Evidence on differences between recognition and disclosure: A comparison of inputs to estimate fair values of employee stock options

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Journal of Accounting and Economics

Journal of Accounting and Economics 51 (2011) 77–94

0165-41

doi:10.1

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E-m1 Fo

are incl

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journal homepage: www.elsevier.com/locate/jae

Evidence on differences between recognition and disclosure:A comparison of inputs to estimate fair values of employeestock options

Preeti Choudhary n

Accounting, Georgetown University, McDonough School of Business, 596 Hariri Building, Washington, DC 20057, USA

a r t i c l e i n f o

Article history:

Received 13 April 2009

Received in revised form

20 July 2010

Accepted 16 September 2010Available online 29 September 2010

JEL classification:

M41

M42

G28

Keywords:

Fair value

Earnings management

Recognition versus disclosure

Employee stock options

01/$ - see front matter & 2010 Elsevier B.V. A

016/j.jacceco.2010.09.004

+1 202 687 5794.

ail address: [email protected]

llowing the FASB’s notion of reliability (FASB

uded in subtotals that appear on the face of fi

es.

a b s t r a c t

I investigate reliability differences across recognition and disclosure regimes to shed

light on differing incentives and reporting of employee stock option (ESO) fair values. I

compare ESO fair values based on firm-reported inputs with ESO fair values based on

benchmark inputs, estimated following authoritative guidance. On average, I find

opportunism increases with recognition as compared with disclosure, and that it is

associated with incentives to manage earnings. Despite the increase in opportunism, I

find that accuracy does not decline for recognizers, and that accuracy differs across

voluntary and mandatory recognition.

& 2010 Elsevier B.V. All rights reserved.

1. Introduction

The evolution of accounting for employee stock options (ESOs) from required disclosure of fair values (under SFAS 123)to required recognition of fair values (under SFAS 123R) provides a setting to investigate possible differences in reliabilitybetween recognized and disclosed amounts.1 I compare recognition and disclosure, as these differences are not wellunderstood. As Schipper (2007) states, we lack a comprehensive theory of required disclosure which might identify anobjective of disclosure and provide a theoretical basis for distinguishing recognition from disclosure. Generally acceptedaccounting principles (GAAP) do not clearly distinguish recognition from disclosure. Although Statement of FinancialAccounting Concepts No. 5, Recognition and Measurement in Financial Statement of Business Enterprises (paragraph 63) (FASB,1984), identifies necessary criteria for recognition, neither Concept Statements nor any other guidance from the FinancialAccounting Standards Board (FASB) states a disclosure objective. I am also unable to identify auditing standards thatindicate that auditors should treat recognized items differently from disclosed ones.

Despite the absence of a specific theoretical, auditing, or financial reporting distinction, participants in the financialreporting process apparently view recognition and disclosure differently. For example, the FASB states in Concepts

ll rights reserved.

, 1980, glossary), I define reliability as the absence of error and bias. Recognition refers to items that

nancial statements. Disclosure refers to items that appear in words, numbers, or descriptions in the

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dx.doi.org/10.1016/j.jacceco.2010.09.004

mailto:[email protected]

dx.doi.org/10.1016/j.jacceco.2010.09.004

P. Choudhary / Journal of Accounting and Economics 51 (2011) 77–9478

Statement 5, paragraph 9 that ‘‘disclosure by any other means is not recognition.’’ Additionally, market participants appearto distinguish recognition from disclosure, specifically in the context of ESOs (Libby et al., 2006; Choudhary et al., 2009;Espahbodi et al., 2002). An Association for Investment Management and Research (AIMR) survey predating SFAS 123R(FASB, 2004) shows 83% of responding fund managers and analysts believe that disclosure of share-based payments isinadequate and they prefer recognition (Bowman, 2002). Thus, in practice, recognition can be interpreted differently fromdisclosure.

If managers believe investors do not adjust recognized values for disclosed values or if contracts are based onrecognized values (Watts and Zimmerman, 1990), then recognition of previously disclosed items can increase incentives toopportunistically report. Alternatively, if recognized amounts are more rigorously audited (Libby et al., 2006), thenrecognition of previously disclosed items can decrease opportunistic reporting ability or increase accuracy (measured asthe unsigned distance between the reported value and benchmark). Using a large sample of firms, I separately analyze theeffect of each of three inputs (stock price volatility, dividend yield, and risk-free interest rate) to ESO fair values estimatedusing Black Scholes. Specifically, I compare ESO fair values to benchmark fair values by replacing one reported input at atime with a benchmark, estimated following authoritative guidance. I refer to signed differences between reported andbenchmark fair values as a measure of bias and unsigned differences between reported and benchmark fair values as ameasure of accuracy. I compare bias and accuracy across recognition and disclosure to test for differences across reportingregimes. I further distinguish the recognition regime by whether it was adopted voluntarily (permitted under SFAS 123,FASB, 1995) or mandatorily (following SFAS 123-R) and test for differences in bias and accuracy accordingly.

I find that once firms are required to recognize the fair values of ESOs in the financial statements, firms underestimateESO fair value cost in comparison with disclosed ESO fair values. Firms accomplish this reduction by lowering theirestimates of stock price volatility resulting in an average 7% reduction of fair value. This reduction translates into anaverage of 3.2% of absolute net income. I also find that systematic underestimation of mandatorily recognized values doesnot compromise the accuracy of those estimates in comparison to disclosed values. Lastly, I compare and contrastvoluntary and mandatory recognition. I find they result in similar magnitudes of bias, but that voluntary recognitionresults in less accurate estimates relative to mandatory recognition.

The implications of recognition versus disclosure are difficult to test due to institutional constraints (Bernard andSchipper, 1994). Few settings permit comparisons between recognition and disclosure. Those that do are typicallycomplicated by one of three issues: (1) simultaneous changes in the valuation of the transaction and recognition;(2) differences in the information quantity (i.e. firms disclose range estimates, but recognize point estimates and provideestimation details); or (3) self-selection problems (firms can choose to recognize/disclose). The first two issues aremitigated by the ESO setting. The valuation method of fair value (specifically Black Scholes) is applied consistently acrossboth regimes, and both regimes require disclosures of inputs to estimate fair value. I use the Heckman procedure toaddress the third issue of self-selection. Johnston (2006) tests for differences in bias between 43 voluntary recognizers and43 disclosers of ESO fair values. Because voluntary recognizers choose this accounting treatment, it is unclear whetherJohnston’s (2006) results are driven by the difference in accounting treatment or by differences in firm characteristics thatcause some firms to elect recognition. My analysis differs from Johnston (2006) in several ways. I address self-selection,test an alternative explanation for the results (firms changing their weights on benchmarks), and test a larger sample offirms that involves both mandatory and voluntary recognition. These features of my study allow me to contribute toexisting research by overcoming typical limitations to comparing recognition and disclosure.

Much of the literature in this area focuses on investor perceptions of recognized versus disclosed values. For example, Davis-Friday et al. (1999), Ahmed et al. (2006), Aboody (1996), and Balsam et al. (2005) find that investors assign different valuationweights to recognized and disclosed values. This difference might result from: cognitive biases, differences in processing costs,or differences in the quality (i.e. reliability) of the recognized versus disclosed values (Schipper, 2007). Rather than inferringdifferences from market prices, I compare the reported fair values to benchmark fair values across the two reporting regimes.Using benchmarks as expected values, I can directly test whether preparers and auditors treat recognized and disclosed valuesdifferently (i.e. for differences in the deviations from benchmarks). This distinction allows me to point to the third explanation –dissimilar quality of recognized and disclosed values – as a potential explanation for why investors weigh them differently.

Several analyses of ESO fair value measures (Aboody et al., 2006; Hodder et al., 2006; Balsam et al., 2003; Bartov et al., 2007)report that managers underestimate disclosed ESO costs given incentives and opportunity (i.e., high CEO compensation or poorgovernance). Relative to disclosure, recognition of ESO fair values can offer greater incentives to underestimate, but lessopportunity. Thus, it is unclear whether recognition of previously disclosed ESO values increases or decreases the reliability ofthe estimates. My results differ from Hodder et al. (2006), which also investigates bias and accuracy of disclosed ESO fair values,but not recognized ESO fair values. Hodder et al. (2006) find that about half the firms in their sample use the latitude(differences between reported values and benchmarks) in ESO fair value estimation to signal future values, but those that uselatitude opportunistically have estimates with lower accuracy. In contrast, my results show that, on average, recognized ESOfair values show evidence of opportunism, but that the opportunism does not degrade accuracy.2

2 Hodder et al. (2006) uses ex-post realized volatility as a benchmark for volatility accuracy, while I use historical and implied volatility as benchmarks.

To reconcile the potential effect of this difference, I present analysis using ex-post volatility in Table 7 and discuss the implications in Section 7.3.

P. Choudhary / Journal of Accounting and Economics 51 (2011) 77–94 79

Evidence presented in this paper can assist standard setters in making recognition versus disclosure decisions as well asdecisions regarding fair value estimation. In particular, results indicate that specificity of the authoritative guidanceregarding estimation potentially limits opportunistic reporting. Such results can have implications for SFAS 157 (FASB,2008), which establishes a framework for measuring fair value. Although ESO fair values are explicitly excluded from SFAS157, the authoritative guidance for estimating inputs to ESO fair values resembles SFAS 157’s guidance to estimate level 2inputs. Specifically, both use market corroborated data to estimate fair values.3 Some research asserts fair value estimatesthat are not based on prices in active markets are subject to opportunistic estimation (e.g. Holthausen and Watts, 2001).4

My evidence of opportunism in fair value estimation augments existing research (e.g. Ramanna and Watts, 2008; Ramanna,2008; Beatty and Weber, 2006). I am able to both quantify reliability differences and show how level 2 inputs affectreliability. I find that firms primarily use volatility, the input with the greatest latitude and effect on fair values, tounderestimate fair value cost. In addition, I find greater underestimation in the presence of earnings managementincentives (measured via proximity to earnings management thresholds such as small changes in EPS of less than $0.02from the prior year).

