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The Impact of XBRL-tagged Financial Notes on Information Environment � 

Joung W. Kim Huizenga School of Business Nova Southeastern University

3301 College Ave. Davie, FL 33314

954-262-5110 joung@nova.edu

Jee-Hae Lim University of Waterloo

School of Accounting & Finance 200 University Ave W (HH 289G) Waterloo ON N2L 3G1, Canada Phone: (519) 888-4567 (x35702)

Fax: (519) 888-7562 Email: jh2lim@uwaterloo.ca

Current Version:

May 6, 2015

Prof. Lim acknowledges funding by SSHRC (435-2015-0630), UW-SSHRC, and CGA/CAAA research grant awards, as well as PWC professorships at the University of Waterloo.

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ABSTRACT

This study investigates whether block or detail tagged financial statement note information

enhances the readability of 10-K and increases value relevance of earnings and book value of

equity. In addition, we examine whether firm size is a factor in determining the benefit of tagged

information. Our findings show that block or detail tagged financial note improves the readability

of 10-K, and also increase the overall value relevance of the accounting figures such as earnings

and book value of equity. However, the improvement in readability disappears when we use non

accelerated filers. When comparing the smaller non accelerated filers to the large accelerated filers,

the increase in value relevance of earnings is not significant. Thus, we provide evidence that the

benefits of block or detail tagged financial note does enhance readability and increase value

relevance through this improvement is diminished as firm size becomes smaller.

Keywords: XBRL (eXtensible Business Reporting Language); interactive data; financial notes; Information Environment

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I. INTRODUCTION

The communication between firms and users in the market is now a very challenging task,

and the ability of users to process the information disclosed by firm has been in question. It is also

impossible for firms to provide all the necessary details in their financial statements. The notes,

therefore, are used to provide major accounting policies and methods and additional information

and details, which are considered to be an integral part of financial statements. To improve

efficiency, speed, and comparability in the delivery of financial information to all parties, the US

Securities and Exchange Commission (SEC) mandates the adoption of eXtensible Business

Reporting Language (XBRL) under the phase-in schedule over three years (SEC 2009). In

particular, to give time to become familiar with tagging footnotes, the SEC requires that firms

comply with blocking tagging in the first year of XBRL filings, but are required to use detailed

tagging (e.g., each table and amount within each footnote is tagged separately) after the first year.

Thus, the XBRL adoption is expected to reduce information processing cost by improving

timeliness in data analysis and by increasing the accessibility of firm-specific financial information

for a variety of user groups, including outside investors, financial analysts, credit rating agencies,

regulators, and other stakeholders (e.g., Kim et al., 2012; Liu et al. 2014; Li et al. 2013; Dong et

al. 2015).

In fact, financial statement note information has been perceived as an important source of

valuation (e.g., De Franco, Wong, and Zhou 2011; You and Zhang 2009, Lehavy, Li, and Merkley

2011). De Franco et al. (2011) report that financial statement users including equity analysts utilize

the note information to make accounting adjustments, and consequently incorporate this

information into stock prices. Using the Moody’s approach to estimate the accounting adjustments,

they find that stock returns around 10-K filings are associated with the adjustments. As the note

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information is an integral part of financial statements, its complexity is notorious as a key barrier

by users in the market. Increased complexity on the note information can contribute to the

difficulty in measuring, aggregating and reporting accounting information. In this study, we draw

on this strand of accounting literature to examine whether the current mandatory adoption of

XBRL helps investors make informed decisions on the note information by decreasing the

information processing cost.

The objective of this study is to examine the impact of XBRL-tagged financial statement

note information. First, we examine whether block or detail tagged financial notes reduce the

processing cost and improve overall quantity and quality of information available to users in the

market. Block and detail tagging can make the notes to be more “readable”. Detail tagging can be

even better than block tagging. Because all quantitative figures in the footnotes should be tagged

under detail tagging, investors can easily retrieve the footnotes figures they want to compare cross

all firms or for the same firm over several years. Consequently we expect a positive impact of

block or detail tagging on information environment in the market. Using stock return volatility as

a readability measure as employed in Loughran and McDonald (2014), we find that both block

tagging and detail tagging reduce the return volatility after 10-K filing dates. Our results show that

both block tagging and detail tagging mitigate the positive relationship between the file size and

the return volatility and improve the readability of 10-K filings.

