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Keywords:

  • Agency costs;
  • Equity-based compensation;
  • Equity mispricing

Abstract

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

We investigate a link between agency costs and equity mispricing. We employ comprehensive, multi-dimensional measures of agency costs and mispricing, and find that mispricing is significantly and positively related to agency costs. We also explore the effect of equity-based compensation on the impact of agency costs on mispricing. Our investigation extends previous studies that do not separately account for the options and restricted stock grants components of equity-based compensation. We show that stock options, originally intended to resolve conflicts of interest, exaggerate the problem and this phenomenon is pronounced especially when firms are overvalued. Overall, our results imply that compensation packages that are not structured optimally could lead to greater mispricing.


1 Introduction

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

Both theory and empirical evidence support the notion that equity mispricing has an impact on managers’ investment and financing decisions. For instance, misvaluation can drive firms’ takeover behavior (Shleifer and Vishny, 2003; Rhodes-Kropf and Viswanathan, 2004; Rhodes-Kropf et al., 2005; Dong et al., 2006). The levels of firms’ investment are affected by inefficient market valuations (Baker et al., 2003; Polk and Sapienza, 2009). Furthermore, firms may try to time equity issues to take advantage of misvaluation (Ritter, 1991; Loughran and Ritter, 1995; Rajan and Servaes, 1997; Baker and Wurgler, 2002).

A stream of research establishes two different views on equity mispricing. These are related to market imperfections such as information asymmetry, transactions costs, lack of investor sophistication, or unequal access to prices or information (e.g. noise trading, see Roll, 1988). In efficient markets with rational asset pricing, stock mispricing can be either a short-term, temporary phenomenon quickly reversible by arbitrageurs (Friedman, 1953), or rational compensation for systematic risks that are not accounted for in asset pricing models (see, e.g. Fama and French, 1993, 1996). On the other hand, behavioral finance regards persistent mispricing as the result of an irrational (behavioral) component to asset prices.

This paper extends the current understanding by providing evidence that equity mispricing could be the result of conflicts of interest and incentive problems within the firm, rather than suggesting that equity mispricing is solely determined by the markets. According to agency theory, agency costs are associated with divergent objectives between agents (managements) and owners (shareholders). These conflicts of interest are caused by the presence of information asymmetry where agents discriminately have better/more information than owners. If there is no information asymmetry, conflicts of interest can be solved simply by fully informed stockholders. However, even without conflicts of interest, information asymmetry can cause mispricing. Suppose, for instance, that there are large differences in the quality and availability of information between managers and outside investors of a particular firm. Then, one may expect that the firm's stock is likely to be valued incorrectly because ambiguity about future cash flows leads to stock mispricing (Zhang, 2006). The question that we want to examine is what happens to the size of mispricing, given the serious information asymmetry, in cases where there is also conflict of interest between managers and shareholders. While some prior studies have argued that there is a link between information asymmetry and stock misvaluation (Nanda and Narayanan, 1999; Healy and Palepu, 2001), there is little direct empirical evidence in the literature of the effect of agency costs on equity mispricing. We employ several measures of information asymmetry and conflicts of interest to devise a composite measure of firms’ likelihood to display agency problems, which we call the agency costs index. We also devise a composite equity mispricing measure, the mispricing index, that combines four different relative valuation measures and an abnormal return measure. We test whether the two indices are significantly related after controlling for other factors that are associated with mispricing.

