (0.77) 15.11 (<.01)

Industrials 2.66 0.67 *** -0.55 *** -2.06 ** -0.71 1.56 ***

2.01 (0.15) 78.40 (<.01) 25.76 (<.01) 5.86 (0.01) 0.05 (0.82) 18.98 (<.01)

Cnsmr

Discret.

3.10 0.49 *** -0.47 *** 1.16 -2.70 0.23

2.19 (0.13) 16.42 (<.01) 11.05 (<.01) 0.38 (0.53) 0.21 (0.64) 2.46 (0.11)

Health Care 2.39 *** 0.96 *** -0.99 *** 4.52 *** -0.47 0.20

6.74 (<.01) 90.36 (<.01) 45.54 (<.01) 105.53 (<.01) 1.42 (0.23) 0.62 (0.43)

Info Tech 2.90 *** 0.45 *** -0.30 ** 0.14 29.74 1.33 ***

18.72 (<.01) 28.17 (<.01) 6.07 (0.01) 0.67 (0.41) 0.29 (0.59) 52.61 (<.01)



explanatory variable, the Quick Ratio (QR). The model is given by


+ α6FVL12it + α7FVL3it + α8 QRit + α9QRit ×FVA3it + β1NIit + εit


148


Panel E. Descriptive Statistics of the Operating Cash Flow Ratio (CR) by GICS Sector


Energy 125 -2.39 0.48 1.09 1.77 14.57 1.40 1.80

Materials 60 -11.16 -0.01 0.34 0.65 4.06 0.00 2.12

Industrials 182 -6.07 0.14 0.39 0.66 8.86 0.51 1.08

Cnsmr Discret. 132 -1.21 0.19 0.48 0.89 3.31 0.58 0.67

Cnsmr Staples 36 -1.33 0.25 0.51 0.81 1.36 0.48 0.53

Health Care 207 -13.47 -2.49 -0.05 0.58 4.39 -1.08 2.64

Financials 25 -1.31 -0.25 0.29 1.52 18.80 1.28 3.84

Info Tech 255 -10.50 0.05 0.41 0.81 2.18 0.30 1.18

Telecom Svcs. 26 -5.42 0.39 0.91 1.34 3.22 0.63 1.65

Utilities 52 -6.92 0.30 0.42 0.79 1.61 0.38 1.09

Total 1100 -13.47 0.06 0.40 0.86 18.80 0.26 1.86

This panel shows descriptive statistics of Operating Cash Flow Ratio (CR) by GICS Sector.

Q1 is the first quartile and Q3 is the third quartile. STD is the standard deviation.

149


Panel F1. First Half of the Estimated Coefficients from an IRLS Regression with CR

Alone

GICS Sector FVA1 FVA2 FVA3 CR N Adj R2

Energy -0.28 1.11 *** 0.75 ** 0.48 125 0.47

0.50 (0.47) 16.69 (<.01) 4.32 (0.03) 2.09 (0.14)

Materials -0.02 6.89 *** 2.03 -0.07 60 0.57

0.00 (0.98) 12.33 (<.01) 1.62 (0.20) 0.03 (0.85)

Industrials 0.92 *** 0.31 0.20 -0.65 182 0.43

14.34 (<.01) 1.76 (0.18) 0.19 (0.66) 1.74 (0.18)

Cnsmr Discret. 0.78 2.22 *** 1.27 2.83 *** 132 0.39

2.40 (0.12) 44.70 (<.01) 1.00 (0.31) 13.45 (<.01)

Cnsmr Staples 5.66 *** 56.69 *** -269.80 *** 5.92 ** 36 0.73

24.19 (<.01) 60.97 (<.01) 57.71 (<.01) 5.91 (0.01)

Health Care 1.81 *** 1.66 *** 3.28 *** 0.13 207 0.42

92.42 (<.01) 208.67 (<.01) 13.90 (<.01) 1.12 (0.28)

Info Tech 1.33 *** 1.82 *** 1.17 *** 0.26 255 0.50

101.49 (<.01) 106.65 (<.01) 20.99 (<.01) 1.81 (0.17)

Telecom Svcs. 0.57 6.29 *** 7.82 1.27 26 0.59

0.53 (0.46) 9.98 (<.01) 2.32 (0.12) 2.19 (0.13)

Utilities 1.32 ** 0.33 -0.28 -0.02 52 0.44

3.99 (0.04) 0.17 (0.68) 0.01 (0.92) 0.00 (0.98)

Total 1075


150

Table 16 (cont’d)

Panel F2. Remaining Estimated Coefficients from an IRLS Regression with CR Alone

GICS


Energy -0.73 0.98 *** -1.02 *** -0.95 *** -0.59 0.51 ***

0.41 (0.52) 134.96 (<.01) 61.87 (<.01) 41.70 (<.01) 0.27 (0.60) 16.20 (<.01)

Materials -0.02 1.28 *** -1.27 *** -1.45 -3.82 2.35 ***

0.00 (0.98) 82.73 (<.01) 51.80 (<.01) 2.31 (0.12) 0.09 (0.77) 39.02 (<.01)

Industrials 1.80 ** 0.71 *** -0.59 *** -1.14 *** -0.85 1.30 ***

4.72 (0.02) 199.37 (<.01) 82.72 (<.01) 10.06 (<.01) 0.27 (0.60) 38.24 (<.01)

Cnsmr

Discret.

1.91 * 0.58 *** -0.56 *** -0.81 ** -0.07 0.54 ***

3.65 (0.05) 55.15 (<.01) 35.13 (<.01) 5.41 (0.02) 0.00 (0.98) 37.33 (<.01)

Cnsmr

Staples

-2.49 0.84 *** -0.57 *** -1.18 -

123.16

*** 2.61 ***

1.04 (0.30) 19.55 (<.01) 7.18 (<.01) 2.46 (0.11) 9.92 (<.01) 8.83 (<.01)

Health Care 0.99 * 1.27 *** -1.47 *** 2.06 *** -1.12 ** -0.05

3.79 (0.05) 297.47 (<.01) 240.11 (<.01) 41.42 (<.01) 6.61 (0.01) 0.07 (0.78)

Info Tech 1.57 *** 0.57 *** -0.40 *** 0.02 2.99 1.10 ***

17.45 (<.01) 97.16 (<.01) 25.79 (<.01) 0.02 (0.89) 0.98 (0.32) 66.22 (<.01)

Telecom

Svcs.

5.34 ** -0.01 0.00 -0.20 3.12 3.67 ***

5.06 (0.02) 0.00 (0.97) 0.00 (0.99) 0.02 (0.89) 1.34 (0.24) 8.68 (<.01)

Utilities 6.54 ** 0.32 -0.16 -2.14 *** -1.35 2.59 **

4.34 (0.03) 1.68 (0.19) 0.27 (0.60) 8.24 (<.01) 0.49 (0.48) 4.69 (0.03)

Total



explanatory variable, the operating cash flow fatio (CR)


+ α5NVFLit + α6FVL12it + α7FVL3it + α8 CRit + β1NIit + εit .


151

Table 16 (cont’d)

Panel G1. Several Estimated Coefficients from an IRLS Regression with FVA3 x CR

GICS

Sector FVA1 FVA2 FVA3 CR FVA3xCR N

Adj

R2

Energy -0.26 1.10 *** 0.97 0.54 -0.14 125 0.46

0.43 (0.51) 16.15 (<.01) 1.59 (0.20) 2.27 (0.13) 0.10 (0.75)

Materials -0.26 6.90 *** 1.96 -0.07 -1.51 60 0.54

0.10 (0.74) 11.97 (<.01) 1.39 (0.23) 0.03 (0.87) 0.60 (0.43)

Industrials 0.95 *** 0.29 -0.04 -0.59 -1.27 182 0.42

14.16 (<.01) 1.49 (0.22) 0.01 (0.92) 1.37 (0.24) 0.86 (0.35)

Cnsmr

Discret.

0.82 2.21 *** 1.60 3.06 *** -0.99 132 0.39

2.62 (0.10) 43.87 (<.01) 1.23 (0.26) 12.75 (<.01) 0.25 (0.61)

Cnsmr

Staples

6.37 *** 58.04 *** -

320.59

*** 5.74 ** 80.60 36 0.65

27.94 (<.01) 58.40 (<.01) 34.55 (<.01) 5.03 (0.02) 0.78 (0.37)

Health Care 1.69 *** 1.65 *** 4.68 *** 0.07 0.49 ** 207 0.43

84.47 (<.01) 218.00 (<.01) 26.52 (<.01) 0.35 (0.55) 4.84 (0.02)

Info Tech 1.34 *** 1.81 *** 1.00 *** 0.23 0.56 255 0.50

103.12 (<.01) 105.91 (<.01) 9.94 (<.01) 1.26 (0.26) 1.03 (0.31)

Telecom

Svcs.

