Stock Option Compensation Incentives and R&D Investment Returns

Transcription

1 Stock Option Compensation Incentives and R&D Investment Returns Bruce K. Billings Florida State University James R. Moon, Jr. Georgia State University Richard M. Morton Florida State University Dana M. Wallace University of Central Florida October 2014 Abstract: This study investigates the effects of risk-taking incentives (i.e., the sensitivity of CEO wealth to stock return volatility, or vega) on the profitability of research and development (R&D) investment. While prior research documents that vega encourages managers to invest in higher levels of R&D, we show that the profitability of that R&D is also sensitive to vega. Based on models predicting that higher levels of option compensation can induce risk-aversion, we show that the return on R&D investment is a concave function of vega, implying that moderate (excessive) levels of vega increase (reduce) the return on R&D investments. We also find that investor pricing of R&D conditional on vega reflects a similar non-linear relation. As expected, these relations are concentrated among firms with less investor oversight. Our evidence is consistent with executive stock options encouraging managers to invest in risky, but profitable projects, with the caveat that excessive levels of risk-taking incentives lead to diminishing returns on investment. JEL classification: G31, G32, G34, J33 Keywords: Executive compensation; Managerial incentives; Risk taking; Research and Development We thank Jeff Paterson, Kenny Reynolds, Sarah Shaikh, workshop participants at Florida State University, and participants at the 2013 AAA Annual Meeting for helpful comments and suggestions.

2 1. Introduction We investigate whether higher levels of stock options can have unintended consequences by inducing some managers to invest in a greater proportion of less profitable R&D investments. The prevailing view in prior literature is that stock option compensation helps to align managers and investors risk preferences, incentivizing otherwise under-diversified, risk-averse managers to pursue riskier positive NPV opportunities (Jensen and Meckling 1976; Guay 1999; Core and Guay 1999). 1 The risk-taking incentives associated with stock options primarily arise because options enhance the sensitivity of managers wealth to their firms stock price volatility (i.e., vega). While this sensitivity is generally expected to encourage managers to pursue riskier and commensurately profitable investments, such as R&D projects, prior research also suggests that, in some circumstances, higher levels of options have the potential to make managers more risk averse. Several theoretical papers argue that the convex payoff structure of options may not sufficiently compensate for a manager s increased exposure to total firm risk (Lambert, Larcker, Verrecchia 1991, Carpenter 2000, Meulbroek 2001, Ross 2004). Although the specific assumptions and settings vary, these analytical models collectively suggest that increasing the variance of the expected payoff could lead the risk-averse manager to reduce the volatility of the underlying assets. In other words, giving the manager more options can make him or her seek less risk. In sum, this alternative theory raises doubts about the ability of option compensation to consistently incentivize managers to pursue higher risk/higher return investments. In addition to these analytical findings, limited empirical research also challenges the notion that greater levels of stock options encourage risk-taking. For instance, Dittmann and 1 Empirical research finds a positive association between risk-taking incentives and risky choice related to financing decisions (Dong et al. 2010, Chava and Purnanandam 2010), investing decisions (Rajgopal and Shevlin 2002, Coles et al. 2006) and tax policy (Rego and Wilson 2012). 1

3 Maug (2007) apply efficient contracting models to the compensation of a sample of CEOs and derive the optimal mix of stock, option, and salary to properly align incentives. Surprisingly, the empirical predictions from these models suggest a minimal role for executive stock options, a result difficult to reconcile with the extensive use of options observed in practice. Hayes et al. (2012) similarly question the importance of stock options in motivating policy choice. Specifically, they investigate the impact of Statement of Financial Accounting Standards (SFAS) 123R, which requires firms to recognize an expense related to option compensation. They report a dramatic decline in stock option usage and vega after this accounting change without a corresponding decrease in risk-taking (i.e., investing or financing decisions). They infer that the prevalence of executive stock options prior to SFAS 123R was likely driven by preferential accounting treatment, not risk-taking incentives induced by option-based compensation. In light of the research discussed above, we consider whether option compensation unambiguously leads to more risky investment that enhances firm value. The theoretical work to date suggests that a manager s increased exposure to firm risk brought on by additional stock option awards can more than offset the options convex payoff function, prompting the manager to reduce asset volatility. Thus, while low-to-moderate levels of stock options have the potential to encourage risky investment by rewarding managerial risk-taking, we posit that high levels of options will accentuate a manager s risk exposure and lead to investments with a lower risk/lower return profile. To assess the potential incentive effects of stock options on risky investment, we build on prior literature and focus on the relation between vega and R&D investment in particular. As noted above, prior research finds that vega is associated with relatively more R&D (Coles et al. 2006). However, a greater level of spending does not necessarily equate to greater investment 2