In our analysis we also examine the role of managerial compensation structures, in particular equity-based (incentive) compensation, in the relationship between agency costs and mispricing. Equity-based compensation, in general, is theoretically known as the most effective tool firms can use to align managerial interests with those of shareholders but not necessarily as a tool suited to resolving information asymmetry. However, recent financial scandals (e.g. those associated with Enron or Worldcom) and academic evidence (see, e.g. Bergstresser and Philippon, 2006) have raised serious questions about the validity of this view. If incentive-laden compensation packages are not structured optimally, they may fail to resolve or they may even exacerbate conflicts of interest, thereby causing an even stronger positive effect of agency problems on equity mispricing. Thus, if our conjecture is correct, the level of mispricing should be related to components of managerial compensation packages that are intended to resolve the conflicts of interest. In addition, the two components of incentive compensation, options grants and restricted stock grants, have been shown to induce opposite results. While options have been shown to induce managerial myopia (i.e. shorter-term orientation, see Watts and Zimmerman, 1986; Aboody and Kasznik, 2000; Sanders, 2001; Gao and Shrieves, 2002; Bergstresser and Philippon, 2006), restricted stock grants have been shown to induce managers to become less myopic (i.e. longer-term orientated, see Narayanan, 1996; Bryan et al., 2000). Therefore, based on the conflicting theoretical and empirical evidence, the role of equity-based compensation on the relationship between agency costs and mispricing remains an empirical question. Our study sheds light on this important issue by examining both major equity-based compensation components, that is, stock options and restricted stock grants, and testing whether and how they enhance the relationship between agency conflicts and mispricing.

Our results show a significant positive relation between agency problems and equity mispricing. Furthermore, using chief executive officer (CEO) incentive compensation data, we find evidence consistent with the notion that conflicts of interest are significantly exacerbated in certain cases, leading to greater mispricing. When we interact agency costs proxies with variables that capture managerial equity-based compensation components intended to resolve the conflicts of interest between CEO and owners, our models explain an additional significant proportion of mispricing. Our findings obtained from several univariate and regression tests support the notion that the positive relation of agency costs with mispricing is, to some extent, driven by myopia-inducing stock options’ awards to the CEO. This evidence may indicate that stock option grants increase the value of overvalued firms, but decrease the value of undervalued firms. One may argue that shareholders would not be willing to provide stock options if the latter case were expected. Therefore, we retest the mispricing models to examine if the effects of information asymmetry and conflicts of interest on mispricing are different for overvalued and undervalued firms. When we examine overvalued and undervalued firms separately, we find that the overall effect of agency costs on mispricing is much stronger for undervalued firms. The total impact of agency conflicts on poor performing firms’ value is almost double that of the corresponding impact of agency conflicts on good performers’ value. Furthermore, option awards are solely responsible for the positive relationship between agency conflicts and overvaluation, while the impact of agency conflicts on undervaluation is not affected by options grants. This evidence is consistent with Jensen's (2004) argument that several aspects of managerial behavior act as potential sources of the agency costs of overvalued equity. It also complements prior studies that document the opportunistic behavior of executives with respect to options grants (Yermack, 1997; Aboody and Kasznik, 2000; Lie, 2005). Collectively, our results imply that agency conflicts may lead to undervaluation and, if the CEO compensation package includes substantial option grants, agency conflicts may also cause overvaluation.

It is worth emphasizing that our results are not driven by the particular estimation methodology used. We conduct various robustness tests, using several other methods such as a panel regression, the time-series average of cross-sectional annual regressions (Fama and MacBeth, 1973), and a cluster-correcting model. We note that our results are not altered by these robustness checks.

The rest of the paper is organized as follows. In the next section, we develop the main hypotheses to prove the relation between incentive conflicts and mispricing. Section 'Data and Measures' describes the data sources and measures of main variables. Section 'Agency Costs and Equity Mispricing' introduces the empirical methodology and reports test results. Section 'Equity-based Compensation and the Effect of Agency Costs on Mispricing' conducts additional tests utilizing managerial compensation data and provides a more detailed explanation of the relation between agency costs and mispricing. Section 'Robustness' presents the results of the robustness tests. Section 'Summary and Conclusions' provides a summary and concluding remarks.