This paper is organized as follows: Section 2 discusses the background and benchmarks for reported inputs. Section 3describes the hypotheses. Section 4 details the research design. Section 5 describes the sample and data. Section 6discusses the results and Section 7 addresses robustness tests and alternative explanations. Finally, Section 8 concludes.

2. Background

ESO financial reporting provides a useful setting to analyze potential differences between recognition and disclosure aswell as issues related to fair value measurement. Prior to SFAS 123, all firms followed Accounting Principles Board (APB) 25(APB, 1972) to value ESOs. Under APB 25, fixed term options were measured using intrinsic value (the difference betweenthe market price and the exercise price) at the grant date. For ESOs granted at the money, this measurement resulted inzero recognized compensation costs.

In 1993 the FASB proposed to modify ESO financial reporting, requiring companies to recognize the ESO fair values onthe grant date using estimates of the risk-free interest rate, stock price volatility, dividend yield, and expected option life.During its deliberations, the FASB received over 1500 comment letters, including letters from 20 Senators and 61Congressman. The FASB issued SFAS 123 in October 1995, advocating fair value recognition, but requiring only fair valuedisclosure of ESOs. Firms were permitted to choose between recognizing ESO values at fair value or intrinsic value in thefinancial statements, while the disclosures required fair value as the measurement attribute. In December 2004, the FASBissued SFAS 123R, requiring companies to recognize ESO fair values in their financial statements for fiscal years beginningafter June 15, 2005.

There are at least three unique features to the ESO setting. First, most firms use the same valuation model (BlackScholes) to estimate option fair values, limiting the potential of differences in fair value that could be driven by differencesin estimation techniques.5 Second, both standards require firms to estimate grant-date ESO fair values using models basedon four inputs: stock price volatility, risk-free interest rate, dividend yield, and option life. The first three of these inputscorrespond to level 2 in SFAS 157’s discussion of inputs to fair value measurement in that they are market (or marketcorroborated) inputs. I investigate the effect of each input separately to determine what drives reliability differences in ESOfair values. Third, SFAS 123 and SFAS 123R provide benchmarks for the inputs to fair value based ESO measurement.6

I compare ESO fair values based on firm-reported inputs with ESO fair values based on benchmark inputs to measure(un)reliability. I then compare (un)reliability across reporting regimes.

I use authoritative guidance (SFAS 123, SFAS 123R, and Staff Accounting Bulletin (SAB) 107) to identify publiclyavailable firm-specific and time-specific benchmarks as bases for reasonable estimates of three inputs that underlie the fairvalue estimates. I use these benchmarks to address time trends inherent in the inputs (see Fig. 1). SFAS 123 (paragraph273) specifies that ‘‘an entity issuing an option on its own stock must (emphasis added) use as the risk-free interest rate theimplied yield currently available on zero-coupon US government issues with a remaining term equal to the expected life ofthe option that is being valued.’’ The most precise benchmark is the implied risk-free interest rate on the day(s) optionswere granted. Option grant dates are not reported in the 10-K, so I use the average monthly zero-coupon yield (fromOptionmetrics) over the fiscal year with a term closest to the expected life. As Fig. 1 illustrates, interest rates declined

3 The SFAS 157 hierarchy describes three levels of inputs to fair value measurement: level 1 inputs are quoted prices for identical items in active

markets, level 2 inputs are data that are observable in marketplace, and level 3 are inputs are not observable in the marketplace.4 Examples of recent standards using fair value measurement include: accounting for ESOs in SFAS 123/123R (1995/2004), goodwill in SFAS 142

(2001), impairment of long-lived assets in SFAS 144 (2001), and derivatives in SFAS 133 (1998). SFAS 155, 156, and 159 permit fair value measurement.5 In my sample 96% of firms use the Black Scholes Model; 4% use the binomial model; 0.3% use an unspecified ‘‘other’’ model. Balsam et al. (2007)

survey reports 86% of firms in their sample use the Black Scholes model, though 56% of them considered alternative models.6 I do not analyze the expected life input (level 3 per SFAS 157) because I focus my analysis on market corroborated inputs; expected life benchmarks

are not available from public data. FAS 123 (paragraph 280) specifies that the vesting period, volatility, and the ‘‘average length of time similar grants

have remained outstanding in the past’’ are useful to consider when estimating option life. While 67% of my sample of firms report the vesting period, I

am unaware of a source of data for option life. In unreported tests, I estimate firm-specific changes in the reported ESO expected life for voluntary and

mandatory recognizers (expected life recognition�expected life disclosure). Differences between recognized and disclosed expected life estimates are

insignificantly different from zero at the 10% level.

Fig. 1. Volatility, interest rate, and dividend yields from 1996 to 2008. This figure represents the annual averages of input assumption benchmarks

calculated for firms in the same four-digit SIC code as firms included in Table 1 sample, (both voluntary and mandatory adopters). Interest is computed as

the average continuously compounded zero coupon bond yields calculated from LIBOR rates that mature in five years; data is from Optionmetrics.

Dividend is the sum of the dividends per share paid over each year divided by the mean monthly price for the year for firms that pay dividends. Volatility

is the standard deviation of the annualized log returns over a rolling five year period. Value-weighted volatility is the standard deviation of the value

weighted returns over a rolling five year period. Implied volatility is for S&P 500 indexed call options with 4 =540 days to expiration.


(increased) during the disclosure (recognition) sample period from 2001 to 2004 (2005–2008). Systematic patterns ofoption grants towards the end of the year (beginning of the year) bias against (towards) finding increased downward biasof recognized ESO fair values.

SFAS 123 and SAB 107 (SEC, 2005) recommend historical volatility as a benchmark for stock price volatility. Though theguidance does not specify a frequency interval (daily, weekly or monthly), I measure historical volatility using daily prices.7

According to SFAS 123 (paragraph 285), historical volatility should be calculated ‘‘over the most recent period that isgenerally commensurate with the expected option life. If the available period is shorter than the expected life of theoptions, then the volatility should be computed for the longest period for which trading is available’’ and firms should also‘‘consider historical volatilities of similar entities following a comparable period in their lives.’’ The sample contains firmswith sufficient price data; therefore, I estimate the historical volatility benchmark over the expected life.

Both SFAS 123R (paragraphs A32, A34, A43) and SAB 107 (page 18) identify implied volatility as a benchmark. In mysample, the Spearman (Pearson) correlation between implied and historical volatility is 0.73 (0.72). Implied volatility islower on average than historical volatility by ten basis points, likely because traded options have shorter lives than ESOs. Ireport analyses using implied volatility as an additional benchmark to historical volatility. Finally, I discuss a thirdbenchmark for accuracy, ex-post realized volatility in Section 7.3.

With respect to expected dividend yields, SFAS 123 (paragraph 287) indicates that historical dividend patterns shouldbe considered, and the ‘‘assumption about expected dividends should (generally) be based on publicly availableinformation.’’ I estimate the dividend benchmark as four times the sum of dividends paid per share over the last quarter,divided by the average monthly price per share (from CRSP). I exclude non-dividend paying firms (approximately 50% ofmy sample) from dividend reliability analysis.

While the authoritative guidance provides estimation guidelines, there is evidence that some latitude exists and differsacross the inputs. Authoritative guidance specifies a benchmark for risk-free interest (the zero coupon bond yield), butpermits greater estimation latitude for volatility by (providing multiple benchmarks) and dividend yield (by specifyingonly that the estimate should be based on publicly available information). Several comment letters sent to the FASBexpress concern over the latitude. For example, Intel states: ‘‘In an environment where corporate officers must certify theaccuracy of their financial reports under penalty of jail time if such certification is in error, we urge the Board to mandateeither the use of zero volatility or a specific method of arriving at expected volatility estimates if it maintains the currentfair value at grant date measurement approach,’’ (FASB, 2004a). On the other hand, others reason that latitude ‘‘providescompanies the ability to appropriately consider their own facts and circumstances’’ (FASB, 2004a).

3. Hypotheses

I first test for differences in the reliability of inputs to fair value ESO estimates that are recognized versus disclosed. I thenaddress whether incentives to manage earnings are incrementally associated with underestimation of recognized values. Thesetests shed light on whether recognition and disclosure differ and whether earnings management incentives affect the degree ofthe differences. Managers potentially treat recognized values and disclosed values differently for at least two reasons.

First, managers might believe investors do not adjust income for disclosed values. Graham et al. (2005)’s surveyevidence indicates that CFOs believe net income is the most important financial metric for public firms, and that managers

7 In unreported tests, analyses using monthly volatility as a benchmark yield qualitatively similar results. Daily, rather than monthly, estimates are

more highly correlated with reported volatility estimates.


are willing to sacrifice firm value to achieve smooth earnings or meet earnings targets.8 Further evidence of the importanceof earnings benchmarks is documented in Burgstahler and Dichev (1997), and Degeorge et al. (1999). I use proximity toearnings benchmarks as a cross-sectional measure of incentives to manage earnings during recognition.

Managers can believe that recognition of previously disclosed information not only affects reported net income, but alsoaffects firm valuation. Anecdotal evidence from the business press suggests that at least some managers believe fair valuerecognition of ESO costs lowers stock prices (BNA Pensions & Benefits, March 14, 1994, and Pensions & Investments,January 10, 1994). Harper et al. (1987) find that both sophisticated and unsophisticated users calculate significantlydifferent debt-to-equity ratios when unfunded pension liability is recognized versus when it is disclosed. Theory (e.g.Hirshleifer and Teoh, 2003; Barth et al., 2003) predicts that if there are limits to informed/attentive investors’ ability toexploit mispricing of shares by less informed/inattentive investors, then recognition versus disclosure affects stock prices.

A second reason managers may bias recognized values differently from disclosed values is where contracts are based onrecognized values (Watts and Zimmerman, 1990). Inadvertent contract violations, such as debt covenant violations, that donot reflect the probability of default can be costly and time consuming to address. While Beatty et al. (2002) finds thatlenders reduce borrowing costs by 71 basis points when debt covenants use frozen GAAP (which is not affected bymandatory accounting changes), doing so increases record-keeping costs because firms must maintain multiple sets ofaccounting records.