4 risk. To proxy for the ex ante risk, which is unobservable, we examine the ex post return on R&D investment. Consistent with the classic risk-return relation, more risky investments should yield greater returns, on average. Accordingly, we expect that moderate levels of vega encourage greater risk-taking, which manifests in greater returns on R&D investment. As options comprise a higher proportion of the manager s wealth, however, the potential for vega to accentuate the manager s risk aversion increases, and we expect this to be reflected in relatively lower risk/lower return R&D investments. Thus, our study addresses the important and unresolved issue of whether stock option compensation may be counterproductive for some managers as an incentive mechanism for investment behavior. 2 To test our predictions, we regress future, industry-adjusted ROA (using earnings before R&D) on current R&D expense and several control variables. We allow the coefficient on R&D to vary with the level of vega, delta, and firm size (Ciftci and Cready 2012). 3,4 If higher vega more generally incentivizes managers to invest in higher risk/higher return R&D investments, we should observe a positive coefficient for R&D conditional on vega. Interestingly, while we find that the coefficient on the vega-r&d interaction term is positive, it is insignificantly different from zero at conventional levels. In other words, we fail to find that vega linearly increases the profitability of R&D investment, despite theory suggesting vega should lead managers to take on 2 Although the use of executive stock options has declined in the past decade, they continue to play an important role in compensation packages. Equilar, a provider of executive compensation and governance data, reports that 65.2% of S&P 1500 firms awarded new stock options to employees in 2012, and the median number of options granted was 578,000. The median number of options outstanding for S&P 1500 firms was over 2.8 million (Equilar 2013). 3 Delta is the sensitivity of compensation to the firm s stock price. As Armstrong, Larcker, Ormazabal, and Taylor (2013) point out, the conflicting effects of higher expected returns to R&D investment and greater investment risk make predictions related to delta ambiguous. Therefore, rather than making predictions about the effects of delta, we control for it in our analysis, and focus on the role of vega, which is generally presumed in prior research to have an unambiguous incentive effect to encourage risky investment. 4 We use vega and delta values from year t-1 to ensure that option-based incentives are in place in the period in which the R&D investments are chosen. 3

5 riskier projects with higher expected returns. However, this result is consistent with riskaversion induced by high levels of vega partially offsetting the expected positive effect on investment risk. To assess whether vega has a non-linear effect on risk-taking, we extend our model to allow for a concave relation between ROA and R&D conditional on vega. Specifically, we introduce interactions between R&D and vega-squared (and delta-squared) to the model. Including the R&D*vega-squared interaction term models the return on R&D as a quadratic function of vega. 5 Consistent with our predictions, we find that the return on R&D increases in the linear vega term, suggesting vega leads to a higher return per dollar of R&D, up to a point. More importantly, we find a negative coefficient on the R&D*vega-squared interaction term, indicating relatively lower returns on R&D investment as vega increases beyond an inflection point. Given this result, a natural question that arises is whether investors are aware of and efficiently price the concavity in the rate of return on R&D investments attributable to vega. Efficient pricing of this effect would indicate that investment choices by high vega managers have the potential to reduce firm value (relative to more moderately incented managers). To investigate this possibility, we regress current annual abnormal stock returns on the R&D*vega and R&D*vega-squared interactions, controlling for the direct effects these and other variables may have on abnormal returns. Consistent with the future profitability results, the coefficient on R&D*vega is significantly positive, while the coefficient on R&D*vega-squared is significantly 5 We choose to use a quadratic specification for the return on R&D because a quadratic function allows for (rather than imposes) both a concave relation (a second derivative less than zero) and an inflection point (a point at which the first derivative switches from positive to negative). Alternative concave functions, such as a natural logarithm or square root, do not allow for the presence of an inflection point and impose a concave functional form. We believe the use of this specification is most appropriate in testing whether excessive vega leads to declining returns on R&D. 4

6 negative. Thus, our results suggest that investors perceive and price the diminishing rate of return on R&D investment for firms with higher levels of vega. To assess the efficiency of this pricing, we estimate this same model after replacing current abnormal stock returns with threeyear ahead abnormal stock returns. We fail to find significant coefficients for R&D conditional on either vega or vega-squared. Therefore, we find no evidence indicating that investors inefficiently price the implications of higher levels of vega for future profitability as it relates to R&D investments. In sum, these results suggest that, for certain high-vega CEOs, firm value declines with R&D, consistent with these managers investing in a higher proportion of low risk/low return projects. Given our expectation that investors prefer a level of vega that overcomes managers risk aversion while still producing wealth-maximizing investments, we next investigate whether diminishing returns on R&D that result from higher vega are concentrated in firms with weaker investor oversight. Such settings create a greater opportunity for managers to make suboptimal investment decisions. We examine this possibility by partitioning our sample into two subsamples based on the level of institutional holdings (i.e., high and low) and re-estimating our R&D profitability model within each partition. Institutional investors, such as pension funds and mutual funds, control over half of publicly traded shares in the United States (Blume and Kleim 2012). By virtue of their size, institutional owners are likely to exercise more oversight and vigilance in monitoring the profitability generated by managers investment in R&D projects and exerting influence to avoid lower risk/lower returns that are not commensurate with greater stock option compensation. Our results, as expected, indicate that the non-linear relation between future ROA and R&D induced by higher levels of vega is indeed largely concentrated in firms with lower institutional holdings. In fact, we fail to find any relation between vega and the 5

