Cashing in on Managerial Malfeasance

8/12/2019 Cashing in on Managerial Malfeasance

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September/October 2010 www.cfapubs.org 85

Financial Analysts Journal

Volume 66 Number 5

2010 CFA Institute

Cashing In on Managerial Malfeasance:

A Trading Strategy around Forecasted

Executive Stock Option GrantsIvo Ph. Jansen and Lee W. Sanning

This study examined the profitability of a trading strategy that exploits the manipulation of stockprices around the grant date of executive stock options. The strategy generates annualizedabnormal returns of 1.45.2 percent net of transaction costs and is relatively unaffected by theSarbanesOxley Act of 2002.

xecutive stock option compensation createsan incentive for managers to temporarilymanipulate their companies stock pricedownward before an option grant. This

incentive stems from the fact that the option strikeprice is typically set equal to the market price of thestock on the date the option is granted and thepayoff at exercise equals the difference between thestock price and the strike price. Therefore, optionvalue and strike price are negatively related, andexecutive stock options are more valuable thelower the stock price (and thus the strike price) onthe grant date. Aboody and Kasznik (2000) andChauvin and Shenoy (2001) argued that managersact on these incentives and manipulate stock pricesdownward by accelerating the release of badnews before an option grant and delaying therelease of good news until after an option grant.Consistent with this argument, Chauvin andShenoy (2001) documented significant negativeabnormal returns in the days preceding executivestock option grants, and Aboody and Kasznik(2000) documented significant positive abnormalreturns following such grants. In our study, wedesigned and evaluated a trading strategy thatseeks to profit from such managerial manipulationof stock pricesand the resulting pattern of abnor-

mal returnsaround the dates of option grants.

Motivation and Prior LiteratureStock option awards have become a popular andsignificant part of executive compensation. Thepurpose of stock option compensation is to align

executive interests with those of the stockholders.Options are well suited to this purpose because theirvalue increases with stock price. A stock optionsexercise payoff is the difference between the stockprice on the exercise date and the strike price, whichis set when the option is granted. For an overwhelm-ing majority of companies, the strike price is setequal to the market price on the grant date. Indeed,Hall and Murphy (2002) documented that 94 per-cent of the options granted to CEOs of S&P 500companies in 1998 were granted at the money.1

Because option value is higher, all else beingequal, when the strike price is lower, researchershave argued that the granting of at-the-moneyoptions has led to two types of manipulation: (1) amanipulation of the grant date such that optionsare backdated to, or awarded, when the marketprice is low (Yermack 1997; Lie 2005; Narayananand Seyhun 2005a, 2005b; Heron, Lie, and Perry2007) and (2) a downward manipulation of thestock price before an option grant (Aboody andKasznik 2000; Chauvin and Shenoy 2001). Withrespect to both types of manipulation, these studiesdocumented negative abnormal returns precedingan option grant and/or positive abnormal returnsfollowing it. They further documented that the pat-tern of abnormal returns exists for companies withboth fixed and nonfixed award schedules but that

it is more pronounced for the latter. These findingssuggest that manipulation of the stock price and thegrant date occurs because (1) companies with fixedaward schedules, by definition, cannot manipulatethe grant date, and so the pattern of abnormalreturns likely arises from a manipulation of thestock price, and (2) the abnormal returns are morepronounced for companies with nonfixed awardschedules, and so they are likely manipulatingmore than the stock price alone (i.e., they are alsomanipulating the grant date).

Ivo Ph. Jansen is assistant professor of accounting atRutgers University, Camden, New Jersey. Lee W.Sanning is assistant professor of finance at theUniversity of Wyoming, Laramie.

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The notion that companies manipulate thegrant date has been advanced by Yermack (1997),Lie (2005), Narayanan and Seyhun (2005a, 2005b),and Heron, Lie, and Perry (2007). Yermack (1997)argued that executives exert pressure on compensa-tion committees to time the option grant to occurjust after bad news or just before good news. He

reported that positive abnormal returns begin toaccumulate immediately after an option grant andreach a maximum of 3.42 percent after approxi-mately 50 trading days (10 weeks). Lie (2005),Narayanan and Seyhun (2005a, 2005b), and Heron,Lie, and Perry (2007) argued that companies manip-ulate the grant date after the fact. This practice isknown as backdating, whereby companies pick thegrant dateand thus the strike priceby lookingback and choosing a date on which the stock pricewas lowest in recent months. Lie (2005) andNarayanan and Seyhun (2005a, 2005b) documentedaverage abnormal returns of about 3 percent