1.29 ** -2.02 -36.65 *** 0.38 20.97 *** 26 0.47

6.41 (0.01) 2.04 (0.15) 23.31 (<.01) 0.38 (0.54) 23.24 (<.01)

Utilities 1.47 ** 0.40 -9.09 -0.27 8.37 * 52 0.43

5.07 (0.02) 0.25 (0.61) 2.48 (0.11) 0.05 (0.82) 3.00 (0.08)

Total 1075


152

Table 16 (cont’d)

Panel G2. Remaining Estimated Coefficients from an IRLS Regression with

FVA3 x CR

GICS


Energy -0.70 0.97 *** -1.01 *** -0.94 *** -0.65 0.52 ***

0.37 (0.54) 129.90 (<.01) 59.63 (<.01) 40.05 (<.01) 0.32 (0.57) 16.59 (<.01)

Materials -0.11 1.32 *** -1.33 *** -1.48 -3.11 2.60 ***

0.01 (0.93) 80.77 (<.01) 52.11 (<.01) 2.34 (0.12) 0.05 (0.81) 41.83 (<.01)

Industrials 1.75 ** 0.71 *** -0.59 *** -1.15 *** -0.96 1.31 ***

4.47 (0.03) 197.61 (<.01) 81.61 (<.01) 10.15 (<.01) 0.34 (0.56) 39.06 (<.01)

Cnsmr

Discret.

1.78 * 0.58 *** -0.55 *** -0.83 ** -0.01 0.54 ***

2.97 (0.08) 54.70 (<.01) 34.72 (<.01) 5.64 (0.01) 0.00 (0.99) 37.70 (<.01)

Cnsmr

Staples

-3.33 0.92 *** -0.65 *** -1.25 -

104.06

** 2.30 **

1.70 (0.19) 21.94 (<.01) 8.56 (<.01) 2.55 (0.11) 5.51 (0.01) 6.23 (0.01)

Health Care 0.85 * 1.30 *** -1.49 *** 1.95 *** -1.14 *** -0.13

2.94 (0.08) 323.93 (<.01) 261.68 (<.01) 39.07 (<.01) 7.24 (<.01) 0.54 (0.46)

Info Tech 1.58 *** 0.56 *** -0.39 *** 0.03 2.97 1.09 ***

17.59 (<.01) 94.07 (<.01) 24.45 (<.01) 0.05 (0.82) 0.97 (0.32) 64.79 (<.01)

Telecom

Svcs.

-1.04 1.65 *** -1.29 *** -1.38 -4.90 *** -0.28

0.38 (0.53) 45.49 (<.01) 22.28 (<.01) 1.99 (0.15) 7.13 (<.01) 0.12 (0.72)

Utilities 6.16 ** 0.40 * -0.26 -2.20 *** -0.19 2.57 **

3.93 (0.04) 2.73 (0.09) 0.72 (0.39) 8.87 (<.01) 0.01 (0.92) 4.67 (0.03)

Total


regression on the entire sample using the modified Ohlson model of the previous half-panels

with an additional term investigating the interaction of the operating cash flow ratio (CR)

and the coefficient of the Level 3 fair value of assets, FVA3


+ α6FVL12it + α7FVL3it + α8 CRit + α9 CRit ×FVA3it + β1NIit + εit .

The other variables are those given in Panel A of Table 9

153

Table 17. Investigating the Value Relevance of the FAS 157 Ratio

Panel A1. Certain Coefficients of IRLS Regression including the Fair Value of Assets

Ratio

GICS Sector FVA1 FVA2 FVA3 FVArat N Adj R2

Energy -0.45 1.17 *** 0.77 * 0.86 125 0.46

1.00 (0.31) 10.75 (<.01) 3.04 (0.08) 0.00 (0.94)

Materials 0.14 6.89 *** 2.31 1.49 60 0.56

0.03 (0.86) 15.21 (<.01) 1.96 (0.16) 0.05 (0.81)

Industrials 0.90 *** 0.55 ** 0.34 7.47 182 0.42

7.82 (<.01) 5.17 (0.02) 0.47 (0.49) 0.96 (0.32)

Cnsmr Discret. 1.40 ** 2.25 *** 0.29 -2.57 132 0.39

3.84 (0.04) 40.48 (<.01) 0.30 (0.58) 0.15 (0.70)

Cnsmr Staples 8.42 *** 64.30 *** -337.64 *** -7.47 36 0.72

33.83 (<.01) 103.14 (<.01) 100.18 (<.01) 0.30 (0.58)

Health Care 1.47 *** 1.38 *** 5.12 *** 1.24 207 0.42

68.11 (<.01) 158.67 (<.01) 53.97 (<.01) 1.21 (0.27)

Info Tech 1.43 *** 1.93 *** 1.22 *** -1.47 255 0.50

86.31 (<.01) 97.92 (<.01) 22.26 (<.01) 1.11 (0.29)

Telecom Svcs. 0.97 6.36 *** 7.12 -2.15 26 0.56

0.42 (0.51) 8.14 (<.01) 1.43 (0.23) 0.03 (0.85)

Utilities 1.17 0.20 -1.38 29.36 52 0.42

2.42 (0.12) 0.05 (0.81) 0.16 (0.68) 0.39 (0.53)

1075


154

Table 17 (cont’d)

Panel A2. Remaining Estimated Coefficients

GICS


Energy -0.32 1.05 *** -1.11 *** -1.05 *** -0.78 0.55 ***

0.06 (0.80) 154.68 (<.01) 72.78 (<.01) 50.18 (<.01) 0.47 (0.49) 18.66 (<.01)

Materials -0.36 1.36 *** -1.40 *** -0.96 *** -2.53 2.14 ***

0.06 (0.80) 98.86 (<.01) 69.51 (<.01) 7.87 (<.01) 0.04 (0.84) 36.26 (<.01)

Industrials 1.63 0.72 *** -0.65 *** -1.20 *** -1.59 1.15 ***

2.42 (0.11) 161.21 (<.01) 91.61 (<.01) 10.42 (<.01) 1.00 (0.31) 28.28 (<.01)

Cnsmr

Discret.

3.82 *** 0.56 *** -0.55 *** -1.20 *** -1.54 0.49 ***

16.82 (<.01) 50.23 (<.01) 32.60 (<.01) 7.49 (<.01) 0.10 (0.74) 30.99 (<.01)

Cnsmr

Staples

-1.15 1.26 *** -1.02 *** -1.96 *** -

101.20

*** 1.19

0.37 (0.54) 58.23 (<.01) 30.62 (<.01) 9.32 (<.01) 8.16 (<.01) 2.40 (0.12)

Health Care 0.32 1.37 *** -1.57 *** 1.87 *** -1.34 *** -0.12

0.22 (0.63) 343.67 (<.01) 299.68 (<.01) 31.52 (<.01) 10.47 (<.01) 0.41 (0.52)

Info Tech 1.87 *** 0.57 *** -0.42 *** 0.00 3.32 1.15 ***

12.36 (<.01) 90.45 (<.01) 30.10 (<.01) 0.00 (0.99) 1.21 (0.27) 73.19 (<.01)

Telecom

Svcs.

5.31 * -0.10 0.16 0.17 1.66 4.25 ***

3.34 (0.06) 0.06 (0.80) 0.14 (0.71) 0.01 (0.91) 0.32 (0.56) 11.71 (<.01)

Utilities 4.48 0.40 -0.24 -2.43 *** -1.29 2.65 **

0.99 (0.31) 2.32 (0.12) 0.56 (0.45) 8.26 (<.01) 0.42 (0.51) 4.83 (0.02)


regression using the modified Ohlson model with the Fair Value of Assets Ratio, FVARat,

as an additional explanatory variable.


+ α5NVFLit + α6FVL12it + α7FVL3it + α8FVAratit + β1NIit + εit .

The basic model and other variables are those given in Panel A of Table 9.

