article summary- Stock Price Reaction and Value Relevance

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Gunny, K. A. (2010). The relation between earnings management using real activities
manipulation and future performance: Evidence from meeting earnings
benchmarks. Contemporary accounting research, 27(3), 855-888.
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Name / Login ID
Section #
Article Reference
Gunny, K. A. (2010). The relation between earnings management using real activities
manipulation and future performance: Evidence from meeting earnings
benchmarks. Contemporary accounting research, 27(3), 855-888.
Article Summary
(Rephrase from
Abstract)
Key Definitions in
the article
Research Method
(Design, samples,
variables)
Key Contributions
and Implications
Personal Insight
In a few sentences, provide an overview of what this article is communicating or
asserting.
State key definitions or concepts used in this article.
Describe the research methodology of this article. It usually includes the design,
samples, and the key variables being tested. Do not need to list all the variables used
in all the tests done in the article.
What are the key contributions, both academically to the literature and practically in
the industry?
Provide your own insight after reading this article.
Journal of Accounting and Economics 33 (2002) 343–373
Stock price reaction and value relevance of
recognition versus disclosure: the case
of stock-based compensation$
Hassan Espahbodia, Pouran Espahbodia, Zabihollah Rezaeeb,
Hassan Tehranianc,*
a
College of Business and Technology, Western Illinois University, Macomb, IL 61455-1390, USA
b
College of Business, The University of Memphis, Memphis, TN 38152, USA
c
Department of Finance, Carroll School of Management, Boston College, Chestnut Hill,
MA 02467-3808, USA
Received 28 October 1999; received in revised form 23 August 2001
Abstract
This study examines the equity price reaction to the pronouncements related to accounting
for stock-based compensation and assesses the value relevance of recognition versus disclosure
in ﬁnancial reporting. We document that ﬁrms exhibit signiﬁcant abnormal returns around the
issuance of the Exposure Drafts proposing to require recognition of stock-based compensation
costs, and also around the event reversing that decision to require disclosure only (while
encouraging recognition). We also document that the abnormal returns are most pronounced
for high-tech, high-growth, and start-up ﬁrms. Our results are consistent with the contracting
theory, and show that disclosure is not a substitute for recognition. r 2002 Elsevier Science
B.V. All rights reserved.
JEL classification: G14; M41; C39
Keywords: Capital market; Accounting standard; Stock option; Security price reaction
$
This research was supported by a grant from the College of Business Alumni Funded Research
Program at Western Illinois University. Special thanks are to Ross Watts (the managing editor) and an
anonymous reviewer for many helpful comments that are incorporated in this version of the paper. The
authors also thank Elizabeth Bagnani (Strock), Susan Chu, Gil Manzon, Ken Schwartz, and Billy Soo, for
their helpful comments on previous drafts of this paper. Thanks are ﬁnally due Gang Hu and Hairong Liu
for their research assistance. All remaining errors are those of the authors.
*Corresponding author. Tel.: +1-617-552-3944; fax: +1-617-552-0431.
E-mail address: hassan.tehranian@bc.edu (H. Tehranian).
0165-4101/02/$ – see front matter r 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 5 – 4 1 0 1 ( 0 2 ) 0 0 0 4 8 – 4
344
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
1. Introduction
The past two decades have witnessed a signiﬁcant growth in the number of public
companies offering stock options and similar equity instruments (known as stockbased compensation, or SBC) to their executives and employees. Among others,
start-up, high-tech, and high-growth companies, starving for cash, have been able to
grant stock options, in lieu of a cash bonus or salary, to compensate their executives.
The spread of SBC as an incentive plan was not only inﬂuenced by companies’ desire
to motivate employees and conserve cash outlays, but also by the fact that no
compensation expense had to be recognized for most SBC plans under the
Accounting Principles Board (APB) Opinion No. 25.1
As SBC plans were becoming more popular as a means of motivating
and compensating employees, the accounting profession and the ﬁnancial
community became concerned about the inconsistency and inadequacy of APB
No. 25 in accounting for SBC. In response to this concern, the Financial
Accounting Standards Board (FASB) placed accounting for SBC on its agenda in
1984, but it took the Board until June 1993 to issue its highly controversial Exposure
Draft (ED) entitled ‘‘Accounting for Stock-Based Compensation.’’ The ED
proposed recognition of compensation cost for all awards of SBC plans that would
eventually vest, based on their fair value at the grant date (a proposal that was
reversed later).
The responses to the ED on accounting for SBC were overwhelmingly
negative. The vast majority of respondents objected to the recognition of
compensation cost for ﬁxed employee stock options. Signiﬁcant objections
were especially voiced by high-tech, high-growth, and start-up, companies on
the grounds that the recognition of SBC expense would place them at a
competitive disadvantage. After a long deliberation process, and under pressure
from the ﬁnancial community, the accounting profession, and the Congress, the
FASB reached a decision to ‘‘encourage,’’ but not require, recognition of
compensation cost based on the fair-value method and to pursue expanded
disclosure in the Statement of Financial Accounting Standards (SFAS) No. 123.
Employers are permitted to continue application of APB Opinion No. 25 (the
intrinsic-value method), but they are required to disclose the pro forma effect on net
income and earnings per share (EPS, if presented) had the fair-value method been
applied.
The main impetus for the criticism by Congress, the business community, and the
accounting profession, was the prediction that recognition of SBC costs could lower
the reported earnings by as much as 50% and would adversely affect stock prices
(Berton, 1993). It was also predicted that high-tech, high-growth, and start-up,
1
Under APB Opinion No. 25 (the intrinsic-value based method), compensation cost to the company is
the excess, if any, of the quoted market price of the stock at the grant date or other measurement date over
the amount an employee must pay to receive the stock. Most stock option plans are ﬁxed with no intrinsic
value and therefore require no cost recognition. Some SBC plans, however, including those with variable,
performance based, features do require recognition of compensation cost under APB Opinion No. 25.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
345
companies relying heavily on SBC programs would be more affected than other ﬁrms
(see Wall Street Journal, e.g., on 2/26/92, 4/8/93, and 6/3/93).
In this study, we: (1) investigate whether equity prices reacted to the
SBC pronouncements; (2) investigate whether abnormal returns varied
cross-sectionally with ﬁrm-speciﬁc variables; and (3) assess the value relevance
of recognition versus disclosure in ﬁnancial reporting. The ﬁrst two inquiries
are similar to previous studies investigating the market reaction to various
accounting pronouncements that required recognition (e.g., Espahbodi et al.,
1991, 1995) or disclosure of some information (e.g., Clinch and Magliolo, 1992;
Espahbodi and Tehranian, 1989). They not only provide insight into the market’s
assessment of the relative importance of each event, they offer some rationale for
lobbying efforts and concerns by Congress, the business community, and the
accounting profession, that eventually forced the FASB not to require recognition of
SBC costs.
The distinguishing feature of this paper stems from the fact that the FASB never
retreated from requiring recognition to allow a choice between disclosure and
recognition for the same item of information. Thus, analysis of the economic
consequences of SBC pronouncements not only allows us to test the signiﬁcance of
the contracting theory, but also to assess the value relevance of disclosure versus
recognition.
Our results indicate that ﬁrms did exhibit signiﬁcant abnormal returns around
the issuance of the Exposure Drafts proposing to require recognition of stockbased compensation costs, and also around the event reversing that decision
to require disclosure only (while encouraging recognition). We show that the
abnormal returns are most pronounced for high-tech, high-growth, and start-up
ﬁrms. We also document that the stock price impact is positively related to the
existence of tax loss carry-forward, the extent of stock option usage (as reﬂected by
its effect on EPS), and retained-earnings related debt constraint; and negatively
related to the noise in stock price performance, free cash ﬂows over total assets, and
ﬁrm size.
The signiﬁcance of the abnormal return around the event reversing the decision to
only require disclosure is consistent with the contracting theory, and shows that
market participants value disclosure and recognition differently (or that disclosure is
not a substitute for recognition). Requiring companies to only disclose the cost of
SBC rather than forcing recognition as was proposed earlier would involve no new
information and should not affect security prices, except through the contracting and
political cost hypotheses (as future earnings will be affected by recognition, but not
by disclosure, of SBC costs).
Our results are in contrast with those of Dechow et al. (1996) who also examined
the economic consequences of accounting for SBC, although their focus was on
determining the characteristics of ﬁrms lobbying against (writing comment letters
on) the exposure draft on SBC and those using employee stock options. Dechow
et al. used three samples: companies writing a comment letter; bio-technology ﬁrms;
and all ﬁrms in industries with high median option usage, as measured by the
proportion of common shares reserved for conversion of stock options to common
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H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
shares outstanding. They did not ﬁnd any equity price reaction to the ED
announcement or the decision to rescind its requirements. Neither did they ﬁnd any
of their four ﬁrm-speciﬁc variables to be related to equity price reaction to the SBC
pronouncements. We attribute all these differences to the sample and the variable
set, and explore these differences in the results section. We contend that our sample
selection criteria, and the use of the more recently available data on the impact of
stock options on EPS, enable us to perform a more powerful test of the market
reaction to SBC pronouncements.
The remainder of this paper is organized as follows. A review of the accounting
pronouncements and issues pertaining to SBC appears in Section 2. Hypotheses
on the capital market effects of these pronouncements are developed in Section 3.
The fourth section describes the events, sample and data, and methodology.
Section 5 presents the empirical results. The ﬁnal section contains a summary and
conclusion.
2. Accounting pronouncements and issues pertaining to SBC
Prior to the issuance of SFAS No. 123, accounting for SBC was governed by APB
Opinion No. 25 and its interpretations. Under APB Opinion No. 25, most SBC plans
were considered non-compensatory in nature and, accordingly, did not require
recognition of compensation expense if the exercise price was equal to or less than
market price on the measurement date (the ﬁrst date on which both the number of
shares and the exercise price were known). For ﬁxed awards the expense was
measured when the option was granted. For a variable plan, the expense was
estimated and accrued between the date of the grant and the ﬁnal measurement date.
Variable plans typically have an exercise price that is below the stock’s market price
at the measurement date resulting in expense recognition while ﬁxed plans generally
have an exercise price that is equal to the market price of the stock on the grant date,
resulting in no expense recognition. Thus, many companies have been taking
advantage of the ﬂexibility of APB Opinion No. 25 and avoiding expense recognition
for SBC.