Secondly, we investigate whether the tagging affects value relevance of earnings and equity

figures. We create three portfolios: No Tagging (NT), Block Tagging (BT), and Detail Tagging

(DT). Then we employ the Ohlson (1995) model to test the value-relevance of earnings and equity.

We run the model using three different portfolios, and compare the incremental explanatory power

of earnings and equity in each portfolio. In general, block or detail tagging increases the

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explanatory power of earnings and equity. The R2 value for BT or DT is larger than that for NT

The US Securities and Exchange Commission, while the R2 for DT is larger than that for BT. The

results indicate that detail tagged financial note is more effective in increasing the value-relevance

of the accounting figures than block tagged financial note. However, when we use the subsamples

based on the SEC’s filer classifications (large accelerated filers, accelerated filers, non-accelerated

filers), the evidences become weaker or disappears. Therefore, our results should be cautiously

interpreted. In addition, we compare the information content of earnings and the information

content of the book value of equity, as measured by the coefficients of earnings and equity in the

model. Again we run the model using the three portfolios, and compare the coefficient of earnings

(or equity) in each portfolio. Overall, our results support that the tagging increases the information

content of earnings only.

The remainder of the paper is organized as follows. The next section discusses related

research and develops the hypotheses. Sections three and four explain the research design and

discuss the results. Section five concludes.

II. BACKGROUND AND HYPOTHESIS DEVELOPMENT

Background

In April 2009, the SEC enacted the Final Rule Release No. 33-9002, known as the

“Interactive Data to Improve Financial Reporting Rule”, which required firms to use XBRL for

preparing, communicating, and exchanging financial statements. The primary objective of the

amendments is to provide financial information easier for financial users to retrieve and analyze

while assisting in automating regulatory filings and business information processing. In fact, it

provides key benefits in the form of increased efficiency, speed, and comparability in the delivery

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of financial information to all parties, regardless of their varying needs. The requirement in the

SEC rules for firms to provide an interactive data file under the phase-in schedule over three years. 

In the first year, large accelerated filers with a common equity float over $5 billion, also known as Phase 1 filers, were required to provide 10-K or 10-Q filings using XBRL for fiscal years or quarters ending on or after June 15, 2009. For Phase 1 filers, each account balance in the financial statements is tagged in XBRL, and each note to the financial statements and certain financial schedules are tagged as a block of text, (e.g., an entire footnote disclosure, a significant accounting policy, or a table). Block tagging applies a single tag to a block of text.

In the second year, other accelerated filers with a common equity float over $700 million, were required to begin providing XBRL-tagged financial statements for fiscal years or quarters ending on or after June 15, 2010. In the second year of XBRL filings, firms must complete the first-year requirements but also include detail tagging of footnotes and schedules. Detail tagging means that every data point (e.g., monetary value, percentage, or number) must be tagged separately.

All remaining firms, or Phase 3 filers, were to begin providing XBRL-tagged 10-K or 10-Q filings for fiscal periods ending on or after June 15, 2011.

The notes to financial statements, frequently referred to as "footnotes" or "financial notes",

obtain some of the most important information in corporate financial reporting. It is not always

possible for firms to provide all the necessary details in their financial statements. The notes,

therefore, are used to provide major accounting policies and methods and additional information

and details, which are considered to be an integral part of financial statements. To give time to

become familiar with tagging footnotes, the SEC requires that firms comply with blocking tagging

(or Level I) in the first year of XBRL filings, but are required to use detailed tagging (e.g., each

table and amount within each footnote is tagged separately) after the first year. Footnote tagging

requirements fall into four levels:

Level I: Each complete footnote gets tagged as a single block of text. Level II: Each significant accounting policy within the significant accounting policies gets

tagged as a single block of text. Level III: Each table within each footnote gets tagged as a separate block of text.

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Level IV: Within each footnote, each amount (monetary value, percentage, and number) gets its own tag.

This new rule provides a 30-day grace period for both the initial interactive data submission and

for the first filing of footnotes and schedules tagged in detail. Thus, firms must comply with all

four levels of XBRL tagging is called detail XBRL tagging or Level IV by the second year of XBRL

filings.1

Information Processing Cost of Financial Statement Note Information

Prior studies have examined the valuation of financial statement note information at the

time of 10-K filings. Investors have used financial statement note information which is available

to the public when companies file their 10-K or 10-Q. For example, the note information is

essential for users to compare the accounting figures such as earnings, assets, and liabilities of

firms whose accounting policies for leases are different. Investors can capitalize the assets and

liabilities related to operating leases and can transform the statements to be more comparable by

using the note information for operating leases. DeFranco, Wong, and Zhou (2011) find that the

market processes the note information in 10-K reports and incorporates it into stock price. Aboody,

Barth, and Kasznik (2004) document the value relevance of stock-based employee compensation

in the notes to the financial statements.