before an option grant and 48 percent thereafter.Aboody and Kasznik (2000) pointed out that

the argument that companies manipulate the grantdate can be maintained only for those companiesthat have varying option award dates. They sug-gested that companies manage news announce-ments around option grants to temporarily depressthe stock price before the options are granted. Theyinvestigated companies with fixed award sched-ules and reported positive abnormal returns of 4percent three months after the option grant. Chau-vin and Shenoy (2001) argued that companiesmanipulate the stock price by accelerating therelease of bad news and delaying good news. Theydocumented abnormal returns of 0.79 percent inthe 10 days preceding option grants for companieswith fixed award schedules.

Taken together, prior research provides strongevidence that stock option grants are preceded bynegative abnormal returns and followed by positiveabnormal returns. In our study, we designedandinvestigated the profitability ofa trading strategythat exploits this pattern of abnormal returns bytaking a short position before an expected optiongrant and a long position afterward.

Trading StrategyTo implement our trading strategy, we needed toeither know or be able to anticipate future grantdates. Current U.S. SEC disclosure rulesunderSection 16(a) of the Securities Exchange Act of 1934and in line with Section 403 of the SarbanesOxleyAct of 2002 (SOX)require companies to discloseoption grants within two days of the grant. Com-panies are not required, however, to announce

upcomingoption grants and, indeed, seldom do so.2

Therefore, we limited our strategy to companieswith fixed award schedules, for which we couldform reasonable expectations about upcominggrant dates. This approach had two importantimplications. First, our trading strategy excludedcompanies that manipulated the grant date,including backdaters. Second, our trading strategythus sought to take advantage of managers manip-ulation of their companies stock prices aroundscheduled awards.

Although fixed award schedule, fixedgranter, and scheduled award have no formaldefinition, they do notrefer to companies that pub-licly announce upcoming grants. Rather, theseterms were introduced in previous research to dis-tinguish between companies that grant on approx-imately the same calendar date each year andcompanies that do not. For example, Yermack(1997) defined scheduled awards as those that

occur 1113 months after a previous grant (about56 percent of his sample). Aboody and Kasznik(2000) and Lie (2005) defined scheduled awards asthose that occur within a week of the one-yearanniversary of the prior years grant (about 46percent of their samples). We identified fixedgranters as companies that have at least a four-yearhistory of consecutive awards within one week ofthe preceding years option grant date.

Once we had identified a company as a fixedgranter, we forecasted next years grant date as theone-year anniversary of the most recent optiongrant. We had to rely on forecasted, as opposed to

actual, grant dates to avoid hindsight bias and thusbe able to implement our trading strategy.3We thentook a short position 20 trading days before theforecasted grant date to take advantage of anydownward manipulation of the stock price preced-ing an option grant. Finally, on the expected grantdate, we reversed our short and took a long posi-tion for 60 trading days to exploit the reversal ofany downward manipulation of the stock price.

We chose our trading strategy window on thebasis of findings by Aboody and Kasznik (2000)and Chauvin and Shenoy (2001) for companieswith scheduled awards. With regard to the pre-

grant window, those studies used a 30-day and 10-day period, respectively, and reported mixedresults. Furthermore, although a manager mighthave difficulty manipulating the stock price down-ward for an extended period, a month is likely nottoo long to do so successfully. We thus chose a 20-day pre-grant window. Because Aboody andKasznik (2000) found that cumulative abnormalreturns continue to increase until approximatelythree months after a scheduled award, we chose a60-day trading period for our post-grant window.4


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Cashing In on Managerial Malfeasance

Data and Descriptive StatisticsWe evaluated the profitability of our trading strat-egy by using both cumulative and buy-and-holdabnormal returns. We investigated cumulativeabnormal returns (CARs) to maintain comparabilitywith previous research. We examined buy-and-holdabnormal returns (BHARs) because they capture

actual changes in the value of investor portfolios(Barber and Lyon 1997; Lyon, Barber, and Tsai 1999).We defined a companys abnormal return relative tothree expected return benchmarks: (1) the returnpredicted by the market model, (2) the return on theS&P 500, and (3) the return predicted by theFamaFrench (1993) three-factor model.