As SBC plans were becoming more popular as a means of motivating and
compensating employees, the accounting profession and the ﬁnancial community
became concerned about the inconsistency and inadequacy of APB No. 25 in
accounting for SBC. In response to this concern, the FASB placed accounting for
SBC on its agenda in 1984. From 1985 to 1988, the Board considered the issues and
conducted research on various aspects of SBC plans. Late in 1988, the Board set
aside the stock compensation issue to ﬁrst deliberate the related concepts of equity
versus liability instruments. Based on comments received in response to a Discussion
Memorandum, issued in August 1990, distinguishing between liability and equity
instruments and accounting for instruments with characteristics of both, the Board
decided that an entity’s obligation to issue its own stocks is an equity (and not a
liability) instrument because the entity has no obligation to transfer its assets. In
February 1992, the Board decided not to pursue possible changes to the conceptual
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
347
distinction between equity and liability, and to resume work on the compensation
project within the conceptual framework. The expectation that the Board may
require companies to deduct the cost of stock options from their income was, for the
ﬁrst time, reported in New York Times on January 22, 1992. Thus, this was the ﬁrst
event that may have inﬂuenced investors’ expectations about future reported
earnings.
A highly controversial Exposure Draft (ED) entitled ‘‘Accounting for StockBased Compensation’’ was issued in June 1993. The ED proposed recognition
of compensation cost for all awards of SBC plans that would eventually vest,
based on their fair value at the grant date (a proposal that was reversed later).
The ED would have required: (1) recognition of SBC cost, on the grant date, initially
in the Balance Sheet as an asset (prepaid compensation) and as an element of
stockholders’ equity (stock options outstanding); and (2) amortization of the
recognized SBC asset as compensation expense over the service period, usually the
vesting period.
By proposing recognition of SBC expense at the grant date for both ﬁxed and
variable plans (the fair-value method), the FASB intended to mitigate the
controversial issues surrounding APB Opinion No. 25. The Board argued that
stock options and other forms of stock-based awards to employees represent
compensation and should be treated as such. The proposal would have provided
better uniformity in accounting for SBC and resolved the inconsistency among
various forms of SBC plans.
The proposal, however, received great attention and criticism by Congress,
the business community, and the accounting profession. Opponents of the fairvalue method of accounting for SBC (see, e.g., Rouse and Barton, 1993;
Derieux, 1994) argued that: (1) there is no out-of-pocket cost to the company
for SBC; (2) SBC cannot be reliably measured; and (3) recognition of SBC
expense puts most companies, especially high-tech, high-growth, and start-up
companies, at a competitive disadvantage. Proponents of compensation
expense recognition (see, e.g., Pacter, 1994) argued that: (1) mere disclosure
of SBC costs ignores the fact that these options give employees valuable rights
and are compensation for services already performed; (2) like other types of deferred
compensation arrangements such as pensions and postretirement beneﬁts, SBC costs
should be accrued as expense over the service period; and (3) mere disclosure of SBC
costs produces ﬁnancial statements that are neither credible nor representationally
faithful.
In October of 1995, after more than a decade of deliberation, the FASB issued
Statement No. 123, ‘‘Accounting for Stock-based Compensation.’’ However, under
pressure from companies and even the Congress, SFAS No. 123 only encourages
employers to recognize compensation expense for SBC based on their estimated fair
value at the grant date. Speciﬁcally, companies offering SBC have a choice of either
applying the fair-value based accounting method or the intrinsic-value based
method. However, companies that use the intrinsic-value method of accounting
under APB Opinion No. 25 must disclose pro forma net income and EPS ﬁgures as if
they had used the fair-value method.
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H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
3. Hypotheses on the capital market effects of SBC accounting
Under SFAS No. 123, publicly held companies may either recognize or disclose
compensation costs for ﬁxed (as well as variable) SBC. Compared to APB Opinion
No. 25, under which most companies reported no compensation expense for ﬁxed
options, recognition (disclosure) of SBC costs can have a signiﬁcant effect on
reported (pro forma) income. Bear, Stearns & Co. estimated that recognition of SBC
costs could lower the reported earnings by as much as 50% which would adversely
affect stock prices (Berton, 1993). In addition to reducing reported (or pro forma)
earnings, SFAS No. 123 also increases the number of shares considered outstanding
and thus results in a lower reported (or pro forma) EPS.
In general, the announcements that increased the probability of recognizing the
SBC costs could cause a decrease in stock prices by eliminating an efﬁcient
contracting deﬁnition of earnings (i.e., one that avoids recognition of present and
future SBC costs). As Watts and Zimmerman (1986) point out, accounting practice
tends to trade-off information asymmetry (reliability) against timeliness. The
comments to the FASB suggest that reliability was a serious problem with the
Exposure Draft (ED) requirements so that earnings based on the ED would be less
useful for contracting, causing stock prices to drop. Stock prices could also decrease
for ﬁrms with retained-earnings related debt constraints, as the ED would increase
the probability that debt covenants would be violated and thus increase the expected
cost of a technical default (Watts and Zimmerman, 1986, p. 286). (Leverage related
debt covenants would have actually loosened, i.e., debt to assets or debt to equity
ratios would have decreased, as both assets and owners’ equity would have increased
under the proposed standard.) Finally, if recognizing the SBC costs was expected to
result in rewriting compensation contracts (or in curtailing the use of options), a
negative price reaction to the events that would increase the probability of
recognizing SBC costs would be observed since a dead-weight cost would be imposed
on the ﬁrms.
On the other hand, announcements related to recognition of SBC costs could
result in stock price increases, especially for larger ﬁrms, through reduction of
political costs. Prices could also increase slightly for ﬁrms with income-based
compensation plans, as recognition of SBC costs would reduce managers’ ability to
increase their compensation (Watts and Zimmerman, 1986, pp. 286–287). Although
the direction of the net stock price reaction to SBC related announcements is an
empirical issue, based on the ﬁndings of previous studies (e.g., Espahbodi et al.,
1991; Collins et al., 1981; Lys, 1984), we formulate our ﬁrst hypothesis as follows:
H 1:
Firms with SBC plans should experience a negative (positive) abnormal
return around the events that increase (decrease) the probability of forcing
companies to ‘‘recognize’’ the cost.
The impact of the pronouncements on SBC, of course, is not expected to be the
same for all ﬁrms that provide these beneﬁts to their employees. Among others, startup and high-tech companies, starving for cash and relying heavily on SBC plans,
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
349
would be more affected than other ﬁrms (see, e.g., Akresh and Fuersich, 1994).2
Therefore, our next hypothesis is:
H2: The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for start-up and high-tech companies.
Smith and Watts (1992) and Gaver and Gaver (1993) document that executive
compensation policies are related to measures of investment opportunity set (such as
the availability of growth options and ﬁrm size). They show that ﬁrms with more
growth options have higher executive compensation and make greater use of stockoption plans. Thus, ﬁrms with higher growth options should be more adversely
affected by the requirement of expensing the SBC costs.
Following Smith and Watts (1992), we use the book value of total assets over total
ﬁrm value as the base measure of investment opportunity set (growth option). Since
the investment opportunity set is unobservable and only imperfectly measured by
any single proxy measure, we test the robustness of our results to the deﬁnition of
investment opportunity set by considering two alternative measures: book over
market value of common equity, and research and development expense over total
assets. The greater the investment opportunity set available to a ﬁrm (as measured by
a higher research and development cost over total assets, a lower book value of total
assets over total ﬁrm value, and a lower book over market value of common equity),
the larger is the option usage and thus the greater is the economic consequence of
expensing the SBC costs. Thus, our third hypothesis is:
H3: The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms that have higher investment
opportunities.
In addition to the investment opportunity set, option usage has been shown to
vary with the amount of noise in stock price performance and tax position (see, e.g.,
Sloan, 1993; Matsunaga, 1995). The lower the noise in stock prices in measuring
managerial performance, the better option values reﬂect that performance and hence
the more they are used. Firms with tax loss carry-forward beneﬁts also use options to
a greater extent because they can not take advantage of the tax deductibility of cash
compensation (or they may not have the cash). Thus, ﬁrms with a tax loss carryforward and low noise in their stock prices are expected to experience a more
signiﬁcant price movement in reaction to SBC pronouncements. Consistent with
Sloan and Matsunaga, we presume the market effect is beyond the managers’ control
hence represents noise in stock prices measuring the managers’ performance. We
thus measure noise in stock price performance as the proportion of variance in the
2
We are assuming here that, on average, start-up and high-tech ﬁrms use more options; similarly, in
hypothesis 3, we assume that high-growth companies make greater use of stock-option plans. These two
hypotheses test whether the opposition to the ED on SBC by such ﬁrms was justiﬁed on economic
grounds. We realize not all heavy users of stock option are start-up, high-tech, or high-growth ﬁrms. In
hypothesis 6, therefore, we examine the effect of SBC pronouncements on stock prices of high option
users.
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H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
daily stock return attributed to market movement, i.e., the systematic risk (beta)
squared times the variance of the market return based on a two-parameter market
model for each ﬁrm. Tax position is measured by a dummy variable taking a value of
one if the ﬁrm has a tax loss carry-forward and zero otherwise. Our next two
hypotheses therefore are:
H 4:
H 5:
The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms with tax loss carry-forward
beneﬁts.
The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms that have lower noise in their
stock prices.
A direct measure of option usage is obtained by examining the percentage decrease
in 1996 primary (basic) EPS resulting from the use of SBC. This information was
only required to be disclosed in the footnotes to ﬁnancial statements starting with the
1996 ﬁscal year. Following Dechow et al. (1996), we also use the proportion of
common shares reserved for stock options to common shares outstanding as an
alternative measure of option usage, although this is a noisy (see Dechow et al., p. 8)
and indirect measure of the potential impact of expensing SBC costs on EPS. Our
hypothesis is:
H6: The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms with higher option usage.
The debt hypothesis usually considered in the literature was not relevant for the
proposed SBC accounting. Debt covenants based on leverage ratios would have
actually loosened, i.e., debt to assets or debt to equity ratios would have decreased.