Although the note information is very beneficial, investors are more likely process the

information only when the processing cost is lower enough to justify the benefits of processing the

note information. Prior studies show that information processing cost plays a key role in valuation

in the market. Hirst et al. (2004) report that the footnote information for banks’ fair-value-income

measurement affects commercial bank equity analysts’ risk and value judgments, and that note

                                                            1 SEC does not require the XBRL format for MD&A.

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disclosure may not be well perceived by the analysts. Plumlee (2003) shows that the magnitude of

the errors in analysts’ forecasts of effective tax rates increases with the effects of the more complex

tax-law changes. You and Zhang (2009) show that investors are less likely to incorporate 10-K

information into stock price when 10-K is less readable. Investors’ capacity to process information

is limited. They have time and resource constraints, and they cannot fully utilize the public

information in the market. If the information is complex or less readable, the processing cost

increases, and investors are less likely to use the information. Consequently investors are unable

to fully incorporate all publically available information into price. Especially when investors need

to process the footnote data, they cannot rely on most commercial databases to reduce the costs of

gathering and analyzing the data because many databases do not include the details for the

footnotes. Therefore, the processing cost of the note information is relatively high in the market.

Impact of XBRL Adoption on Readability and Value Relevance of the Note Information

The SEC rule allows XBRL adoption to be phased in over time. In the first year of XBRL

adoption, each account balance in the financial statements is tagged in XBRL, and each note to the

financial statements and certain financial schedules are tagged as a block of text which is referred

to as “block tagging”. In the second year, each amount in the notes and financial schedules must

also be tagged in XBRL. Within each footnote, each amount (i.e., monetary value, percentage, and

number) is separately tagged. This requirement is commonly referred to as “detail tagging”. When

the figures in the notes are tagged individually as the account balance on the financial statements

are tagged, investors may significantly reduce the costs to gather the note information and to

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analyze the information (e.g., cross-industry comparison). The SEC rule does require that detail

tagging should be implemented for the notes from the second year of XBRL adoption and onward.2

A considerable body of recent research has been explored the impact of XBRL on the

equity market. Kim et al. (2012) find that the mandatory adoption of XBRL reduces the

information asymmetry and the associated information risk through its impact on the improved

accessibility and comparability. Kim et al. (2012) also show that the aforementioned observed

effects are further magnified, when outside investors are faced with more uncertain and complex

information environments. The improved disclosure of data in XBRL format further leads to a

decrease in the cost of equity capital and an improvement in the information environment as

manifested in an increase in analyst coverage and analyst following, an increase in analyst forecast

accuracy, and a decrease in forecast dispersion during the post-XBRL-adoption period (Li et al.

2012; Liu et al. 2014). In a similar vein, Dong et al. (2015) claim that the effect of XBRL adoption

on synchronicity is larger for firms covered by financial analysts and for firms in financial distress

and with less outside monitoring.

Although Li et al. (2012) argue that XBRL adoption results in a significant reduction in the

cost of equity capital because of more effective information processing from analysts and claim

the effect is stronger in small and high growth firms, Blankespoor et al. (2014) show that more

sophisticated investors gain more benefits from XBRL filings due to their superior resources and

processing capabilities. In a similar vein, Chen et al. (2013) also emphasize the view that the XBRL

mandate brings about a more enhanced information environment, which enables more

sophisticated users (e.g. banks, among the earliest adopters of XBRL and more experienced with

                                                            2 SEC does not require the XBRL format for MD&A.

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XBRL) to effectively monitor borrowers at a reduced cost. Using the XBRL mandate as an

exogenous shock to information processing costs, Blankspoor (2012) finds that firms increase their

quantitative disclosures following the adoption of XBRL detailed tagging requirements. She

interprets the evidence as consistent with the notion that information processing costs to investors

can be significant enough to impact a firm’s disclosure decisions.