7 return on R&D for firms with high institutional ownership, suggesting that higher levels of shareholder activism may serve as a substitute for risk-increasing incentives in affecting the return on R&D investments. Our analysis contributes to existing research related to the influence of stock options on executive decision making, with a particular focus on R&D investment policy. A number of prior studies suggest that vega encourages managers to make investment decisions that are perceived as relatively more risky (Rajgopal and Shevlin 2002, Coles et al. 2006). However, our study suggests that the incentive effects of vega have a limit beyond which they may be counterproductive, prompting managers to invest in R&D projects that yield lower rates of return. These results are consistent with studies that suggest that risk-averse, less-diversified managers who receive a higher proportion of their wealth in option compensation may reduce the volatility, and therefore return, of the option s underlying assets (Lambert et al. 1991; Carpenter 2000; Meulbroek 2001; Ross 2004). We also extend Hanlon, Rajgopal, and Shevlin (2003), who document a positive and nonlinear relation between future profitability and the fair value of stock option grants. Their study investigates whether option awards are more consistent with incentive alignment or rent extraction. 6 Alternatively, we assess whether the incentive structure of stock options (i.e., vega) affects a specific manager behavior (i.e., R&D investment) that we expect will directly contribute to future profitability. Thus, in contrast to Hanlon et al. (2003), we focus on the effectiveness of the incentive alignment mechanism with respect to investment choice. 6 Hanlon et al. (2003) view stock options as an investment by the firm and assess whether that investment is associated with future earnings after controlling for other factors that affect performance, including R&D expenditures. They suggest that greater stock option values imply higher quality managers, who enhance future performance. 6

8 Our study also contributes to the stream of research examining the pricing of R&D. In general, prior research finds that R&D investment relates to increased firm value (Lev and Sougiannis 1996; Chambers et al. 2002; and Ciftci and Cready 2011). Our evidence suggests investors recognize and efficiently price the rate of return on R&D investments as it varies with vega. This pricing behavior implies that, for firms with excessive levels of vega, the less profitable incremental investment induced by vega has negative value-implications for shareholders. Finally, we contribute to research on the governance role of external parties (i.e., institutional investors). We document that the non-linear effect of vega on R&D profitability is concentrated in firms with less oversight (i.e., lower institutional ownership). This result suggests that increased oversight and activism associated with institutional investors may act as a substitute for option-based risk-taking incentives. Overall, our results provide a unique perspective on the incentive effects of executive stock options, particularly those related to vega on the profitability (rather than level) of R&D investment. In the next section of the paper, we further discuss the related literature and develop our hypotheses. We provide the research design and results in Section 3, and we conclude in Section Hypothesis Development 2.1 Studies favoring stock option compensation as a means to align incentives Theory suggests that stock option compensation can be beneficial by aligning manager s interests with shareholders (Jensen and Meckling 1976; Haugen and Senbet 1981; Smith and Stulz 1985; Lambert 1986). Specifically, managers compensated with share-based pay theoretically have fewer agency conflicts because firm ownership motivates managers to behave 7

9 more like investors. However, under-diversified managers with large levels of wealth invested in their organization may exhibit a lower appetite for risk than the firm s investors. Therefore, option-based compensation theoretically overcomes this risk-aversion tendency by introducing a convex payoff function whereby managers benefit from stock price gains but not losses. Despite theory suggesting otherwise, the widespread use of stock options to compensate executives has led to criticism that it is a form of hidden compensation, rather than incentive alignment. That is, managers exercise influence over their compensation packages to extract rents. In an effort to test these contrasting views of stock option compensation, Hanlon et al. (2003) relate option awards to future profitability. They argue that stock option grant fair values proxy for a firm s investment in a higher quality manager. If options align incentives, the cost of investing in the manager should be positively related to the benefit realized through higher future earnings. However, if options enable managers to instead engage in rent extraction, then the correlation between stock option values and future earnings will be less positive, zero, or negative. Hanlon et al. s (2003) initial results indicate a negative relation between stock option values and future earnings. However, after introducing a second order term, the squared value of stock option grants, they observe a positive coefficient for the first order term and a negative coefficient for the second order term. They conclude that their evidence of positive payoffs to stock option grants is consistent with options aligning incentives rather than creating opportunities for rent extraction. Relatedly, an extensive body of research examines how specific components of stock options, such as the sensitivity of compensation to the firm s stock price (i.e., delta) and the sensitivity of a manager s portfolio to stock price volatility (i.e., vega), influence managers behavior and corporate policy (see Murphy 1999). For example, Coles et al. (2006) document 8

10 that higher vega is associated with greater R&D spending and lower investment in property, plant, and equipment. Rajgopal and Shevlin (2002) find that oil and gas firms with higher vega engage in more risky exploration activities. Dong et al. (2010), Chava and Purnanandam (2010), and Coles et al. (2006) link higher vega to higher leverage. 7 Rego and Wilson (2012) report that vega is associated with more aggressive tax avoidance strategies. Each of these studies supports the premise that the manager, acting in the best interests of the shareholder, takes on greater risk. 2.2 Studies challenging stock option compensation as a means to align incentives A separate stream of analytical research, however, challenges the notion that stock options unambiguously incentivize managers to increase the risk profile of the firm. Lambert et al. (1991) show that a manager s risk aversion may dominate in-the-money option payoffs such that the manager becomes averse to increasing the variance of the payoffs. Similarly, Meulbroek (2001) investigates the tension between incentive alignment and the manager s lack of diversification, noting that the manager is exposed to total firm risk while diversified investors are only exposed to systematic firm risk. She shows that managers value their options at less than fair market value, indicating that managers expected returns may be too low to compensate them for their risk exposure. Carpenter (2000) also suggests that option compensation does not necessarily lead managers to be more risk seeking. Her model demonstrates that increasing the proportion of options in a manager s total portfolio value can increase the manager s exposure to the underlying assets risk. This, in turn, can cause the manager to decrease the volatility of the 7 Chava and Purnanandam (2010) also find that CEO vega incentives are associated with lower cash balances, but Liu and Mauer (2011) find the opposite relation. To our knowledge, the reason for this discrepancy has not been investigated. One possibility is that Liu and Mauer (2011) scale vega by total compensation whereas most studies do not employ this scalar. 9