Compared to accounting for SBC under APB Opinion No. 25, the exposure draft
would have increased assets (prepaid compensation) and owners’ equity (options
outstanding), although the increase would not have been signiﬁcant (e.g., Akresh
and Fuersich,1994, show that the initial increase in assets and equity can be up to
2%) because no retroactive adjustments were to be required. The asset was to be
amortized over the vesting period, i.e., gradually recognized as expense, reducing
retained earnings. However, total assets and owners’ equity would have been higher
until the vesting period was over.
On the other hand, as retained earnings were to be reduced, debt covenants based
on retained earnings would have tightened, possibly resulting in a decline in stock
prices. Consistent with Dechow et al. (1996) and Healy and Palepu (1990), we use a
dummy variable that takes a value of one if a ﬁrm is close to its retained-earnings
related constraint (speciﬁcally if less than 2 years of dividends are available in
retained earnings), and zero otherwise. We posit the following hypothesis:
H7: The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms that have a retained-earnings
related debt constraint.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
351
One of the claims for opposing the new rules on SBC was that, by reducing
reported earnings, expensing SBC costs increases the cost of raising new capital. If
that claim is true, then the stock price of ﬁrms needing new capital should be more
negatively affected by pronouncements requiring expensing of SBC costs. Consistent
with Dechow et al. (1996), we measure the need for additional ﬁnancing by free cash
ﬂows (cash ﬂows from operation plus that from investing activities) divided by total
assets (although this variable is related to many other variables, including ﬁrm size
and the investment opportunity set). The lower this variable, the higher is the need
for additional ﬁnancing. We thus pose the following hypothesis:
H 8:
The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms that have higher need for
additional ﬁnancing (or lower free cash ﬂows over total assets).
Watts and Zimmerman (1978, 1990) suggest an accounting standard that reduces
earnings reduces the political costs associated with regulatory pressures. Since larger
ﬁrms are expected to have larger decreases in political costs, they will have a less
negative stock price reaction to the pronouncements requiring expensing of SBC
costs. Smaller companies also rely more heavily on options and, thus, will be
signiﬁcantly affected by accounting pronouncements on SBC while the impact on
larger companies may be minimal (see, e.g., Ciccotello and Grant, 1995). There are,
at least, two plausible explanations regarding this prediction for large companies.
First, the stock price volatility of a large and well-established company is lower than
that of a small company, and lower volatility will reduce the option value. Second,
the relative magnitude of options as a percentage of the number of outstanding
shares is more signiﬁcant for a small compared to a larger ﬁrm. Using natural log of
total assets to proxy for ﬁrm size, our last hypothesis is:
H 9:
The stock price reactions to pronouncements that increased the probability of
expensing SBC costs are more negative for ﬁrms with lower market values.
4. Events, sample and data, and methodology
4.1. Events considered
The events leading to the issuance of SFAS No. 123 indicate that participants
(e.g., Congress, business community, accounting profession) in the lobbying process
believed that recognition of SBC costs would adversely affect ﬁnancial statements
and place companies which rely heavily on SBC as an incentive plan at a competitive
disadvantage. Although, according to Beresford (1996), no accounting pronouncements before SFAS No. 123 (except for oil and gas in 1970s) have been so highly
publicized and criticized, pronouncements on accounting for SBC consisted of the
normal chain of open and inclusive standard-setting process. During this
promulgation process, various pieces of information were disseminated to the
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H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
market that may have formed or revised investors’ expectations regarding the ﬁnal
provisions of the Statement and their possible ﬁnancial impact.
Table 1 shows the chronology of important events leading to the issuance of SFAS
No. 123 and their expected market reaction. Consistent with prior research, we used
multiple information sources since the ﬁnancial community receives news from a
variety of sources. We obtained a list of events and dates from the project manager
for SFAS No. 123. We also searched the New York Times and the Wall Street
Journal (WSJ), as well as the WSJ index, to conﬁrm and/or identify the event dates.
In our analysis, however, we only included 12 events that we believed would form/
revise investors’ expectations regarding the likelihood of passage of accounting
standards requiring recognition of SBC costs.3
Events 1, 2, 4, and 5, are considered ‘‘bad news’’ because they provided
information indicating eventual recognition of SBC costs. The remaining events are
considered ‘‘good news’’ because they either provided information indicating FASB
was compromising its position, or they provided news regarding lobbying efforts
opposing the expense recognition. In our regressions, each event is represented by a
dummy variable. As suggested by Schipper and Thompson (1983), to give
differential treatment to the events involving good news, each dummy variable is
set equal to one if an unfavorable event occurred, minus one for a favorable event (a
reversal), and zero otherwise.
To the extent that the SBC pronouncements provide new information not
previously known to market participants and/or affect SBC plans, security prices of
affected entities should change. Based on prior research (e.g., Espahbodi et al., 1991,
1995; Salatka, 1989), the change in security prices should be most signiﬁcant around
the issue of the exposure draft (event 5). Event 11 (a shift in FASB’s policy from
intending to require recognition of SBC cost to mere disclosure of such costs), on the
other hand, should only be signiﬁcant if the market participants value disclosure and
recognition differently (as no new information was provided to the market at that
time, and no change was made to the calculation of stock options value). This
difference in valuation is consistent with the contracting and political cost
hypotheses, as future earnings will be affected by recognition, but not by disclosure,
of SBC costs.
4.2. Sample and data
The sample ﬁrms meet the following criteria: (1) they are listed on the CRSP daily
returns and the COMPUSTAT Annual Industrial ﬁles; (2) they offer stock-based
compensation plans to employees and disclose their impact on EPS in the footnotes
to 1996 ﬁscal year ﬁnancial statements in the DISCLOSURE data base; (3) data on
the ﬁrm-speciﬁc variables are available on the COMPUSTAT data base; and (4) they
are not ﬁnancial institutions or utility companies. The ﬁrst three criteria are designed
3
We repeated our analysis, considering 23 other events reported in the same sources. None of these
events, however, were signiﬁcant and stock returns did not vary cross-sectionally on those dates with the
ﬁrm characteristics.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
353
Table 1
Important events leading to the issuance of SFAS No. 123 and their expected market reactiona
Event
number
Event
date
1
1/22/92
2
6/26/92
3
4/6/93
4
4/7/93
5
6/30/93
6
11/5/93
7
3/28/94
8
4/22/94
9
11/16/94
10
12/14/94
11
12/15/94
12
10/23/95
Description
FASB is reviving a long-term project to require
companies to deduct the cost of stock options from
their income
Urged by the SEC, FASB began an 8-year project on
stock-option accounting to require companies to
subtract the value of unexercised stock options from
their proﬁts
In a letter to FASB the day before the Board is
scheduled to vote on the issue, Treasury Secretary
said he had reservations about forcing companies to
expense the value of stock options.
FASB voted 6–1 to require companies to expense the
estimated value of stock options by 1997, in the
interim requiring disclosure of options’ value in 1994
ﬁnancial statements
The Exposure Draft ‘‘Accounting for Stock-Based
Compensation (SBC)’’ was issued, requiring
recognition of SBC expense
FASB may delay for a year (push to 1995 report) the
requirement that companies disclose the cost of
employee stock options, a step that many companies
would favor
In the wake of many protests, FASB will look at
many new proposals, among them a compromise
plan that would reduce the damage to company
earnings by considering some options as equity
FASB is likely to postpone until at least 1995 the
expensing proposal to redeliberate and reconsider
alternative ideas. In addition, FASB is expected to
postpone the disclosure of the cost of options for at
least a year (until 1995)
Following a storm of protest from industry, FASB is
looking at several ways to ease corporate concerns.
Although FASB hasn’t yet changed its proposal, it is
looking at several possible changes including
disclosure only
FASB will consider softening its position on the SBC
project. FASB would allow ﬁrms to choose
disclosure alone or deduction of cost
FASB voted 5–2 to rescind the requirement to
expense the estimated value of stock options, instead
requiring disclosure while encouraging recognition of
stock option costs (no effective date for disclosure yet)
FASB issued SFAS No. 123 requiring disclosure
while encouraging recognition of SBC cost
Expected market
reaction
Negative (bad news)
Negative (bad news)
Positive (good news)
Negative (bad news)
Negative (bad news)
Positive (good news)
Positive (good news)
Positive (good news)
Positive (good news)
Positive (good news)
Positive (good news)
Positive (good news)
a
Events 4, 5, and 12, are from the FASB records. All other events were reported in the Wall Street
Journal and/or the New York Times; for completeness, 23 other events reported in the same sources were
considered, but none of them were signiﬁcant.
354
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
to ensure data availability for a sample of ﬁrms offering SBC to test the nine
hypotheses. The last criterion excludes ﬁnancial institutions and utilities because: (a)
these ﬁrms’ equity prices react differently to SBC pronouncements and there is no
control group to account for these differences; and (b) some of the required ﬁrmspeciﬁc data are not available on these companies.
Application of these selection criteria resulted in a sample of 595 ﬁrms.
Speciﬁcally, a total of 1492 ﬁrms met criterion number 2, i.e., disclosed the impact
of the SBC on their EPS. (About 17% of the 1492 ﬁrms, and 16% of the 595 ﬁrms,
indicated no impact on their EPS.) There were 30 ﬁnancial institutions and utility
ﬁrms; 599 and 165 ﬁrms were not listed on the CRSP and COMPUSTAT,
respectively (out of which 154 did not exist on either data base); and 257 ﬁrms had
missing data on COMPUSTAT. A comparison of the 595 ﬁrms in the ﬁnal sample
with the 897 dropped from the analysis indicated that the latter ﬁrms were much
smaller in size, but reported a higher mean (median) percentage decline in their 1996
EPS resulting from the use of SBC [24.79 (7.41) % versus 17.25 (5.40) % for the 595
ﬁrms]. Both of these facts suggest that the stock price reaction to the SBC
pronouncements would have been more signiﬁcant for the excluded ﬁrms.
Of the 595 ﬁrms, 117 are deﬁned as high-tech, 297 as high-growth, and 187 as
start-up companies. High-tech ﬁrms are those with four digit SIC codes of 3570–
3579, 3670–3679, and 8730–8734; namely, computer, electronics, semi-conductors,
biological research, and similar ﬁrms. High-growth ﬁrms as those with lower than
median book to market ratio of common equity (i.e., higher market to book ratios).