As alluded to in the above discussion, one problem that most investors face when reading

footnotes is that they often deal with unreadable format of complex issues. When the financial

figures in the notes are tagged individually as the account balance on the financial statements are

tagged, investors may significantly reduce the costs to gather the information and to analyze the

information (e.g., cross-industry comparison). As the SEC noted, each piece of business and

financial data with a standardized official tag (element) from an agreed on taxonomy (i.e., the

official U.S. GAAP elements) should reduce investors’ information acquisition and processing

costs and promote the comparability of financial information across firms. In addition, Kim et al.

(2013) find that the XBRL-induced information environment facilitates external monitoring and

scrutiny by outside users of accounting information, which in turn constrains managerial

opportunism in financial reporting, especially for firms that use more standardized official

elements.

We extend the prior research by examining the impact of XBRL tagging on the readability

of 10-K and value relevance of accounting figures. Loughran and McDonald (2014) define

readability in 10-K as the ability to assimilate valuation-relevant information. They report that 10-

K file size provides a better readability proxy that outperforms the Fog Index. Using 10-K file size

as a readability measure, they document that the larger file size (proxy for lower readability) of

10-K is, the higher stock return volatility in post-filing period exists. When the document is more

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readable, the information will produce less ambiguity in valuation, as reflected by the lower price

volatility in the period following the 10-K filing. If block or detail tagging improves readability of

10-K, then post-filing stock return will be lower when the note information is tagged.

H1: Block or detail tagging for footnote information lowers stock return volatility right after the 10-K release.

Block or detail tagging is expected to change the value relevance of accounting figures.

Investors are more willing to reflect the tagged note information into stock price when the tagged

information can be easily processed. Consequently tagged note information can make accounting

figures such as earnings and book value of equity more associated with price after the filings dates.

We employ the Ohlson (1995) model to test the change in value-relevance of earnings and equity

after the adoption.

H2: Block or detail tagging for footnote information affects value relevance of earnings and equity right after the 10-K release

III.METHODOLOGY

Sample

We first extract all XBRL filings submitted to the SEC from the EDGAR database of

Interactive Data Filing and the monthly Really Simple Syndication (RSS) feeds archived from

EDGAR, the program used for all interactive data submitted to the SEC, for the sample period

from January 1, 2010, to December 31, 2013. We restrict our sample to mandatory XBRL adopters.

We identify total of 21,122 filings3. We screen these filings by requiring our variables be from

COMPUSTAT and CRSP. After deleting the filings with a missing variable from COMPUSTAT,

there are 13,249 filings left. We remove 3,804 submissions that have a missing value from CRSP.

                                                            3 Our sample includes all 10-K, 10-K405, 10KSB, and 10KSB40

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Also we delete 4,450 submissions from firms who are not included in any one of our three

portfolios: No Tagging, Block Tagging, and Detail Tagging. For the NT portfolio, we obtain 5,174

filings from Jan. 1, 2005 to Dec. 31, 2007. Our final sample consists of 10,169 observations from

1,616 firms. Table 1 shows the sample composition by year (Panel A) and by industry (Panel B).

Panel A presents the number of filings in each year and in each portfolio (NT, BT, and DT). 5,174

filings are non-XBRL formatted, while 1,636 filings have block tagged notes information and

3,359 filings have detail tagged notes information.

[Insert Table 1]

Variables and Models

Table 2 shows the definitions of variables in our models. First, to test any increase in

readability, we employ the following model used in Loughran and McDonald (2014):

R_VOLit = β1 + β2 FSIZEit + β3 FSIZE_BTit + β4 FSIZE_DTit + β5 ALPHAit + β6 PRE_VOLit

+ β7 ABS_ABNit + β8 SIZEit + β9 BMit + β10 NASQit + ∑ β industryit + ∑ β yearit + eit- Eq. (1)

R_VOLit is a root mean square error of a market model regression of firm i using trading days

starting from six days after the filing date in year t to 28 days after the filing date. In Eq. (1), the

variable of interest, FSIZE_BT (FSIZE_DT) is interaction variable between FSIZE and BLOCL

(DETAIL) which is equals one for the block (detail) tagged financial note year t. Hypothesis 1

translates into a negative coefficient for the interaction variables (β3 < 0 and β4 < 0), which implies

that the tagged note information leads to an increase in readability (decrease in R_VOL), all else

being equal. To isolate the effect of the tagged note information from the effect of other variables,

we control for firm-specific variables. We employ the same control variables used in Loughran

and McDonald (2014).