2 Related Literature and Hypothesis Development

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

A sizeable component of stock mispricing can be caused by lack of transparency at the corporate level. Outside investors’ ambiguity about firms’ future cash flows increases when they have limited access to information or when investors’ information is of poor quality relative to that of firm insiders. Therefore, the more opaque the information available to investors about a firm's true but unobservable distribution of future cash flows, the greater the degree of deviation of market value from intrinsic value. Since Myers and Majluf (1984) showed that firms subject to higher information asymmetry are more likely to refuse valuable investment opportunities and to suffer from unfavorable misvaluation, many authors have documented the impact of information asymmetry on misvaluation. Nanda and Narayanan (1999) formally develop an information-related argument in the context of divestitures through a model of asymmetric information about firm value between the managers and the market. They assume that the market can observe the aggregate cash flows of the firm but not the individual divisional cash flows, which results in misvaluation of the firm's securities. Healy and Palepu (2001) argue that misvaluation arises when there is information asymmetry between managers and investors that is not fully resolved. For a given level of information asymmetry, the lack of transparency can be exacerbated by the severity of the conflicts of interest. Therefore, our first hypothesis is that firms with greater agency costs are more likely to display high levels of equity mispricing.

As addressed above, mispricing is associated with information asymmetry, which is a necessary condition for agency problems. We seek to identify how important are conflicts of interest in explaining the impact of agency costs on mispricing for a given information asymmetry. As discussed above, if mispricing is brought about substantially by conflicts of interest, it should be related to components of managerial incentive compensation. The finance literature has adopted two different views on the linkage between agency problems and executive compensation.

First, many authors regard managerial compensation as a potential agency conflict resolution mechanism (Bhagat et al., 1985; Jensen and Murphy, 1990a,b; DeFusco et al., 1990; Mehran, 1995; Core and Guay, 2001; Datta et al., 2001; Loughran and Vijh, 1997; Frye, 2004; Core and Larcker, 2002; Nam et al., 2006). Under this view, corporate boards design compensation packages to provide managers with the correct incentives to maximize shareholder value. Several studies found that a firm's stock performance is positively related to the fraction of equity-based compensation suggesting that equity-based compensation resolves agency problems. Bhagat et al. (1985) find that the adoption of employee stock purchase plans result in an increase in shareholder wealth, and that equity-based compensation schemes motivate top managers more than lower-level employees. Jensen and Murphy (1990a,b) suggest that equity-based, rather than cash-based, compensation is more efficient in aligning the interests of managers and shareholders. DeFusco et al. (1990) find that implicit share price variance and stock return variance increase after the firm approves an executive stock option plan. Moreover, their event study analysis results indicate that the announcement of approval of stock option plans leads to an increase in stock price along with a significant negative reaction in the bond market, suggesting that executive stock options may transfer wealth from bondholders to stockholders. Mehran (1995) shows that firm performance is positively related to managers’ ownership and the amount of shares provided by their compensation packages. He also shows that firms with a higher percentage of shares held by outside blockholders use less equity-based compensation. Based on these findings, he suggests that monitoring by outside blockholders can be a substitute for incentive equity compensation for executives. Core and Guay (2001) show that firms use options to attract and retain certain types of employees as well as to create incentives to increase firm value. Datta et al. (2001) document a positive relation between equity-based compensation received by acquiring managers’ equity-based compensation and acquirer firms’ stock price response around and following corporate acquisition announcements. They also find that acquiring firms with high equity-based compensation do not show underperformance as documented by Loughran and Vijh (1997) and others. Frye (2004) provides evidence that firms with a high percentage of equity-based compensation show better performance measured by Tobin's q. Core and Larcker (2002) show that mandatory increases in the level of managerial equity ownership result in improvements in accounting returns and stock returns. Nam et al. (2006) examine the effectiveness of equity-based compensation in mitigating the agency costs in single- and multi-segment firms, and find that the effect for multi-segment firms, where agency costs are expected to be higher, is much greater than for single-segment firms.