Start-up companies are those that have been in existence for less than 5 years,
indicated by not being listed on the CRSP data base in 1987 (5 years before FASB
indicated for the ﬁrst time that the value of options is to be deducted from earnings).
The SIC distribution of the full sample and those of the sub-samples (i.e., high-tech,
high-growth, and start-up samples) are shown in Table 2. Note that entries in the full
sample do not equal the sum of those in the sub-samples, as there are overlaps
among the sub-samples and some ﬁrms are not in any sub-sample. Overall, 36 ﬁrms
are in both the start-up and high-tech samples, 51 in both high-tech and high-growth
samples, 112 in both high-growth and start-up samples, and 23 ﬁrms are in all the
three sub-samples. One hundred and seventy ﬁrms in the full sample are not in any of
the sub-samples.
To test the ﬁrst hypothesis, daily return data are collected from the CRSP data
base. For hypotheses 2 through 9, additional data are obtained from the
COMPUSTAT and CRSP data bases to measure the following attributes of sample
ﬁrms: (1) growth; (2) debt constraint; (3) size; (4) free cash ﬂow divided by total
assets; (5) option usage; (6) noise in stock price performance; (7) the tax loss carryforward indicator; (8) the high-tech company indicator; and (9) the start-up
company indicator. All these variables are measured as averages over the 4-year
period of events (1992–1995), unless indicated below. A list of these variables, their
deﬁnition, and the expected sign of their relation with abnormal security returns, are
shown in Table 3.
Following Smith and Watts (1992), the book value of total assets (Compustat item
6) over total ﬁrm value, BKOV, is used as a base proxy for growth (investment
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
355
Table 2
Industry distribution of sample ﬁrms
Number of ﬁrms
Industry
classiﬁcation
Agriculture, forestry,
and mining
Construction
Manufacturing
Transportation and
communication
Wholesale
Services
Total number of ﬁrms
SIC codes
Full
sample
0100–1499
53
1500–1999
2000–3999
4000–4899
1
448
3
5000–5199
7000–8999
11
79
595
High-tech
sample
106
11
117
High-growth Start-up
sample
sample
19
16
1
213
1
131
1
6
57
297
4
35
187
The full sample is composed of all the ﬁrms in the sample. The other three samples are sub-samples of
these 595 ﬁrms. Speciﬁcally, high-tech sample consists of ﬁrms with SIC codes of 3570–3579, 3670–3679,
and 8730–8734; high-growth ﬁrms are deﬁned as those with lower than median book to market ratio of
common equity (i.e., higher market to book ratios); and start-up sample ﬁrms are those in existence for less
than 5 years.
Note that entries in the full sample do not equal the sum of those in the sub-samples, as there are overlaps
among the sub-samples and some ﬁrms are not in any sub-sample. Overall, 36 ﬁrms are in both the startup and high-tech samples, 51 in both high-tech and high-growth samples, 112 in both high-growth and
start-up samples, and 23 ﬁrms are in all the three sub-samples. One hundred and seventy ﬁrms in the full
sample are not in any of the sub-samples.
opportunity set). Total ﬁrm value is deﬁned as the market value of equity plus the
book value of total assets minus the book value of equity (Compustat item 24 times
item 25 plus item 6 minus item 60). To check the robustness of the results to the
speciﬁc proxy used for growth, two alternative measures of growth are also
considered: (1) the book over the market value of common equity (BKOM,
calculated as Compustat item 60 divided by the product of items 24 and 25); and (2)
research and development expense over the book value of total assets (RDOA,
computed as Compustat item 46 divided by item 6).
Because the leverage ratio (long-term debt over total assets, LTDOA, Compustat
item 9 divided by item 6) would have actually decreased based on the proposed
standard, the base proxy for debt constraint used in this study is retained-earnings
related. Consistent with Dechow et al. (1996), and Healy and Palepu (1990), we use a
dummy variable (RECOV) that takes a value of one if retained earnings is less than 2
years of dividends, i.e., if the proportion of retained earnings at year-end plus
dividends and purchase of treasury stock during the year (sum of Compustat items
36, 127, and 115) over the last year’s dividends and purchase of treasury stock (sum
of Compustat items 127 and 115) is less than two. For completeness, we do repeat
the analysis, using the leverage ratio instead of the dummy variable.
Size (SIZE) is measured as the natural log of total assets, Compustat item 6. Free
cash ﬂows (FCFOA) is the cash ﬂows from operations plus that from investing
activities divided by the book value of total assets (sum of Compustat items 311 and
Variablea
Stock option usage—common shares reserved for conversion of stock options over common
shares outstanding
Noise in stock price performance—the proportion of variance in the daily stock return
attributed to market movement, i.e., the systematic risk (beta) squared times the variance
of the market return based on a two-parameter market model for each ﬁrm over the 5-year
period of mid-1991 through mid-1996 (Lambert, 1993)
Tax loss carry-forward dummy in ﬁscal year 1991d
High-tech dummy indicator in ﬁscal year 1991e
Start-up dummy indicatorf
a
Variable
abbreviation
Expected
sign
Annual compustat data
items/other sources
BKOV
BKOM
RDOA

+
6/(24 25+6–60)
60/(24 25)
46/6
RECOV
+
LTDOA
SIZE
FCFOA

(36+127+115)/
last year’s(127+115)
9/6
6
(311 + 308)/6
SOEFF
+
SOUSE
+
Footnotes from disclosure
data base
215/25
NOISE

CRSP data base
TAX
HT
START
+
+
+
52
SIC codes
CRSP data base
All variables are measured as averages over the 4-year period of 1992–1995, unless otherwise indicated.
Equals one if the fraction is less than two, zero otherwise.
c
Percentage decrease in 1996 primary (basic) EPS obtained from the footnotes to ﬁnancial statements. The year 1996 was the ﬁrst year companies were
required to disclose the pro forma effect of the options on income and EPS.
d
Equals one if the ﬁrm has an operating tax loss carry-forward, zero otherwise.
e
Equals one for high-tech ﬁrms, zero otherwise. High-tech ﬁrms are deﬁned as those with four digit SIC codes of 3570–3579, 3670–3679, and 8730–8734;
namely, computer, electronics, semi-conductors, biological research, and similar ﬁrms.
f
Equals one if the company has been in existence for less than 5 years, indicated by not being listed on the CRSP data base in 1987 (5 years before FASB
indicated for the ﬁrst time that the value of options is to be deducted from earnings); zero otherwise.
b
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
Growth (investment opportunities)—use as alternatives
Book value of total assets over total ﬁrm value
Book over market value of common equity
Research and development over total assets
Debt constraint—use as alternatives
Retained-earnings related—dummy variable based on (retained earnings+div.+purchase
of treasury stock)/(last year’s div.+purchase of treasury stock)b
Leverage-related—long-term debt over total assets
Size—natural log of total assets
Free cash ﬂows over total assets—cash ﬂows from operations and investments over total assets
Option usage—use as alternatives
Pro-forma percentage decrease in earnings per share in 1996c
356
Table 3
List of variables
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
357
308 divided by item 6). The extent of option usage by a ﬁrm is measured by the
percentage decrease in pro forma (in two cases, actual) EPS in 1996, as that was the
ﬁrst year such data were available. We obtained this data item (SOEFF) through a
search of the footnotes to the ﬁnancial statements of ﬁscal year 1996 in the
Disclosure data base. For completeness, however, we repeat the analysis using the
number of common shares reserved for conversion of stock options over the total
number of common shares outstanding (SOUSE, Compustat item 215 divided by
item 25) as a proxy for stock option usage (see Dechow et al., 1996, p. 8, for the
problems with this proxy). We measure noise in stock price performance (NOISE)
for each ﬁrm as the proportion of variance in the daily stock return attributed to
market movement, i.e., the systematic risk (beta) squared times the variance of the
market return based on a two-parameter market model for each ﬁrm over the 5-year
period of mid-1991 through mid-1996.4
The last three variables are all indicator variables. Tax loss carry-forward (TAX)
takes a value of one if the ﬁrm has a tax loss carry-forward beneﬁt as of the end of
1991, as indicated by Compustat item 52 (because the expectation of recognizing an
expense ﬁrst surfaced in early 1992). High-tech dummy (HT) takes a value of one for
ﬁrms with primary SIC codes in ﬁscal year 1991 of 3570–3579, 3670–3679, and 8730–
8734, i.e., for computer, electronics, semi-conductors, biological research, and
similar ﬁrms. Start-up dummy (START) takes a value of one if the company has
been in existence for less than 5 years, speciﬁcally if the company is not listed on the
CRSP data base in 1987 (5 years before FASB indicated for the ﬁrst time that the
value of options is to be deducted from earnings).
Descriptive statistics on the 13 variables (nine ﬁrm characteristics and four
alternative proxies) for the full sample (595 ﬁrms) are presented in Panel A of Table
4. Panel B of Table 4 presents the Pearson correlation coefﬁcients between these ﬁrm
characteristics. The high correlation among these variables underscores the need to
use the Sefcik and Thompson (1986) methodology that considers such interrelations.
This methodology will be described in the next section.
Table 5 reports the mean, the lower quartile, the middle quartile (median), and the
upper quartile of eight selected variables for the full sample and seven sub-samples to
provide a better understanding of the characteristics of these ﬁrms and why they
could be potentially affected by the SBC pronouncements. The full sample is
composed of all the ﬁrms in the sample. Other samples are a subset of these 595
ﬁrms. Speciﬁcally, high-tech sample consists of ﬁrms with SIC codes of 3570–3579,
3670–3679, and 8730–8734; start-up sample ﬁrms are those in existence for less than
5 years; high-growth ﬁrms are deﬁned as those with lower than median book to
market ratio of common equity (i.e., higher market to book ratios); high-EPS-impact
sample ﬁrms are those with higher than median percentage decrease in their
4
Speciﬁcally, a two parameter market model is estimated for each and every ﬁrm over the 5-year period
of mid-1991 through mid-1996. The noise in the stock price of each ﬁrm is the estimated beta for that ﬁrm
squared times the variance of the market return; equivalently, the noise for each ﬁrm is the sum of squares
regression divided by the total degrees of freedom (number of observations minus one) in that regression
(see Lambert, 1993, p. 117).