In the ESO setting, latitude to bias fair values is likely affected by the absence of remeasurement (i.e. there is no reversalfor accruals or reconciliation with ex-post cash payments). In typical accrual settings, current underestimation of expenseswill lead to higher recognized expenses in the future. The cost of earnings management (modeled by Stein, 1989 as thefuture reversal of current estimation error) is zero in the ESO setting. While increases in bias may translate into lessaccurate values under recognition as compared to disclosure, I am unaware of a direct incentive to decrease the accuracy ofrecognized inputs.

Counterbalancing possible preparer incentives to manage recognized ESO values, recognized values could be auditedmore rigorously than disclosed ones, thereby increasing accuracy and/or reducing bias. Libby et al. (2006) reports thatauditors permit less misstatement of recognized than disclosed ESO fair values. This result implies differences ininformation reliability (bias or accuracy) based on placement. I address three hypotheses, stated in null form:

H10. Inputs to recognized ESO fair values are equally biased, relative to benchmarks, as inputs to disclosed ESO fair values.

H20. Inputs to recognized ESO fair values are equally accurate, relative to benchmarks, as inputs to disclosed ESO fairvalues.

H30. Incentives to manage earnings do not affect the bias of inputs to recognized ESO fair values.

While all three inputs can be estimated from corroborating market data, they differ in the ease of verifiability (Hodderet al., 2006). Stock price volatility estimates are firm-specific and time-specific, allowing for relatively more reportinglatitude than dividend yield and risk-free interest inputs. In contrast, dividend yield is firm-specific, but does not varymuch over time (see Fig. 1). Risk-free interest varies over time, but not across firms and is specified in authoritativeguidance as the implied interest on a zero coupon bond. Thus, the magnitude or existence of bias (accuracy) potentiallyvaries across the three inputs.

4. Research design

4.1. Reliability measures

I decompose reliability into bias and accuracy; the decomposition is similar to the analyst forecast literature except theanalyst literature uses an outcome measure (reported earnings), whereas I use market-based benchmarks. I measure biasand accuracy as follows:

Bias=(Reported Fair Value�Benchmark Fair Value)/Reported Fair Value9

Accuracy=9(Reported Fair Value�Benchmark Fair Value)9/Reported Fair Value10

Bias captures the signed difference between the reported estimate and a benchmark. Accuracy captures the unsigneddifference between the reported value and a benchmark. Bias may, but need not, lead to less accurate estimates.11

8 Interviewed CFOs state four reasons for this belief: income is comparable across firms, it gets the broadest distribution and coverage by the media, it

simplifies analysts’ tasks, and it allows for evaluation of analyst performance.9 Results scaled by benchmark fair values for both bias and accuracy are qualitatively similar, thus not reported.10 Unreported tests of accuracy that weight larger deviations more heavily (measured as bias squared) are qualitatively similar. In Section 7.3 and

Table 7 I present an alternate measure of volatility accuracy (9reported volatility�ex-post volatility9).11 Note the conceptual similarity between accuracy and the square root of the mean square error:

ffiffiffiffiffiffiffiffiffiffiMSEp

¼ Accuracy=ffiffiffinp

, and the statistical

decomposition of MSE: MSE¼ Bias2þVariance. It follows that accuracy is a function of bias and the standard deviation (and a cross product). Thus, it is

possible to obtain better accuracy with more bias, provided there is a sufficiently large reduction in variance.


Similar to Johnston (2006), I measure the reported fair value using the Black Scholes formula with input estimatesreported in the 10-K. I compute the benchmark fair value by replacing one reported input with its benchmark, retainingother inputs at their values reported in the 10-K. For example, when estimating risk-free interest reliability, I estimate boththe reported and benchmark fair values using the reported stock price volatility, dividend yield, and expected option life.However, I estimate the reported fair values using reported risk-free interest and the benchmark fair values using thebenchmark risk-free interest (implied interest on a zero coupon bond). When estimating fair values, I assume that optionsare granted at the money using the fiscal year end share price.12 This approach explicitly takes into account the sensitivityof the fair value calculation to changes in inputs. For example, the dollar value effect of reducing the interest rate by 0.5%can vary with the option’s ‘‘Rho’’ (partial derivative of Black Scholes value with respect to interest rate). The advantage ofmeasuring the impact in fair values is that auditors typically measure materiality thresholds in terms of relative orabsolute dollar values (Friedberg et al., 1989; Icerman and Hillison, 1991). An alternative approach is to measure bias oraccuracy as the difference between the reported and benchmark inputs; I present changes in bias and accuracy using rawinputs in Section 7.2.

My measure of accuracy is conceptually similar to Hodder et al. (2006), but differs in two ways. First, I scale reliabilitymeasures by reported values to control for cross-sectional and temporal differences in the level of option compensation.Second, I use historical and implied benchmarks rather than ex-post values.13 Some criticisms of using ex-post realizedvalues include: realized values may not be good proxies for expectations, insufficient time series of ex-post values due todata limitations, and realized values can be inconsistent with the objective of SFAS 123/123R and SAB 107.14 However, toreconcile differences in our volatility accuracy conclusions that may result from using different benchmarks, I presentresults using ex-post realized volatility in Table 7 with corresponding discussion in Section 7.3.

4.2. Empirical model

Firms can choose to recognize ESOs at fair value under SFAS 123 (voluntary recognition). Firms that do not elect fairvalue recognition under SFAS 123 are required to recognize fair values under SFAS 123R (mandatory recognition). I includeboth types of recognition in my analysis. If the choice to recognize is correlated with incentives to under or overestimatefair value, then this feature of the reporting environment potentially creates self-selection, which can affect the results. Iuse a two stage Heckman procedure to address the self-selection issue. I estimate the first stage for voluntary andmandatory recognizers separately as follows:

Voluntaryit or ðMandatoryitÞ ¼ a0þa1ðEquity IssuanceitÞþa2ðAcquisitionitÞþa3ðDebt to EquityitÞ

þa4ðInterest CoverageitÞþa5ðBonus %itÞþa6ðCEO OwnershipitÞ

þa7ðOutside Director OwnershipitÞþa8ðInstitutional HoldingitÞ

þa9ðSizeitÞþak

XðIndustryitÞþeit ð1Þ

Voluntary (mandatory) is an indicator variable for each respective type of recognition, zero otherwise. The regression (1)variable definitions follow Aboody et al. (2004) and appear in Appendix A. I identify voluntary recognizers using a BearStearns list dated December 16, 2004 (McConnell et al., 2004). I include the inverse Mills ratio from regression (1) toaddress self-selection in my analysis of bias and accuracy, as follows:

BiasitðAccuracyitÞ ¼ a0þa1ð123R PassitÞþa2ðRecognitionitÞþa3ðLog OptionsitÞþa4ðROEitÞþa5ðMTBitÞ

þa6ðVRETitÞþa7ðOption LifeitÞþa8ðLog MVEitÞþa9ðInverse Mills RatioitÞþSakðIndustryitÞþeit

ð2Þ

I estimate regression (2) separately for voluntary and mandatory recognizers, separately for bias and accuracy, andseparately for each input (stock price volatility, risk-free interest rate, and dividend yield). Recognition is comprised ofthree separate indicator variables. It is one for voluntary recognizers beginning in the first year of voluntary recognition.Mandatory recognition is one for mandatory recognizers after the effective date of SFAS 123R (i.e. for fiscal year ends after6/15/06). I include an additional mandatory recognition indicator ‘123R Pass’ which equals one if the firm is a mandatoryadopter and the fiscal year end is between the passage of SFAS 123R and the SFAS 123R effective date (1/1/05–6/15/06).SFAS 123R requires recognition of all unvested options granted after 1995 and outstanding as of the effective date. ESO fairvalue cost is estimated on the grant date and allocated over the vesting period. In my sample, the mean vesting period isfour years; thus, fair value estimates prior to the effective date are likely to be recognized in future financial statements.

12 The correlation between disclosed fair values of ESOs and my estimate are 0.95 in my sample. In sensitivity checks, I infer the weighted average

exercise price from the weighted average fair value of options granted (disclosed in the 10-K in most cases). Results are qualitatively similar, thus not

presented.13 A third way to measure accuracy is the standard deviation of the difference between the reported value and the benchmark. Because I do not have

sufficient time series to estimate a firm-specific standard deviation, and the pooled deviations of each sample will be affected by sample and incentive

differences, I do not use standard deviation to measure accuracy.14 ‘‘The measurement objective for equity instruments awarded to employees is to estimate the fair value at the grant date of the equity instruments’’

(FAS 123-R, paragraph 16). A similar statement is made in paragraph 17 of FAS 123. ‘‘Estimates of fair value are not intended to predict actual future

events, and subsequent events are not indicative of the reasonableness of the original estimates of fair value made under Statement 123-R’’ (SAB 107,

page 6).


Even though these inputs are not recognized in the financial statements of the grant year, financial statement preparersknow estimates made after the passage of SFAS 123R will be recognized in future financial statements until the vestingperiod elapses. Thus, ‘123R Pass’ captures any changes to inputs firms make in anticipation of future recognition. Negativea2 coefficients for each recognition indicator are consistent with recognizers increasing the bias or accuracy of theirreported ESO fair values.

I control for cross-sectional differences related to size using the lagged market value of equity and the log of optionsgranted, performance using return on equity, growth using the book to market ratio and industry using the two-digitNAICS code. For specifications with volatility bias or accuracy, I include the standard deviation of market returns (VRET)estimated over the same period as the firm-specific volatility estimate controls for time effects of stock price volatility.Similarly, the implied volatility of S&P 500 index options controls for time period effects when stock price volatility bias/accuracy is estimated using implied volatility as a benchmark. Lastly, reported option life controls for cross-sectionaldifferences in the length of estimation periods.