The alternative view of executive compensation found in the literature is that of executive compensation being part of the agency problem itself. Recent corporate scandals involving excessive managerial pay coupled with abysmal performance and wealth expropriation of outside shareholders, such as those at Enron and WorldCom, have cast doubts over prior beliefs about the effectiveness of equity-based compensation. Moreover, researchers suggesting stock-based compensation as an efficient mechanism for solving agency problems typically treated all stock-based incentives equally and related them to lowered agency costs as well as enhanced firm stock value. The skepticism about the effectiveness of equity-based compensation motivated our decision to analyze equity-based compensation by separately considering its stock options and restricted stock grants components.

It is intuitively appealing to think that incentive stock options should have a positive impact on firm performance. But options may also impose a penalty on the firm because they tend to make managers more myopic. In particular, because managers’ gains from stock option grants are greater than stock appreciation returns, managers have an incentive to maximize short-term stock price appreciation to increase their options exercise value. It is conceivable then that an increase in stock value could lead to a substantial enough increase in the value of the stock option grants to provide managers with an incentive to cash out and leave the company. Such a scenario would be especially true if projects and investments chosen by the managers have a short-term focus at the expense of long-term wealth creation.

The finance and accounting literatures broadly document that executives have the ability to manage the timing of stock option grants and/or the information flow around option grants. Yermack (1997) investigates corporate managers’ influence over the terms of their own compensation by analyzing the timing of CEO stock option awards. He finds that CEO option awards are followed by significantly positive abnormal returns. Aboody and Kasznik (2000) suggest that CEOs make opportunistic voluntary disclosure decisions to maximize their stock option compensation. Chauvin and Shenoy (2001) show that stock price significantly decreases in the ten days prior to stock option grants. Carpenter and Remmers (2001) find that abnormal stock returns after exercises by top managers at small firms are significantly negative. Huddart and Lang (2003) examine the stock option exercise decisions of over 50 000 employees at seven corporations and present evidence that stock exercise is high before the stock price decreases and low before the stock price increases. They suggest that the timing when both senior and junior employees exercise their stock options can be used to predict future stock returns.

In a recently published study, Lie (2005) proposes an alternative way for explaining the abnormal return pattern around options grants (i.e. abnormally negative returns before executive option grants and abnormally positive returns thereafter). Unlike previous studies that argue conventional grant timing, Lie (2005) argues that, to enrich their senior executives, firms may simply backdate the stock option grant date to a time period where the market price was particularly low. Heron and Lie (2007) look at a 2002 change in response to changes to Section 16 reporting of the Securities and Exchange Act of 1934 mandated by the Sarbanes-Oxley Act. The Securities and Exchange Commission (SEC) changed the reporting regulations that require companies to report option grants within 48 hours. They document that the aforementioned abnormal return pattern becomes weak after the SEC requirement. They find that when companies reported options the same day they were granted, there was no pattern of share prices quickly rising. But the pattern continued when companies delayed reporting option grants. These findings support Lie's backdating theory. The backdate theory has been also supported by recent anecdotal evidence from the SEC's investigation of many cases (e.g. Mercury Interactive): “SEC investigators previously had posited that companies were timing grants to benefit from positive corporate news that would drive up stock prices, such as strong earnings. But increasingly they are focusing on backdating” (Maremont, 2005).

Another negative aspect of option grants is that options appear to lead executives to take risks that might not be in the best interest of shareholders. This can occur because stock option grants offer substantial upside potential, but impose little downside risk on managers (Sanders, 2001). They serve as motivational “carrots” but lack the complementary disciplinary “stick.” Thus, executives may view the potential option payouts as a form of compensation lottery. Watts and Zimmerman (1986) argue that managers of firms with earnings-based compensation incentives maximized their awards by choosing income-increasing accounting methodologies. Gao and Shrieves (2002) find that option grants and exercisable in-the-money options are positively correlated with earnings management intensity. Lee et al. (2011) also find that higher values and price incentives for executive stock options have a significant positive influence on discretionary accruals. Bergstresser and Philippon (2006) provide evidence that during years of high discretionary accruals CEOs exercise unusually large numbers of options and sell large quantities of shares.