358
Variable
BKOV BKOM
RDOA
RECOV
LTDOA
SIZE
FCFOA
SOEFF
SOUSE
NOISE
TAX
HT
START
0.067
0.017
0.196
17.249
5.399
37.905
0.139
0.118
0.088
0.00005
0.00004
0.00005
0.442
0
0.497
0.197
0
0.398
0.314
0
0.465
0.353b
0.069
0.588b
0.060
0.032
0.359b
1.000
0.129b
0.011
0.100c
0.127b
0.093c
0.173b
0.110c
1.000
0.064
0.024
0.210b
0.026
0.164b
0.275b
0.063
0.180b
1.000
0.374b
0.190b
0.376b
0.003
0.169b
0.081c
0.277b
0.057
0.173b
1.000
0.210b
0.103c
0.292b
0.182b
0.033
0.266b
0.302b
0.129b
0.111b
0.192b
1.000
0.038
0.037
0.044
0.012
0.089c
0.018
0.021
0.057
0.164b
0.252b
0.011
1.000
0.187b
0.021
0.186b
0.101c
0.088c
0.164b
0.209b
0.161b
0.077
0.299b
0.112b
0.007
1.000
a
Panel A: Descriptive statistics of ﬁrm characteristics
Mean
0.577
0.436
0.133
0.199
Median
0.575
0.411
0.092
0
Std.
0.266
0.832
0.150
0.400
Deviation
0.125
0.076
0.175
4.588
4.174
2.105
Panel B: Pearson product-moment correlations between ﬁrm characteristics
BKOV
1.000
0.393b
0.380b 0.041
0.102c
0.166b
BKOM
1.000
0.111b 0.028
0.045
0.028
RDOA
1.000
0.032
0.203b
0.322b
RECOV
1.000
0.052
0.168b
LTDOA
1.000
0.221b
SIZE
1.000
FCFOA
SOEFF
SOUSE
NOISE
TAX
HT
START
a
Variables are abbreviated according to Table 3.
Correlation is signiﬁcant at the 0.01 level (2-tailed).
c
Correlation is signiﬁcant at the 0.05 level (2-tailed).
b
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
Table 4
Descriptive statistics of, and correlations between, ﬁrm characteristics for the full sample (N ¼ 595)
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
359
pro-forma EPS; high option users are ﬁrms with higher than median proportion
of common shares reserved for conversion of stock options; bio-technology ﬁrms
are those with SIC codes of 2830–2836 and 8731; and comment-letter sample
consists of ﬁrms submitting a written response to the exposure draft on option
accounting. Six of the selected variables represent the ﬁrm characteristics that were
discussed earlier and deﬁned in Table 3, namely natural log of total assets (SIZE),
book over market value of common equity (BKOM), long-term debt over total
assets (LTDOA), pro-forma percentage decrease in earnings per share in 1996
(SOEFF), free cash ﬂows over total assets (FCFOA), and noise in stock price
performance (NOISE). The other two variables are market value of common equity
(MVEQ) measured as an average over the 4-year period of 1992–1995, and actual
earnings per share in 1996 (ACTEPS). MVEQ is an alternative measure of size and is
calculated as Compustat item 24 times item 25, same as the denominator of BKOM.
ACTEPS is Compustat item 58; in absolute terms, it is the denominator of the
SOEFF.
The descriptive statistics in Table 5 show that the comment-letter sample has the
highest mean and median size (both SIZE and MVEQ) and actual EPS (ACTEPS),
but the lowest mean and median percentage decrease in EPS (SOEFF). The portfolio
return for this sample, therefore, should be least affected by the SBC pronouncements. All samples (including the comment-letter sample), however, could be
potentially affected to various degrees by the SBC pronouncements. The degree to
which any of the sample (portfolio) returns are inﬂuenced by various SBC
pronouncements depends on whether the equity prices of ﬁrms in the corresponding
portfolio systematically reacted to the events under consideration (a test of
hypothesis 1). It is worth however to note that, compared to other dummy
variables, the correlations (not reported) between SOEFF and dummy variables
identifying high option users, bio-tech, and comment-letter samples are the smallest
(in fact, the correlation between SOEFF and comment-letter dummy variable is
negative). The low (or negative) correlations for these three portfolios might thus
explain the lack of equity price reaction to SBC pronouncements in Dechow et al.
(1996).
4.3. Methodology
Hypothesis 1 in this study examines the average impact of each of the 12 events
on stock prices of sample ﬁrms. While a mean effect may not be observed on
a particular event date, stock returns may vary cross-sectionally on that date with
the ﬁrm characteristics. Hypotheses 2–9, therefore, examine the effect of
ﬁrm characteristics on the stock market reaction to these events. Hypothesis 1
is tested by employing a Multivariate Regression Model (MVRM) proposed
by Schipper and Thompson (1983). The MVRM incorporates both the crosssectional heteroscedasticity and the contemporaneous correlation of the residuals
into the estimation process, allowing joint hypotheses to be tested utilizing the F statistic deﬁned by Rao (1973). The joint hypothesis tests are of special importance
360
Samplea
Variableb
Full Sample (N ¼ 595)
Mean
Quartiles
High-tech sample (N ¼ 117)
Start-up sample (N ¼ 187)
Mean
Quartiles
Mean
Quartiles
High-growth sample
(N ¼ 297)
Mean
Quartiles
High-EPSimpact sample
(N ¼ 297)
Mean
Quartiles
SIZE
MVEQ
BKOM
LTDOA
ACTEPS
SOEFF
FCFOA
NOISE
Lower
Middle
Upper
4.588
3.082
4.174
5.965
1161.607
29.122
106.432
554.610
0.436
0.248
0.411
0.621
0.125
0.012
0.076
0.187
0.373
0.378
0.240
0.930
17.249
1.475
5.399
16.667
0.067
0.100
0.017
0.029
0.00005
0.00002
0.00004
0.00008
Lower
Middle
Upper
4.665
3.317
4.448
6.069
1136.416
43.606
145.507
559.696
0.499
0.295
0.452
0.682
0.093
0.007
0.053
0.153
0.478
0.410
0.310
1.120
21.760
2.985
6.796
16.667
0.059
0.089
0.026
0.021
0.00008
0.00004
0.00007
0.00011
Lower
Middle
Upper
4.079
3.006
3.928
5.111
344.049
33.551
96.425
342.877
0.410
0.208
0.342
0.549
0.102
0.003
0.053
0.147
0.049
0.593
0.025
0.723
26.263
2.705
10.000
28.116
0.128
0.196
0.065
0.006
0.00007
0.00003
0.00007
0.00011
Lower
Middle
Upper
4.412
2.800
4.057
5.907
1509.248
43.317
140.140
1101.199
0.166
0.164
0.248
0.327
0.121
0.003
0.069
0.169
0.259
0.450
0.120
0.890
19.280
2.480
6.667
20.612
0.111
0.201
0.034
0.030
0.00006
0.00002
0.00005
0.00009
Lower
Middle
Upper
3.980
2.725
3.728
4.982
614.803
27.363
92.191
289.334
0.430
0.204
0.359
0.585
0.091
0.003
0.040
0.140
0.076
0.365
0.070
0.495
32.717
9.784
16.667
34.423
0.107
0.156
0.034
0.016
0.00006
0.00002
0.00005
0.00009
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
Table 5
Mean and quartiles of selected variables for the full sample and sub-samples
Mean
Quartiles
Bio-tech sample (N ¼ 88)
Mean
Quartiles
Comment-letter sample
(N ¼ 64)
a
Mean
Quartiles
Lower
Middle
Upper
4.046
2.973
3.959
4.896
588.148
26.120
85.626
227.967
0.471
0.235
0.403
0.632
0.092
0.003
0.039
0.133
0.304
0.468
0.220
0.918
19.657
3.313
8.957
20.581
0.086
0.126
0.033
0.016
0.00007
0.00002
0.00006
0.00011
Lower
Middle
Upper
3.999
2.668
3.520
4.986
1441.157
37.105
91.096
313.959
0.292
0.181
0.271
0.379
0.084
0.006
0.042
0.121
0.063
0.755
0.060
0.390
21.360
2.449
6.667
27.004
0.210
0.340
0.131
0.018
0.00008
0.00003
0.00006
0.00011
Lower
Middle
Upper
6.608
4.615
6.896
8.543
4429.198
221.471
1419.676
6132.810
0.410
0.229
0.378
0.537
0.116
0.030
0.069
0.212
1.418
0.060
0.790
2.960
15.601
1.229
3.396
14.772
0.018
0.035
0.000
0.035
0.00006
0.00001
0.00005
0.00009
The full sample is composed of all the ﬁrms in the sample. Other samples are a subset of these 595 ﬁrms. Speciﬁcally, high-tech sample consists of ﬁrms with
SIC codes of 3570–3579, 3670–3679, and 8730–8734; start-up sample ﬁrms are those in existence for less than 5 years; high-growth ﬁrms are deﬁned as those
with lower than median book to market ratio of common equity (i.e., higher market to book ratios); high-EPS-impact sample ﬁrms are those with higher than
median percentage decrease in their pro-forma EPS; high option users are ﬁrms with higher than median proportion of common shares reserved for conversion
of stock options; bio-technology ﬁrms are those with SIC codes of 2830–2836 and 8731; and comment-letter sample consists of ﬁrms submitting a written
response to the exposure draft on option accounting.
b
Variables are abbreviated according to Table 3, except for the two new variables: (1) MVEQ is the market value of common equity, Compustat item 24
times item 25, measured as an average over the 4-year period of 1992–1995; and (2) ACTEPS is the actual Earnings Per Share in 1996, Compustat item 58.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
High-option-users sample
(N ¼ 297)
361
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
362
in this study, as ﬁrms are expected to be differentially affected by the
pronouncements on stock option.