To calculate CARs, we first computed theabnormal return (AR) for company i on day t asfollows:

where Ritis the return for company ion day tand

is the expected return for company ion day t.Using the CRSP equally weighted market index(Markett), we computed the expected return accord-ing to the market model:

where the market model parameters (iand i) areestimated over a 180-day period that ends 21 daysbefore the forecasted grant date to avoid overlap ofthe estimation and event windows. We computedthe expected return according to the FamaFrenchthree-factor model:

where, as with the market model, parameters areestimated over a 180-day period that ends 21 daysbefore the forecasted grant date. In the FamaFrenchmodel, SMB captures the excess returns of smallversus large stocks and HML captures the excessreturns of high versus low book-to-market stocks.

We measured abnormal returns over severalwindows, extending from 20 days before a fore-casted grant to 60 days after a forecasted grant. Wecalculated CARifor a given company over a win-dow of length Tas follows:

The average CARfor an event window is the aver-age across Ncompanies:

To compute BHARi for company i, we sub-tracted the compounded expected return over a

window of length T from the compounded rawreturn for company i:

The average BHAR for an event window is theaverage across Ncompanies:

5

Data Sources. We obtained the return datafor our study from the CRSP daily stock file and theoption data from the Compustat ExecuComp data-base. ExecuComp provides detailed informationabout top-management compensation packagesfor S&P 1500 companies, including informationabout stock option grants, collected from annualstatements. Option grant dates are available for2006 onward. The database does not provide infor-

mation about the date on which the option granttook place before 2006; as in prior research, how-ever, we inferred the option grant date from theoption expiration date (i.e., other than being indifferent years, the option grant date almost alwayshas the same calendar date [e.g., 12 January] as theoption expiration date).

Sample and Descriptive Statistics. Oursample spanned all years available on ExecuComp:19922008. Consistent with prior research (see, e.g.,Yermack 1997; Aboody and Kasznik 2000; Chauvinand Shenoy 2001; Lie 2005), we limited our analyses

to option grants awarded to CEOs. When a companyawarded options more than once a year, we usedonly the first option grant in a given year to establishwhether a company was a fixed granter and, if so, toforecast future grant dates.6Our sample contained16,959 option grants with available return data.

Table 1reports the pattern of abnormal returnsaround all option grants to establish that the pat-tern documented in prior research is also presentin our sample. Consistent with the general findingsin the literature, we found that CARs in the 20 dayspreceding option grants are significantly negativeand CARs in the 60 days following option grants

are significantly positive. We found the same pat-tern for BHARs. Figure 1graphically demonstratesthis pattern for CARs. It clearly shows downward(upward) abnormal stock price movements pre-ceding (following) option grants, and it highlightsthe potential profitability of our trading strategy.

Because ExecuComp data start in 1992 andbecause we defined fixed granters as companiesthat had established a four-year history of sched-uled awards, our trading strategy sample covered19962008. We forecasted a grant date and took a

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position for 2,088 company-year observations offixed granters.7The breakdown of the observations

by year is given in Panel A of Table 2.Panel A of Table 2 also describes how many

positions were taken incorrectly. If a fixed granterdid not award options in a given year, the strategywould miss the date and trade on an incorrect

forecast. To avoid hindsight bias, our strategyallowed this to happen. Of the 2,088 forecastedgrant dates, 1,509 (72 percent) were within oneweek of an actual grant. We considered these fore-casts to be correct.8For the remaining 579 fore-casted grant dates, 353 (17 percent) missed an actual

grant date by more than one week and 226 (11percent) forecasted a grant that was never made.9

Panel B of Table 2 provides a breakdown by

month of the 2,088 positions we took. The results

show that we took as few as 64 positions in July and

as many as 510 in February, aggregated over 13

years. They also show that we took more than halfof all positions in December, January, and Febru-

ary. Although the distribution of positions is

clearly not uniform across the months, enough

positions were taken throughout the year to allow

for annualization of our trading strategy returns.

