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Investor perception of managerial discretion in valuing stock options: an empirical examination

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Abstract

Managers have significant discretion over option-pricing model input assumptions for reported stock option values in SEC filings. However, it is unclear how investors perceive such discretion. By modeling the discretionary component of employee stock option (ESO) value, we examine what determinants drive the ESO discretions and whether the discretions convey value-relevant information to investors or represents merely managerial opportunism. We find that the changes in firms’ future operating risk drive understatement of ESO values, and that the understatement of ESO value is significantly negatively associated with stock price, suggesting that the market does incorporate the information underlying this component into its valuation assessment. This result is consistent with the notion that outside investors perceive ESO discretion as a signal of estimated future operating risk.

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Notes

  1. For example, Yermack (1998), Aboody et al. (2006), Hodder et al. (2006), Johnston (2006), Lam and Chng (2006), and Bartov et al. (2007).

  2. For example, Aboody (1996), Aboody et al. (2004a, b), Bell et al. (2002), and Rees and Stott (2001).

  3. Hodder et al. (2006) defined the Black–Scholes (hereafter, BS) value using the expected input assumptions as the expected ESO value or the nondiscretionary component of ESO value. In contrast to the expected ESO value, the unexpected ESO value or the discretionary component of ESO value represent the extent to which the reported ESO value deviates from its expected one. Hodder et al. label this difference as the “discretionary component of ESO value.” We use the same terminology in this study.

  4. For example, Subramanyam (1996), Sankar and Subramanyam (2001) and Hann et al. (2007).

  5. For example, Dye (1988), Trueman and Titman (1988), Teoh et al. (1998a, b, c), Burgstahler and Dichev (1997) and Burgstahler and Eames (2006).

  6. Nondiscretionary ESO value is measured by the BS value using expected pricing model input assumptions, which are the fitted values obtained from regressing firm reported inputs on individual firm historical data and industry averages. The discretionary ESO value is then determined by subtracting the nondiscretionary ESO value from the BS value using the reported inputs.

  7. The reasonableness of these assumptions is discussed in Sects. 2.4 and 5.3.

  8. For example, Yermack (1998), Balsam et al. (2003), Bartov et al. (2007), Aboody et al. (2006), Johnston (2006), and Hodder et al. (2006).

  9. When examining whether discretionary or nondiscretionary ESO value is associated with future operating risk, the presence (nonexistence) of such an association would be consistent with the information communication hypothesis (opportunism hypothesis).

  10. In December 2002, the FASB issued SFAS 148, Accounting for Stock-Based Compensation-Transition and Disclosure, which amended the transition method specified in SFAS123 and increased the frequency with which firms disclosed their pro forma numbers (FASB 2002). In addition to annual disclosures, SFAS148 required new quarterly disclosures for all firms with stock-based compensation starting in the first quarter of 2003.

  11. By contrast, Rees and Stott (2001) found that disclosed ESO expenses in 1996 are positively associated with annual stock returns. However, the results should be interpreted with caution, as they failed to address the simultaneity issue related to ESO variables. See Sect. 3 for a detailed discussion on the simultaneity issue. Similarly, Bell et al. (2002) found a positive relationship between share prices and disclosed, but not recognized, ESO expenses and concluded that the market appears to value these firms’ ESO expenses not as an expense but as an intangible asset. However, Aboody et al. (2004a, b) argued that Bell et al.’s inferences, drawn from computer software firms, cannot be generalized to other firms, including in their study profitable firms in a variety of other industries and firms in knowledge-based industries beyond computer software.

  12. Consistent with this view, Subramanyam (1996) finds that discretionary accruals are, on average, priced by the markets, suggesting that the flexibility afforded by GAAP allows managers to improve the value relevance of earnings by communicating private information regarding a firm’s future profitability that is not captured in nondiscretionary accruals.

  13. If we set option exercise price equal to the average stock price at fiscal year-end and other inputs equal to their sample mean, an option value is roughly proportional to volatility. Specifically, if stock return volatility declines by 10 % from its sample mean of 35.70–32.13 %, option values will decrease by 8 %.

  14. One might question why or how firm-specific volatility could be relevant to stock prices. Prior studies suggest that share price can be expressed as a function of equity book value, earnings growth potential, and option values (Aboody 1996; Aboody 2004a; Li 2002). Further, the option pricing model shows that option values are positively correlated with a firm’s future stock return volatility (Black and Scholes 1973). Therefore, firm-specific stock return volatility can be considered an important determinant of firm valuation and is relevant to share price. As a result, investors may be under-diversified if they overweigh stocks with higher volatility in their investment portfolios (Goetzmann and Kumar 2008).