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[Insert Table 2]

The second model, which investigate the second hypothesis, is based on the Ohlson

model (1995):

PRCit = β1 + β2 IBit + β3 BVit + ∑ β industryit + ∑ β yearit + eit Eq. (2)

PRC is stock price of firm i seven days after the filing date.4 IBit is earnings per share of firm i for

year t and BVit is book value of equity per share of firm i at the end of fiscal year t. Using three

different portfolios such as NT, BT, and DT, we run this model three different times. Then we

compare the coefficients (β2 and β3) of IB and BV. Overall, we expect that the tagged note

information increases the value relevance of IB and BV. Consequently the coefficient of IB (BV)

using BT or DT is expected to be larger than that using NT.

Panel A of Table 3 reports descriptive statistics for full sample. In our sample, about 16.1%

of total filings have block tagged note information, while about 33% have detail tagged note

information. Panel B compares the mean values of variables among the three portfolios: NT, BT,

and DT. The mean value of PRC is significantly higher in DT. The mean value of R_VOL is

significantly lower in BT or in DT than that in NT. The mean value of FSIZE is significantly higher

in BT or DT than NT. The mean value of IB (BV) is higher in BT or in DT than in NT.

With respect to other control variables, we find that the mean values of all control variables

except NASQ are significantly different among three portfolios. The mean value of NASQ present

that about 74.3% of total filings in NT are submitted by the NASDAQ firms (73.8% in BT and

71.3% in DT).

[Insert Table 3]

                                                            4 We employ stock price seven days prior to the next quarter earnings announcement date. The results are similar.

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IV. RESULTS

Table 4 presents the multivariate regression results for Eq. (1). Column 1 shows the results

using our full sample, and columns 2 through 4 show the results using the subsamples based on

the SEC’s phase-in schedule over three years. The SEC classifies all filers into one of the three

categories based on firm size: Large accelerated filers (a public float of $ 700 MM or more),

accelerated filer (a public float of $ 75 MM or more and less than $700), and non-accelerated filer

(a public float of less than $75MM). The SEC doubts that the benefit of tagging is not big enough

for smaller filers. 5 Therefore, we repeat our analysis using the subsamples.

We find that the coefficient of FSIZE_BT is negative and highly significant (p < 0.01) in

columns (1) and (2) and negatively significant (p < 0.05) in column (3). These significantly

negative coefficients for FSIZE_BT are consistent with the prediction in Hypothesis 1. However,

we do not find a negatively significant coefficient in column (4). Similarly, the coefficient of

FSIZE_DT is negative and highly significant (p < 0.01) in columns (1), (2), and (3), and not

negatively significant in column (4). Overall, the results in Table 4 support the view that block or

detail tagged note information enhances the accessibility of financial data, improves timeliness

and comparability in data analyses, and decreases information processing costs to outside users by

improving the readability of 10-K. Conversely, the improvement in readability is disappeared

when non-accelerated filer sample is used.

[Insert Table 4]

                                                            5 The SEC posted the staff observation for the survey for XBRL adoption on July 7, 2014. The SEC analyzed the numbers of each element in XBRL used by firms from 2009 through October 2013. It shows that overall larger filers correctly tagged all and the cost for tagging is minimal. However, for smaller filers (not large accelerated filers whose common equity total is $700 million or more), the number of incorrectly tagged accounts increase. 

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Barth, et al., (2012) use the incremental explanatory power of regression of stock price on

net income and equity book value. They construct their value relevance measure as the difference

in explanatory power of the regression that includes net income, book value of equity, the industry

and year fixed effects, and the nested model that includes only the fixed effect. They explain that

the difference in explanatory power is expected to reflect only the explanatory power of net income

and book value of equity for the dependent variable. Before examining the coefficients of IB and

BV in equation (2), we investigate whether the tagging affects value relevance by comparing the

incremental explanatory power of the regressions using NT, BT, and DT.

Table 5 presents the incremental explanatory power of IB and BV in Eq. (2). We estimate

the difference between the adjusted R2 from Eq. (2) and the adjusted R2 from the nested version

of Eq. (2) that includes only industry dummies and year dummies. If the tagged note information

enhances value relevance of IB and BV in general, we may expect an increase of the adjusted R2

in Eq. (2). Consequently the difference between the R2 from Eq. (2) and the R2 from the nested

version would be larger when the tagged information is used.