Table 1. Abnormal Returns around Actual Option Grants, 19922008

Cumulative Abnormal Returns (%) Buy-and-Hold Abnormal Returns (%)

Window Market Model S&P 500 FamaFrench Market Model S&P 500 FamaFrench

(20, 1) 1.17**** 0.00 1.12**** 1.44**** 0.05 1.38****

(0, +20) 0.97**** 2.33**** 0.98**** 0.71**** 2.32**** 0.75****

(0, +40) 1.24**** 3.47**** 1.25**** 0.49*** 3.45**** 0.55****

(0, +60) 1.66**** 4.53**** 1.55**** 0.20 4.49**** 0.14

Note: Abnormal returns are reported for four windows, measured in trading days relative to the grantdate (i.e., Day 0).

***Significant at the 1 percent level under a one-tailed test.****Significant at the 0.1 percent level under a one-tailed test.

Figure 1. Abnormal Returns around Actual Option Grants, 19922008

Notes: This figure shows cumulative abnormal returns around all actual option grants. The option grantdate is Day 0 in event time. The figure graphs cumulative abnormal returns from Day 20 to Day +60.

Cumulative Abnormal Return (%)

S&P 500

Fama

French Market Model

5

4

3

2

1

0

1

2

20 6010 20 300 10 40 50

Event Time (days)


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Finally, Table 3compares several characteris-

tics of our trading strategy companies with those ofthe ExecuComp companies that award options ona nonfixed schedule. The market value of equity ofour trading strategy companies is almost $13billion, on averageabout twice as large as that ofthe companies without scheduled awards. Ourtrading strategy companies are also more profitableand more levered. Their beta is very close to 1, onaverage, whereas the beta for the nonfixed grantersis significantly greater than 1. In short, the compa-nies in our trading strategy sample are bigger, less

risky, and apparently stronger than the other com-

panies in the ExecuComp database.

Trading Strategy ReturnsTable 4reports the abnormal returns from our trad-ing strategy. We computed its profitability as thenegative value of the abnormal return from the (20,1) window plus the abnormal return from the (0,+60) window, which corresponds to our short andlong positions, respectively.10 We found that theprofitability of our trading strategy is significantlypositive and ranges between 1.00 percent and 2.15

Table 2. Number of (Forecasted) Grants by Year/Month and Accuracy of Forecast, 19922008

YearTotal No.of Grants

No. of ForecastedGrants (positions)

Actual Grant ThatYear within OneWeek of Forecast

Actual Grant ThatYear but Not within

One Week of ForecastNo Actual

Grant That Year

A. Breakdown of (forecasted) grants by year

1992 224

1993 701

1994 9461995 992

1996 1,057 69 78.3% 14.5% 7.2%

1997 1,112 148 76.4 7.4 16.2

1998 1,200 176 72.7 17.6 9.7

1999 1,273 166 74.1 18.7 7.2

2000 1,265 172 65.1 22.7 12.2

2001 1,238 174 70.1 19.5 10.3

2002 1,231 190 75.3 18.4 6.3

2003 1,184 183 72.7 19.1 8.2

2004 1,147 183 76.0 14.2 9.8

2005 1,080 200 72.0 17.0 11.0

2006 1,259 186 67.2 19.5 13.3

2007 1,050 150 75.5 18.1 6.52008 91 65.6 4.3 30.1

Total 16,959 2,088 72.3% 16.9% 10.8%

MonthNo. of

Positions% of TotalPositions

B. Breakdown of forecasted grants (i.e., positions taken) by month

January 475 22.7

February 510 24.4

March 99 4.7

April 99 4.7

May 125 6.0

June 89 4.3

July 64 3.1August 104 5.0

September 73 3.5

October 102 4.9

November 102 4.9

December 246 11.8

Total 2,088 100.0

Notes: This table gives a breakdown, by year and month, of the number of (forecasted) option grants. Panel A provides the breakdownby year for all grants to CEOs and for the grants that we forecasted for fixed granters. The last three columns of Panel A describe theaccuracy of the forecasted grant dates. Panel B provides the breakdown by month for all forecasted grants (i.e., the positions taken).


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percent, depending on the specification of abnormalreturns. For example, the buy-and-hold strategyreturn, relative to the return on the S&P 500, is 2.04percent. On an annualized basis, our strategy thus

beats the S&P 500 by about 6.2 percent. Dependingon the measure of expected returns, our tradingstrategy earns annual abnormal returns of about 3percent to 6.5 percent. These numbers are statisti-cally and economically significant.