The MVRM model conditions the return generating process on the occurrence
or nonoccurrence of an event by adding a dummy variable for each event to
the market model. Each dummy variable is set equal to one if an unfavorable
event occurred, minus one for a favorable event (a reversal), and zero
otherwise. Since the exact timing of the information release is unknown, a
3-day event period is used corresponding to trading days t ¼ 1; t ¼ 0; and
t ¼ þ1 relative to the announcement date shown in Table 1. The coefﬁcient
of each dummy variable measures the corresponding event’s impact on stock
returns. The model is a system of eight portfolio return equations—the entire
sample of ﬁrms offering stock options and the seven sub-samples: the
high-tech,
start-up,
high-growth,
high-EPS-impact,
high
option-users,
bio-tech, and comment-letter sub-samples. The equation for each portfolio
is:
R$ jt ¼ aj þ bj R$ mt þ
K
X
gjk Dkt þ e$jt ;
ð1Þ
k¼1
where:
R$ jt = the return on portfolio j (j ¼ 1; 2; y8) on day t (t ¼ 1; 2; yT). T is the
total number of daily return observations from mid-1991 through mid1996. Returns for each portfolio are weighted based on the full
estimated variance-covariance matrix of residuals in order to increase
the efﬁciency of parameter estimates. Speciﬁcally,
R$ jt ¼ P0j Rijt ; where
Rijt =the vector of returns on all i ﬁrms in portfolio j on day t;
P0j =the transpose of portfolio j weights, Pj ;
Pj ¼ ð10 Sj1 1Þ1 Sj1 1;
1=a vector of ones, and
Sj =
the full estimated variance–covariance matrix of residuals
from ﬁrst-pass OLS regressions similar to Eq. (1), but on
each ﬁrm in portfolio j;
R$ mt = the return on the Standard and Poor’s 500 Index on day t;
aj = intercept coefﬁcient for portfolio j;
bj = risk coefﬁcient for portfolio j;
gjk = the effect of event k (k ¼ 1; 2; yK) on portfolio j’s return. K is the total
number of events examined, which is 12 in this study;
Dkt = dummy variable for the kth event which equals one during the
3-day period (t ¼ 1; t ¼ 0; and t ¼ þ1 relative to the announcement
date) if event k is unfavorable, minus one if favorable, and zero
otherwise; and
e$jt = random disturbance which is assumed to be normal and independent of the
return on the market and the event announcement variables.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
The system of portfolio regressions in Eq. (1) can be generalized as:
3 2 3 2 3
2 3 2
R$ 1
X$ 0 0
b1
e$1
7
6 7 6
7 6 7
6 R$ 2 7 ¼ 6 0 X$ 0 7 6
þ
b
5 4 2 5 4 e$2 5
4 5 4
b3
e$3
0 0 X$
R$ 3
363
ð2Þ
or
$ þ e$;
R$ ¼ Xb
ð3Þ
where:
R$ j = T 1=vector, (the elements of the vector are R$ j1 ; R$ j2 ; y; R$ jT );
X$ = T C matrix of independent variables which is the same for each equation in
the system, C ¼ K þ 2 ¼ 14: (The ﬁrst column of this matrix is of 1’s, the
second column is of the daily returns on Standard and Poor’s 500 index, R$ m ;
and the last 12 columns are of dummy variables, Dk ; for the 12 events);
bj = C 1 vector of coefﬁcients; and
e$j = T 1 vector of disturbances.
Estimation of the multivariate regression model in Eq. (3) assumes that the
residuals are independently, identically distributed within each equation. As Smith
et al. (1986) suggest, however, this is not likely to be true. The estimation of the
system in Eq. (3), therefore, must be adjusted for possible time-series heteroscedasticity (see Smith et al., 1986, p. 477, for a detailed discussion of this situation). To
correct for time-series heteroscedasticity a procedure developed by White (1980), in
which the variance–covariance matrix of the residuals are allowed to vary across
observations, is employed.
To test the effect of ﬁrm characteristics on stock market reaction to the events
under consideration (i.e., to test Hypotheses 2–9), the portfolio weighting procedure
proposed by Sefcik and Thompson (1986) is used. The procedure involves three
steps. First, form a matrix F having a column of ones and (P-1) columns of ﬁrm
characteristics, namely growth, debt constraint, size, free cash ﬂows, option usage,
noise in stock price performance, tax loss carry-forward status, high-tech status, and
start-up status. This matrix is deﬁned as follows:

F ¼ 1 X2 y Xp ;
ð4Þ
where Xp is an N 1 vector of the pth ﬁrm characteristic (P ¼ 10 and N ¼ 595 ﬁrms
used to test Hypotheses 2–9).
Second, create P ¼ 10 sets of portfolio weights (W ) and compute the portfolio
returns (R$ pt ) for each set as follows:
2
3
W10
6 07
6 W2 7
1 0
0
7
W ¼6
ð5Þ
6 ^ 7 ¼ ðF F Þ F ;
4
5
Wp0
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
364
R$ pt ¼ Wp0 Rit ; p ¼ 1; 2; ?; P; t ¼ 1; 2; y; T; i ¼ 1; 2; y; N;
ð6Þ
where:
W ¼ P N matrix of portfolio weights (P ¼ 10 and N ¼ 595 ﬁrms);
Wp0 = pth row of portfolio weights which are inﬂuenced by the pth ﬁrm
characteristic (a single column of F );
F ¼ N P matrix deﬁned in Eq. (4);
R$ pt = return on portfolio p on day t; and
Rit ¼ N 1 vector of individual ﬁrms’ security returns on day t:
Third, run p portfolio time-series OLS regressions (p ¼ 1; 2; y; 10) of the form:
R$ pt ¼ ap þ bp R$ mt þ
K
X
gpk Dkt þ e$pt ;
ð7Þ
k¼1
where R$ mt =the return on the Standard and Poor’s 500 Index on day t; ap =the
intercept coefﬁcient for portfolio p; bp =risk coefﬁcient for portfolio p; gpk =the
parameter estimate for event k (k ¼ 1; 2; y12); Dkt =dummy variable for the kth
event which equals one during the 3-day period (t ¼ 1; t ¼ 0; and t ¼ þ1 relative to
the announcement date) if event k is unfavorable, minus one if favorable, and zero
otherwise; and e$pt =random disturbance which is assumed to be normal and
independent of the return on the market and the event announcement variables.
The event parameter estimates (gpk ) in each of the above regressions reﬂect the
effect of one (and only one) ﬁrm characteristic on stock market reaction to the events
under consideration. These estimates are the same as those in cross-sectional
regression of abnormal returns (or dummy variable coefﬁcients) on ﬁrm
characteristics. However, ‘‘unlike cross-sectional regressions, the standard errors
of these estimates account fully for the cross-correlation and (cross-sectional)
heteroscedasticity in ﬁrm disturbances’’ (Sefcik and Thompson, 1986, p. 324). In
addition, the weighting procedure takes into account potential collinearities among
the ﬁrm characteristics, and provides an opportunity to evaluate the relative
importance of different ﬁrm characteristics in explaining the market reaction to
pronouncements related to stock options.
5. Results
Table 6 reports the portfolio abnormal returns and the t-statistics for the full
sample as well as the seven sub-samples based on the Multivariate Regression Model
(MVRM) for the 3-day period (t ¼ 1; t ¼ 0; and t ¼ 1 relative to the announcement day) around each of the twelve events. The full sample is composed of all the
ﬁrms in the sample (595 ﬁrms) and other samples are a subset of these ﬁrms, as
discussed before in Table 5. The ﬁrst four portfolios relate to our hypotheses, and the
last four are added for comparison with Dechow et al. (1996). The estimates in Table
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
365
Table 6
Test of hypothesis 1 for the 12 events
Portfolio abnormal returns (in %) and t-statistics (in parentheses) for the full sample and seven sub-samplesa
around each of the 12 events. These estimates are the coefﬁcients of the dummy variables in a regression of
portfolio returns (weighted based on the full estimated covariance matrix of residuals) on the market return and
dummy variables corresponding to the 12 events (represented by Eq. (3)). Each dummy variable equals ‘‘+1’’
during the 3-day period (t ¼ 1; t ¼ 0; and t ¼ 1 relative to each announcement) of an unfavorable event, ‘‘1’’
for a favorable event, and zero otherwise
Event
Event
number b dateb
1
1/22/92
2
6/26/92
3
4/6/93
4
4/7/93
5
6/30/93
6
11/5/93
7
3/28/94
8
4/22/94
9
11/16/94
10
12/14/94
11
12/15/94
12
10/23/95
HighHighoptionEPSHighBio-tech
users
impact
High-tech Start-up growth
Full
sample
sample
sample
sample
sample
sample
sample
(N ¼ 595) (N ¼ 117) (N ¼ 187) (N ¼ 297) (N ¼ 297) (N ¼ 297) (N ¼ 88)
0.19
(0.46)
0.18
(0.55)
0.15
(0.49)
0.37
(0.78)
0.85
(2.17)d
0.65
(1.85)e
0.21
(0.50)
0.19
(0.57)
0.09
(0.19)
0.18
(0.45)
0.89
(2.15)d
0.30
(0.80)
0.29
(0.67)
0.32
(0.72)
0.12
(0.30)
0.53
(1.31)
1.27
(3.15)c
0.94
(2.58)d
0.24
(0.59)
0.25
(0.65)
0.11
(0.26)
0.36
(0.80)
1.10
(2.87)c
0.35
(0.79)
0.22
(0.65)
0.30
(0.71)
0.14
(0.32)
0.50
(1.37)
1.14
(2.85)c
0.94
(2.39)d
0.31
(0.81)
0.27
(0.75)
0.18
(0.61)
0.29
(0.78)
1.03
(2.68)c
0.38
(0.94)
0.40
(1.07)
0.21
(0.55)
0.08
(0.18)
0.37
(0.90)
1.36
(3.47)c
0.69
(1.93)e
0.31
(0.84)
0.11
(0.30)
0.24
(0.65)
0.19
(0.51)
0.92
(2.41)d
0.37
(1.04)
0.37
(1.35)
0.15
(0.41)
0.13
(0.43)
0.35
(1.05)
1.23
(2.91)c
0.82
(2.27)d
0.29
(0.83)
0.20
(0.56)
0.20
(0.47)
0.12
(0.45)
0.97
(2.54)d
0.27
(1.12)
0.54
(1.23)
0.11
(0.25)
0.14
(0.28)
0.29
(0.35)
0.72
(1.56)
0.58
(1.31)
0.26
(0.56)
0.18
(0.42)
0.19
(0.44)
0.12
(0.28)
0.55
(1.27)
0.30
(0.70)
0.45
(1.16)
0.20
(0.70)
0.33
(0.80)
0.07
(0.23)
0.52
(1.40)
0.50
(1.21)
0.41
(0.83)
0.23
(0.65)
0.05
(0.17)
0.38
(0.92)
0.54
(1.32)
0.09
(0.17)
Commentletter
sample
(N ¼ 64)
0.18
(0.50)
0.23
(0.61)
0.10
(0.28)
0.17
(0.43)
0.08
(0.20)
0.14
(0.38)
0.27
(0.68)
0.16
(0.44)
0.22
(0.57)
0.17
(0.49)
0.11
(0.47)
0.38
(0.99)
a
The full sample is composed of all the ﬁrms in the sample. Other samples are a subset of these 595 ﬁrms.