  15. If we set option exercise price equal to the average stock price at fiscal year-end and other inputs equal to their sample mean, the option value drops by only 4 % for a 10 % decrease in option life assumption.

  16. The equity cost of capital is equal to the cost of capital for operations, and the financial leverage times the spread between the cost of capital for operations and that for debt. In other words, the equity cost of capital has two components, namely, operating risk and financial risk; both are the basic fundamental determinants of equity risk (Penman 2001).

  17. In this study, we measure nondiscretionary ESO value based on individual firms’ historical experience and the average input assumptions of their peer firms within the same industry sector, as suggested by SFAS 123. Section 3 discusses in detail the measurement procedure for nondiscretionary ESO value.

  18. Hodder et al. (2006) also find that about half of their sample firms use the latitude in ESO value estimation to signal information, but those using latitude opportunistically have estimates with low accuracy. By contrast, Choudhary (2011) examine reliability differences in ESO value estimates across recognition and disclosure regimes and find that recognized ESO values on average show evidence of opportunism, but that the opportunism does not undermine accuracy.

  19. Managers may use their discretion when estimating future dividend yields and to influence expected stock return volatility estimates, which may relate to future operating risk. Such discretion and its resulting effects are what we would expect, although the actual factors chosen by managers are unobservable. Thus, we test the resulting discretionary ESO value to infer unobservable manager discretion through market reactions.

  20. Hodder et al. (2006) find that about half of their sample firms overstated their ESO value, and their results are consistent with the conjecture that, for overstating firms, managers increase their ESO value estimates to convey private information, primarily about future operating risk. More recently, Cheng and Smith (2013) compare firms mandatorily recognizing ESO expense with those voluntarily choosing to do so. They find that mandatory firms with more intensive ESO grants are more likely to undervalue their ESOs, especially in the post SFAS 123R period, which is consistent with Choudhary (2011). Further, they find that mandatory firms with lower future operating risk have better accuracy in the post SFAS123R period, as compared to themselves in the pre SFAS123R period and voluntary firms in the post SFAS123 period. This finding supports the notion that the informativeness of option estimates accounts for the level of accuracy.

  21. Under SFAS 123, ESO expenses are not reported but are disclosed in the footnotes to financial statements. However, firms must disclose pro forma earnings, which show what earnings would have been if firms had adopted the fair value approach. Accordingly, for those who consider ESO expense an expense of the firm, the overstatement of ESO value may affect the perception of firm profitability and its variance. Consistent with this position, Standard & Poor's includes SFAS 123 expense in “core earnings”, which it presents as a standard, comparable number that represents ongoing operating earnings (Byrnes and Derhovanesian 2002; Aboody et al. 2004a).

  22. Overstatement of an ESO value estimate is also likely to receive political scrutiny. However, given the great managerial discretion in estimating ESO value, detecting overstated ESO values could be more difficult than other forms of compensation (e.g., cash pay).

  23. The methodology that we use to measure historical annualized volatility is analogous to Yermack’s (1995). However, Yermack (1995) measures historical volatility by arbitrarily using the last 120 trading days of the fiscal year as the estimation period. We refine his estimation procedure by using “the most recent period that is generally commensurate with the expected option life” as our estimation period, which is suggested by FASB 123R.

  24. Accounting measures are more subject to managerial manipulation (Dechow and Dichev 2002) as well as reported earnings change as accounting standards evolve. In addition, Gombola and Ketz (1983) argue that the cash flow ratio may contain information not found in the accounting profitability ratio with respect to firm performance. Bowen et al. (1986) also suggest that accrual earnings and cash flow numbers convey different signals in terms of predicting a firm’s future cash flows, and that earnings numbers may not provide better forecasts of future cash flows than do cash flow numbers. Ismail and Kim (1989) suggest that cash flow risk measures provide significant incremental explanatory power over that provided by earnings risk measures in explaining the variability in market betas.

  25. These predictions of the relationship between the discretionary component of ESO value and a firm’s operating or financial risk are consistent with the role of information in affecting a firm’s cost of capital. Easley and O’Hara (2004) show that firms can influence their cost of capital by affecting the precision and quantity of information available to investors.