155

155

Table 18. Sensitivity Analysis

Panel A. Steps Taken to Develop the Sample

Step Description 2,008 2,009 Total

Change Firms Change Firms Change Firms

0 Extract from Compustat for fiscal year with:

11 <= Stock Exchange Code <= 14

6,631 6,631 6,477 6,477 13,108 13,108

1 Less: Not Major Market (NYSE, AMEX, or NASDAQ) (580) 6,051 (556) 5,921 (1,136) 11,972

2 Less: Not Final Data (keep UPD = 3 only) (787) 5,264 (809) 5,112 (1,596) 10,376

3 Less: Firms with fiscal year-end not December (1,284) 3,980 (1,252) 3,860 (2,536) 7,840

4 Less: Missing AT, LT, NI, or CSHPRI (87) 3,893 (40) 3,820 (127) 7,713

5 Less: Missing complete set of FVH observations (1,240) 2,653 (942) 2,878 (2,182) 5,531

6 Less: Firms missing a GICS code (18) 2,635 (2) 2,876 (20) 5,511

7 Merged with CRSP Monthly 3/31/2008 and 3/31/2009,

and Google Finance on 3/31/2011 with PRC > $1.00

(471) 2,164 (318) 2,558 (789) 4,722

8 Less: Firms where the computed sum of Level 1, Level 2,

and Level 3 Assets or Liabilities did not equal the

respective observation of the Total from Compustat.

(346) 1,818 (433) 2,125 (779) 3,943

9 Less: Outliers (|Studentized Residual| > 2) (24) 1,794 (104) 2,021 (128) 3,815

This table shows the steps taken to develop the sample. More details on Step 9, identification of outliers, are provided in Panel D below.

156

156

Table 18 (cont’d)

Panel B. Composition of the Sample by GICS Sector and Fiscal Year at Step 8

GICS Sector 2008 2009 Total

Code Description N %(Row) N %(Row)

10 Energy 151 47 173 53 324

15 Materials 82 43 110 57 192

20 Industrials 228 46 270 54 498

25 Consumer Discretionary 173 43 229 57 402

30 Consumer Staples 49 48 53 52 102

35 Health Care 262 45 314 55 576

40 Financials 507 48 560 52 1,067

45 Information Technology 284 47 316 53 600

50 Telecommunication Services 31 41 45 59 76

55 Utilities 51 48 55 52 106

Total 1,818 46 2,125 54 3,943

This table shows the composition of the sample by Global Industry Classification Standard

(GICS®) Sector by year. For each year, the number of firms and the row percentage are shown.

157

Table 18 (cont’d)

Panel C1. Ratio of Levels 1–3 of Assets Reported at Fair Value to Total Assets (Percent) by GICS Sector for 2008, 2009, and

Combined

GICS Sector

FVA1 FVA2 FVA3

2008 2009 Combined 2008 2009 Combined 2008 2009 Combined

Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv

Energy 0.9 3.3 2.0 8.6 1.5 6.7 3.0 5.5 1.1 3.9 2.0 4.8 1.3 3.7 0.5 2.1 0.9 3.0

Materials 2.9 8.2 5.6 11.6 4.5 10.4 1.0 3.2 0.8 2.5 0.8 2.8 0.4 1.6 0.5 2.4 0.4 2.1

Industrials 3.5 7.5 4.2 9.5 3.8 8.7 1.0 3.5 1.1 4.2 1.0 3.9 0.6 2.7 0.8 4.8 0.7 4.0

Consmr Discret 5.0 11.4 5.9 13.0 5.5 12.3 2.0 7.3 2.0 5.8 2.0 6.5 1.1 3.6 0.9 3.7 1.0 3.7

Consmr Stapls 4.8 11.8 5.7 11.6 5.2 11.6 0.9 2.9 0.8 2.8 0.8 2.8 0.1 0.5 0.1 0.3 0.1 0.4

Health Care 19.0 25.2 21.7 28.1 20.5 26.8 12.3 21.3 10.1 19.2 11.1 20.2 1.6 5.1 1.1 4.1 1.3 4.6

Financials 2.8 7.6 3.8 10.8 3.3 9.4 16.7 19.8 17.9 20.9 17.3 20.4 2.8 12.6 2.9 11.9 2.9 12.2

Info Tech 15.4 18.8 15.5 19.7 15.5 19.3 7.2 13.5 8.1 14.6 7.7 14.1 2.0 5.5 1.8 5.7 1.9 5.6

Telecom 3.6 10.3 5.4 10.0 4.6 10.1 0.9 2.4 0.9 2.7 0.9 2.6 0.5 1.7 0.2 1.0 0.3 1.3

Utilities 2.5 5.1 2.7 4.4 2.6 4.8 2.1 3.5 1.7 3.2 1.9 3.3 0.4 0.9 0.3 0.8 0.4 0.8

This table shows the ratio of fair value of assets for each of the three levels of the fair value hierarchy (FVH) to the firm’s total assets expressed

as a percentage. The ratios are shown by Sector of the Global Industry Classification Standard (GICS®) for fiscal 2008, 2009, and both years

combined together.

158

Table 18 (cont’d)

Panel C2. Ratio of Liabilities Reported at Fair Value to Total Liabilities (Percent) by GICS Sector for 2008, 2009, and Combined

GICS Sec

FVL1 FVL2 FVL12 FVL3

2008 2009 Comb 2008 2009 Comb 2008 2009 Comb 2008 2009 Comb

Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv Avg StDv

Energy 1.0 6.2 1.8 9.6 1.4 8.2 2.6 8.4 3.3 9.6 3.0 9.1 3.6 11.0 5.1 13.8 4.4 12.6 0.6 2.5 1.5 7.2 1.1 5.6

Materials 1.1 6.2 1.2 7.2 1.1 6.8 3.7 8.9 2.9 9.6 3.2 9.3 4.7 13.1 4.1 11.9 4.4 12.4 0.1 0.4 0.1 0.6 0.1 0.5

Industrials 0.6 3.5 0.2 1.3 0.4 2.5 2.5 6.9 2.3 8.4 2.4 7.7 3.1 7.9 2.4 8.4 2.7 8.2 0.7 5.6 1.0 6.4 0.9 6.0

Cnsr Disc 1.3 7.9 1.6 9.7 1.5 9.0 1.3 4.5 3.1 11.1 2.3 8.9 2.6 9.3 4.7 14.7 3.8 12.7 0.5 4.9 0.7 5.4 0.6 5.2

Cnsr Stpls 1.2 6.7 1.9 9.5 1.6 8.2 1.9 5.7 3.9 10.1 2.9 8.3 3.1 9.7 5.8 13.4 4.5 11.8 0.1 0.7 0.4 2.4 0.3 1.8

Hlth Care 0.6 5.7 0.6 5.7 0.6 5.7 1.5 7.7 3.1 12.5 2.4 10.6 2.2 9.5 3.7 13.7 3.0 12.0 0.7 4.7 3.1 12.1 2.0 9.5

Financials 0.2 2.8 0.5 5.0 0.4 4.1 1.4 8.1 0.9 6.5 1.1 7.3 1.6 8.6 1.4 8.1 1.5 8.4 1.1 7.7 1.0 7.3 1.0 7.5

Info Tech 0.3 2.6 0.8 6.1 0.6 4.8 1.1 5.2 1.5 7.5 1.3 6.5 1.4 5.7 2.4 10.5 1.9 8.6 0.3 4.2 0.9 5.2 0.6 4.8

Telecom 1.4 7.6 3.1 14.5 2.4 12.2 3.5 11.7 2.8 12.0 3.1 11.8 4.9 13.8 5.9 18.5 5.5 16.6 0.4 1.5 0.8 2.5 0.6 2.2

Utilities 1.1 4.0 0.3 1.4 0.7 3.0 2.4 3.9 1.4 2.5 1.9 3.3 3.5 6.7 1.8 3.2 2.6 5.2 0.5 1.2 2.3 12.8 1.4 9.0

This table shows the ratio of fair value of liabilities for each level of the FVH to the total liabilities of each firm expressed as a percentage.

FVLn, where n = 1, 2, or 3, is the ratio of the fair value of Level n liabilities to total liabilities expressed as a percentage. FVL12 is the sum of

Level 1 and Level 2 fair value of liabilities divided by total liabilities. The ratios are shown by Sector of the Global Industry Classification

Standard (GICS®) for fiscal 2008, 2009, and both years combined together.