Speciﬁcally, high-tech sample consists of ﬁrms with SIC codes of 3570–3579, 3670–3679, and 8730–8734; start-up
sample ﬁrms are those in existence for less than 5 years; high-growth ﬁrms are deﬁned as those with lower than
median book to market ratio of common equity (i.e., higher market to book ratios); high-EPS-impact sample
ﬁrms are those with higher than median percentage decrease in their pro-forma EPS; high option users are ﬁrms
with higher than median proportion of common shares reserved for conversion of stock options; bio-technology
ﬁrms are those with SIC codes of 2830–2836 and 8731; and comment-letter sample consists of ﬁrms submitting a
written response to the exposure draft on option accounting. The last four portfolios are added for comparability
with Dechow et al. (1996).
b
Event numbers and dates are described in Table 1.
c
Signiﬁcant at the 0.01 level.
d
Signiﬁcant at the 0.05 level.
e
Signiﬁcant at the 0.10 level.
366
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
6 are the coefﬁcients of the dummy variables in a regression of portfolio returns
(weighted based on the full estimated covariance matrix of residuals) on the market
return and dummy variables corresponding to the twelve events (represented by
Eq. (3)). Each dummy variable equals ‘‘+1’’ during the 3-day period (t ¼ 1; t ¼ 0;
and t ¼ 1 relative to each announcement) of an unfavorable event, ‘‘1’’ for a
favorable event, and zero otherwise. Thus, all the dummy variables should have a
negative coefﬁcient.
The results for the ﬁrst four portfolios indicate that events 5, 6, and 11, are
associated with signiﬁcant abnormal returns.5 These events represent the exposure
draft proposing to require recognition of SBC costs (bad news), a pronouncement
that the FASB may delay disclosure of employee stock options cost (the ﬁrst sign of
backing up by FASB, good news), and the vote to rescind the requirement to expense
the cost of employee options and require disclosure only while encouraging expense
recognition (also good news). None of the other dummy variables’ coefﬁcients are
signiﬁcant at any meaningful level, and all signiﬁcant coefﬁcients have the expected
sign. Overall, the results support Hypothesis 1.
The signiﬁcance of the abnormal return around event 11 is consistent with the
contracting theory, and shows that disclosure is not a substitute for recognition.
Requiring companies to only disclose the cost of SBC rather than forcing recognition
as was proposed earlier would involve no new information and should not affect
security prices, except through the contracting and political cost hypotheses (as
future earnings will be affected by recognition, but not by disclosure, of SBC costs).
Thus, there must be some truth in FASB’s claim in general that disclosure is not a
substitute for recognition, and in the exposure draft that (even with improved
disclosure) only the most sophisticated users could reasonably estimate the impact
on ﬁnancial statements of SBC costs. An anecdotal evidence of this claim is that only
two companies, out of over 1400 ﬁrms we considered before screening them for data
availability, indicated in the footnote to their 1996 annual report that they elected to
recognize the SBC costs.
The estimated coefﬁcients of event dummy variables for the high-tech, start-up,
and the high-growth sub-samples are always larger (in absolute values because, as
discussed earlier in this section, all the dummy variables should have a negative
coefﬁcient) than those for the full sample. The estimated coefﬁcients associated with
the full sample are 0.85, 0.65, and 0.89 for events 5, 6, and 11, respectively; the
corresponding values are 1.27, 0.94, and 1.10 for the high-tech, 1.14, 0.94,
and 1.03 for the start-up, and 1.36, 0.69, and 0.92 for the high-growth
companies. Furthermore, for each of these three sub-samples, the three F -statistics
for events 5, 6, and 11 (not reported in Table 6) show that differences between the
coefﬁcients for ﬁrms in the sub-sample and those for the remaining ﬁrms combined
5
The signiﬁcant abnormal returns reported in Table 6 could have been driven by a few outliers. To rule
out this possibility, the number of signiﬁcant 3-day abnormal returns around each of the events 5, 6, and
11 for the full sample and each of the next four sub-samples was determined. Except for the numbers
corresponding to events 6 and 11 for the full sample that were signiﬁcant at 0.05 level, these numbers were
signiﬁcant at the 0.01 level based on binomial tests, suggesting that results reported in Table 6 are not
driven by outliers.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
367
(e.g., for the 595–117=478 ﬁrms in the case of high-tech sample) are signiﬁcant at
0.01 level; exception is for event 6 in the case of high-growth sample, where the
signiﬁcance level is 0.05. These results indicate that high-tech, high-growth, and
start-up companies would have been generally more adversely affected by expensing
the SBC costs, as claimed.
The last four portfolios in Table 6 are designed to explore the differences between
our results and those of Dechow et al. (1996) in terms of equity price reaction to SBC
pronouncements. Dechow et al. used three samples: companies writing a comment
letter; bio-technology ﬁrms; and all ﬁrms in industries with high median option
usage, as measured by the proportion of common shares reserved for conversion of
stock options to common shares outstanding. They observed a signiﬁcant mean
abnormal return on 4/8/93, the date the WSJ and NYT announced the FASB’s
formal vote to require expensing of SBC costs, for the comment- letter sample but
not the other two samples. They did not, however, ﬁnd any equity price reaction to
the ED announcement or the decision to rescind its requirements (our event numbers
5 and 11).
The results for the high-EPS-impact sample (ﬁfth portfolio) show signiﬁcant
abnormal returns around events 5, 6, and 11, for half of the sample ﬁrms with higher
than median percentage decline in their pro-forma EPS (SOEFF). However, no
signiﬁcant abnormal return is observed for high option users, deﬁned as half of the
sample ﬁrms with higher than median fraction of shares reserved for conversion of
stock options (SOUSE). This difference in results shows that the fraction of shares
reserved for conversion of stock options is a noisy proxy for option usage.6 Dechow
et al. (1996) acknowledged this problem but could not have used our measure, as
1996 was the ﬁrst year companies were required to disclose the pro forma effect of
the options on income and EPS.
The last two portfolios in Table 6 show that, consistent with the results in Dechow
et al., no signiﬁcant abnormal returns are observed around any of the 12 events for
bio-tech ﬁrms and those writing comment letters to FASB. One might think that
these ﬁrms are high option users (as conﬁrmed in Table 5 for the bio-tech ﬁrms).
However, the correlations (not reported in Table 4 because of space limitation)
among dummy variables identifying these ﬁrms and the two measures of stock
option usage are very insigniﬁcant (the highest correlation is 0.046, which is
signiﬁcant at 0.29). It is possible, therefore, that companies in the comment-letter
sample (those most concerned) took steps to mitigate the negative effects of
recognizing the SBC costs. For example, The WSJ (11/23/93) reported that some
banks were devising ways for ﬁrms to hedge against the proposed stock option rule.
Alternatively, ﬁrms strongly opposing the Exposure Draft (as evidenced by writing
comment letters) may have somewhat curtailed the use of stock options. Finally, as
Dechow et al. suggest, these managers’ lobbying behavior may have been driven by
concern over compensation rather than concern over investors misunderstanding the
6
Although our high-option-users sample has a higher mean (also median) percentage decrease in EPS
(SOEFF) than the full sample, as indicated in Table 5, the correlation between SOEFF and a dummy
variable identifying high-option-users is only 0.053, which is signiﬁcant at 0.22 level.
368
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
information about stock option expense. The fact that the comment-letter writers
have lower mean and median percentage decline in their EPS (mean and median of
15.6% and 3.4% versus 17.5% and 5.8% for the remaining ﬁrms in our sample) is
consistent with the managers’ lobbying motive proposed by Dechow et al. and with
the absence of a signiﬁcant market reaction to SBC events for the comment-letter
sample.
Table 7 presents the results for Hypotheses 2–9. This table reports the results for
the full sample only because the growth opportunities, as well as high-tech and startup status, are captured by book value of assets over total ﬁrm value (BKOV) and the
dummy variables, and because many of the other variables account for the extent of
option usage. Each column in Table 7 reports the coefﬁcients of dummy variables in
one of the ten portfolio regressions described by Eq. (7). The dummy variable
coefﬁcients (gpk ) for each portfolio measure the effect of the corresponding ﬁrm
characteristic (and only that characteristic, as the potential collinearities among
different ﬁrm characteristics are taken into account by employing the Sefcik and
Thompson (1986) methodology) on stock market reaction to each of the twelve
events.
The estimated coefﬁcients of dummy variables reported in the second through
tenth columns are all signiﬁcant for events 5 and 11 (except for retained-earnings
related constraint for event 11). For event 6, which had a smaller impact on the
overall mean return, only the coefﬁcients of dummy variables in option usage, noise
in stock price performance, tax loss carry-forward, high-tech, and start-up portfolios
are signiﬁcant. None of the other coefﬁcients were signiﬁcant at any meaningful
level, and all signiﬁcant coefﬁcients had the expected sign.7
In general, the results support Hypothesis 2–9 that the negative stock price impact
of the proposed standard on SBC (the exposure draft) was more pronounced for
high-tech, high-growth (represented by a low book value of total assets over total
ﬁrm value), and start-up ﬁrms. The stock price impact was also positively related to
the existence of tax loss carry-forward, the extent of stock option usage (as reﬂected
by its effect on EPS), and retained-earnings related debt constraint; and negatively
related to the noise in stock price performance, free cash ﬂows over total assets, and
ﬁrm size.