To consider incentives to manage earnings (H3), I test for cross-sectional variation in the bias of mandatorily recognizedESO fair values conditioned on earnings management indicators drawn from prior research. Small positive EPS (less than$0.02 in my study) and small changes in EPS (a change in EPS of $0.02 or less in my study) are two EPS thresholds that priorresearch shows to be associated with incentives to manage earnings (Burgstahler and Dichev, 1997; Graham et al., 2005;Degeorge et al., 1999). In my sample, 22 firms have small positive EPS defined by $0.02 or less and 62 firms have changes inEPS of $0.02 or less after the effective date of SFAS 123R. A negative coefficient on mandatory recognition interacted withsmall changes in EPS or low EPS, indicates that these firms have relatively greater bias (i.e. greater underestimation) ininput estimates relative to other firms that recognize ESO fair values. Regression (3) below includes these proxies forearnings management.

Biasit ¼ a0þa1ð123R PassitÞþa2ðMandatory RecognitionitÞþa3ðMandatory�small D in EPSitÞ

þa4ðMandatory�small positive EPSitÞþa5ðLog OptionsitÞþa6ðROEitÞþa7ðMTBitÞ

þa8ðVRETitÞþa9ðOption LifeitÞþa10ðLog MVEitÞþa11ðInverse Mills RatioitÞþSakðIndustryitÞþeit ð3Þ

5. Sample selection

The sample includes mandatory and voluntary recognizers of ESO fair values listed in the Equilar database (inputs toESO fair values and number of options granted), that have sufficient CRSP returns data to estimate volatility benchmarks,and that have both Compustat data (net income, market value of equity, debt, book value of equity, operating income, andinterest) and Boardex data (CEO bonus, CEO ownership, and outside director stock ownership).15 The final sample consistsof 7730 firm year observations, of which 852 are observations for voluntary adoption and 6878 are observations formandatory adoption. Table 1, Panel A describes the sample attrition. Table 1, Panel B (C) reports descriptive data formandatory (voluntary) recognizers pre- and post-recognition. Consistent with Aboody et al. (2004), voluntary adopters arelarger, with mean sales (assets) pre-recognition of $14,766 million ($48,249 million), as compared with mandatoryrecognizers $3,603 million ($4,554 million). Consistent with Carter et al. (2007), voluntary (and mandatory) recognizersgrant more options pre-recognition with a mean of 8,830,000 (4,640,000) options than post-recognition with a mean of3,762,000 (2,863,000) options.

Fig. 1 depicts the average annual implied interest yield from the zero coupon bond with a five year term, the averagefive-year volatility and annual dividend yield of CRSP firms in the same industries and the value weighted market volatilitycalculated over a five year rolling window (from CRSP) from 1996 to 2008. Implied volatility is also included for the S&P500 indexed call options (from Optionmetrics). The figure illustrates the cyclical nature of the inputs and the importance ofcontrolling for time trends. Reported inputs, benchmarks, and reliability estimates before and after mandatory (voluntary)recognition are in Table 1 Panel B (Panel C). For mandatory adopters, pre-recognition (post-recognition) mean reportedvolatility is 52% (43%). Similarly, mean historical volatility is 56% pre-recognition (47% post-recognition). The similardecline across reported values and historical benchmarks indicates the benchmarks should address time period effects. Themean bias estimates are negative for volatility (risk-free interest) both before and after recognition at �8.0% and �8.7%(�2.2% and �2.4%), suggesting that reported inputs are on average underestimated relative to the benchmarks.

6. Empirical results

Spearman and Pearson correlations between bias and accuracy measures for each input assumption appear in Table 2,Panel A. Bias (accuracy) estimates across inputs have positive Spearman correlations, suggesting that the latitude iscorrelated across firms. Implied interest and volatility bias are positively correlated with a Pearson (Spearman) correlationof 0.10 (0.02), significant at the 1% level. Pearson correlations across input accuracy range from 0.10 to 0.25 significant at

15 The Equilar database contains the interest, volatility, dividend, expected option life, fair value and number of options granted (in most cases),

vesting period (in some cases), and valuation model use. While financial statements include estimates of Black Scholes inputs for disclosed ESO fair values

beginning after December 15, 1995, the Equilar database includes reported inputs between 2001 and 2008.

Table 1Sample description for analysis of reliability differences.

Panel A: Sample selection for reliability analysis

Data criteria Mandatory

adopters

sample

Control Sample (voluntary

adopters pre- and

post-voluntary recognition)

Firm year observations in Equilar between 2001 and 2007 32,324 3400

Firms that have all input assumptions available (mostly deleted due to firms without option

grants)

25,998 2598

Firms with CRSP data for volatility and dividend benchmarks 16,933 2390

Firms with Compustat data (net income, market value of equity, debt, book value of equity,

operating income, and interest)

12,408 1341

Firms with CEO Bonus, CEO ownership, and outside director stock data from Boardex 6878 852

Total sample size 6878 852

Firms with FYEs before 1/1/2005, pre-FAS 123-R passage 2904

Firms with FYEs after 6/15/2006, post-FAS 123-R passage 3974

Firms prior to voluntary recognition 226

Firms post voluntary recognition 626

Panel B: Sample descriptive data for mandatory recognizers

Pre-recognitiona (n=2904) Post-recognitiona (n=3974)

Median Mean Std dev Median Mean Std dev

Sales 1114 3603 8045 1308 4640 11,224

Assets 1184 4554 11,414 1500 5702 14,447

Net income 40 143 832 72 311 1085

# Options (thousands) 1435 4640 13,524 934 2863 8988

Return on equity 3.8% �3.9% 120% 4.4% 0.8% 21.2%

Market to book 2.3 3.1 3.9 2.5 3.3 4.0

Reported Life (yr) 5.0 5.2 1.6 5 5.1 1.3

Volatility 46.5% 52.1% 23.9% 38% 43% 18.5%

Interest 3.7% 3.7% 1.0% 4.3% 4.2% 0.6%

Dividend 0 0.7% 1.25% 0 0.7% 1.2%

Value-weighted return volatility 18.7% 18.5% 1.2% 15.3% 14.5% 2.9%

Historical (daily) Volatility 50.4% 56.0% 22.4% 41.9% 47.1% 18.9%

Dividend 0 1.1% 4.3% 0 1.0% 3.9%

Implied interest 4.2% 4.2% 0.9% 4.7% 4.7% 0.5%

Bias Historical volatility �4.2% �8.0% 18.0% �4.4% �8.7% 22%

Historical dividend 0 1.1% 10.7% 0 0.6% 10.8%

Implied interest �1.5% �2.2% 3.5% �1.9% �2.4% 2.6%

Accuracy Historical volatility 7.5% 12.5% 15.2% 7.0% 12.2% 20.3%



Panel C: Sample descriptive data for control sample (voluntary adopters)

Pre-recognitionb (n=226) Post-recognitionb (n=626)


Sales 4106 14,766 32,719 5532 21,842 44,067

Total assets 6483 48,249 143,084 9425 73,382 215,757

Net income 170 728 2362 296 1338 3933

# Options (thousands) 2695 8830 21,878 1045 3762 7335

Return on equity 4.9% 1.9% 16.9% 5.8% 1.6% 34.3%

Market to book 1.9 2.4 3.4 2.2 2.7 4.1

Reported

Life (yr) 5.9 5.8 1.4 5.5 5.6 1.5

Volatility 35.0% 39.3% 15.3% 30.0% 33.6% 13.6%

Interest 4.5% 4.3% 0.8% 4.0% 3.9% 0.9%

Dividend 1.0% 1.5% 1.6% 1.0% 1.5% 2.9%

Value-weighted return volatility 18.2% 17.8% 1.3% 16.4% 18.9% 2.9%

Historical (daily)

Volatility 37.2% 41.5% 15.0% 34.3% 37.9% 15.2%

Dividend 1.2% 2.7% 9.8% 1.2% 1.9% 3.7%


Bias

Historical volatility �3.0% �6.0% 17.5% �7.0% �10.5% 16.4%


Implied interest �2.4% �3.3% 4.6% �3.1% �3.5% 3.6%

Accuracy

Historical volatility 7.8% 12.1% 14.0% 8.6% 12.9% 14.6%

Historical dividend 3.2% 8.1% 15.5% 3.2% 7.1% 13.2%


Table 1 (continued )

Panel C: Sample descriptive data for control sample (voluntary adopters)

Pre-recognitionb (n=226) Post-recognitionb (n=626)



Panel A includes a breakdown of how the samples were determined. Bias=(reported fair value�benchmark fair value)/reported fair value;

Accuracy=9Bias9. Mandatory adoption firms are firms who did not voluntarily adopt fair value recognition which are subject to mandatory recognition

according to FAS 123-R with fiscal years ending after June 15, 2006; disclosing firms are the same firms with fiscal year observations two years prior when

FAS 123 required disclosure. Sales, Total Assets, and Net Income are data items 16, 6, and 18 from COMPUSTAT, respectively. #Options is the disclosed

options granted from the 10-K, included in the Equilar database. Reported input assumptions and options granted were purchased from Equilar (hand

collected from annual 10-ks). Value-weighted volatility is the value weighted return volatility for each corresponding time period. Historical volatilityis estimated for the period equal to reported life, ending 6 months prior to FYE using daily stock price from CRSP. Historical dividend is estimated using

the quarterly dividend of the most recent quarter divided by the average stock price for the year. Implied interest is obtained from Optionmetrics (zero

coupon bond yields) using the maturity that matches expected life, averaged over the firm’s fiscal year.a Pre-recognition (post-recognition) firms are mandatory recognizing firms with fiscal year ends on or before 12/15/2004 (after 12/15/2004).b Pre-recognition (post-recognition) firms are voluntary adopters of ESO fair value recognition under FAS-123 with fiscal year ends prior to their fair

value adoption (post fair value adoption). Firms in the control sample are identified using a Bear Stearns report dated December 16, 2004.

Table 2Pearson\Spearman correlations for reliability analysis.