11 underlying assets. In other words, giving the manager more options can make him seek less risk. 8 Empirical evidence also challenges the relation between risk-taking and option-based compensation. Dittmann and Maug (2007) analyze the optimal balance of executive stock, option, and base salary contracts using efficient contracting models. Their results rarely predict option compensation. They conclude that either current contracting models are flawed or observed compensation practice suffers from significant defects. Hayes et al. (2012) investigate the impact of SFAS 123R, which requires firms to expense stock options at their fair values rather than intrinsic values. This change eliminated an accounting advantage of stock option compensation relative to other forms of compensation. Hayes et al. (2012) report a dramatic decline in stock option usage without a corresponding decline in investment or financial policy related to risk taking following implementation of SFAS 123R. Their findings do not support the view that creating incentives for risk taking is the primary rationale for stock option compensation. To the extent that option compensation is intended to align managers and investors utility for risk, these studies collectively raise the concern that the high levels of option compensation observed in practice may be counterproductive for some managers by increasing their risk aversion. 2.3 Synthesis and Hypotheses Development As described in the previous section, prior studies indicate that vega motivates managers to take on greater risk by investing in greater levels of R&D (i.e., Coles et al. 2006). We extend these studies by considering how vega relates to the profitability of that R&D. If the intent of 8 As Meulbroek (2001, p. 7) points out, if stock-based compensation were purely designed to align incentives, there would be no natural stopping point, and managers compensation would be 100% equity-based. 10

12 stock options (i.e., vega) is to encourage managers to engage in more risk-taking, the ultimate benefit to investors should then be greater returns. Given prior evidence that investors positively price investment in R&D and under the presumption that vega incentives are expected to induce managers to take on riskier but profitable projects, we expect that there is a positive relation between future profitability and R&D investment conditional on vega. Therefore, our first hypothesis specifies this basic risk-return relation that follows from compensating managers with incentive stock options. H1: The rate of return on R&D investment increases with vega. As discussed previously, Hanlon et al. (2003) examine the relation between stock award grants and future profitability. While they reach the conclusion that stock option compensation is more consistent with incentivizing managers, we consider how the degree of riskiness in investment (induced by stock options) affects future profitability. Thus, in contrast to Hanlon et al. (2003), the question we address is not whether managers are incentivized or are opportunistic, but whether greater incentives are effective at inducing greater risk and commensurately greater returns. As a result, we specifically examine the risk-inducing incentives (i.e., vega) in stock options, rather than their fair value. Furthermore, we evaluate how the incentive-intensity captured by vega affects manager behavior vis-à-vis the risk/return profile of the R&D investment portfolio rather than the direct relation between option value and future income as studied in Hanlon et al. (2003). As we note in the prior section, despite the extensive theory and empirical evidence suggesting vega should encourage risk taking, theory and evidence also supports the notion that higher levels of stock options may lead to less risky investment. We expect that the potential 11

13 negative consequences will be more concentrated in firms that rely on higher levels of stock option compensation. Higher stock option compensation likely increases the manager s proportional option wealth and places the risk-averse manager at greater risk of holding a lessthan-fully diversified portfolio. 9 While stock options provide the manager with upside gain potential, they do not protect the manager from downside risk that can adversely affect an underdiversified manager. 10 Managers facing higher risk exposure resulting from a higher proportion of their wealth in stock options may reduce the volatility of the underlying assets by investing in relatively less risky R&D projects. Consistent with the classic risk/return relation, we expect that these less risky projects will be evidenced by a lower return on R&D investment, implying diminishing marginal returns as vega increases. Note that we are not simply predicting diminishing returns to greater R&D investment, but rather that the accounting rate of return on R&D conditional on vega is concave. Our second hypothesis captures this notion by modifying H1 to allow for increasing returns on R&D investment for low to moderate levels of vega but relatively lower returns for high levels of vega. H2: The rate of return on R&D investment is a concave function of vega. Alternatively, we might expect that shareholders who anticipate that greater vega could lead to greater risk aversion will take active measures to prevent such outlays in the first place. This requires some level of investor activism, an issue we discuss later in conjunction with H4, which would work against us finding support for H2. 9 Hall and Liebman (1998) show that mean stock option grant values increased by 682.5% from 1980 to 1994, while mean salary and bonus only increased by 97.3% over this same period. 10 Ross (2004) argues that granting put options as compensation protects managers from downside risk and encourages risk-taking. 12