159

Table 18 (cont’d)

Panel D. The Number of Outliers in Each GICS Sector (Step 9)

Absolute Value of Studentized Residual

<= 2 > 2 (Outliers) Total

GICS Sector 2008 2009 Comb 2008 2009 Comb

Energy 151 159 310 0 14 14 324

Materials 78 104 182 4 6 10 192

Industrials 226 247 473 2 23 25 498

Consumer Discretionary 171 220 391 2 9 11 402

Consumer Staples 46 51 97 3 2 5 102

Health Care 258 305 563 4 9 13 576

Financials 501 530 1,031 6 30 36 1,067

Information Techonlogy 281 314 595 3 2 5 600

Telecommunication Services 31 40 71 0 5 5 76

Utilities 51 51 102 0 4 4 106

Total 1,794 2,021 3,815 24 104 128 3,943

This table shows the distribution of outliers, identified as the observations with Studentized

Residuals greater than two, by GICS Sector.

160

Table 18 (cont’d)

Panel E. Subsamples for OLS Models for Tests of Hypotheses

GICS Sector Trimmed H1

(BE/ME)

H4

(QR)

H4

(CR)

H4’

(CR & BE>=0)

Energy 310 305 174 310 287

Materials 182 167 118 181 161

Industrials 473 462 235 449 402

Consumer Discretionary 391 374 184 376 322

Consumer Staples 97 88 44 97 89

Health Care 563 532 212 558 504

Financials 1031 740 28 64 59

Information Technology 595 537 267 585 564

Telecommunication Services 71 61 37 71 63

Utilities 102 98 29 100 96

Total 3815 3364 1328 2791 2547

This table shows the composition of the samples for the tests of the hypotheses. The column

marked “Trimmed” is the result of Step 9 of Table C1, and is also given as the Combined

(“Comb”) column where Studentized Residual <= 2 in Table C4. This sample is closest to

that of STY. The column with the heading “H1 (BE/ME)” shows the composition of the

sample where the ratio of Book Equity-to-Market Equity is not missing. The numerator,

book equity (BE), was computed from Computstat variables as BE = AT – LT – MIB, where

AT is the total assets, LT is the total liabilities, and MIB is the minority interest as stated on

the balance sheet. The denominator was the Compustat variable MkValT (Market Value,

Total). The absence of this variable led to the missing value for the BE/ME ratio. OLS

models on this sample test hypothesis H1. The column with the heading “H4 (CR)” shows

the composition of the sample where the operating cash flow ratio (CR) is not missing. The

CR is the ratio of the Compustat variables OANCF (operating cash flow ratio), as the

numerator and LCT (Liabilities, Current, Total) as the denominator. Missing values for

either of these observations result in missing values of their ratio. This sample was used to

test hypothesis H4. The final column, with heading “H4’ (CR & BE >=0)” shows the

composition of the CR sample where the book equity, BE as defined above, is not negative.

161

Table 18 (cont’d)

Panel F. OLS Regressions of the Modified Ohlson Model on the Trimmed Sample by GICS Sector

Variable Energy Materials Indstrls Consmr

Discret

Consmr

Staples

Health

Care Financials

Info

Tech

Telecom

Svcs Utilities

Icept 11.13*** 5.43*** 7.47*** 7.44*** 9.04*** 7.37*** 3.53*** 1.91*** 5.08*** 5.45***

fva1 0.98** 3.56*** 0.86*** 0.06 1.01 1.45*** 0.76*** 2.2*** 0.09 0.49

fva2 0.05 4.85*** 0.68*** 1.93*** 2.82* 1.26*** 0.73*** 3.96*** 1.78* 0.39

fva3 -0.23 -3.93 0.45 -0.51 -21.59 3.8*** 0.49*** 3.06*** 2.89 2.23

fvl12 -0.23 -0.82*** -

1.31*** -1.43*** -0.08 -0.05 -0.96*** -1.06*** 0.23

-

1.26***

fvl3 0.5 -3.52 -2.53 -1.19 -71.13* -1.29 -0.99*** 10.91*** 0.06 -1.85*

nfva 0.32*** 0.82*** 0.73*** 1.37*** 0.19 1.33*** 0.71*** 1.37*** 0.24* 0.35**

nfvl -0.2** -0.59*** -

0.68*** -1.42*** -0.04

-

1.51*** -0.72*** -1.64*** 0.02 -0.17

nipsh 0.94*** -0.11 1.06*** 0.95*** 7.04*** 2.64*** 0.97*** 3.23*** 3.54*** 2.83***

N 310 182 473 391 97 563 1031 595 71 102

AdjR2 0.37 0.54 0.95 0.86 0.70 0.63 0.79 0.72 0.70 0.70

162

Table 18 F. (cont’d)

This table shows the results of regressions of the modified Ohlson model by ordinary least squares (OLS) for each sector of the Global

Industry Classification System (GICS). The first column on the left lists the variables of the model and subsequent columns list the

estimated coefficients followed by a number of stars that indicates the statistical significance as follows: no star indicates p-value >

0.1, * indicates the p-value <= 0.1, ** indicates the p-value <= 0.05, and *** indicates the p-value <= 0.01. N indicates the number of

observations in the data subset, and AdjR2 provides the adjusted coefficient of determination, Adjusted R2. The model and variables

are those given in Panel A of Table 9.

163

Table 18 (cont’d)

Panel G. OLS Regressions of the Modified Ohlson Model with BE/ME by GICS Sector (Test of H1)


Discret

Consmr

Staples

Health

Care Financls

Info

Tech

Telecm

Svcs Utilities

Icept 14.06*** 10.07*** 12.15*** 7.26*** 10.08*** 10.18*** 6.71*** 6.45*** 9.29*** 12.8***

fva1 1.2*** 3.74*** 0.72*** 0.3 1.66* 2.2*** 0.79*** 2.55*** 0.14 0.56

fva2 0.27 5.9** 0.42** 1.57*** 3.51** 1.96*** 0.68*** 3.97*** 1.99** 1**

fva3 0.13 -0.37 0.72** -0.44 -41.25 3.22*** 0.47*** 3.49*** 2.28 -0.93

fvl12 -0.33** -1.1*** -1.59*** -1.51*** -0.73 -0.82** -0.93*** -1.95*** -0.11 -1.3***

fvl3 0.7 3.94 -2.42* -1.29 -57.79 -2.19*** -0.8*** 4.15 0.5 -1.75**

beme -4.52*** -6.94*** -6.43*** -0.01 -3.54 -10.84*** -2.4*** -9.1*** -6.55** -9.77***

nfva 0.41*** 0.99*** 0.88*** 1.41*** 0.48*** 1.61*** 0.65*** 1.77*** 0.5** 0.5***

nfvl -0.34*** -0.84*** -0.85*** -1.46*** -0.35* -1.81*** -0.66*** -2.06*** -0.38 -0.33*

nipsh 0.93*** -0.18 0.51*** 1.01*** 6.2*** 2.05*** 0.56*** 2.11*** 2.69*** 1.75**

N 296 153 430 351 84 516 725 524 56 93

Adj R2 0.43 0.64 0.97 0.83 0.67 0.73 0.72 0.80 0.65 0.80

This table shows the results of regressions by OLS of the modified Ohlson model including a term for the book-to-market equity

(BE/ME) for each sector of the GICS. Other formatting conventions follow those of Panel F. The model and variables are those

given in Panel A of Table 9.

164

Table 18 (cont’d)

Panel H. IRLS Regression of the Modified Ohlson Model with QR (for H4a)


Discret

Consmr

Staples

Health

Care Financials Info Tech

Telecom

Svcs Utilities

Icept. 6.19*** 3.97* 6.68*** 6.34*** 8.4** 8.31*** 14.77*** 4.74*** 5.51** 11.09*

fva1 -0.07 3.77*** 0.41 1.33*** 2.33* 1.01** -0.27 1.48*** 0.32 4.09**

fva2 -0.46 5.05*** 0.6** 2.97*** -0.97 1.04*** 0.36 2.74*** 2.1** -0.92

fva3 0.13 -6.53** 0.21 -2.19 12.28 1.17 0.4 1.57*** 25.46* -8.74

fvl12 -0.86*** -1.02*** -1.68*** -0.83*** 0.52 1 -0.74 -0.16 -1.99 1.54

fvl3 0.32 7.6 -3.1 -2.48* 25.1 -1.65* -0.32 47.38*** 0.84 1.57

qr -0.07 -0.08 0.18 -0.28 -0.58 0.02 -0.54 -0.35*** -0.26 -7.18

nfva 0.85*** 0.88*** 0.64*** 1.02*** 0.32* 0.97*** 0.04 0.62*** -0.04 0.11

nfvl -0.79*** -0.63*** -0.51*** -0.97*** -0.31 -1.24*** 0.33 -0.48*** 0.34 -0.01

nipsh 0.56*** -0.12 1.73*** 1.1*** 7.73*** 3.64*** 2.7** 2.01*** 3.89*** 5.48**

N 174 118 235 184 44 212 28 267 37 29

AdjR2 0.48 0.63 0.97 0.94 0.80 0.55 0.98 0.41 0.68 0.55

This table shows the results of regressions by Iteratively Reweighted Least Squares (IRLS) of the modified Ohlson model including a

term for the quick ratio (QR) for each sector of the GICS. Other formatting conventions follow those of Panel F. The model and

variables are those given in Panels C1 and C2 of Table 16.