Panel A: Pearson\Spearman correlation matrix for bias and accuracy, total sample

N=7730 Bias Accuracy

Historical volatility Historical dividend Implied interest Historical volatility Historical dividend Implied interest

Bias

Historical volatility 1 0.04 0.15 �0.65 �0.03 �0.18

o0.001 o0.001 o0.001 0.01 o0.001

Historical dividend �0.00 1 0.03 �0.04 0.02 �0.04

0.002 0.01 0.32 0.07 0.01

Implied interest 0.20 �0.02 1 �0.12 �0.36 �0.91

o0.001 0.03 o0.001 o0.001 o0.001

Accuracy

Historical volatility �0.87 0.01 �0.20 1 0.05 0.14

o0.001 0.56 o0.001 o0.001 o0.001

Historical dividend �0.07 0.55 �0.17 0.08 1 0.39

o0.001 o0.001 o0.001 o0.001 o0.001

Implied interest �0.24 0.02 �0.81 0.23 0.17 1

o0.001 0.14 o0.001 o0.001 o0.001

Panel B: Pearson\Spearman correlations for incentive/control variables, total sample

N=7730 Mandatory pass Mandatory effective Voluntary recognize Log options ROE MTB VRET Option life Log MVE

Mandatory pass 1 �0.35 �0.18 �0.02 0.00 0.07 �0.07 �0.05 �0.02

o0.001 o0.001 0.03 0.78 o0.001 o0.001 o0.001 0.05

Mandatory effective �0.35 1 �0.17 �0.15 0.02 0.01 �0.69 �0.03 0.06

o0.001 o0.001 o0.001 0.13 0.22 o0.001 0.02 o0.001

Voluntary recognize �0.18 �0.17 1 �0.02 0.15 �0.03 �0.05 0.08 0.21

o0.001 o0.001 0.15 o0.001 0.01 o0.001 o0.001 o0.001

Log options �0.02 �0.14 �0.03 1 �0.13 0.21 0.17 �0.15 0.50

0.03 o0.001 0.01 o0.001 o0.001 o0.001 o0.001 o0.001

ROE 0.02 0.01 0.01 �0.02 1 �0.06 �0.07 0.13 0.20

0.13 0.46 0.40 0.12 o0.001 o0.001 o0.001 o0.001

MTB 0.03 0.01 �0.03 0.12 0.04 1 �0.03 �0.03 0.26

o0.01 0.40 o0.01 o0.001 o0.01 0.03 0.01 o0.001

VRET 0.07 �0.74 �0.03 0.14 0.00 �0.02 1 �0.01 �0.11

o0.001 o0.001 0.01 o0.001 0.72 0.10 0.25 o0.001

Option life �0.05 �0.04 0.08 �0.14 �0.01 �0.02 0.11 1 0.05

o0.001 o0.001 o0.001 o0.001 0.44 0.06 o0.001 o0.001

Log MVE �0.03 0.04 0.23 0.49 0.06 0.15 �0.09 0.05 1

0.02 o0.001 o0.001 o0.001 o0.001 o0.001 o0.001 o0.001


the 1% level, indicating that estimation error may be systematic across firms. Panel B Pearson and Spearman correlationsamong the control variables are consistent with prior research. Consistent with Aboody et al. (2004), I find a positiveSpearman (Pearson) correlation between voluntary recognition and the log MVE (size), of 0.21 (0.49), significant at the 1%


level. Consistent with Carter et al. (2007), recognition variables (mandatory pass/effective and voluntary recognize) arenegatively correlated with the log of option grants.

Table 3, Panel A presents tests of bias (H1) and accuracy (H2) for the volatility input using two benchmarks—historicaland implied volatility. Estimates of the inverse Mills ratio are typically significant, indicating that self-selection isimportant to address in this setting. Specifications where bias is the dependent variable and historical volatility is thebenchmark indicates that firms on average have greater underestimation when ESO fair value costs are recognized thandisclosed. The negative coefficient on voluntary recognition indicates voluntary adopters underestimate volatility inputsleading to a 7.2% reduction in fair value cost of options post-recognition (po0.01). Similarly, mandatory adoptersunderestimate fair value option cost by 7.5% post-recognition (po0.01). Tests for volatility accuracy indicate thatvoluntary recognition results in a 2.7% average decline in accuracy, (po0.10), while mandatory recognition results nosignificant change in accuracy (p40.10).

I perform similar tests using an alternate benchmark for volatility, implied volatility, for firms that haveexchange traded options. Implied volatility (which is correlated with historical volatility, correlation=0.72) is lowerthan historical volatility by 10 basis points on average, likely because exchange traded options have shorter livesthan ESOs. Results using implied volatility are directionally similar to those with historical volatility, with the exception ofbias for voluntary recognizers. There is an insignificant difference in bias between voluntarily recognized anddisclosed values when using implied volatility as the benchmark (po0.10). I explore the possibility of changingbenchmarks to explain this difference in Section 7.1. Overall the results indicate that firms on average underestimatevolatility during both voluntary and mandatory recognition, while only voluntary recognition yields less accurateestimates. These results reject the null hypothesis (H1) that bias is similar between recognized and disclosed ESO fairvalues, and partially reject the null hypothesis (H2) that accuracy is similar between recognized and disclosed ESO fairvalues.

Panel B presents test of bias and accuracy differences for dividend and interest inputs. Similar to stock pricevolatility analysis, self-selection appears to play an important role, as the inverse Mills ratio is significant in mostregressions (po0.01). Significant differences between recognition and disclosure appear in two places. When thedependent variable is interest bias, the coefficient on mandatory recognition is significant (po0.10). However, themagnitude is small indicating mandatorily recognized values have greater underestimation of 0.2% relative to disclosedvalues. Dividend bias results reveal that voluntary adopters have greater underestimation of dividend inputs leading to a2.9% reduction in ESO fair value cost for voluntary recognition as compared with disclosure (po0.10). Overall, the resultsdo not support systematic differences in bias of interest and dividend inputs across (voluntary or mandatory) recognitionand disclosure. Accuracy analysis reveals insignificant differences in ESO fair value cost for interest and dividend inputs(po0.10).

One possible explanation of the differences between the volatility versus interest and dividend inputs is that volatilityhas both firm and time-specific variation and typically has the largest effect on ESO fair values. Thus, firms have bothgreater latitude and incentives to bias volatility in comparison to the other inputs. While latitude exists for dividend yield,there is no precise method of measurement specified in the authoritative guidance. The absence of a well specifiedbenchmark makes it difficult to detect evidence of opportunism. Fig. 1 shows little variation in dividend yield over time,perhaps limiting estimation latitude. Latitude with respect to interest rates is the least, as authoritative guidelines providea specific benchmark. Different magnitudes of bias across estimates are consistent with the existence of differential costsand benefits of manipulation across inputs.

In Panel C, I test whether voluntary and mandatory recognition differ. Results indicate insignificant differences in biasbetween voluntary and mandatory recognition (p40.10), but voluntary recognition is less accurate relative to mandatoryrecognition (po0.10). Similar differences between accuracy exist between voluntary and mandatory recognition whenusing ex-post volatility as a benchmark (Section 7.3). These results are consistent with evidence in Frederickson et al.(2006) that users perceive voluntary recognition to be less reliable than mandatory recognition.

Table 4 presents tests of H3, cross-sectional variation in bias for mandatory recognition of ESO fair values for firms withincentives to manage earnings. I find evidence that firms with small positive changes in EPS between $0.00 and $0.02underestimate ESO fair value cost by an incremental 3.2% (po0.05) when recognized. I find no evidence to supportearnings management measured as low positive EPS (between $0.00 and $0.02) and volatility bias (po0.10). This lack ofassociation could be driven by low power; only 22 firms have low EPS in my sample post mandatory recognition, while 62firms have small changes in EPS.

7. Robustness tests and alternative explanations

7.1. Changing weights on implied and historical benchmarks

One possible explanation for an increase in volatility bias after mandatory recognition of ESO fair values is differences inthe weights firms place on implied volatility and historical volatility benchmarks. Implied volatility is on average ten basispoints lower than historical volatility, so managers can reduce fair value expense by increasing (decreasing) the weight onimplied (historical) volatility estimates. On the other hand, the weight on implied volatility might decrease because of

Table 3Reliability analysis for mandatory and voluntary recognition.

Panel A: Volatility reliability

Volatility Biasit (Accuracyit)=a0+a1(Recognitionit)+a2(Log Optionsit)+a3(ROEit)+a4(MTBit)+a5(VRETit)+a6(Option Lifeit)+a7(Log MVEit)+a8(Inverse Mills

Ratioit)+Sak(Industryit)+eit

Bias Accuracy

Historical volatility Implied volatility Historical volatility Implied volatility

Intercept 0.156 0.266 1.50 0.747 �0.052 0.116 0.119 0.222

1.32 4.86nnn 6.44nnn 10.49nnn�0.51 1.75n 0.67 8.20nnn

123R pass �0.056 �0.058 0.002 0.002

�9.27nnn�6.02nnn 0.88 0.58

Mandatory recognition �0.075 �0.143 �0.006 �0.001

�8.06nnn�13.45nnn

�0.49 �0.36

Voluntary recognition �0.072 0.034 0.027 0.079�4.43nnn 1.44 1.91n 4.01nnn

Log # Options �0.006 �0.006 0.004 �0.0005 0.002 �0.000 �0.008 �0.001

�1.24 �2.57nnn 0.51 �0.02 0.47 �0.33 �1.56 �0.59

Return on equity �0.031 �0.011 0.119 0.023 0.008 0.001 �0.050 0.003

�2.52nn�2.00nn 2.46nn 1.33 0.54 0.20 �0.75 0.15

Market to book �0.002 �0.001 0.002 0.001 0.000 0.000 �0.002 �0.001

�1.15 �1.23 1.90n 1.69n 0.35 0.09 �1.49 �0.67

VRET (market volatility) �1.14 �1.338 0.923 0.180

�4.65nnn�10.28nnn 4.27nnn 0.10

Ivol index (market volatility) �1.11 �3.101 1.076 �0.000

�2.14nn�18.56nnn 2.74nnn

�0.00

Option life 0.008 �0.005 �0.002 �0.003 �0.004 0.000 �0.008 �0.002

1.24 �1.43 �0.25 �0.90 �0.76 0.02 �1.83n�0.33

Log(MVE) 0.006 0.012 �0.084 0.008 �0.002 �0.011 �0.000 �0.017

0.81 2.94nnn�5.95nnn 1.99nn

�0.32 �3.34nnn�0.04 �7.50nnn

Inverse Mills ratio 0.044 �0.207 �0.321 �0.169 �0.027 0.191 �0.002 0.1513

2.49nnn 6.41nnn�8.13nnn 15.56nnn

�1.91n 6.24nnn 0.05 32.90nnn

Observations 852 6878 455a 3782a 852 6878 423a 3782a

Panel B: Interest and dividend reliabilityInterest or Dividend Biasit (Accuracyit)=a0+a1(Recognitionit)+a2(Log Optionsit)+a3(ROEit)+a4(MTBit)+a5(Option Lifeit)+a6(Sizeit)+a7(Inverse Mills