14 Related to these analyses, Shen and Zhang (2013) examine the relation between vega and future profitability, but focus on a small sample (approximately 900 firm-year observations) of firms that substantially increase R&D spending in a given year. They report lower future profitability for their portfolio of high vega firms relative to low vega firms. 11 Our analysis of the relations predicted in H1 and H2 differs substantially from their study in several important ways. First, we examine variation in the level of R&D, which reflects firms more persistent investment policy; Shen and Zhang (2013) examine large R&D increases, which are affected more by transitory spending. More importantly, we directly estimate R&D profitability conditional on vega. In contrast, Shen and Zhang (2013) report mean aggregate earnings for vega portfolios. They also report a positive correlation between vega and R&D increases, making it difficult to unambiguously interpret their results. By not controlling for the magnitude of the R&D increase across the vega portfolios, their evidence of lower mean profitability for the high vega portfolio could be due to the effects of vega, excessive R&D, or some combination of the two. Unlike Shen and Zhang (2013), our research design focuses on the interactive effect of R&D and vega for future profitability while controlling for their direct and separate effects. After examining the relation between R&D and future profitability conditional on vega, we next consider the impact of R&D on firm value conditional on vega. If moderate levels of vega encourage managers to increase their risk exposure leading to more profitable investments, we expect the market to respond positively. 12 To the extent that higher levels of vega lead to 11 Shen and Zhang (2013) also examine future (but not contemporaneous) stock returns within vega quintiles. They find higher future returns for low vega relative to high vega firms (though abnormal returns are non-monotonic). This research design represents a test of investor efficiency; however, it is unclear whether any inefficiency is related to large increases in R&D or the magnitude of vega. 12 As mentioned previously, prior research documents a positive association between R&D investment and abnormal stock, bond, and firm returns (Eberhart et al. 2008). 13

15 declining rates of return on R&D, the natural question is whether this non-linear relation is reflected in stock prices. We view this as a joint hypothesis. The first part of that hypothesis is whether the lower accounting-rate-of-returns to high vega-induced R&D are in fact valuedecreasing, and the second component is whether investors perceive and price the consequences of those lower returns to R&D. Although we may observe lower returns on R&D investment for higher vega firms, it is not necessarily the case that the activity is value-decreasing. Without knowledge of the next best alternative use of those resources, we cannot definitively conclude that the investment is not in the best interest of the firm. Even if the incremental investment in R&D is value-decreasing, there may be reasons to believe that prices will not reflect that. For example, prior research argues that information asymmetries are greater for firms with higher levels of R&D. 13 This suggests that investors may be hindered from fully appreciating the consequences of vega for future returns to R&D. Alternatively, value enhancements attributable to R&D investment may not be fully realized in the period over which we measure future earnings. Nevertheless, assuming some ability of investors to perceive and price the implications of R&D, our next hypothesis proposes that the non-linear effect of vega on R&D profitability similarly affects firm value. H3: The stock price reaction per dollar invested in R&D is a concave function of vega. Finding evidence consistent with H3 suggests that investors perceive the lower return on R&D investment due to high levels of vega. However, it also suggests a general inability on the 13 For example, Aboody and Lev (2000) report substantially greater insider trading gains for R&D intensive firms relative to non-r&d firms 14

16 part of stakeholders to prevent managers from investing in marginally lower risk/lower return R&D projects. Accordingly, we next consider whether different levels of investor activism can explain cross-sectional differences in the future performance of vega-induced R&D. Gillan and Starks (2000) report that shareholder proposals sponsored by institutions garner substantially more support than those sponsored by individuals, suggesting that institutional investors are more successful at shareholder activism. Moreover, Bushee (1998) finds that when institutional ownership is high, managers are less likely to myopically cut R&D to maintain earnings growth. This evidence supports the view that the sophistication of institutional owners allows them to better monitor and discipline managers by ensuring managers focus on long-term value rather than short-term growth. We consider whether increased attention and activism by institutional investors may constrain managers from succumbing to the incentives created by high vega to lessen the volatility of firm assets by investing in R&D projects that yield lower returns. Alternatively, we expect that a lack of attention or activism on the part of individual investors may fail to constrain high vega managers from investing in R&D projects that yield lower returns on investment. We express this in the following hypothesis in alternative form: H4: The concave relation between vega and the rate of return on R&D investment is mitigated for firms with higher levels of institutional oversight. 3. Research Design and Results 3.1. Sample and Data We obtain required annual financial statement information from the Compustat Annual Fundamentals file, returns data from the Center for Research in Security Prices (CRSP) monthly 15