In Table 5, columns (1), (2) to (3) shows the incremental explanatory power measured by

the difference between the adjusted R2 and the adjusted R2 from the nested version of Eq. (2) that

includes only industry dummies and year dummies, using each sample (full, large accelerated

filers, accelerated filers, and non-accelerated filers). Columns (4), (5), and (6) show the differences

between the incremental explanatory power for BT and that for NT, between the incremental

explanatory power for DT and one for BT, between the incremental explanatory power for DT and

one for NT respectively. The incremental explanatory power for NT is lower than that for BT in

all sample groups (full, large accelerated filers, and accelerated filers) except in non-accelerated

filers group. In addition, the incremental explanatory power for DT is larger than that for NT in all

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samples. These results suggest that block or detail tagged information increases the explanatory

power of IB and BV. Using the full sample, we find the incremental explanatory power for DT is

higher than that for BT, which indicates that detail tagged financial note is more likely to increase

value relevance than block tagged financial note. However, when we use the subsamples, the

results are somewhat mixed.

[Insert Table 5]

Table 6 reports the results of regression in Eq. (2). We find that the coefficient of IB for

BT (DT) are significantly higher than that for NT when we use the full sample or large accelerated

filers sample (p<0.01 for BT and p<0.10 for DT). The coefficient of IB is 2.256 for NT, while it

is 4.82 for BT (4.795 for DT). When we use non-accelerated filers, we find that the coefficient of

BV for DT is significantly higher over that for NT (p<0.10) and BT (p<0.01). Additionally using

accelerated filers, we find that the coefficient of IB for BT is significantly higher than that for NT,

whereas the coefficient of IB for DT is not significantly different from that for NT. Surprisingly

the coefficients of IB and BV for DT are significantly lower than those for BR. Overall, these

findings are in line with the prediction in Hypothesis 2, suggesting that the block or detail tagged

information affects value relevance of IB and BV. However, the impact of the tagging depends

on firm size.

[Insert Table 6]

V. CONCLUSION

This study investigates whether block or detail tagged financial statement note information

enhances readability of 10-K and increases value relevance of earnings and book value of equity.

In addition, we examine whether firm size is a factor in determining the benefit of tagged

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information. Our findings show that block or detail tagged financial note improves the readability

of 10-K, and also increase overall value relevance of the accounting figures such as IB and BV.

However, the improvement in readability disappears when we use non accelerated filers. Again,

when we compare the smaller non-accelerated filers to the large accelerated filers, the increase in

value relevance of IB is not significant. Our results suggest that the benefits of block or detail

tagged financial notes are diminished as firm size becomes smaller.

However, our findings should be interpreted cautiously, because, as in other studies, this

study is subject to limitations. First, firms that terminate their securities registration or delay the

filing of their 10-K reports in XBRL are excluded from the sample. It is not clear whether and how

the addition of these firms would change our findings. Second, one cannot completely rule out the

possibility that our reported results are driven by XBRL formatted financial statements, not by

tagged note information. Our indicator variable for block tagging is somewhat correlated with the

indicator for XBRL adoption.

Notwithstanding these caveats, our findings provide important policy implications. Our

study identifies an important but yet unrecognized benefit of XBRL adoption: the impact of tagged

financial statement note information. Further, our results are in line with the SEC’s view that

XBRL adoption may not be beneficial when firm size is small.

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You, H., and X.J. Zhang. 2009. Financial reporting complexity and investor underreaction to 10-K information. Review of Accounting Studies 14: 559–86.

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Table 1: Sample Composition

Panel A: Distribution by Year

Year  Number of filings 

  NO Tagging  Block Tagging  Detail Tagging 

2005  1223  0  0

2006  1268  0  0

2007  1305  0  0

2008  1378  0  0

2009  0  14  0 

2010  0  556  16 

2011  0  1055  520 

2012  0  8  1494 

2013  0  3  1329 

Total  5174  1636  3359 

Panel B: Distribution by Industry

SIC 2‐Digit  Number of filings 

00‐09  31 

10‐19  760 

20‐29  1450 

30‐39  2453 

40‐49  943 

50‐59  543 

60‐69  2226 

70‐79  1287 

80‐  476 

Total  10169 

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Table 2: Variable Definitions