Ratioit)+Sak(Industryit)+eit

Bias Accuracy

Implied interest Dividend Implied interest Dividend

Intercept �0.13 0.007 �0.075 0.003 0.033 0.008 0.148 0.112

�1.82n 1.19 0.68 1.06 1.26 0.80 1.47 3.77nn

123R pass �0.001 �0.010 �0.001 �0.001�1.38 �1.33 �1.46 �0.26

Mandatory recognition �0.002 �0.008 �0.001 0.001

�1.74n�0.82 �0.96 0.12

Voluntary recognition 0.002 �0.029 �0.002 �0.013

0.46 �1.76n�0.35 �0.84

Log # options 0.003 0.002 0.003 0.003 �0.003 �0.001 0.004 �0.000

2.01nn 3.81nnn 0.45 1.06 �1.72 �4.52nnn 0.79 �0.01

Return on equity �0.005 �0.001 �0.054 0.002 0.006 0.001 �0.058 �0.007

�2.33nn�2.60nnn

�1.32 0.10 2.69nn 2.01nnn�1.66n

�0.38

Market to book �0.000 0.000 �0.002 �0.004 0.000 �0.0001 �0.003 �0.000

�0.22 0.50 �0.44 �2.05nn 0.50 �0.89 �0.09 �0.19

Option life �0.004 �0.004 �0.008 �0.005 0.004 0.002 �0.001 0.000

�4.26nnn�11.08nnn

�1.44 �2.53nn 4.15nnn 6.60nnn�0.19 0.43

Log(MVE) 0.003 �0.003 �0.008 �0.015 0.003 0.002 �0.008 �0.015

0.75 �6.47nnn�1.00 �3.67nnn 2.45nn 4.13nnn

�1.43 �5.70nnn

Inverse Mills ratio 0.032 �0.016 �0.027 0.126 �0.005 0.027 �0.020 0.142

1.71n�3.49nnn

�1.59 9.33nnn�1.70nnn 19.54nnn

�1.63n 16.39nnn

Observations 852 6878 647b 3135b 852 6878 647b 3135b

Panel C: Tests for differences between mandatory and voluntary recognition

Biasit (Accuracyit)=a0+a1(123R Passit)+a2(Mandatory Recognitionit)+a3(Voluntary Recognitionit)+a4(Log Optionsit)+a5(ROEit)+a6(MTBit)+a7(Market

Volatilityit)+a8(Option Lifeit)+a6(Sizeit)+a7(Inverse Mills Ratioit)+Sak(Industryit)+eit

Mandatory recognition=voluntary recognition

P-value

Bias

Historical volatility 0.84

Implied interest 0.43

Historical dividend 0.25


Table 3 (continued )

Panel C: Tests for differences between mandatory and voluntary recognition

Biasit (Accuracyit)=a0+a1(123R Passit)+a2(Mandatory Recognitionit)+a3(Voluntary Recognitionit)+a4(Log Optionsit)+a5(ROEit)+a6(MTBit)+a7(Market

Volatilityit)+a8(Option Lifeit)+a6(Sizeit)+a7(Inverse Mills Ratioit)+Sak(Industryit)+eit

Mandatory recognition=voluntary recognition

P-value

Accuracy

Historical volatility 0.06n

Implied interest 0.84

Historical dividend 0.39

Bias=(reported fair value�benchmark fair value)/reported fair value; Accuracy=9Bias9. Bias and accuracy analysis is presented for each of three inputs

separately. Recognition is comprised of three indicators. Voluntary recognition equals one in all periods after voluntary fair value recognition, per Bear

Stearns list 12/16/2004. Mandatory recognition equals one for firms that do not voluntarily elect fair value option recognition and have FYEs after 6/15/

2006. 123R Pass equals one for mandatory recognizers with FYEs between 12/14/2004 and 6/15/2006. These values will be recognized in future financial

statements as the unvested value of options granted on and after the effective date. Log of options is the log of the number of options granted. Return on

equity is net income divided by market value of equity. Market to book is the market value of equity divided by the book value of equity. Option life is the

reported estimated life of the options granted. Market volatility is comprised of two values. VRET is the value weighted volatility estimated over the same

period as historical volatility when historical volatility is the benchmark. Ivol index is the average implied volatility for Russell 2000 firms over the firm’s

fiscal year when implied volatility is the benchmark. Industry controls are included in all regressions. Regressions are estimated using the Heckman two

step procedure with maximum likelihood estimation and firm clustering. The inverse Mills ratio is estimated from the first stage regression of Heckman

following Aboody et al. (2004). Variable definitions are provided in Appendix A. The first stage regression follows:

Voluntaryit (Mandatoryit)=a0+a1(Equity Issuanceit)+a2(Acquisitionit)+a3(Debt to Equityit)+a4(Interest Coverageit)+a5(Bonus %it)+a6(CEO Ownershipit)+

a7(Outside Director Ownershipit)+a8(Institutional Holdingit)+a9(Sizeit)+ak

P(Industryit)+eit.

Tests in Panel C above are F-tests for a2=a3 from Table 3, Panels A and B.n Significance at the 10% level.nn Significance at the 5% level.nnn Significance at the 1% level.a Sample size is limited to firms with exchange traded options from Optionmetrics.b Sample is limited to firms that grant dividends.


differences in guidance between SFAS 123 and SFAS 123R.16 I test for differences in weights placed on the historical andimplied volatility benchmarks across accounting regimes using the following regression:

Reported Volatilityit ¼ d0þd1ðHistorical VolatilityitÞþd2ðImplied VolatilityitÞþd3ðHistoricalit�RecognitionitÞ

þd4ðImpliedit�RecognitionitÞþd5ðRecognitionitÞþd9ðVRETitÞþd6ðIndex IvolitÞ

þd7ðInverse Mills RatioitÞþdj

XðIndustryÞþeit ð4Þ

Similar to earlier analysis I address self-selection issues by using the Heckman procedure, where the inverse Mills ratio isestimated from a first stage regression (Eq. (1) above). Results displayed in Table 5 indicate several points worth noting. First,firms with exchange traded options tend to rely more heavily on historical volatility than implied volatility as a benchmark. Thecoefficient is on historical volatility 0.69 (0.52) is more than three times that of the coefficient on implied volatility 0.19 (0.15)for mandatory (voluntary) recognizers. Second, coefficients on the interaction between mandatory recognition areinsignificantly different from zero at the 10% level, suggesting that the increase in underestimation during mandatoryrecognition is not driven by changing weights across benchmarks. However, voluntary recognizers appear to weight historicalvolatility benchmarks more heavily post voluntary recognition with a coefficient of 0.23 (po0.01). Though there is nosignificant change in the weight of implied volatility (p40.10) post voluntary recognition, the increased weight on historicalvolatility potentially explains why there is no evidence of volatility bias when using implied volatility as a benchmark forvoluntary recognizers (i.e. due to low power). Third, despite including both benchmarks simultaneously and permitting forchanges in benchmarks, indicators for mandatory (voluntary) recognition are negative and significant (po0.01) indicating thatfirms with exchange traded options lower their volatility estimates by 2.9% (8.3%) on average post recognition.

7.2. Difference in difference analysis

Fig. 1 depicts patterns of interest, volatility, and dividends over time. Regressions control for time series effects by usingbenchmarks with similar time series patterns and by including value-weighted volatility for volatility analysis. However, firmscan change through time for reasons other than the change from recognition to disclosure. A difference in difference design canmitigate this concern subject to the caveat that firms not changing from recognition to disclosure might not be affected indifferent periods in similar ways. I regress changes in reported volatility (measured as reported volatilityt�reported

16 FAS 123 paragraph 275 states that if multiple volatility estimates of equal quality are available, firms cans use the lowest estimate. In contrast, FAS

123-R paragraph A20 states that if several reasonable estimates are available, then the average should be used.

Table 4Cross-sectional analysis of volatility bias.