17 and daily files, and institutional ownership from Thomson Reuters Institutional Holdings (13f) database. We obtain estimates of vega and delta from the website of Dr. Lalitha Naveen. 14,15 To test H4, we proxy for investor oversight using the percentage of institutional ownership. For our empirical analyses, we use lagged values of vega and delta to ensure the risk-taking incentives are in place at the start of the period in which managers select R&D. Further, given we use three years of future earnings to estimate the return on R&D, we require each observation to have financial statement and return data from t to t+3. Thus, our data span from 1992 to 2012, but our sample period is restricted to 1993 to For most analyses, we restrict our sample to firms with positive, non-missing values for R&D (Compustat data item XRD). 16 The intersection of these data sources and data screens for our sample time period results in 7,639 firm-year observations. Table 1 provides descriptive statistics of our sample. Consistent with most prior research, we use the natural logarithm of vega and delta in our empirical analyses as our measures of risk and price sensitivity (Vega and Delta, respectively) to control for extreme skewness in the untransformed distributions. The median of unlogged Vega (Delta) is 53.1 (224.0). Thus, a one-percent increase in implied volatility (stock price) increases the median CEO s wealth by approximately $53,000 ($224,000). These compare favorably with Coles et al. (2006) who report median vega and delta estimates of $34,000 and $206,000, respectively, for an earlier time period. Given we require that all sample firms report R&D expense, we report a relatively high mean (median) R&D of 7.0% (4.4%) of assets. To assess 14 We graciously thank Dr. Lalitha Naveen for providing vega and delta estimates and explanations on her website ( Details of these calculations can be found in Coles, Daniel, and Naveen (2013). 15 Detailed variable definitions can be found in Appendix A. 16 The one exception to this restriction is when we analyze the relation between R&D and vega, using R&D as the dependent variable. Dropping firms with zero reported R&D would yield a censored distribution, violating a requirement of OLS. 16

18 the return on R&D investment, we calculate future return on assets (ROA) using industryadjusted average earnings over the three years following the R&D expenditure. Specifically, we use earnings before interest, taxes, depreciation, and amortization (EBITDA) plus R&D and advertising expense, scaled by total assets in year t-1 (ROA3). We industry-adjust future earnings to increase the variability in future earnings (since abnormal earnings are less persistent) and to reduce the likelihood that industry differences in R&D profitability explain our results. Further, we expect new R&D investments more likely correspond to new abnormal earnings in future periods. Note that we add back R&D expense to earnings to avoid inducing a mechanical relation between R&D and earnings (since R&D is a negative component of earnings, and the level of R&D is relatively persistent). The mean ROA3 is 0.00%, suggesting a fairly symmetric distribution of abnormal future earnings. Data requirements imposed on the sample also result in a sample of fairly large firms. The mean (median) market value of equity (MVE) is 9.5 billion (1.5 billion). In addition, our sample firms have a heavy concentration of institutional ownership (InstHold), averaging 64%, and modest levels of leverage (Lev), averaging 18.9%. Table 2 reports Spearman (Pearson) correlations among select variables above (below) the diagonal. Italics indicate statistically (and economically) insignificant correlations (p>0.05). As expected, the correlation between Vega and Delta is positive, and both are positively related to firm size. These two measures of executive compensation incentives typically move in tandem, highlighting the importance of controlling for Delta in evaluating the role of Vega. We also observe that ROA3 is positively related to R&D, Vega, and Delta. Despite prior research documenting a link between Vega and R&D, we do not find a positive bi-variate relation between the two constructs. To investigate the relation between R&D and vega further, we 17

19 estimate a multivariate model similar to that in Coles et al. (2006). Table 3 displays the results of regressing R&D on Vega, Delta and other determinants of R&D. Column (1) reports results using standard OLS regression, and column (2) reports results using a Tobit model to address censoring of the dependent variable. Reported t-statistics reflect standard errors clustered by firm. We observe a significant positive coefficient on Vega in both columns (1) and (2). 17 Thus, we are confident our sample firms support the notion that stock option vega leads to greater investment in R&D Tests of H1 and H2 Consistent with the view that stock options incentivize managers to invest in worthwhile projects, H1 specifies that the rate of return on R&D investment increases with Vega. To test this hypothesis we regress ROA3 on R&D and R&D interacted with Vega, along with various controls as shown below. Note that this specification does not allow for a non-linear effect from Vega, as predicted in H2.!"#3!,! = +!&!!,!!! +!!! Vega!,!!! +!!!!"#$%!,!!! +!!!!"#!,! +!!!"#$!,!!! +!!!!"#$%!,!!! +!!!!"#!,! +!!!!"#!,! +!!!!"#!,! +!!"!"#!,! +!!!!!"#$!,! +!!" (1) In this model, the terms in parentheses determine the earnings generated by a dollar of R&D over the following three years, referred to as the return on R&D. Our parameter of interest, β 2, captures how that relation varies with the level of Vega. If Vega leads to more risky investment with greater expected returns, β 2, should be positive. 17 We also run two additional estimations of this model using: (1) Fama-MacBeth regression (Fama and MacBeth 1973) and (2) standard OLS with firm fixed effects. Vega remains significantly positively associated with R&D in the Fama-MacBeth estimation, but loses significance when firm fixed effects are added to the OLS model. The latter result suggests that the within-firm variation in the level of R&D is insensitive to risk-seeking incentives. As discussed shortly, this result differs significantly from the effect of vega on within-firm variation in the profitability of R&D investment. 18