For the trading strategy to be implementable,the abnormal returns need to exceed transactioncosts. Transaction costs comprise explicit andimplicit trading costs (see, e.g., Keim and Madha-van 1997). Explicit transaction costs are the directcosts from trade executionmainly brokeragecommissions and bidask spreads. Implicit transac-tion costs arise from the price impact of trading andopportunity costs. Transaction costs have declinedsignificantly over time (see, e.g., Stoll 1995; Keimand Madhavan 1997, 1998; Hanna and Ready 2005;Stoll 2006), primarily because of electronic trading.Stoll (2006) estimated that in 2001, brokerage com-missions were 0.21 percent and bidask spreadsplus implicit trading costs were 0.07 percent for a

round-trip trade. Because our strategy requires tworound-trip trades, we estimated its transaction coststo total 0.56 percent. Net of transaction costs, ourtrading strategy thus generates abnormal returns of

about 0.44 percent to 1.59 percent, which corre-sponds to annualized abnormal returns of about 1.4percent to 5.2 percent. We conclude, therefore, thatour strategy is implementable.

Researchers who use abnormal returns in theiranalyses always face the concern that the riskadjustment in the calculation of abnormal returnsis incomplete, leading to overstated or under-stated abnormal returns, on average. This con-cern, however, does not explain our findings

because it is inconsistent with the asymmetry in thepattern of abnormal returns surrounding the fore-casted grant date. First, with the market model andthe FamaFrench model as benchmarks forexpected returns, the abnormal returns are signifi-cantly negative in the pre-grant window and sig-nificantly positive in the post-grant window.11

Second, with the S&P 500 return as a benchmark,the abnormal returns are significantly positive in

both windows, but the magnitude of the abnormal

Table 3. Comparison of Company Characteristics, 19922008

Trading StrategyCompanies (n= 2,088)

ExecuComp Companieswith Nonfixed Grants

(n= 14,871)p-Value ofDifference

Variable Mean Median Mean Median Mean Median

Market value of equity 12,853.11 3,453.66 6,239.89 1,422.43 0.001 0.001

Total assets 22,159.76 4,571.50 11,123.23 1,417.99 0.001 0.001

Return on assets 7.31% 7.38% 5.57% 6.86% 0.001 0.001Debt-to-equity ratio 3.283 1.669 2.755 1.263 0.001 0.001

Beta 1.050 1.009 1.274 1.159 0.001 0.001

Notes: This table compares means and medians and provides p-values of difference on the basis of at-test and a chi-square test, respectively. Market value of equity and total assets are in millions of dollars.Return on assets is net income plus interest expense divided by average total assets. Beta is estimatedover a 180-trading-day window that ends 21 days before a (forecasted) option grant.

Table 4. Trading Strategy Abnormal Returns, 19962008



(20, 1)

0.60*** 0.45**

0.61***

0.84**** 0.39**

0.85****(0, +20) 0.49** 1.33**** 0.53*** 0.25 1.26**** 0.29*

(0, +40) 0.83*** 1.96**** 0.78*** 0.24 1.80**** 0.16

(0, +60) 1.43**** 2.61**** 1.21**** 0.34 2.43**** 0.15

Profit 2.03**** 2.15**** 1.82**** 1.28*** 2.04**** 1.00**

Note: Abnormal returns are reported for four windows, measured in trading days relative to the grantdate (i.e., Day 0).

*Significant at the 10 percent level under a one-tailed test.**Significant at the 5 percent level under a one-tailed test.



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returns in the (0, +20) window is much larger thanin the (20, 1) window. This asymmetry surround-ing a forecasted option grant is difficult to reconcilewith a risk-based explanation for the profitabilityof our trading strategy, but it follows logically fromthe argument that stock prices are manipulatedaround the option grant time.

On the whole, our trading strategy is signifi-cantly profitable, regardless of the expected returnbenchmark. The buy-and-hold abnormal returns areslightly lower than the cumulative abnormal returns

but are still significantly positive. Given the magni-tude of abnormal returns documented in priorresearch around actualscheduled grantsAboodyand Kasznik (2000) reported cumulative abnormalreturns of up to 4 percentour trading strategy doesvery well to generate profits of about 2 percent onthe basis offorecastedgrants. Our results thus indi-cate that our method of forecasting option grantdates, despite the resulting errors as documented in

Panel A of Table 2, enables a trading strategy thatgenerates significant economic profits.