Based on the above evidence we expect that options grants effectively make CEOs more myopic. In other words, as the proportion of options in a CEO's compensation package increases, so does the incentive to make short-term wealth maximization decisions that might not be in the best interests of long-term stakeholders.

Restricted stock grants endow managers with a number of shares of a firm's equity, but also restrict managers from reselling or transferring shares and contain provisions that invalidate the award if managers quit or are fired before the restricted period. While options have been shown to induce managerial myopia, restricted stock grants have been shown to reduce managerial myopia. Narayanan (1996) theoretically investigates the relationship between two types of compensation, cash and non-cash, and the manager's decision horizon. He does not investigate the effect of options as a form of non-cash compensation but rather focused on restricted stock grants. He finds that all-cash contracts induce managers to underinvest in the long term while restricted stock grants induce managers to overinvest in the long term. He concludes that a combination of both cash and restricted stock produce efficient investment. Kole (1997) finds that stock options and restricted stocks are common in research and development (R&D) intensive industries but the difference in corporate use of restricted stocks between high- and low-R&D-intensive industries is economically and statistically more significant than the difference of corporate use of stock options. However, Ryan and Wiggins (2002) report that R&D investment is positively related to stock options but negatively related to restricted stocks. This finding is, they interpret, because the linear payoff of restricted stock encourages managers to avoid risky investment and the nonlinear payoff of options motivates risk-taking behavior.

Another important difference between restricted stock and options is that restricted stock grants have more of a linear payoff relative to stock option grants. Bryan et al. (2000) and Ryan and Wiggins (2002) contend that restricted stock grants, due to their linear payoffs, are relatively inefficient in inducing risk-averse CEOs to accept risky, value-increasing investment projects. On the other hand, it is plausible that the linear payoff of restricted stock grants does not adversely affect CEO decisions because it precludes the potential of earning a windfall in the short-term and discourages CEOs from making decisions that could be harmful to stakeholders’ long-term interests. It is also reasonable to argue that restricted stock grants provide less incentive for earnings management because the reversion of earnings management accruals will likely manifest before managers can realize large personal gains (Gao and Shrieves, 2002). Therefore, it is expected that restricted stock grants are effective in resolving agency problems and thereby improving firm performance.

Because, as discussed above, options have been shown to induce managerial myopia, while restricted stock grants have been shown to induce managers to become less myopic, we focus on the two incentive compensation plans separately. On one hand, mispricing can be reduced when incentive conflicts are resolved by a compensation package, which contains a high proportion of restricted stocks. On the other hand, mispricing can be exaggerated when firms provide CEOs with compensation packages, which have many stock options. Therefore, our second hypothesis is that equity mispricing caused by agency conflicts should be mitigated (exaggerated) by the use of restricted stock grants (stock option grants) in CEO compensation packages.

3 Data and Measures

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

We extract return data from the Center for Research in Securities Prices (CRSP) where NYSE, AMEX, and Nasdaq stocks are listed. The initial sample includes all firms in CRSP from 1985 to 2004, omitting financial (SIC 6000–6999) and utility (SIC 4900–4999) firms. Accounting and financial data are drawn from COMPUSTAT. Firms with market value of equity less than USD$20 million are excluded in order to avoid cases of firms with distorted valuation multiples in the mispricing measures. We collect CEO compensation data from the sample of firms in Standard and Poor's (S&P) ExecuComp database. The S&P's ExecuComp database covers the period from 1992 to 2003, and includes executive compensation data for firms in the S&P 1500 index, which comprises the S&P 500, the S&P 400 mid cap, and the S&P 600 small cap indices. ExecuComp also contains information on firms that are not currently in the S&P 500, the S&P 400, and the S&P 600 indices, but were previously included in one of the aforementioned indices. According to ExecuComp, CEOs’ total compensation is comprised of seven items: (i) salary; (ii) bonus; (iii) stock options granted; (iv) restricted stock grants; (v) long-term incentive plan; (vi) other annual compensation; and (vii) all other compensation. In this paper, we calculate the dollar value of “granted” stock options instead of using the value of exercised options. This is because the value of options that are already exercised can be viewed as a regular salary or bonus, as opposed to (unexercised) options grants that are pure incentive compensation. The value of stock options granted is computed as the aggregated dollar value (in thousands of dollars) of stock options granted to the CEO during the year as valued using S&P's Black–Scholes methodology. Details of all compensation variables are provided in Table 1 and summary statistics are documented in Table 2.