Volatility Biasit=a0+a1(123R Passit)+a2(Mandatory Recognitionit)+a3(Mandatorynsmall D in EPSit)+a4 (Mandatorynsmall positive EPSit)+a5 (Log

Optionsit)+a6(ROEit)+a7(MTBit)+a8(VRETit)+a9(Option Lifeit)+a10(Log MVEit)+a11(Inverse Mills Ratioit)+Sak(Industryit)+eit

Historical volatility

Intercept 0.267

4.88nnn

123R Pass �0.056�9.20nnn

Mandatory recognition �0.075�8.00nnn

Mandatorynsmall positive change in EPS (o =0.02) �0.032�1.96nn

Mandatorynlow positive EPS (o =0.02) �0.026�1.02

Log # options �0.006

�2.56nnn

Return on equity �0.011

�2.01nn

Market to book �0.001

�1.26

VRET �1.34

�10.30nnn

Option life �0.006

�1.44

Log(MVE) 0.011

2.91nnn

Inverse Mills ratio �0.207

6.32nnn

Observations 6782

Bias=(reported fair value�benchmark fair value)/reported fair value. 123-R Pass equals one for FYEs between 12/14/2004 and 6/15/2006. These values

will be recognized in future financial statements as the unvested value of options granted on and after the effective date. Mandatory recognition equals

one for firms that do not voluntarily elect fair value option recognition and have FYEs after 6/15/2006. Small positive change in EPS is an indicator

variable that is one when $0.00o =(EPSt�EPSt�1)o =$0.02 for FYEs after 6/15/2004 and the firm is a mandatory recognizer; 62 firms in my sample meet

this criteria. Low positive EPS is an indicator variable that equals one when $0.00o =EPSo =$0.02 for FYEs after 6/15/2004 and the firm is a mandatory

recognizer; 22 firms in my sample meet this criterion during mandatory recognition. Log of options is the log of the number of options granted. Return on

equity is net income divided by market value of equity. Market to book is the market value of equity divided by the book value of equity. Option life is the

reported estimated life of the options granted. VRET is the value weighted volatility estimated over the same period as historical volatility. Industry

controls are included in all regressions. Regressions are estimated using the Heckman two step procedure with maximum likelihood estimation and firm

clustering. The inverse Mills ratio is estimated from the first stage regression of Heckman following Aboody et al. (2004). Variable definitions are provided

in Appendix A. The first stage regression follows:

Voluntaryit (Mandatoryit)=a0+a1(Equity Issuanceit)+a2(Acquisitionit)+a3(Debt to equityit)+a4(Interest coverageit)+a5(Bonus %it)+a6(CEO Ownershi-

pit)+a7(Outside director ownershipit)+a8(Institutional Holdingit)+a9(Sizeit)+ak

P(Industryit)+e.

nn Significance at the 5% level.nnn Significance at the 1% level.


volatilityt�1) estimates on recognition indicators, changes in firm specific volatility benchmarks, changes in market volatility,and changes in firm size. Similar to prior analysis, I include the inverse Mills ratio to address self-selection.

Results in Table 6 show that firms reduce reported volatility estimates on average by 1.5% during the year of voluntaryadoption, significant at the 10% level. Similarly, mandatory adopters reduce reported volatility estimates by 1.5% aftermandatory recognition is adopted (these options are recognized in the financial statements in the subsequent fiscal year),and by an incremental 0.80% in the first year recognition is required for concurrent option grants (po0.01 and 0.05,respectively). Similar to analysis in Table 5, the difference in difference analysis reveals similar magnitudes of increasedunderestimation of reported volatility during recognition.

In column (2) of Table 6, I present difference in difference analysis of accuracy where accuracy is defined as 9reportedvolatilityt�historical volatilityt�19. Results show insignificant changes in accuracy during voluntary and mandatoryrecognition (p40.10), i.e. accuracy does not decline post recognition. These results suggest that main tests for accuracydifferences between voluntary and mandatory recognizers are sensitive to fair value translations.

In unreported tests I compare univariate differences in firm reported stock price volatility, volatility bias and volatilityaccuracy across both voluntary and mandatory disclosure regimes on a subsample of firms with minimal time series differencesin benchmark volatility. I find mandatory (voluntary) recognition firms lower reported volatility estimates by an average of 5.4%(2.14%), significant at the (po0.01) post recognition. Mandatory (voluntary) recognition firms also increase theirunderestimation of ESO fair value cost by 3.6% (3.4%) (po0.05 and 0.10, respectively), but increase their accuracy by 2.8%(no change in accuracy) (po0.05 and p40.10, respectively). The unreported tests support differences between voluntary andmandatory recognition and suggest that accuracy does not worsen post recognition despite the increase in bias.

Table 5Tests for changing benchmarks during voluntary and mandatory recognition.

Reported Volatilityit=d0+d1(Historical Volatilityit)+d2(Implied

Volatilityit)+d3,4(HistoricalitnRecognitionit)+d5,6(ImplieditnRecognitionit)+d7,8(Recognitionit)+d8(VRETit)+d9(Index Ivolit)+d11(Inverse Mills

Ratioit)+d10

P(Industry)+eit

Voluntary adopters Mandatory adopters

Intercept 0.068 0.127

1.60 4.14nnn

Historical volatility 0.522 0.688

5.47nnn 25.38nnn

Implied volatility 0.150 0.187

2.12nn 6.81nnn

Mandatory recognitionnHistorical �0.025

�0.69

Mandatory recognitionnImplied �0.004

�0.12

Mandatory recognition �0.029�2.47nn

123R Pass �0.018�3.15nnn

Voluntary recognitionnHistorical 0.225

2.93nnn

Voluntary recognitionnImplied �0.047

�0.58

Post voluntary recognition �0.083�2.71nnn

VRET 0.027 �0.364

0.19 �4.11nnn

Index Ivol 0.211 �0.055

1.38 �0.64

Inverse Mills ratio 0.020 �0.062

2.39nn 8.81nnn

Observations 455 3782

Reported volatility is the volatility estimate provided by the firm in the 10-K (Equilar database). Historical volatility is estimated for the period equal to

reported life, ending 6 months prior to FYE using daily stock price from CRSP. Implied volatility is obtained from Optionmetrics using a strike price

during the year closest to FYE stock price. Voluntary adopters are identified using a Bear Stearns list dated 12/16/2004. 123-R Pass equals one for FYEs

between 12/14/2004 and 6/15/2006. These values will be recognized in future financial statements as the unvested value of options granted on and after

the effective date. Mandatory recognition equals one for firms that do not voluntarily elect fair value option recognition and have FYEs after 6/15/2006.

Voluntary recognition equals one in all periods after voluntary fair value recognition, per Bear Stearns list 12/16/2004. VRET is the value weighted

volatility estimated over the same period as historical volatility. Ivol index is the average implied volatility for Russell 2000 firms over the firm’s fiscal

year. Standard errors are robust to firm clustering, and estimated using maximum likelihood procedure. The inverse Mills ratio is estimated following the

Heckman two step procedures. Variable definitions are provided in Appendix A. The first stage regression is as follows:

Voluntary (Mandatory)= a0+a1(Equity Issuance)+a2(Acquisition)+a3(Debt to Equity)+a4(Interest Coverage)+a5(Bonus %)+a6(CEO Ownership)+a7(Out-

side Director Ownership)+a8(Institutional Holding)+a9(Size)+ak

P(Industry)+e.

nn Significance levels of 5%.nnn Significance levels of 1%.


7.3. Ex-post volatility as an alternate measure of accuracy

Using a rational expectations framework, ex-post volatility is a useful benchmark for accuracy.17 Ex-post volatility isalso more closely associated with the actual cost of granting the ESOs. One limitation to using ex-post data as a benchmarkin my setting is that it can be estimated on a limited basis as CRSP data is available only through 2009. Many firms (54% ofmandatory and 55% of voluntary adopter observations) have insufficient time series of ex-post data to estimate volatilityover the entire reported life of the option. The recognition sample has a greater proportion of insufficient ex-post datarelative to the disclosure sample (i.e. 70% of voluntary and 77% of mandatory recognizers have insufficient ex-post dataduring recognition). Assuming insufficient data leads to systematically lower ex-post volatility estimates, data limitationscould bias towards finding more accurate estimates during recognition. To mitigate this concern, I use daily intervals toestimate ex-post volatility, which allows for more observations, and I include a control variable (short) that is equal to thedifference between data available and expected life (in months). Despite data limitations, using ex-post volatility permitsbetter comparisons with Hodder et al. (2006) who find that opportunistic fair value estimates leads to less accurateestimates where accuracy is defined by comparing reported values to ex-post volatility.

17 One criticism of this benchmark follows SAB 107 (page 6): ‘‘estimates of fair value are not intended to predict actual future events, and subsequent

events are not indicative of the reasonableness of the original estimates of fair value made under Statement 123-R.’’

Table 6Difference in difference analysis across recognition and disclosure.

D Reported Volatility or D Accuracyit=d0+d1,2(Recognitionit)+d3,4(D Volatilityit)+d5(DVRETit)+d6(D Log MVEit)+d7(Inverse Mills Ratioit)+eit

Dependent variable (1) D Reported volatility (2) D Accuracy

Sample Voluntary adopters Mandatory adopters Voluntary adopters Mandatory adopters

Intercept �0.019nn�0.014nnn 0.007 �0.007nn

�2.76 �8.12 1.36 �3.92

123R Pass �0.015nnn 0.003�5.48 1.50

Mandatory recognition �0.008nn�0.001

�2.26 �0.25Voluntary recognition �0.015n

�0.003�1.81 �0.36

D Historical volatility 0.340nnn 0.353nnn

6.97 9.62

D VRET 0.235 0.008 0.596nnn 0.410nnn

1.50 0.10 3.88 6.52

D Log MVE –0.023nnn�0.128nnn

�0.003 �0.002

�3.60 �3.51 �0.45 �0.72

Inverse Mills ratio 0.008nn 0.020nnn�0.004 0.025nnn

1.92 6.53 �1.04 3.76

Observations 572 4217 572 4217

The sample size is reduced because difference in difference analysis results in a one year loss of data. D Reported Volatility is reported

volatilityt�reported volatilityt�1; negative values correspond to greater underestimation. D Accuracy is equals (accuracyt�accuracyt�1) where accuracy

in column (2) is defined as 9reported volatilityt�historical volatilityt9; negative values correspond to more accurate estimates. Changes are reported as

valuet�valuet�1, such that negative values represent decreases. 123-R pass equals one for FYEs between 12/14/2004 and 6/15/2006. These values will be

recognized in future financial statements as the unvested value of options granted on and after the effective date. Voluntary (Mandatory) recognitionequals one in the first year of recognition only. Voluntary adopters are identified using a Bear Stearns list dated 12/16/2004. Historical volatility is

estimated for the period equal to reported life, ending 6 months prior to FYE using daily stock price from CRSP. VRET is the value weighted volatility

estimated over the same period as historical volatility. Standard errors are robust to firm clustering, and estimated using maximum likelihood procedure.