Profitability of the Trading Strategy overShorter Periods and Post-SOX. We also investi-gated the profitability of our trading strategy over19962002 and 20032008. The latter period corre-sponds to the years after the implementation ofSOX in 2002. The results are reported in Table 5,which shows that our trading strategy is signifi-cantly profitable in both periods. Depending onthe expected return benchmark, the overall profitsrange from 0.76 percent to 2.98 percent in the first

half of our sample period and from 1.13 percent to1.89 percent in the second half of our sampleperiod. The profitability is statistically significantin all cases excepting the 0.76 percent for theBHARs under the FamaFrench model for 19962002. The consistency in the trading strategysprofitability between the two periods suggests that

investors did not learn from the pattern of abnor-mal returns around scheduled grants in earlieryears to devise a trading strategy and arbitrageaway the abnormal returns in later years. On anannualized basis, the abnormal returns range fromabout 2.5 percent to 9 percent, or 0.6 percent to 7.5percent after transaction costs, depending on thespecification of abnormal returns.

As mentioned earlier, SOX established therequirement that companies disclose optiongrants within two days of the award. Thisrequirementcombined with the overall increasedscrutiny of corporate practices following the Enron,WorldCom, and similar scandalscould arguablyeliminate the profitability of our trading strategy ifmanagers would cease to manipulate their compa-nies stock prices around option grants. Our results,however, show that the strategy continues to gen-erate significantly positive abnormal returns. Thetotal profitability may have decreased somewhat,

but it is significantly positive in all cases. In fact,with the FamaFrench model as the expectedreturn benchmark, the strategy is more profitableafter than before the implementation of SOX. Thus,despite the increased scrutiny in recent years and

Table 5. Trading Strategy Abnormal Returns by Period, 19962008



A. 19962002 (n = 1,089)

(20, 1) 0.95*** 0.32 0.78** 1.23**** 0.28 1.07***

(0, +20) 0.55* 1.91**** 0.72** 0.20 1.79**** 0.38

(0, +40) 0.78* 2.40**** 0.69* 0.02 2.19**** 0.07

(0, +60) 1.30** 3.30**** 0.99* 0.02 3.04**** 0.31

Profit 2.25*** 2.98**** 1.77*** 1.39** 2.76**** 0.76

B. 20032008 (n = 999)

(20, 1)

0.22 0.59**

0.43**

0.41** 0.50**

0.61***(0, +20) 0.43** 0.71*** 0.31* 0.30 0.69*** 0.19

(0, +40) 1.00*** 1.49**** 0.88*** 0.53* 1.37**** 0.42

(0, +60) 1.57**** 1.86**** 1.46**** 0.72** 1.76**** 0.66*

Profit 1.79**** 1.27*** 1.89**** 1.13*** 1.26*** 1.27***

Note: See Table 4 for details regarding our trading strategy.

*Significant at the 10 percent level under a one-tailed test.**Significant at the 5 percent level under a one-tailed test.



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the disclosure requirements of SOX, our tradingstrategy still generates significant economic profits.The continued post-SOX manipulation surround-ing stock option grants is consistent with resultsdocumented by Narayanan and Seyhun (2005b).

Finally, to check for sensitivity regarding the(20, +60) window of abnormal returns for our

trading strategy, we investigated abnormal returns(not tabulated) starting 60 days before a forecastedoption grant and continuing to 90 days after theforecasted grant. We found significant negativeabnormal returns as early as 30 days before theforecasted grant; we also found that the abnormalreturns cease to be significantly positive 50 daysafter the grant. The concentration of significantabnormal returns in the weeks surrounding theforecasted option grant suggests a substantialmanipulation of stock prices.

Conclusion

In this article, we documented the profitability of atrading strategy that forecasts option grant datesand takes a position to exploit managerial manip-ulation of stock prices surrounding option grants.The granting of executive stock options creates anincentive for managers to manipulate stock pricesdownward in anticipation of a grant because theoptions strike price is usually set equal to the stockprice on the grant date and stock option value isinversely related to strike price. Prior research (see,e.g., Aboody and Kasznik 2000; Chauvin and She-noy 2001) has shown that abnormal stock pricedeclines do indeed precede option awards andabnormal stock price increases immediately followthem. Moreover, previous research has docu-mented that these abnormal stock price movementsare not exclusively manifestations of the practice ofbackdating option grants (see, e.g., Lie 2005;

Narayanan and Seyhun 2005a) but are apparentlyrelated to managers timing of information releases(see Aboody and Kasznik 2000).