Table 1. Variable definitions
Mispricing
Mispricing1The absolute value of excess value based on Ohlson's (1995) residual income value approach. M1it = Ln[Pricei,t/I(Value)i,t], where Priceit is the stock price at the end of June of each year from CRSP, and I(Value)it is the intrinsic value using the residual income model (Ohlson, 1995) and median values of analysts’ forecasts issued in June, as in Frankel and Lee (1998)
Mispricing2The absolute value of excess value based on the Berger and Ofek (1995) approach. M2it = Ln[Capitali,t/I(Capital)i,t], where Capitali,t is total capital that is the market value of equity plus the book value of debt, I(Capitali,t) is the imputed value derived as the product of firm sales and the median capital-to-size ratio in the firm's industry. The industry classification here is based on the Fama–French 48 sectors. This measure of mispricing is constructed in a similar fashion as the first one, but uses the firm's total capital instead of price and computes imputed value based on the Fama–French 48 industry classification. Thus the intrinsic value here is a size and industry benchmark
Mispricing3The absolute value of the excess value based on Rhodes-Kropf et al., 2005. Fundamental value, V is estimated by decomposing the market-to-book into two components: a measure of price to fundamentals (Ln(M/V)), and a measure of fundamentals to book value (Ln(V/B)). The first component captures the part of book-to-market associated with mispricing. This component is further decomposed into firm-specific and industry-specific mispricing. We use the firm-specific mispricing component based on Model III of Rhodes-Kropf et al., 2005 that also accounts for net income and leverage effects. Ln(Mi,t)= α0j,t + α1j,t Ln(Bi,t+ α2j,t Ln(NI)+i,t + α3j,t I(<0) Ln(NI)+i,t+ α4j,t Ln(LEVi,t+ εi,t, where M is firm value, B is book value, NI+ is absolute value of net income, I(<0) is an indicator function for negative net income observations, and LEV is the leverage ratio
Mispricing4The absolute value of the industry-adjusted market-to-book ratio. M4it = Ln[MBi,t/Median(MB)j,t], where MBi,t is the market-to-book ratio for firm i at time t, and Median(MBj,t) is the jth industry median of MBt
Mispricing5The absolute value of a firm's average monthly abnormal return for each year. The expected return of month t is computed using benchmarks from the Fama–French three-factor model estimated over the five-year period immediately preceding month t. The estimation of the parameters is based on the model, E(Ri,t)−Rf,t = β0 + βM (Rm,t−Rf,t+ βSMB SMBt βHML HMLt + εi,t, where E(Rit) is the rate of return on the ith company's common stock in month t, Rf,t is risk-free rate, Rm,t is the value-weighted market portfolio return, and SMBt and HMLt are the size and book-to-market factors as in Fama and French (1993, 1996). Abnormal returns, ARETi,t, are computed as differences of actual returns from expected returns derived from the parameters of model, and the mispricing value from the asset pricing model is |ARETi,t| or |Ri,t−E(Ri,t)|
Mispricing indexThe mispricing index that is constructed each year for each observation i = 1,…,N as: inline image, where Rankk(Mispricingi,k) is the rank function which assigns a rank for each observation from least misvalued (rank of one) to most misvalued (rank of N). Mispricingi,k is the kth measure of mispricing for firm i in our sample, and λ represents the dimensions of mispricing measures. The denominator, λ, averages the ranks by the number of mispricing values available for each firm in the sample in a particular year. Finally, dividing by N, we scale the mispricing index from 0 (least mispriced) to 1 (most mispriced)
Agency costs
Free cash flows(Free cash flowsi,t/Total assetsi,t) × Growth dummyi,t, where Free cash flow = operating income before depreciation−(taxes + interest expense + dividends paid). Growth dummy = 1 if the firm's Tobin's q is less than 1 and 0 otherwise. Tobin's q = [market value of common equity + preferred stock liquidating value + long-term debt − (short-term assets − short-term liabilities)]/(total assets)
Expense ratioOperating expensei,t/Salesi,t measures the inefficiency in the management control of operating costs
Asset utilization ratioSalesi,t/Total assetsi,t measures the effectiveness of a firm's management in deploying assets
Proportion of independent directorsThe number of independent directors/the number of all directors on the corporate board