165

Table 18 (cont’d)

Panel I. IRLS Regression of the Modified Ohlson Model with Interaction between QR and FVA3 (for H4b)


Discret

Consmr

Staples

Health

Care Financials Info Tech

Telecom

Svcs Utilities

Icept. 6.33*** 4.23* 6.68*** 6.36*** 6.93* 8.57*** 14.02*** 4.51*** 5.98 9.76*

fva1 -0.04 3.86*** 0.4 1.34*** 1.96 0.93** -0.24 1.51*** 0.35 3.95***

fva2 -0.46 4.85*** 0.6** 2.97*** -9.98 1.03*** 0.44 2.74*** 2.17** -1.2

fva3 0.88 -18 0.38 -2.26 -111.54 -2.91 10.85 2.64*** 23.78 23.94

fvl12 -0.86*** -0.99*** -1.68*** -0.82*** 0.57 0.89 -0.82 -0.22 -1.89 2.02

fvl3 0.32 10.07 -3.11 -2.48 530.74 -1.64* -0.15 45.67*** 0.6 1.95

nfva 0.85*** 0.86*** 0.64*** 1.02*** 0.38** 0.95*** 0.12 0.66*** -0.03 0.02

qr -0.06 -0.2 0.18 -0.3 -0.48 -0.1 -0.53 -0.26** -0.59 -4.01

fva3 × qr -0.43 11.29 -0.16 0.04 91.94* 2.55 -0.57 -0.84 0.67 -36.89

nfvl -0.79*** -0.61*** -0.51*** -0.97*** -0.28 -1.21*** 0.21 -0.54*** 0.32 0.11

nipsh 0.58*** -0.12 1.74*** 1.1*** 8.16*** 3.7*** 2.62** 2.02*** 3.88*** 5.09**

N 174 118 235 184 44 212 28 267 37 29

AdjR2 0.48 0.63 0.97 0.94 0.78 0.56 0.98 0.41 0.68 0.67

166

Table 18 (cont’d)

Panel I

This table shows the results of regressions by IRLS of the modified Ohlson model including a term for the QR and its interaction with

the Level 3 fair value assets (FVA3) for each sector of the GICS. Other formatting conventions follow those of Panel F. The model

and variables are those given in Panels D1 and D2 of Table 16.

Panel J

This table shows the results of regressions by IRLS of the modified Ohlson model including a term for the operating cash flow ratio

(CR) for each sector of the GICS. Other formatting conventions follow those of Panel F. The model and variables are those given

Panels F1 and F2 of Table 16.

Panel K

This table shows the results of regressions by IRLS of the modified Ohlson model including a term for the CR and its interaction with

the FVA3 for each sector of the GICS. Other formatting conventions follow those of Panel F. The model and variables are those

given Panels G1 and G2 of Table 16.

167

Table 18 (cont’d)

Panel J. IRLS Regressions on Modified Ohlson Model that includes the Operating Cash Flow Ratio, CR (H4a)


Discret

Consmr

Staples

Health

Care Financials

Info

Tech

Telecom

Svcs Utilities

Icept. 7.31*** 4.17*** 6.02*** 3.36*** 5.47** 6.29*** 8.85*** 3.35*** 2.64 6.24***

fva1 0.69** 3.01*** 0.8*** 1.51*** 1.2 1.45*** 0.36 1.55*** 0.22 0.32

fva2 0.33 4.52*** 0.51*** 3.06*** 2.7* 1.24*** 0.32 2.5*** 1.69** 0.99*

fva3 -0.13 -3.73 0.4 -0.42 -22.78 1.78 -0.4 1.57*** 1.07 1.78

fvl12 -0.47*** -1*** -1.15*** -0.89*** 0.43 0.06 -0.57 -0.18 0.14 -1.5***

fvl3 0.2 -5.53 -1.12 -0.94 -65.68* -1.35* 0.54 9.8*** 1.01 -1.79*

cr 0.89*** 0.48 0.3 3.39*** 4.02* 0.16 0.31 0.68*** 2.26** -0.27

nfva 0.51*** 0.8*** 0.71*** 0.99*** 0.19 1.23*** 0.12 0.71*** 0.3** 0.3*

nfvl -0.43*** -0.54*** -0.62*** -0.94*** -0.02 -

1.35*** 0.26

-

0.54*** -0.08 -0.09

nipsh 0.7*** 0.04 1.15*** 0.85*** 6.87*** 2.15*** 0.76** 1.99*** 3.02*** 2.22***

N 310 181 449 376 97 558 64 585 71 100

AdjR2 0.52 0.52 0.95 0.87 0.74 0.55 0.92 0.33 0.64 0.72

168

Table 18 (cont’d)

Panel K. IRLS Regressions on Modified Ohlson Model that includes the Interaction between CR and FVA3 (H4b)


Discret

Consmr

Staples

Hlth

Care Financials

Info

Tech

Telecom

Svcs Utilities

Icept. 7.35*** 3.54** 5.94*** 3.03*** 5.43** 6.35*** 8.84*** 3.35*** 2.72 6.13***

fva1 0.71** 3.31*** 0.8*** 1.57*** 1.21 1.45*** 0.35 1.56*** 0.22 0.29

fva2 0.33 4.59*** 0.47*** 3.04*** 2.83 1.25*** 0.32 2.49*** 1.64* 0.99*

fva3 0.63 0.25 -0.04 1.1 -25.08 1.56 -0.38 1.52*** -1.04 1.6

fvl12 -0.47*** -1.03*** -1.14*** -0.87*** 0.47 0.06 -0.57 -0.18 0.18 -1.44***

fvl3 0 -6.86 -1 -0.89 -65.22 -1.35* 0.53 9.8*** 0.98 -1.76*

cr 0.98*** 0.57 0.16 3.95*** 3.94* 0.19 0.32 0.66*** 1.95 -0.3

cr × fva3 -0.49 -7.5 1.23 -3.21 6.56 -0.3 -0.01 0.29 1.65 0.62

nfva 0.5*** 0.82*** 0.72*** 0.98*** 0.19 1.23*** 0.12 0.71*** 0.29** 0.31*

nfvl -0.42*** -0.55*** -0.61*** -0.93*** -0.02 -1.35*** 0.25 -0.54*** -0.06 -0.09

nipsh 0.71*** 0.05 1.15*** 0.86*** 6.86*** 2.18*** 0.76** 1.98*** 3.07*** 2.18***

N 310 181 449 376 97 558 64 585 71 100

AdjR2 0.51 0.56 0.95 0.87 0.74 0.55 0.92 0.33 0.64 0.72

169

Table 18 (cont’d)

Panel L. IRLS Regressions on sample with BE >= 0 using Modified Ohlson Model that includes the CR (H4’a)


Discret

Consmr

Staples

Hlth

Care Financials

Info

Tech

Telecom

Svcs Utilities

Icept. 6.94*** 3.23** 5.66*** 3.31*** 6.46*** 5.19*** 8.82*** 3.16*** 2.42 6.46***

fva1 0.59 3.24*** 0.79*** 0.97*** 0.84 1.63*** 0.36 1.62*** 0.22 0.6

fva2 0.44* 6.01*** 0.27 3.21*** 2.52* 1.48*** 0.24 2.5*** 2.06** 1.07**

fva3 -0.15 -0.36 0.52 -1.07 -18.72 1.91* -0.41 1.66*** 0.19 -1.68

fvl12 -0.55*** -1.13*** -1.57*** -0.94*** 0.47 0.48 -0.5 -0.27* 0.08 -1.81***

fvl3 0.5 -9.8 -0.88 -2.46* -65.81* -1.6** 0.85 7.7*** 0.73 -1.37

cr 0.86** 0.4 0.17 3.03*** 3.9* 0.11 0.29 0.67*** 2.68** -0.3

nfva 0.58*** 0.88*** 0.78*** 1.09*** 0.1 1.38*** 0.08 0.75*** 0.37*** 0.3*

nfvl -0.53*** -0.62*** -0.68*** -1.09*** 0.1 -1.56*** 0.32 -0.6*** -0.17 -0.09

nipsh 0.74*** 0.05 1.12*** 0.78*** 7.32*** 1.92*** 0.71** 1.98*** 3.17*** 2.25***

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.56 0.59 0.96 0.82 0.78 0.58 0.93 0.33 0.66 0.72

170

Table 18 (cont’d)

Panel L

This table shows the results of regressions by IRLS of the modified Ohlson model including a term for the CR for each sector of the

GICS. Firms with BE/ME < 0 have been removed from the dataset for this sample, as described in the right-most column with

heading H4’ in Panel E. Aside from the difference in dataset, the analysis of this table is the same as that shown in Panel J. Other

formatting conventions follow those of Panel F.