Because option grant dates are not announcedbefore the fact but are disclosed at least severaldaysif not weekslater, we implemented ourtrading strategy on the basis of forecasted grantdates. Specifically, for companies that awardedstock options for four consecutive years within oneweek of the preceding years option grant date, weforecasted next years option grant as the one-yearanniversary of the most recent grant. Next, to takeadvantage of any downward manipulation of stockprices, we took a short position starting 20 tradingdays before the forecasted grant and reversed to along position on the forecasted grant date to takeadvantage of the reversal of that manipulation. Ourresults show that our trading strategy generates asignificantly positive abnormal return of about 1percent to 2.15 percent, which is in excess of typicaltransaction costs. On an annualized basis, our trad-

ing strategy earns abnormal returns of approxi-mately 3 percent to 6.5 percent, or 1.4 percent to 5.2percent after transaction costs. We also documentedthat the profitability of our trading strategy is stableover time and that it continues to be profitable evenafter the implementation of the SarbanesOxley Actof 2002. These results suggest that investors canprofit from implementing our trading strategy asdescribed in this article.

This article qualifies for 1 CE credit.

Notes

1. The incentive to award at-the-money options stems fromthe accounting rules for stock-based compensation. Up to2004, Statement of Financial Accounting Standards (FAS)No. 123 required compensation expense to be recognizedfor the intrinsic value of the option (i.e., the difference

between the market price and the strike price) on the grant

date. Awarding at-the-money options thus led to zero com-pensation expense. In 2004, FAS No. 123 was revised torequire compensation expense to be recognized for thefairvalueof the option on the grant date. Preliminary evidencesuggests that this revision has not caused companies tomove away from granting at-the-money options.

2. Before SOX, companies typically disclosed option grantsweeks or months after the fact. Under SOX, companies mustdisclose stock option awards within two days of the grant.Narayanan and Seyhun (2005b) found that 24 percent of thecompanies in their post-SOX sample failed to report thegrant within the required two days, with 10 percent of thecompanies waiting more than a month to disclose the stock

option award. Incidentally, the SEC has initiated noenforcement actions against these companies, most likely

because the violations are relatively minor and the SECsresources are significantly constrained.

3. This constraint can lead to two types of errors: (1) an incorrectforecast of next years grant date when a grant does occurand (2) an incorrect forecast of a grant that does not occur.The profitability of our trading strategy is likely smaller thanthe abnormal returns documented in prior research aroundactualscheduled awards because of these errors.

4. To check for sensitivity, we assessed the abnormal returns,in 10-day increments, starting on Day 60 and continuingto Day +90. We discuss the findings from these sensitivityanalyses later in the article.

5. See Campbell, Lo, and MacKinlay (1997) for details on theevent study methodology.

6. Less than a quarter of the ExecuComp companies grantedoptions to their CEOs more than once a year.

We thank Charles Trzcinka, Gregory Udell, RichardRosen, Heejoon Kang, Jan Jindra, Katsiaryna Salavei,Sherrill Shaffer, Fred Sterbenz, seminar participants atWhitman College, and conference participants at the2009 FMA and 2009 SFA meetings.


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7. The 2,088 positions were taken in 633 unique companies.The number of positions per company ranges from 1 posi-tion for 196 companies to 13 positions for 3 companies.Regardless of the number of positions per company, thetrading strategys abnormal returns for the correspondingsample partitions are either significantly positive or indis-tinguishable from zero. The latter finding is probably attrib-utable to the small size of some of the sample partitions andthe resultant lack of power. The partition corresponding to

companies for which we took only a single position has thehighest trading strategy abnormal returns (5.69 percent).

8. Being off by even a week, however, could significantlyreduce the profitability of our trading strategy if the stockprice manipulation should occur within that week.

9. Not surprisingly, we found that the abnormal returns fromour trading strategy for these incorrect forecasts are notsignificantly different from zero. They are included in ourreported trading strategy returns to avoid hindsight bias.

10. For the buy-and-hold return, we compounded the negativeof the average pre-grant abnormal return and the positiveof the average post-grant abnormal return. An implicitassumption of this compounding procedure is that theaggregate proceeds from undoing the short positions are

evenly reinvested across the long positions.11. The FamaFrench BHARs are positive but not significant.

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