Institutional ownershipThe percentage of shares that are owned by institutional investors
Governance indexThe index constructed by Gompers et al. (2003) to proxy for the level of shareholder rights. The Governance Index is constructed by counting 28 provisions listed in five categories: Delay, Protection, Voting, Other, and State. Among 28 provisions, 24 are unique and equally weight in index. A firm with high governance index (i.e. many anti-takeover provisions) is expected to have a high level of agency problems
Product market competitionThe inverse value of Herfindahl concentration index, inline image, where Salesj is the annual sales of jth firm belonging to the industry in which firm i is included. A higher CMPT (i.e. lower Herfindahl index) thus indicates that a product market is more competitive
Analysts’ coverageResidual value from the regression of analyst coverage on firm size
Analysts’ earnings forecast error|Med(AF)i,t − EPSi,t+1|/|Med(AF)i,t|, where Med(AF)i,t is the median forecast and the actual earnings per share EPSi,t+1 is the actual earnings per share
Analysts’ earnings forecast dispersionStd.Dev.(AF)i,t/|Med(AF)i,t |, where Std.Dev.(AF)i,t is the standard deviation of one-year-ahead forecasts
Agency cost indexThis is constructed by using the same methodology for the mispricing index and by combining all ranks of five variables (free cash flows, expense ratio, governance index, analysts’ earnings forecast error, and analysts’ earnings forecast dispersion) and inverse ranks of five variables (asset utilization ratio, proportion of independent directors, institutional ownership, product market competition, and analysts’ coverage). The index is scaled from 0 (least agency costs) to 1 (greatest agency costs)
CEO compensation
Total compensationTotal compensation (in thousand $) that comprises seven items: (i) salary; (ii) bonus; (iii) restricted stock grants; (iv) stock options; (v) long-term incentive plan; (vi) other annual income; and (vii) all other compensation
SalaryBase salary (in thousand $) of the base salary (cash and non-cash)
BonusBonus (in thousand $)
Restricted stock grantsThe value (in thousand $) of restricted stock granted determined as of the date of the grant
Stock optionsThe aggregated dollar value (in thousand $) of stock options granted to the CEO during the year as valued using S&P's Black–Scholes methodology
Long-term incentive planThe dollar value (in thousand $) paid out to the CEO under the company's long-term incentive plan
Other annual incomeThe dollar value (in thousand $) of other annual income not properly categorized as salary or bonus. This includes items such as: (i) perquisites and other personal benefits; (ii) above-market earnings on restricted stock, options/SARs, or deferred compensation paid during the year but deferred by the officer; (iii) earnings on long-term incentive plan compensation paid during the year but deferred at the election of the officer; (iv) tax reimbursements; and (v) the dollar value of the difference between the price paid by the officer for company stock and the actual market price of the stock under a stock purchase plan that is not generally available to shareholders or employees of the company
All other compensationThe dollar value (in thousand $) of all other items including: (i) severance payments; (ii) debt forgiveness; (iii) imputed interest; (iv) payouts for cancellation of stock options; (v) payment for unused vacation; (vi) tax reimbursements; (vii) signing bonuses; (viii) 401K contributions; and (ix) life insurance premiums
Proportion of restricted stock grantsRestricted stock grants/total compensation
Proportion of stock optionsStock options/total compensation
Other firm characteristic
SizeThe log of total assets
LeverageLong-term debt/total assets
ProfitabilityNet income/total assets
Firm ageln(1 + age), where age is the number of years since the stock inclusion in the CRSP database
Business diversificationA dummy that equals 1 if a firm operates in multi-segments and 0 otherwise
Dividend payerA dummy that equals 1 if a firm pays dividends and 0 otherwise
Table 2. Descriptive statistics
Variables N MeanStandard deviation5th percentileMedian95th percentile
  1. Reported are descriptive statistics for our sample firms. The sample contains 38 781 firm-year observations (6446 firms) over the period 1985–2004. Refer to Table 1 for variable definitions.