Panel M

This table shows the results of regressions by IRLS of the modified Ohlson model including a term for the CR and its interaction with

FVA3 for each sector of the GICS. Firms with BE/ME < 0 have been removed from the dataset for this sample, as described in the

right-most column with heading H4’ in Panel E. Aside from the difference in dataset, the analysis of this table is the same as that

shown in Panel K. Other formatting conventions follow those of Panel F. The model and variables are those given Panels F1 and F2

of Table 16.

171

Table 18 (cont’d)

Panel M. IRLS Regressions on sample with BE >= 0 using Modified Ohlson Model that includes the Interaction between CR

and FVA3 (H4’b)


Discret

Consmr

Staples

Hlth

Care Financials

Info

Tech

Telecom

Svcs Utilities

Icept. 6.97*** 3.06* 5.59*** 3.09*** 6.46*** 5.23*** 8.82*** 3.16*** 2.48 6.19***

fva1 0.6* 3.35*** 0.79*** 0.98*** 0.84 1.64*** 0.36 1.62*** 0.23 0.41

fva2 0.44* 6.1*** 0.23 3.21*** 2.5 1.48*** 0.24 2.49*** 2.05** 1.18**

fva3 0.42 1.02 0.09 -0.3 -18.38 1.76* -0.4 1.61*** -1.36 -10.48**

fvl12 -0.54*** -1.14*** -1.55*** -0.94*** 0.47 0.48 -0.5 -0.27* 0.14 -1.56***

fvl3 0.35 -10.06 -0.77 -2.46* -65.85 -1.6** 0.84 7.69*** 0.62 -0.74

cr 0.93*** 0.48 0.03 3.39*** 3.9* 0.13 0.29 0.64*** 2.46* -0.47

cr × fva3 -0.37 -6.38 1.2 -1.71 -0.69 -0.23 -0.01 0.3 1.27 8.39**

nfva 0.58*** 0.88*** 0.78*** 1.09*** 0.1 1.38*** 0.08 0.75*** 0.35** 0.28*

nfvl -0.53*** -0.62*** -0.68*** -1.08*** 0.1 -1.56*** 0.32 -0.6*** -0.13 -0.04

nipsh 0.74*** 0.06 1.13*** 0.78*** 7.32*** 1.94*** 0.71** 1.98*** 3.23*** 2.23***

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.56 0.60 0.96 0.82 0.77 0.58 0.93 0.33 0.67 0.73

172

Table 18 (cont’d)

Panel N. IRLS Regression on a sample with BE/ME >=0 with CR and an Indicator Variable for the Year


Discret

Consmr

Staples

Hlth

Care Financials

Info

Tech

Telecom

Svcs Utilities

Icept. 1.71 -0.18 1.56* -0.78 4.92* 3.39*** 6.17*** 1.19** 1.64 4.84**

fva1 0.37 3.08*** 0.67*** 0.98*** 0.85 1.59*** 0.32 1.66*** 0.16 0.35

fva2 0.51** 5.95*** 0.22 3.3*** 2.66* 1.49*** 0.02 2.43*** 2.01** 0.97*

fva3 0.29 -0.64 0.46 -1.04 -19.66 2.5** -0.31 1.69*** 0.53 -0.25

fvl12 -

0.53*** -1.11*** -1.31*** -0.98*** 0.12 0.36 -0.39 -0.31** 0.04 -1.49***

fvl3 0.03 -9.75 0.16 -3.14** -68.46* -1.57** 1.79 7.35*** 1.2 -1.71*

cr 1.1*** 0.45 0 3.82*** 3.41 0.12 0.3 0.81*** 2.62** -0.43

yr09 7.52*** 6*** 7.17*** 6.24*** 4.29* 3.15*** 4.55* 3.39*** 1.28 2.46

nfva 0.59*** 0.83*** 0.76*** 1.07*** 0.1 1.39*** 0.08 0.77*** 0.37** 0.28*

nfvl -0.5*** -0.55*** -0.64*** -1.05*** 0.1 -

1.57*** 0.34

-

0.62*** -0.18 -0.06

nipsh 0.86*** 0.06 1.35*** 0.67*** 7.04*** 1.8*** 0.52 1.84*** 3.15*** 2.47***

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.65 0.61 0.96 0.82 0.76 0.59 0.93 0.34 0.66 0.72

173

Table 18 (cont’d)

Panel N

This table shows the results of regressions by IRLS of the modified Ohlson model including a

term for the CR and an indicator variable for the year, yr09, for each sector of the GICS. The

indicator variable, yr09, takes the value 0 when the year is 2008 and 1 when the year is 2009.

Firms with BE/ME < 0 have been removed from the dataset for this sample, as described in the

right-most column with heading H4’ in Panel E. Aside from the difference in dataset, the

analysis of this table is the same as that shown in Panel J. Other formatting conventions follow

those of Panel F. The model is given by

PRCit = α0 + α1NFVAit + α2FVA1it + α3FVA2it + α4FVA3it + α5NVFLit+ α6FVL12it

+ α7FVL3it + α8 CRit + α9 Yr09it + α10 CRit ×FVA3it+ α11 CRit ×Yr09it

+ α12 FVA3it × Yrit + α13 CRit ×FVA3it ×Yr09it + β1NIit + εit

where other variables are given in Panel A of Table 9.

Panel O

This table shows the results of regressions by IRLS of the modified Ohlson model including the

CR, yr09, and their interaction for each sector of the GICS. Firms with BE/ME < 0 have been

removed from the dataset for this sample, as described in the right-most column with heading

H4’ in Panel E. Other formatting conventions follow those of Panel F. The model and

variables are those of Panel N.

Panel P


CR, yr09, and the interaction between CR and FVA3 for each sector of the GICS. Firms with

BE/ME < 0 have been removed from the dataset for this sample, as described in the right-most

column with heading H4’ in Panel E. Other formatting conventions follow those of Panel F.

The model and variables are those of Panel N.

Panel Q


CR, yr09, and the interaction between CR and FVA3 for each sector of the GICS. Firms with

BE/ME < 0 have been removed from the dataset for this sample, as described in the right-most

column with heading H4’ in Panel E. Other formatting conventions follow those of Panel F.

The model and variables are those of Panel N.