Variable  Definition 

PRC  Stock price seven days after the 10‐K filing date 

R_VOL 

The root mean square error from a market model estimated using trading days [6,28] relative to the 10‐K file date. At least 10 observations are required 

FSIZE The natural logarithm of the file size in megabytes of the SEC for the 10‐K filing 

IB  Net income before extraordinary items per share 

BV  Book value of equity per share 

BLOCK Dummy variable set to one if the firm is listed on NASDAQ at the time of the 10‐K filing, else zero 

DETAIL Dummy variable set to one if the firm is listed on NASDAQ at the time of the 10‐K filing, else zero 

ALPHA The alpha from a market model using trading days [‐252, ‐6]. At least 60 observations of daily stock returns must be available 

PRE_VOL the root meansquare error from a market model estimated using trading days [‐257,‐6], with a minimum of 60 observations 

ABS_ABN 

The absolute value of the filing date excess return, measured by the market model with the CRSP value‐weighted index over two day period [0, 1] 

SIZE  The natural logarithm of  market capitalization at the end of fiscal year 

BM The natural logarithm of book‐to‐market, which is estimated the book value divided by the market capitalization 

NASQ Dummy variable set to one if the firm is listed on NASDAQ at the time of the 10‐K filing, else zero 

All variables are winsorizied at 1%

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Table 3: Descriptive Statistics

Panel A: Full Sample

STAT  MEAN  SD  25 PERCENTILE  MEDIAN  75 PERCENTILE 

PRC  23.892  33.187  6.950  17.000  32.290 

R_VOL  0.018  0.013  0.009  0.014  0.022 

FSIZE  15.294  1.213  14.294  15.267  16.321 

IB  1.039  3.599  ‐0.005  0.778  1.823 

BV  12.434  13.877  4.004  9.556  16.830 

BLOCK   0.161  0.367  0.000  0.000  0.000 

DETAIL   0.330  0.470  0.000  0.000  1.000 

ALPHA  0.000  0.002  ‐0.001  0.000  0.001 

PRE_VOL  0.018  0.011  0.011  0.015  0.023 

ABS_ABN  0.029  0.039  0.007  0.016  0.036 

SIZE  6.140  1.658  4.984  6.208  7.423 

BM  ‐0.670  0.801  ‐1.112  ‐0.612  ‐0.150 

NASQ  0.732  0.443  0.000  1.000  1.000 See Table 2 for variable definitions.

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Panel B: Mean Comparisons among Three Groups (No Tagging, Block Tagging, and Detail Tagging Groups)

The superscripts ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, using a two-tailed t-test See Table 2 for variable definitions.

 No Tagging 

(NT) Block Tagging 

(BT) Detail Tagging 

(DT) Diff between NT 

and BT Diff between NT 

and DT Diff between BT 

and DT 

PRC  21.1  22.073  29.077  0.973  7.977***  7.004*** 

R_VOL  0.022  0.016  0.013  0.006***  0.008***  0.002*** 

FSIZE  14.303  15.735  16.607  1.432***  2.304***  0.871*** 

IB  0.97  0.919  1.205  0.05  0.235***  0.286*** 

BV  11.247  12.283  14.336  1.036***  3.089***  2.052*** 

ALPHA  0.001  0  0.001  0.000*  0.000***  0.001*** 

PRE_VOL  0.021  0.018  0.015  0.003***  0.006***  0.003*** 

ABS_ABN  0.032  0.029  0.025  0.003***  0.007***  0.004*** 

SIZE  6.022  5.917  6.429  0.105**  0.409***  0.511*** 

BM  ‐0.73  ‐0.493  ‐0.663  0.237***  0.066***  0.171*** 

NASQ  0.743  0.738  0.713  0.004  0.030***  0.025* 

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Table 4: Analysis of Block and Detail Tagging Using Post-Filing Date Stock Return Volatility as the Dependent Variable

The superscripts ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, using a two-tailed t-test. Standard errors are robust and clustered by firm. See Table 2 for variable definitions.

  Column 1: Full   

Column 2: Large Accelerated Filers ($700 MM or more, 4381 obs.) 

Column 3: Accelerated Filers ($75MM or more and less than $700 MM, 4204 obs.) 

Column 4: Filers (less than $75 MM, 1584 obs.) 