20 As mentioned above, Vega and Delta are highly correlated, so it is important to control for any incentive effects Delta may have on R&D profitability. Therefore, we also allow the return on R&D to vary with Delta in Eq. (1). Firm size can also create economies of scale that allow larger firms to better exploit R&D opportunities and generate larger return on investment (Ciftci and Cready 2011). Accordingly, we include MVE in the determinants of the return on R&D in the model. Thus, the return on R&D is a function of Vega, Delta, and MVE. We also include main effects of Vega and Delta to capture other ways these incentive proxies influence manager behavior and impact future earnings. Finally, we include several firm characteristics that likely relate to future profitability. Larger firms, reflected by MVE, are less likely to incur losses. Firms with a higher market-to-book ratio (MTB) have greater expected economic rents or growth opportunities, which likely generate future earnings. Greater leverage (Lev) implies fewer growth prospects and potentially some level of financial distress. Advertising (Adv) and tangible assets (Tang) represent other investment outlays that contribute to future earnings. We estimate Eq. (1) using two specifications. First, we include firm and year fixed effects, which allows R&D, Vega, Delta, and our other control variables to explain the withinfirm variation in future earnings. We believe this is a more powerful setting to study our research questions as it controls for unobserved fixed-firm characteristics that affect future earnings and focuses on the effects of intertemporal fluctuations in Vega and R&D with respect to future ROA. Nevertheless, we also utilize annual, cross-sectional regressions to examine how vega explains the profitability of R&D in the cross-section (Fama and MacBeth 1973). This analysis allows us to assess the effects of variation in the incentive structure and R&D spending across (rather than within) firms. Thus, each provides unique insight. 19

21 We report results from estimating Eq. (1) in Table 4. Columns (1) through (3) provide results using OLS including firm and year fixed effects, with standard errors clustered by firm. Columns (4) through (6) report average coefficients and associated t-statistics based on annual, cross-sectional regressions per Fama and MacBeth (1973). Results reported in columns (1) and (4), which exclude the Vega and Delta interaction terms, confirm Ciftci and Cready s (2011) result that the return on R&D increases in firm size. Specifically, we find a significantly positive coefficient on the interaction between R&D and MVE (β 4 = 0.162, p < 0.01 in column 1 and β 4 =.318, p < 0.01 in column 3). Next, we estimate Eq. (1), this time including Vega and Delta interactions, using both OLS with firm and year fixed effects and Fama-MacBeth regressions, and present the results in columns (2) and (5) of Table 4, respectively. We fail to find that the rate of return on R&D increases with Vega, as predicted in H1. The coefficient on Vega*R&D (β 2 = in column 2 and β 2 = in column 5) is statistically insignificant at conventional levels. Thus, we do not find support for H1. Among the control variables, MTB is positively related to future earnings, as expected. The incremental effect of MVE after controlling for MTB is negative. Lev is negatively related to future ROA, as well. To test H2, we modify Eq. (1) to allow concavity in the effect of Vega on the return on R&D. Specifically, we incorporate Vega 2 as an interaction with R&D and as a main effect in the model, which is presented in equation (2):!!!"#3!,! = α +!&!!,! (!! +!!!!"#$!,!!! +!!!!"#$!,!!! +!!!!"#$%!,!!! +!!!!"#$%!,!!! +!!!!!!"#!,! ) +!!!"#$!,!!! +!!!!"#$!,!!! +!!!!"#$%!,!!! +!!!"!"#$%!,!!! +!!!!!"#!,! +!!!"!"#!,! +!!!"!"#!,! +!!"!"#!,! +!!!"!"#$!,! +!!" (2) 20

22 H2 predicts that the return on R&D is concave in Vega, meaning the second derivative of the return on R&D is negative. This implies that β 2 (the coefficient on the Vega and R&D interaction term) is positive and β 3 (the coefficient on the Vega 2 and R&D interaction term) is negative. For control purposes, we similarly include squared terms for Delta, although we make no prediction of a non-linear relation with Delta. 18 We report results from estimating Eq. (2) in columns (3) and (6) of Table 4. As predicted, we find that the return on R&D varies non-linearly with Vega, as β 2 is now significantly positive (β 2 = 0.256, p-value < 0.05 in column 3 and β 2 = 0.247, p-value < 0.05 in column 6) and β 3 is significantly negative (β 3 = , p < 0.05 in column 3 and β 3 = , p < 0.05 in column 6). This suggests that the accounting return on R&D is a concave function of Vega. That is, Vega enhances the profitability of R&D, but as Vega increases to relatively high levels, the rate of return on R&D increases at a diminishing rate, and, in fact, declines at some point in the distribution of Vega. 19 To get a better idea of where in the distribution the diminishing rate of return begins, we calculate the inflection point using the first derivative of, and coefficient estimates from, Eq. (2) with respect to Vega. Using the OLS (Fama-MacBeth) regression coefficients, we estimate that 18 As a robustness test, we also re-estimate Eq. (2) with the inclusion of additional terms. First, MVE 2 and the interaction between MVE 2 and R&D are included since MVE 2 is arguably correlated with Vega 2 and Delta 2. Second, we include the fair value of stock option grants and fair value of stock option grants squared as control variables given evidence in Hanlon et al. (2003). Including these additional terms does not affect inferences drawn from reported results. 19 We recognize that many papers within the executive compensation literature address the potential endogeneity between executive compensation and firm performance. However, research finds little support for the notion that future pay-offs influence current compensation (Holthausen, Larcker, and Sloan 1995; Rajgopal and Shevlin 2002). Thus, like Hanlon et al. (2003), we treat incentive structure as an exogenous determinant of future returns. 21