174

Table 18 (cont’d)

Panel O. IRLS Regression Modified Ohlson with Interaction between CR and Year Dummy


Discret

Consmr

Staples

Hlth

Care Financials

Info

Tech

Telecom

Svcs28

Utilities

Icept. 2.55* -0.2 1.83** -0.86 4.1 3.25*** 5.96*** 1.34*** -1 4.78**

fva1 0.4 3.08*** 0.69*** 0.99*** 0.96 1.6*** 0.32 1.65*** 0.11 0.37

fva2 0.49** 5.95*** 0.25 3.3*** 2.78* 1.48*** 0.01 2.43*** 2.43** 0.97*

fva3 0.29 -0.6 0.45 -1.04 -22.43 2.41** -0.29 1.7*** 0.86 -0.27

fvl12 -0.52*** -1.13*** -1.25*** -0.98*** 0.2 0.35 -0.39 -0.31** 0.11 -1.51***

fvl3 0.05 -9.95 0.04 -3.11** -69.66* -1.56** 1.86 7.28*** 1.56 -1.7*

cr 0.7 0.32 -0.57 3.92*** 5.23 -0.11 0.36 0.47 5.08** -0.28

yr09 6.65*** 5.95*** 6.06*** 6.39*** 5.8 3.32*** 4.83* 3.19*** 4.62 2.64

cr × yr09 0.65 0.16 2.25** -0.27 -2.92 0.33 -0.14 0.65 -3.06 -0.38

nfva 0.57*** 0.84*** 0.75*** 1.07*** 0.1 1.39*** 0.08 0.77*** 0.36** 0.28*

nfvl -0.48*** -0.56*** -0.64*** -1.05*** 0.1 -1.56*** 0.34 -0.62*** -0.18 -0.06

nipsh 0.87*** 0.07 1.33*** 0.67*** 6.96*** 1.85*** 0.5 1.85*** 3.18*** 2.46***

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.63 0.61 0.96 0.82 0.75 0.59 0.93 0.34 0.67 0.72

28 The IRLS regression for Telecommunication Services did not converge.

175

Table 18 (cont’d)

Panel P. Modified Ohlson with Interaction between FVA3 and Year Dummy


Discret

Consmr

Staples Hlth Care Financials Info Tech

Telecom

Svcs29

Utilities

Icept. 1.63 0.02 1.59* -0.98 5.25** 3.35*** 5.97*** 1.33*** 1.7 5.02**

fva1 0.38 3.1*** 0.67*** 0.98*** 0.7 1.59*** 0.31 1.67*** 0.17 0.34

fva2 0.51** 5.93*** 0.21 3.32*** 0.23 1.49*** 0.01 2.44*** 2.04** 0.98*

fva3 0.43 -2.89 0.16 -0.14 -45.22 2.69* -0.24 1.04*** 0.51 -0.93

fvl12 -0.53*** -1.11*** -1.31*** -0.97*** 0.06 0.37 -0.38 -0.33** 0.06 -1.48***

fvl3 0.41 -9.3 0.19 -3.1** -63.88 -1.56** 1.87 7.34*** 1.15 -1.74*

cr 1.12*** 0.45 -0.02 3.83*** 3.72 0.13 0.32 0.79*** 2.6** -0.43

yr09 7.79*** 5.67*** 7.08*** 6.44*** 2.51 3.2*** 4.85** 3.17*** 1.23 2.23

fva3 × yr09 -1.48 4.18 0.41 -1.25 70.53* -0.56 -0.38 0.88** 0.65 1.32

nfva 0.59*** 0.84*** 0.75*** 1.07*** 0.1 1.39*** 0.08 0.78*** 0.36** 0.27*

nfvl -0.51*** -0.56*** -0.64*** -1.05*** 0.11 -1.57*** 0.34 -0.64*** -0.16 -0.05

nipsh 0.86*** 0.06 1.35*** 0.67*** 7.43*** 1.79*** 0.5 1.87*** 3.17*** 2.48***

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.64 0.61 0.96 0.82 0.78 0.59 0.93 0.34 0.65 0.72


176

Table 18 (cont’d)

Panel Q. IRLS Regression including the Year Dummy with Interaction between FVA3 and CR


Discret

Consmr

Staples

Health

Care Financials

Info

Tech

Telecom

Svcs30

Utilities

Icept. 1.68 -0.35 1.58* -1.06 4.93* 3.43*** 5.97*** 1.2** 1.61 4.75**

fva1 0.39 3.24*** 0.66*** 1*** 0.85 1.59*** 0.3 1.66*** 0.16 0.21

fva2 0.5** 6.03*** 0.2 3.31*** 2.73 1.49*** 0 2.43*** 2.02* 1.1**

fva3 1.08 0.84 0.16 -0.05 -20.97 2.3** -0.21 1.6*** 0.71 -8.95*

fvl12 -0.52*** -1.13*** -1.31*** -0.97*** 0.12 0.36 -0.39 -0.31** 0.03 -1.3***

fvl3 -0.2 -10.28 0.18 -3.17** -68.38* -1.56** 1.82 7.33*** 1.22 -1.06

cr 1.21*** 0.53 -0.13 4.26*** 3.39 0.15 0.33 0.73*** 2.64* -0.59

yr09 7.58*** 5.91*** 7.1*** 6.26*** 4.3* 3.17*** 4.83** 3.41*** 1.31 2.24

nfva 0.58*** 0.85*** 0.76*** 1.07*** 0.1 1.39*** 0.08 0.77*** 0.38** 0.25*

nfvl -0.49*** -0.57*** -0.64*** -1.05*** 0.1 -1.57*** 0.34 -

0.62*** -0.19 -0.01

nipsh 0.87*** 0.08 1.34*** 0.67*** 7.04*** 1.82*** 0.5 1.84*** 3.14*** 2.39***

fva3 × cr -0.5 -5.45 0.78 -2.06 2.94 -0.28 -0.06 0.58 -0.15 7.82**

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.64 0.63 0.96 0.83 0.76 0.59 0.93 0.34 0.65 0.73


177

Table 18 (cont’d)

R. Modified Ohlson with Triple Interaction


Discret

Consmr

Staples Hlth Care Financials Info Tech

Telecom

Svcs Utilities

Icept. 2.15 -0.03 1.77** -1.84 4.67 3.19*** 5.92*** 1.49*** 1 5.04**

fva1 0.39 3.28*** 0.7*** 1.01*** 0.77 1.59*** 0.31 1.67*** 0.05 0.36

fva2 0.5** 5.9*** 0.21 3.25*** 0.38 1.45*** -0.05 2.42*** 2.72*** 1.02*

fva3 1.02 -1.28 0.15 1.72 -107.21 3.06** -0.2 0.84* -10.13 -9.63*

fvl12 -0.52*** -1.15*** -1.24*** -0.99*** 0.22 0.36 -0.42 -0.33** 0.08 -1.41***

fvl3 0.39 -10.22 -0.08 -3.1** -65.74* -1.54** 1.42 7.25*** 1.9 -1.28

cr 0.88* 0.57 -0.62 5.16*** 4.33 -0.25 0.36 0.4 2.82 -0.45

yr09 7.14*** 5.39*** 6.11*** 7.37*** 3.36 3.42*** 4.66* 3.01*** 2.18 1.93

nfva 0.58*** 0.87*** 0.75*** 1.08*** 0.09 1.38*** 0.11 0.78*** 0.32** 0.28*

nfvl -0.5*** -0.6*** -0.63*** -1.05*** 0.12 -1.56*** 0.3 -0.64*** -0.11 -0.05

nipsh 0.88*** 0.06 1.33*** 0.67*** 7.55*** 1.93*** 0.48 1.87*** 3.24*** 2.43***

fva3 × cr -0.39 -9.95 0.22 -3.99 128.59 0.4 -0.07 0.55 9.35 8.47**

fva3 × yr09 -2.34 2.14 -0.64 -4.12 129.68* 0.36 4.29 0.8 28.84 9.11

cr × yr09 0.5 -0.13 1.99* -1.63 -1.54 0.51 -0.09 0.48 -0.46 -0.24

fva3×cr×yr09 0.52 11.62 1.64 6.07 -123.36 -2.39*** -0.57 1.28 -14.96* -8.8

N 287 161 402 322 89 504 59 564 63 96

AdjR2 0.63 0.62 0.96 0.82 0.79 0.59 0.92 0.34 0.68 0.73

178

Table 18 (cont’d)

Panel R (cont’d)

This table shows the results of regressions by IRLS of the modified Ohlson model including the CR,

yr09, their pair-wise interaction with FVA3, and the triple interaction between CR, yr09, and FVA3

for each sector of the GICS. Firms with BE/ME < 0 have been removed from the dataset for this

sample, as described in the right-most column with heading H4’ in Panel E. Other formatting

conventions follow those of Panel F. The model and variables are those given in Panel N.

179

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182

Appendix A

This appendix compares the Standard Deviation to the Median Absolute Deviation, MAD.

The well-known standard deviation is the square-root of the variance, where the well-known

variance is the second central moment. The MAD is the median of the absolute deviations

from the median, and is defined as follows:

where xi is an observation and

A large difference between the standard deviation and MAD suggests the presence of

outliers for a particular variable (SAS 2008).