  Coef.  T‐STAT  Coef.  T‐STAT Coef.  T‐STAT Coef.  T‐STAT

Intercept  0.001  0.44  ‐0.002  ‐0.72 ‐0.001  ‐0.18 0.003  0.27

FSIZE  0.000  2.13  **  0.000  1.52 0.001  2.65 ***  0.000  0.15

FSIZE_BT  ‐0.001  ‐3.03  ***  ‐0.001  ‐3.02 ***  ‐0.001  ‐2.49 **  0.000  ‐0.38

FSIZE_DT  ‐0.001  ‐2.91  ***  ‐0.001  ‐2.92 ***  ‐0.001  ‐2.68 ***  ‐0.001  ‐0.91

ALPHA  ‐0.444  ‐7.29  ***  ‐0.196  ‐2.25 **  ‐0.422  ‐5.32 ***  ‐0.761  ‐4.55 *** 

PRE_VOL  0.794  41.13  ***  0.841  24.36 ***  0.753  26.68 ***  0.788  17.96 *** 

ABS_ABN  0.028  8.09  ***  0.023  5.20 ***  0.033  6.54 ***  0.022  2.78 *** 

SIZE  ‐0.001  ‐6.45  ***  0.000  0.00 ‐0.001  ‐3.91 ***  ‐0.001  ‐2.03 ** 

BM  0.000  ‐0.15  0.000  0.77 0.000  0.20 0.000  ‐0.73

NASQ  0.000  ‐0.26  0.000  ‐0.80 0.000  ‐0.55 0.000  0.15

Industry  Yes Yes Yes Yes Year  Yes Yes Yes Yes

F‐value  248.16*** 122.96*** 78.93*** 31.28*** Adj. R  0.6713 0.6902 0.6003 0.5689

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Table 5: R2 Comparison of the Value Relevance Comparability Regressions

The figures in columns (1) to (3) are the incremental explanatory powers from a regression of stock price on net income per share and book value of equity per share. It is measured by the difference between the adjusted R2 from equation (2) and the adjusted R2 from the nested version of equation (2) that includes only the fixed effects. The figures in columns (4) to (6) are the difference between the explanatory power for one of NT, BT, or DT and the explanatory power for the other.

 Column 1: No Tagging (NT) 

Column 2: Block Tagging (BT) 

Column 3: Detail Tagging (DT) 

Column 4: Difference between NT and BT (BT‐NT) 

Column 5: Difference between BT and DT (DT‐BT) 

Column 6: Difference between NT and DT (DT‐NT) 

Full  0.4756  0.5003  0.5418  0.0247  0.0415  0.0662 

Large Accelerated Filers ($700 MM or more, 4381 obs.)  0.5398  0.6483  0.587  0.1085  ‐0.0613  0.0472 

Accelerated Filers ($75MM or more and less than $700 MM, 4204 obs.)  0.1963  0.5845  0.2848  0.3882  ‐0.2997  0.0885 

Filers (less than $75 MM, 1584 obs.)  0.0714  0.068  0.2754  0.0034  0.2074  0.2040 

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Table 6: Comparison of Value Relevance Coefficients

   

Column 1: No Tagging 

(NT) 

Column 2: Block Tagging (BT) 

Column 3: Detail Tagging (DT) 

Column 4: Difference between NT and BT (BT‐

NT) 

Column 5: Difference between BT and DT (DT‐

BT) 

Column 6: Difference between NT and DT (DT‐

NT) 

Full 

IB  2.256  4.82  4.795  2.564***  ‐0.025  2.539* 

BV  1.173  1.022  1.356  ‐0.151  0.334  0.183 

  Large Accelerated Filers ($700 MM or more, 4381 obs.) 

IB  2.432  5.917  7.447  3.485***  1.53  5.015*** 

BV  1.131  0.979  1.104  ‐0.152  0.125  ‐0.027 

  Accelerated Filers ($75MM or more and less than $700 MM, 4204 obs.) 

IB  0.912  2.867  0.956  1.955**  ‐1.911**  0.044 

BV  0.871  1.056  0.974  0.185  ‐0.082*  0.103 

 

Filers (less than $75 MM, 1584 obs.) 

IB  0.877  0.485  0.127  ‐0.392  ‐0.358  ‐0.75 

BV  0.368  0.289  0.619  ‐0.079  0.33***  0.251* All regressions include an intercept, year dummies and industry dummies. The superscripts ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, using a two-tailed t-test. Standard errors are robust and clustered by firm. See Table 2 for variable definitions.