Based on the relative size of the t-statistics reported in Table 7, growth, high-tech
and start-up dummies, and option usage are the ﬁrm characteristics that have the
most signiﬁcant relation with stock price reaction to the Exposure Draft (event 5).
Stock price reaction to event 11 (the vote not to require recognition of SBC costs) is
7
Alternative speciﬁcations of growth, stock option usage, and debt constraints did not inﬂuence our
ﬁndings. Growth deﬁned as book over market value of common equity, and research and development
over total assets, produced almost identical results except the signs for the latter measure were different, as
expected. With stock option usage deﬁned as the common shares reserved for conversion of stock options
over common shares outstanding, the coefﬁcients were insigniﬁcant at the 0.10 level (perhaps due to the
noise in that measure); the highest t-statistic was 1.49 for event 11. Finally, the leverage-related debt
constraint coefﬁcients were all insigniﬁcant at the 0.10 level. We expected the coefﬁcients of long-term debt
over total assets to be insigniﬁcant, as the loosening effect of the proposed standard on the leverage ratio
was small.
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
369
most signiﬁcantly related to the same variables, except noise in stock price
performance becomes the second (and size the fourth) most signiﬁcant variable. Size
has a strong relation with the stock price reaction and supports the political cost
hypotheses. Retained-earnings related debt constraint has the least pronounced
relation with stock price reaction to event 5, and is not related to events 6 and 11 at
0.10 signiﬁcance level. This result is intuitive because no retroactive adjustments to
retained earnings were to be required, i.e., the proposed standard was to apply to
options issued after 1996 only.
Overall, the results in Table 7 are in contrast with the ﬁndings of Dechow et al.
(1996). Dechow et al. used four ﬁrm-speciﬁc variables to explain cross-sectional
differences in abnormal returns across ﬁrms: free cash ﬂows divided by total assets,
retained-earnings related constraint, leverage-related constraint (total debt divided
by total assets), and size. They did not, however, ﬁnd any of these four variables to
be related to equity price reaction to the SBC pronouncements. We attribute these
differences once again to the sample and the variable set. Speciﬁcally, of the four
variables, as Dechow et al. (1996) explain, the one measuring stock option usage is a
noisy proxy (p. 8) and the leverage-related constraint should not be signiﬁcant as the
ED requirements would have actually reduced leverage (p. 6). The other two
variables’ insigniﬁcance may only be explained by the sample. We examine this issue
by testing the effect of these four variables on the stock market reaction to the twelve
events, using the Sefcik and Thompson (1986) methodology and our comment-letter
sub-sample. Consistent with Dechow et al. (1996), none of the estimated coefﬁcients
of the event dummy variables in these four portfolio regressions are found to be
signiﬁcant at the 0.10 level (the highest t-value is 1.15 for event 5 in the free cash
ﬂow portfolio).
We also explore the differences between our results and those of Dechow et al.
(1996) by adding three portfolios (representing comment-letter and bio-tech dummy
variables and SOUSE, fraction of shares reserved for conversion of stock options) to
our existing ten portfolios in Table 7. None of the event dummy variables for these
three portfolios are found to be signiﬁcant at 0.10 level (the largest t-value is 1.39 for
event 11 in SOUSE portfolio); in addition, except for event 6 in the tax loss carryforward portfolio that becomes insigniﬁcant at 0.10 level, there are no changes in the
signiﬁcance of the events in the existing portfolios.8 These results are consistent with
the lack of signiﬁcant correlations among the dummy variables identifying the biotech and comment-letter ﬁrms and the option usage (SOEFF), and the noise in the
stock option usage measure utilized by Dechow et al. (1996). As mentioned earlier, it
is also possible that companies writing comment letters were motivated by
compensation (as suggested by Dechow et al.) or took steps to mitigate the negative
effects of recognizing the SBC costs. Alternatively, ﬁrms strongly opposing the
Exposure Draft (as evidenced by writing comment letters) may have somewhat
curtailed the use of stock options.
8
The t-values increase for two events and their signiﬁcance level changes from 0.05 to 0.01. Speciﬁcally,
the t-values increase as follows: (1) from 2.50 to 2.71 for event 11 in the option usage portfolio; and (2)
from 2.56 to 2.61 for event 5 in the start-up portfolio.
370
Coefﬁcient estimates (gpk ) of event dummy variables in p (10) portfolio regressions (represented by Eq. (7)) of the form K:
R$ pt ¼ ap þ bp R$ mt þ
K
X
gpk Dkt þ e$pt ;
k¼1
where gpk for each portfolio measures the effect of the corresponding characteristic on stock price reaction to event k: Each dummy variable equals ‘‘+1’’
during the 3day period (t ¼ 1; t ¼ 0; and t ¼ 1 relative to each announcement) of an unfavorable event, ‘‘1’’ for a favorable event, and zero otherwise.
T-statistics are in parentheses
Alternative portfoliosa
Event
numberb
(k)
Constant
term
portfolio
Growth
portfolio
BKOV
Debt
constraint
portfolio
RECOV
Size
portfolio
SIZE
Free cash
ﬂow
portfolio
FCFOA
Option
usage
portfolio
SOEFF
Stock price
noise
portfolio
NOISE
Tax loss
dummy
portfolio
TAX
High-tech
dummy
portfolio
HT
Start-up
dummy
portfolio
START
1
0.54
(0.72)
0.19
(0.26)
0.29
(0.39)
0.51
(0.69)
0.24
(0.33)
0.78
(1.05)
1.01
(1.18)
0.18
(0.21)
0.73
(0.86)
0.58
(0.68)
2.62
(3.05)c
1.37
(1.59)
0.62
(0.70)
0.35
(0.39)
0.98
(1.10)
0.55
(0.62)
1.75
(1.96)e
1.22
(1.37)
0.02
(0.06)
0.14
(0.45)
0.29
(0.93)
0.48
(1.50)
0.75
(2.39)d
0.52
(1.60)
0.77
(1.35)
0.25
(0.44)
0.69
(1.21)
0.64
(1.13)
1.40
(2.46)d
0.77
(1.36)
0.90
(0.96)
0.41
(0.44)
0.74
(0.80)
1.10
(1.18)
2.47
(2.65)c
2.11
(2.26)d
0.41
(0.96)
0.35
(0.82)
0.55
(1.28)
0.50
(1.17)
1.01
(2.37)d
0.87
(2.04)d
0.17
(0.43)
0.25
(0.63)
0.10
(0.20)
0.43
(0.99)
0.89
(2.04)d
0.77
(1.76)e
0.89
(0.97)
0.73
(0.80)
1.02
(1.09)
1.15
(1.25)
2.68
(3.07)c
2.04
(2.35)d
1.01
(1.15)
0.65
(0.74)
0.90
(1.03)
0.92
(1.07)
2.20
(2.56)d
1.76
(2.04)d
2
3
4
5
6
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
Table 7
Test of hypotheses 2–9 for the 12 events
8
9
10
11
12
a
0.78
(0.91)
0.84
(0.99)
0.90
(1.05)
0.68
(0.80)
2.52
(2.94)c
0.49
(0.57)
0.72
(0.80)
0.54
(0.61)
0.80
(0.91)
0.47
(0.50)
1.41
(1.58)
0.88
(0.97)
0.50
(1.53)
0.31
(0.95)
0.32
(0.99)
0.51
(1.57)
0.82
(2.51)d
0.18
(0.55)
0.11
(0.20)
0.23
(0.41)
0.51
(0.89)
0.22
(0.39)
1.31
(2.31)d
0.18
(0.33)
Portfolios are based on the nine base variables described in Table 3.
Events are described in Table 1.
c
Signiﬁcant at the 0.01 level.
d
Signiﬁcant at the 0.05 level.
e
Signiﬁcant at the 0.10 level.
b
0.35
(0.38)
0.15
(0.17)
0.37
(0.40)
0.88
(0.94)
2.38
(2.50)d
0.12
(0.13)
0.40
(0.95)
0.51
(1.20)
0.25
(0.58)
0.48
(1.12)
1.21
(2.85)c
0.15
(0.37)
0.53
(1.19)
0.35
(0.78)
0.52
(1.15)
0.19
(0.39)
1.04
(2.15)d
0.33
(0.68)
1.26
(1.45)
0.65
(0.75)
1.18
(1.36)
0.90
(1.03)
2.40
(2.71)c
0.85
(0.99)
0.85
(0.99)
0.63
(0.76)
0.84
(0.99)
0.90
(1.06)
2.06
(2.42)d
0.58
(0.69)
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
0.56
(0.76)
0.83
(1.12)
0.62
(0.84)
0.34
(0.46)
0.40
(0.54)
0.39
(0.54)
7
371
372
H. Espahbodi et al. / Journal of Accounting and Economics 33 (2002) 343–373
6. Summary and conclusions
The proposed standard to recognize SBC costs was the subject of criticism by
Congress, the business community, and the accounting profession primarily because
it was predicted that recognition of SBC costs could lower the reported earnings by
as much as 50% and would adversely affect stock prices (Berton, 1993). This study
examined the equity price reaction to the SBC pronouncements, the cross-sectional
variation of abnormal returns with ﬁrm-speciﬁc variables, and the value relevance of
recognition versus disclosure in ﬁnancial reporting. Examining the capital market
reaction to the SBC pronouncements not only provided insight into the market’s
assessment of the relative importance of each event, it showed the rationale for
lobbying efforts and concerns by various groups. More importantly, the unique
feature of SBC pronouncements not only allowed us to test the signiﬁcance of the
contracting theory, but also to assess the value relevance of disclosure versus
recognition.
Our results indicate that ﬁrms exhibited signiﬁcant abnormal returns around the
issuance of the Exposure Drafts proposing to require recognition of stock-based
compensation costs, and also around the event reversing that decision to require
disclosure only (while encouraging recognition). We show that the abnormal returns
were most pronounced for high-tech, high-growth, and start-up ﬁrms. We also
document that the stock price impact was positively related to the existence of tax
loss carry-forward, the extent of stock option usage (as reﬂected by its effect on
EPS), and retained-earnings related debt constraint; and negatively related to the
noise in stock price performance, free cash ﬂows over total assets, and ﬁrm size.
These results are consistent with the contracting theory, and show that disclosure is
not a substitute for recognition.
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