183

Table A1. Standard Deviation and Standardized Median Absolute Deviation (MAD)

Untreated Winsorized Trimmed

GIC Sectors Variable StdDev MAD StdDev MAD StdDev MAD

Energy nfva 31.13 26.24 30.36 26.24 26.12 24.23

fva1 1.67 0.00 0.86 0.00 1.72 0.00

fva2 2.56 0.36 1.50 0.36 2.57 0.29

fva3 1.66 0.00 1.04 0.00 1.75 0.00

nfvl 19.70 15.89 19.07 15.89 16.75 14.93

fvl12 5.20 0.07 2.95 0.07 5.51 0.06

fvl3 0.53 0.00 0.48 0.00 0.55 0.00

nidsh 5.14 2.78 4.65 2.78 5.01 2.88

PRC 15.02 12.56 14.95 12.56 13.48 11.33

Materials nfva 24.59 23.50 24.59 23.50 21.98 24.07

fva1 1.05 0.00 1.05 0.00 1.10 0.00

fva2 0.59 0.02 0.59 0.02 0.49 0.01

fva3 0.53 0.00 0.53 0.00 0.57 0.00

nfvl 18.10 15.46 18.10 15.46 16.57 15.19

fvl12 3.71 0.12 3.71 0.12 3.13 0.08

fvl3 0.06 0.00 0.06 0.00 0.06 0.00

nidsh 4.31 1.83 4.31 1.83 2.85 1.80

PRC 22.78 16.55 22.78 16.55 14.68 10.31

Industrials nfva 112.72 22.28 25.99 22.28 114.97 22.68

fva1 7.31 0.03 2.22 0.03 7.46 0.01

fva2 13.26 0.00 1.22 0.00 13.53 0.00

fva3 1.15 0.00 0.53 0.00 1.17 0.00

nfvl 51.09 14.34 22.03 14.34 52.08 14.19

fvl12 1.93 0.08 1.36 0.08 1.97 0.07

fvl3 0.39 0.00 0.32 0.00 0.39 0.00

nidsh 8.77 1.78 2.89 1.78 8.92 1.66

PRC 71.21 10.36 12.78 10.36 72.42 10.13

184

Table A1. (cont’d)



Cnsmr Discret. nfva 42.40 18.64 34.21 18.64 29.63 19.32

fva1 3.71 0.02 2.82 0.02 2.15 0.00

fva2 2.20 0.00 2.11 0.00 1.59 0.00

fva3 1.00 0.00 0.42 0.00 1.05 0.00

nfvl 26.72 13.41 25.31 13.41 24.47 13.99

fvl12 4.18 0.04 4.16 0.04 3.16 0.05

fvl3 0.11 0.00 0.02 0.00 0.11 0.00

nidsh 6.21 2.01 5.51 2.01 6.12 1.95

PRC 37.43 9.37 18.81 9.37 9.05 7.84

Cnsmr Staples nfva 30.72 21.22 30.72 21.22 31.75 18.77

fva1 1.73 0.02 1.73 0.02 1.79 0.03

fva2 5.20 0.00 5.20 0.00 5.41 0.00

fva3 0.79 0.00 0.79 0.00 0.82 0.00

nfvl 24.50 13.34 24.50 13.34 25.44 13.53

fvl12 2.47 0.11 2.47 0.11 2.57 0.12

fvl3 0.68 0.00 0.68 0.00 0.71 0.00

nidsh 2.56 1.57 2.56 1.57 2.65 1.55

PRC 24.07 17.27 24.07 17.27 24.37 15.45

Health Care nfva 19.40 5.10 18.02 5.10 18.78 3.51

fva1 1.84 0.44 1.67 0.44 1.86 0.47

fva2 4.57 0.01 4.03 0.01 4.80 0.00

fva3 0.57 0.00 0.40 0.00 0.58 0.00

nfvl 15.60 2.27 12.30 2.27 16.03 1.79

fvl12 1.01 0.00 0.90 0.00 1.00 0.00

fvl3 0.79 0.00 0.31 0.00 0.84 0.00

nidsh 2.07 1.43 1.90 1.43 1.92 1.24

PRC 16.09 8.00 15.15 8.00 9.96 5.75

185




Financials nfva 109.55 76.12 89.94 76.12 81.12 74.49

fva1 31.23 0.07 16.54 0.07 19.10 0.05

fva2 52.74 16.25 42.36 16.25 41.52 17.67

fva3 6.07 0.00 4.83 0.00 5.19 0.00

nfvl 137.64 93.80 108.90 93.80 102.65 92.39

fvl12 20.33 0.00 2.69 0.00 2.43 0.00

fvl3 2.58 0.00 2.20 0.00 2.55 0.00

nidsh 4.26 1.22 2.90 1.22 2.97 1.16

PRC 23.99 7.43 12.55 7.43 15.24 6.53

Info Tech nfva 10.99 5.06 10.19 5.06 9.19 4.65

fva1 1.93 0.80 1.81 0.80 1.94 0.77

fva2 1.80 0.02 1.34 0.02 1.34 0.02

fva3 0.99 0.00 0.51 0.00 1.04 0.00

nfvl 8.68 2.17 7.25 2.17 6.70 1.96

fvl12 2.21 0.00 0.47 0.00 2.04 0.00

fvl3 0.07 0.00 0.02 0.00 0.07 0.00

nidsh 1.86 0.97 1.79 0.97 1.77 0.94

PRC 17.70 5.92 15.30 5.92 6.88 4.71

Telecom Svcs. nfva 18.43 16.13 18.43 16.13 19.09 17.62

fva1 1.80 0.17 1.80 0.17 1.85 0.28

fva2 1.13 0.00 1.13 0.00 1.17 0.00

fva3 0.33 0.00 0.33 0.00 0.35 0.00

nfvl 13.31 12.19 13.31 12.19 13.69 11.85

fvl12 0.84 0.02 0.84 0.02 0.87 0.02

fvl3 0.58 0.00 0.58 0.00 0.60 0.00

nidsh 1.58 1.13 1.58 1.13 1.63 1.13

PRC 10.46 11.90 10.46 11.90 10.02 10.83

186




Utilities nfva 30.00 25.76 30.00 25.76 30.07 25.24

fva1 3.86 0.59 3.86 0.59 3.89 0.60

fva2 3.64 0.54 3.64 0.54 3.67 0.62

fva3 0.51 0.09 0.51 0.09 0.51 0.10

nfvl 24.72 21.80 24.72 21.80 24.88 19.48

fvl12 4.12 0.60 4.12 0.60 4.12 0.60

fvl3 0.67 0.00 0.67 0.00 0.67 0.00

nidsh 1.86 1.24 1.86 1.24 1.88 1.24

PRC 12.81 12.10 12.81 12.10 12.63 11.98

The Untreated column compares the standard deviation and MAD of the original sample

before Winsorization and the Winsorized column contains the same statistics, but after

Winsorization. If outliers are not present, the standard deviation and MAD should be similar.

The definitions of the variables are provided in Panel A of Table 9.

187

Appendix B. Computation of the Herfindahl of Sales

This appendix describes the computation of the Herfindahl based on net sales using data

from Compustat. Following Hou and Robinson (2006), the Herfindahl for this dissertation

was based on net sales for three years. The SALE variable31 for 2006 through 2008 was

extracted from Compustat (Fundamentals Annual with Monthly Updates). For each year,

the Herfindahl for industry j, Hj, is defined as:

where sij is the share of firm i in industry j, and the summation is for all J firms in industry j.

In order to perform the computation, the following definition of the share:

was used, where SUMj,

is the sum of the net sales for all firms in the industry. Inserting the expression for sij into

the definition of Hj gives

For each industry j, the SUMj is a constant, and can therefore be factored out of the terms of

the sum:

31 Compustat’s SALE variable represents gross sales (the amount of actual billings to customers for

regular sales completed during the period) reduced by cash discounts, trade discounts, and returned

sales and allowances for which credit is given to customers, for each operating segment.

188

Finally, the three-year average

where Hj,2006 is the Herfindahl of net sales for industry j in 2006, etc, and the overbar on the

Hj indicates the computation of an average was computed and used.

Therefore, for each firm, the square was computed as SALEi ×SALEi. Next, for each industry

in each year, the numerator of the Hj was computed as the sum of the SALEi×SALEi, the

denominator as the square of industry sum, SUMj (the sum of the SALEi), and finally Hj

itself as the quotient of the numerator and denominator. Finally, the average of the three Hj

of each year was computed to obtain . Note that when data from Compustat was

extracted, in order to be able to compute the Herfindahl on a wide range of firms, data on

both Active and Inactive firms was included and in particular firms were not eliminated

based on the month on which their fiscal year ends.

Documents

The Value Relevance of the Fair Value Hierarchy of FAS 157the FVH is value relevant for all industry sectors. However, although the degree of value relevance varies across industry