The inverse Mills ratio is estimated following the Heckman two step procedures. Variable definitions are provided in Appendix A. The first stage

regression is as follows:

Voluntary (Mandatory)=a0+a1(Equity Issuance)+a2(Acquisition)+a3(Debt to Equity)+a4(Interest Coverage)+a5(Bonus %)+a6(CEO Ownership)+a7(Out-

side Director Ownership)+a8(Institutional Holding)+a9(Size)+ak

P(Industry)+e.

n T-test significance at the 10% level.nn T-test significance at the 5% level.nnn T-test significance at the 1% level.


In Table 7, I estimate accuracy as the absolute difference between reported volatility and ex-post volatility. I regressfirm specific changes in accuracy on recognition indicators and various controls. Results suggest that accuracy does notworsen after voluntary recognition (p40.10), but that mandatory recognition shows improved accuracy (po0.01).18 Thealternative benchmark (ex-post versus historical volatility) does not appear to explain why my results differ from Hodderet al. (2006). Rather recognition leads to increased bias in reported stock price volatility, but not necessarily less accurateestimates of stock price volatility. Voluntary and mandatory recognition (specifically Pass 123R) still differ (po0.01),supportive of differences between voluntary and mandatory recognition.

8. Conclusions

I test for differences between recognition and disclosure by assessing the reliability for each of three inputs to ESO fairvalues—stock price volatility, dividend yield, and risk-free interest. The unique setting overcomes two typical problemswith studies that compare recognition and disclosure: simultaneous changes in the valuation and accounting regime anddifferences in information quantity. Econometric techniques address a third issue—self-selection. I quantify differencesbetween recognition and disclosure and show how those differences manifest. Finally, I compare voluntary and mandatoryrecognition.

Results indicate that firms treat recognized values and disclosed values differently. In particular, recognized values aremore likely to be underestimated. Firms underestimate recognized values by reducing their estimates of stock pricevolatility, the input with the greatest estimation latitude. Stock price volatility underestimation leads to a 7% reduction infair value cost as compared with disclosed values and corresponds to an average of 3.2% of absolute net income. Firms that

18 Unreported difference in difference tests of bias using ex-post volatility as a benchmark indicate underestimation during recognition (po0.01).

Table 7Changes in accuracy using ex-post volatility.

D Accuracyit=d0+d1,2(Recognitionit)+d3,4(D Ex-post VRETit)+d5(DVRETit)+d6(Unexpected Volatilityit)+d7(D Log MVEit)+d8(Shortit)+d7(DShortit)+d7(Inverse Mills Ratioit)+eit

Sample Voluntary adopters Mandatory adopters

Intercept 0.08nnn 0.0158nnn

3.00 5.84

123R pass �0.042nnn

�12.66Mandatory recognition �0.025nnn

�6.74Voluntary recognition �0.015

�1.13

D Ex-post VRET 0.638nnn�0.305nnn

3.07 �4.44

D VRET 0.067 �0.037

0.23 �0.35

Unexpected volatility 0.075 0.282nnn

0.80 9.59

D Log MVE �0.029nnn�0.010nn

�2.59 �2.04

Short �0.000 �0.004nnn

�1.23 �3.11

D Short �0.001 0.004

�0.26 1.45

Inverse Mills ratio �0.030n 0.056nnn

�1.85 10.63

Observations 572 4217

Accuracy is measured following Hodder et al. (2006) as reported volatility less ex-post daily volatility. Ex-post volatility is measure beginning six months

prior to FYE over the reported useful life or until 12/31/2009 due to data limitations. Changes in accuracy are reported as accuracyt�accuracyt�1, such

that negative values correspond to more accurate estimates. Other change variables are measured as time t less t�1. 123-R pass equals one for FYEs

between 12/14/2004 and 6/15/2006. These values will be recognized in future financial statements as the unvested value of options granted on and after

the effective date. Voluntary (Mandatory) recognition equals one in the first year of recognition only. Voluntary adopters are identified using a Bear

Stearns list dated 12/16/2004. DVRET (D Ex-post VRET) is the change in historical (ex-post) value weighted volatility estimated over the same period as

historical (ex-post) volatility. Unexpected volatility is estimated following Hodder et al. (2006) as the difference between ex-post value weighted

volatility and the implied volatility of S&P index options 6 months prior to fiscal year end. DLMVE is the change is the log of market value of equity. Short(D Short) is the difference (change in the difference) in months between data availability less expected life. Industry controls are included in all

regressions. Regressions are estimated using the Heckman two step procedure with maximum likelihood estimation and firm clustering. The inverse

Mills ratio is estimated from the first stage regression of Heckman following Aboody et al. (2004). The first stage regression follows:

Voluntaryit (Mandatoryit)=a0+a1(Equity Issuanceit)+a2(Acquisitionit)+a3(Debt to Equityit)+a4(Interest Coverageit)+a5(Bonus %it)+a6(CEO Ownershi-

pit)+a7(Outside Director Ownershipit)+a8(Institutional Holdingit)+a9(Sizeit)+ak

P(Industryit)+eit.

n Significance at the 10% level.nn Significance at the 5% level.nnn Significance at the 1% level.


are close to prior year EPS display an incremental 3% of underestimation of fair value cost. The results suggestthat recognized values prepared by managers and reviewed by auditors have different characteristics than disclosedvalues. This potentially explains why prior studies find that investors place greater valuation weights on recognized valuesthan disclosed ones. Investors may increase valuation weights on recognized values to mitigate greater bias in thoseestimates.

Despite the similar increase in bias of both mandatorily and voluntarily recognized values, there is some evidence thataccuracy differs. Mandatorily recognized values are not less accurate than disclosed ones. Specifically, when using ex-postvolatility (historical) to measure accuracy, they appear more accurate (no less accurate). However, voluntarily recognizedvalues are no less (less) accurate relative to disclosed values when using ex-post volatility (historical volatility) as abenchmark. Thus, both benchmarks suggest that voluntary and mandatory recognition differ such that voluntarilyrecognized values are less accurate when compared to mandatorily recognized ones. This disparity between voluntary andmandatory recognition is supported by Frederickson et al. (2006) which documents differences in user reliabilityassessments across voluntary and mandatory recognition.

Such evidence is useful to regulators as they make future financial reporting decisions about recognition and disclosure.While authoritative guidance does not clearly distinguish between these reporting regimes, it appears in practice thatmanagers and auditors treat them differently. Results indicating that the underestimation is driven by volatility suggestthat opportunistic reporting of fair values may be curtailed by providing more specific authoritative guidance regardingestimation. This may be useful in future decisions about guidance and latitude of estimating fair values using marketcorroborated data (as suggested by SFAS 157’s level 2 definition).


Acknowledgements

This paper is from my dissertation ‘‘Effects of Recognition versus Disclosure on the Structure and Financial Reporting ofShare Based Payments.’’ I acknowledge helpful comments from: Bill Baber, Anne Beatty (referee), Melissa Lewis, DJ Nanda,Per Olsson, Lee Pinkowitz, Karthik Ramanna, Katherine Schipper, Jason Schloetzer, Mary Sullivan, Mohan Venkatachalam,Ross Watts (editor), and workshop participants at Columbia University, Emory University, George Mason University,George Washington University, Georgetown University, Massachusetts Institute of Technology, University of Chicago, the2007 Excellence in Financial Reporting Conference, and 2008 Financial Accounting and Reporting Section for their helpfulcomments. I also acknowledge financial support from Georgetown University, Duke University, and the Global CapitalMarkets Research Group at Duke University.

Appendix A.

See Table A1.

Table A1Variable definitions.

Variable Source Computation

Reported volatility, interest,

dividends, and option life

Equilar database Equilar collected data from firm 10-Ks

Historical volatility benchmark CRSP (monthly) Annualized log returns over the reported option life, ending 6

months prior to fiscal year end

Historical dividend benchmark CRSP (monthly) Sum of the cash dividends paid over the prior year divided by the

mean price over the prior year

Implied volatility benchmark Optionmetrics Implied volatility estimates with terms greater than or equal to 540

days, with a strike price closest to fiscal year end price

Implied interest benchmark Optionmetrics Average monthly implied zero curve rate over the FY with a term

equal to reported option life

Voluntary firm Bear Stearns Report 12/16/2004 Indicator variable equal to one post voluntary adoption; adoption

year is included in the report

123-R pass Bear Stearns Report 12/16/2004 Firms not listed as voluntary adopters with fiscal year ends between

1/1/2005 and the effective date of 123-R (6/15/2006)

123-R effective Bear Stearns Report 12/16/2004 Firms not listed as voluntary adopters with fiscal year ends after the

effective date of 123-R (6/15/2006)

Log of options Equilar database Log (# of options granted during fiscal year)

Size COMPUSTAT MVE=CSHOnPRCC_F

Return on Equity COMPUSTAT IB/MVE

Market to Book COMPUSTAT MVE/CEQ

Industry COMPUSTAT Two-digit NAICS code

Variable Definitions for Eq. (1) (following Aboody et al. (2004)).

Acquisition Securities data corporation Indicator variable equals one if the firm used equity to acquire another firm in the last 3 years

Active in equity COMPUSTAT Indicator variable equals 1 if firm SSTK/MVE 4 =3% in any of the last 3 years

Institutional holding Thompson Average quarterly institutional shares held during the last quarter divided by shares outstanding

Debt to equity COMPUSTAT (DLTT+DLTO)/CEQ

Interest Coverage COMPUSTAT XINT/IOBDP

CEO bonus Boardex Bonus/(Salary+Bonus)

CEO ownership Boardex Shares held/shares outstanding

Outside director stock Boardex Shares held by all supervisory (non-employee) directors divided by shares outstanding

Size Compustat Log (MVEt�1)

Industry COMPUSTAT Two-digit NAICS code

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Documents

Evidence on differences between recognition and disclosure: A comparison of inputs to estimate fair values of employee stock options