  26. Research shows that weak board monitoring is associated with increased risk-taking by firms (Wang 2012), and that strong board monitoring is associated with better capital market performance during financial crisis (Leung and Hortwitz 2010). Also corporate governance affects the negative association between cash holdings and firm value (Luo and Hachiya 2005).

  27. This corporate governance score is based on detailed listings of corporate governance provisions and on 24 governance rules that measure the level of shareholder rights compiled by the Investors Responsibility Research Center (IRRC) (Gompers et al. 2003). Higher scores represent weaker shareholder power. We are grateful to the IRRC for permitting academic use of this index as well as the contribution of Gompers et al. (2003).

  28. Market value is the closing price at fiscal year-end multiplied by the number of common shares outstanding (Compustat Executive Compensation variable name: MKTVAL). Book value of equity is the sum of the common stock, capital surplus, preferred stock, and treasury stock adjustments (Compustat Executive Compensation variable name: SEQ).

  29. Liu and Ohlson (2000) and Ohlson (2001) suggest that expected future abnormal earnings is also a relevant independent variable, because it captures “other information” affecting the contemporaneous market value beyond current accounting data. GW is the analysts’ earnings growth forecast and can capture benefits associated with ESO value not yet reflected in net incomes (Aboody et al. 2004a). Skinner (1996) indicates that ignorance of future growth in earnings and book values will give rise to a potentially important omitted variable problem. Therefore, we include GW in the above valuation models.

  30. This procedure suggests regressing each endogenous variable being used as a regressor (i.e., FVREP, FVFIT, and FVDISC) on all the exogenous variables in the system and using the fitted values of these endogenous variables from this regression as the required instrumental variables.

  31. The Hausman (1978) test statistics for FVREP (F-statistics = 17.44) and FVDISC (F-statistics = 23.20) both reject the null hypothesis that the suspected variable is exogenous. However, the Hausman test results for the three instrumental variables FVREP*, FVFIT*, and FVDISC* cannot reject the null hypothesis that the instrumental variables are exogenous, suggesting that using these instrumental variables can effectively mitigate this simultaneity issue.

  32. The key difference between value relevance studies that examine price levels and those that examine price changes is that the former are interested in determining what is reflected in firm value and the latter are interested in determining what is reflected in changes in value over a specific period. Thus, if the research question involves determining whether an accounting amount is timely, examining changes in value is the appropriate research design choice (Barth et al. 2001).

  33. Starting in the summer of 2002, more and more firms have announced their intention to recognize ESO expense in determining net income (Aboody et al. 2006). For example, McConnell et al. (2003) identify 72 recognizing companies in 2002. Further, Aboody et al. (2004b) find 155 firms that recognize ESO expense in 2002 and early 2003. Moreover, after deleting firms without needed data for their tests, Cheng and Smith (2013) find 8, 66, and 81 firms to recognize ESO expense at the year-end of 2002, 2003 and 2004. These studies unambiguously indicate an increasing trend for the number of recognizing firms. Notably, the incentives associated with firms’ decision to recognize and those to disclose ESO expense could be very different (Aboody et al. 2004b). Since the number of firms adopting the fair value method increases since 2002, we choose 2001 as the cut-off year to ensure that the sample set is solely composed of disclosing firms. In addition, sample firms are from the year 2001 list of S&P 500 firms obtained from the COMPUSTAT database.

  34. For example, some firms disclose their expected dividend yield in dollar amounts or disclose their current rather than expected dividend yield.

  35. The discretionary ESO value per option estimated by Hodder et al. (2006) is $0.16, which is lower than our estimate. This difference could be attributable to different sample years as well as to the composition of the sample firms.

  36. Untabulated statistics indicate that the mean (median) weighted average value per option disclosed in firms’ 10-K footnotes for our sample years is $13.32 ($11.26). In addition, the mean (median) difference between the weighted average value per option and OFV_REP is $3.56 ($1.57), and the difference is significantly different from zero (p < 0.01). Further, the mean (median) difference between the weighted average value per option and OFV_FIT, the benchmark value per option, is $3.87 ($1.88), indicating that our measure of the discretionary component of ESO value may not fully capture the effect of managerial discretion on the option value estimates. We examine the measurement error issue in Sect. 6.