23 the rate of return to R&D peaks at the 34 th (17 th ) percentile in the vega distribution. 20 This suggests that fairly low to moderate levels of vega provide substantial incentive to encourage risk-taking that yields higher returns on R&D investment. However, marginal returns to R&D beyond this level of vega fails to increase returns, ultimately leading to diminishing returns as vega increases. To summarize, we find that risk-taking incentives introduced through stock option compensation significantly affect the profitability of R&D investments. This result complements prior research finding a relation between vega incentives and the level of R&D spending (i.e., Coles et al. 2006). More importantly, we show that the return on R&D is a concave function of vega. This relation implies that moderate levels of vega improve the value created by R&D investment. However, greater vega appears to lead to less risky/less profitable investment choices, suggesting that vega does not necessarily equate to greater risk-taking incentives. We next consider whether investors recognize the effect of vega on R&D profitability when valuing R&D investments Tests of H3! If vega affects the nature, or profitability, of R&D investments and investors recognize the implications for firm value, we expect to find a similar concave relation with respect to contemporaneous stock returns. To test this, we estimate a model similar to Eq. (2) but replace the dependent variable ROA3 with abnormal stock returns in year t (AR0). 20 Note that confidence intervals on these estimates are relatively wide and encompass the median of the vegadistribution. Thus, we conclude with confidence that extreme levels of vega lead to diminishing returns on R&D investment, but we do not suggest that vega leads to declining returns for the majority of observations in our sample. 22

24 !!!"0!,! = α +!!&!!,! (!! +!!!!"#$!,!!! +!!!!"#$!,!!! +!!!!"#$%!,!!! +!!!!"#$%!,!!! +!!!!!!"#!,! ) +!!!"#$!,!!! +!!!!"#$!,!!! +!!!!"#$%!,!!! +!!!"!"#$%!,!!! +!!!!!"#!,! +!!!"!"#!,! +!!"!"#0!,! +!!!" (3) The dependent variable, AR0, is defined as the fiscal year buy-and-hold stock return for firm i less the buy-and-hold return of a matching size and book-to-market portfolio. We include industry-adjusted earnings in year t (ROA0) in the model as a proxy for firm-specific news during the period, and we drop MTB, Adv and Tang. 21 We estimate this model using OLS and include year fixed effects. Unlike Eq. (1) and (2), we estimate Eq. (3) without firm fixed effects since there is no reason to expect that abnormal returns contain a fixed firm component. We cluster standard errors by year to control for cross-sectional correlation in abnormal returns (Petersen 2009). 22 Once again, we are primarily interested in β 2 and β 3. We report the results of estimating Eq. (3) in column (1) of Table 5. As in equation (2), estimation results yield a positive value for β 2 (β 2 = 0.385, p-value < 0.05) and a negative value for β 3 (β 3 = , p-value < 0.01), suggesting that the market s pricing of R&D is indeed a concave function of Vega. We fail to find a relation between Delta and AR0, either as a main effect or as an interaction with R&D. Overall, our results support H3 and are consistent with the notion that investors understand the non-linear effect of Vega on the value of R&D investments. More importantly, this result suggests that R&D investments by high vega managers increase firm value less than those made by more moderately incented managers. 21 While MTB is a well-known determinant of expected (and actual) returns, we remove it from the model as a control variable because we compute abnormal returns using size- and MTB-matched portfolios. We leave MVE in the model since we include an interaction between MVE and R&D. 22 We use OLS with clustered standard errors, rather than Fama-Macbeth (1973) regressions, since Fama-Macbeth regressions do not correct for cross-sectional correlation in error terms, a common problem in abnormal return regressions (Petersen 2009). 23

25 We next assess whether the market s pricing of Vega s effect on R&D is efficient. It is possible that investors do not fully price the negative implications for future earnings in year t. It is also possible that the market overreacts in year t. We address this question by re-estimating Eq. (3), but replacing AR0 with AR3, which is the cumulative abnormal return over years t+1 through t+3. As reported in column (2) of Table 5, we fail to observe a concave relation between Vega and R&D profitability. Alternatively, we find some evidence of a positive (negative) relation between Delta*R&D (Delta 2 *R&D) and future returns. This result suggests that future returns are a concave function of R&D with respect to Delta. This could imply initial mispricing of R&D conditional on Delta, but our evidence in Table 4 based on future ROA3 does not provide a ready explanation for this phenomenon. In other words, based on Table 4 it does not appear that Delta affects the profitability of R&D investments Cross-sectional differences in the effect of vega on R&D profitability (Test of H4) Our fourth hypothesis (H4) predicts that institutional holdings will mute the sensitivity of the return on R&D to vega since institutional oversight provides a monitor for management. In essence, we predict that increased oversight prevents managers from neutralizing their personal risk exposure by investing in lower risk/return R&D projects. We first partition our sample based on institutional ownership by identifying observations with institutional holdings (InstHold) above and below yearly sample medians. We then re-estimate Eq. (2) within each partition. We present results of this estimation in Table 6. As in Table 4, we estimate these models using OLS with firm and year fixed effects in columns (1) and (2) and using Fama-MacBeth regression in columns (3) and (4). Consistent with our expectation, the concave relation between 24