  37. The correlation between the predicted ESO value (FVFIT) and the reported ESO value (FVREP) is 99 %. One might be concerned about what the discretionary component captures. As discussed in Sect. 3, the main determinants of the option pricing model input assumptions used by managers to measure either FVREP or FVFIT are the same, i.e., historical data and the inputs used by peer companies in the same industry; therefore, it is not surprising that the two variables are highly correlated. However, the correlation coefficient that measures the linear relation between the two sets of data and their difference is not necessarily related to their linear association. Consequently, an observed high correlation coefficient does not necessarily preclude the possibility of finding a difference between FVFIT and FVREP. More importantly, as revealed in Panel A of Table 4, the discretionary ESO value per option (OFV_DISC) is, on average, $−0.28. Further, untabulated statistics show that, for each sample firm, the mean number of options granted per year is 8.70 million, implying that the total dollar amount of the estimated discretionary value for options granted for the year, on average, is about $2.44 million for each firm. This suggests that the economic effect of managerial discretion on the reported ESO value estimate is nontrivial.

  38. The valuation tests are based on two assumptions. First, the benefits of ESO value are fully reflected in BVE, NI, and GW. Second, our ESO variables capture the dilution effect of an ESO. To assess the reasonableness of these assumptions, we exclude GW from regression (4a). After the exclusion, the estimated coefficient on FVREP* captures not only the dilution effect of an ESO, but also the benefit associated with ESOs as represented in the positive pricing effect of GW. That is, FVREP* now stands for the net benefit effect of ESOs. The positive pricing effect associated with GW will weaken or even reverse the negative association between FVREP* and share prices. Untabulated results show that, after omitting GW from Eq. (4a), the coefficients (t-statistics) on BVE, NI, and FVREP* are 0.55 (t = 2.65), 3.45 (t = 3.35) and 12.42 (t = 6.69), respectively. Notably, the valuation effect of FVREP* becomes positive. This finding is consistent with our assumption that GW serves as an effective proxy for the benefit effect of an ESO and therefore needs to be controlled for when assessing the dilution effect associated with ESOs (Skinner 1996). On the other hand, FVREP* in Eq. (4a) is expected to capture the dilution effect (or the partial cost) of ESOs. Removing FVREP* from the regression may help us assess in what manner the cost of ESOs is reflected in BVE, NI and GW. An a priori expectation is that this exclusion most likely will attenuate the positive pricing effect of BVE, NI, and GW since the negative valuation effect of ESOs will be captured by the three variables. Consistent with our expectation, after omitting FVREP*, the untabulated results show that the coefficients (t-statistics) on BVE, NI, and GW are 1.08 (t = 4.91), 4.28 (t = 4.22) and 1.24 (t = 7.32) and all coefficient estimates become smaller than those reported in Table 5. This finding supports the position that FVREP* may serve as a proxy for the cost of an ESO for generating future earnings. Consequently, after the inclusion of FVREP*, the cost of ESOs is less likely to be reflected in the control variables based on our regression specification. To the extent that the control variables in our model fail to capture the full benefit of ESOs, the coefficient estimates on the ESO variables may reflect partial benefit of an ESO and therefore would lessen the strength of the evidence in favor of the information communication hypothesis.

  39. Untabulated OLS results show that the coefficients (t-statistics) of FVREP (in regression 4a) and FVFIT (in regression 4b) are 4.78 (t = 3.84) and 4.79 (t = 4.28), respectively, both significant at the 1 % level. Additionally, the coefficient (t-statistics) of FVDISC (in regression 4b) is −4.21 (t = −0.69), which is negative but insignificant. These results, however, are probably biased and inconsistent, since there is a simultaneity relationship between ESO value and stock price.

  40. As indicated in Sect. 4, the relative magnitude of the coefficients from Eqs. (1a)–(1b) suggests that firms rely more heavily on historical experience than on their industry’s contemporaneous mean, except for the risk-free interest rate. In other words, FVFIT is mainly comprised of historical information, suggesting a lesser valuation effect of FVFIT since valuation-relevant information contained in the expected inputs will most likely have been disseminated into the market. This result is consistent with H1b.

  41. We also repeat the regression analysis for each sample year. Non-tabulated results show that FVDISC* has a significantly negative sign for all years. Additionally, we find qualitatively the same results as those in Table 5 when using the stock price at the 10-K filing date as the dependent variable in the regression analysis instead of the stock price at the fiscal year-end. To assess whether our results are driven by outliers, we winsorize at 0.5 % on both sides of the distributions of the three instrumental variables. Non-tabulated results show that the coefficients (t-statistics) on FVREP*, FVFIT*, and FVDISC* are −26.70 (t = −3.39), −25.04 (t = −2.94), and −96.96 (t = −6.59), respectively. We note that the coefficient estimate on FVFIT* remains negative but becomes significant. The F-test shows that the difference between the coefficient on FVDISC* and that on FVFIT* is significant at the 1 % level (F-statistics = 24.74), suggesting that FVDISC* is incrementally value-relevant relative to FVFIT* and, thus, our inferences remain unchanged.

  42. We also use an alternative annual stock return measure calculated until the 10-K filing date and find qualitatively similar results.

  43. The coefficient estimates on FVREP* and FVDISC* could be sensitive to the linearity specification required by the 2SLS instrument variable (IV) approach, given that the option pricing model is non-linear in its input assumptions. Although Aboody et al. (2004a, b) argue that the estimated coefficient may suffer a scaling issue, the 4 approach does not drive their findings and inferences (Aboody et al. 2004a, p. 270). Further, reported earnings (NI) consist of permanent and transitory components (Beaver 1998), and the latter portion will attenuate the pricing multiple of earnings (Ohlson 1999; Barth et al. 2001; and Chambers et al. 2007). Therefore, we expect the coefficients on ESO value using reported inputs (FVREP*) and on discretionary ESO value (FVDISC*) to have a larger absolute pricing multiple than that on NI. Consistent with our expectations, we find that the coefficient on FVREP* (−21.24) in Table 5 is greater than that on NI (5.67). Similarly, the coefficient on FVDISC* (−53.33) in Table 5 is also larger compared to that on NI (4.17). We also observe a greater pricing multiple (−139.40) on FVDISC* than that on NI (3.62) for understating firms in Table 7. Finally, we compare our coefficient estimates with those reported in previous studies. For example, the coefficients on the ESO expense variable (COMPX*) reported in Aboody et al. (2004a, b) are −57.54, −49.31, and −10.21 for 1998, 1997, and 1996, respectively (Table 4, p. 264). Aboody et al. (2004a, b) also conducted a pooled cross-sectional regression analysis, and their estimated coefficient on COMPX* is −24.06 (footnote 16, p. 262), which is close to ours (−21.24).

  44. We also use an alternative regression specification to test our hypotheses. We include a dummy UNDER, equal to one if FVDISC is negative and zero otherwise, and two interaction terms, UNDER × FVFIT* and UNDER × FVDISC*, to the model. The coefficient estimates (t-statistics) on FVFIT*, FVDISC*, UNDER × FVFIT*, and UNDER × FVDISC* are −11.08 (−1.23), −19.28 (−1.12), −4.77 (−2.14), and −94.95 (−3.42), respectively. These results are consistent with those documented in Table 7.

  45. We also run the regression for each sample year. Untabulated results show that the overall results are consistent with Table 7. For overstating firms, FVDISC* is significantly negative for only two years, 1996 and 2001. In addition, for understating firms, FVDISC* is significantly and negatively related to the stock price for all years, which is consistent with Table 7 and H2a. This result, again, suggests that the discretionary component of ESO value for understating firms is mainly driven by the information incentive and is robust across different years.

  46. The range of the random distribution is arbitrarily determined. We also use alternative ranges for the uniform distributions, e.g., a five-percent increase or decrease in input assumptions, and arrive at similar results.

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Acknowledgments

We appreciate very helpful comments from an anonymous referee and seminar participants at the 2013 4th Asia-Pacific Business Research Conference at Singapore. Chii-Shyan Kuo acknowledges financial support from the National Science Council of Taiwan (NSC 97-2914-I-007-012-A1).

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Authors and Affiliations

  1. Department of Business Administration, College of Management, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan, ROC

    Chii-Shyan Kuo

  2. Department of Accounting, John Cook School of Business, Saint Louis University, St. Louis, MO, 63108, USA

    Xu Wang

  3. Department of Quantitative Finance, College of Technology Management, National Tsing Hua University, Hsinchu, 30013, Taiwan, ROC

    Shih-Ti Yu

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  1. Chii-Shyan Kuo
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  2. Xu Wang
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  3. Shih-Ti Yu
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Correspondence to Chii-Shyan Kuo.

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Kuo, CS., Wang, X. & Yu, ST. Investor perception of managerial discretion in valuing stock options: an empirical examination. Rev Quant Finan Acc 47, 733–773 (2016). https://doi.org/10.1007/s11156-015-0518-0

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