Mispricing
Mispricing136 1150.7940.8410.0770.6771.889
Mispricing238 5820.6410.6080.0360.4891.739
Mispricing338 7790.3850.3550.0250.2881.081
Mispricing438 7810.4040.3790.0180.3021.155
Mispricing538 7750.1020.0560.0390.0890.207
Mispricing index38 7810.5020.1720.2400.4890.805
Agency costs
Free cash flows27 1290.0130.035000.084
Expense ratio27 8150.3754.1960.0470.2280.692
Asset utilization ratio38 7811.2080.8070.2431.0712.648
Independent board93730.6260.1930.2730.6670.889
Institutional ownership30 4830.5030.2430.0930.5130.892
Governance index15 6139.0732.7485914
Product market competition38 7810.8760.1050.6690.9140.971
Analysts’ coverage36 827−0.0005.517−7.623−0.57410.39
Analysts’ earnings forecast error34 0220.8236.3060.0050.1302.587
Analysts’ earnings forecast dispersion33 0970.2201.2720.0090.0480.706
Agency costs index38 7810.5050.1300.2950.5030.724
CEO compensation
Total compensation10 812376813 820322.9136612 919
Salary10 812592.0338.7206.1530.01102
Bonus10 812580.510610311.82000
Restricted stock grants10 812425.09053001307
Stock options10 81218519684008460
Long-term incentive plan10 812139.4793.400714.0
Other annual income10 81243.20225.900182.1
All other compensation10 812136.8794.4016.64400.3
Proportion of stock options10 8120.1820.296000.858
Proportion of restricted stock grants10 8120.0520.142000.393
Other firm characteristics
Size38 78119.761.67717.3619.5622.86
Leverage38 6470.1630.17900.1030.536
Profitability38 7810.0310.121−0.1820.0490.161
Firm age38 7812.3340.8431.0992.3983.555
Business diversification31 7980.3280.469001
Dividend payer37 3500.3810.486001

The final sample includes 38 781 firm-year observations with 6446 firms during the sample period. For the tests that utilize CEO compensation data the sample is reduced to 8657 firm-year observations.

3.1 Equity Mispricing

Firm mispricing is measured as the deviation of a firm's equity value from its intrinsic or fundamental value. We develop six alternative mispricing measures. The first four measures employ alternative techniques in estimating intrinsic value benchmarks, the fifth measure is based on a standard asset pricing model, and the last one is an index that combines all measures. The mispricing measures are as follows.

The first measure of mispricing is the absolute value of the natural log of the ratio between the stock price and its intrinsic value from Ohlson's (1995) residual income value approach. Mispricing1 = Ln[Price/I(Value)], where Price is the stock price at the end of June of each year from CRSP, and I(Value) is the intrinsic value using the residual income model (Ohlson, 1995) and median values of analysts’ forecasts issued in June, as in Frankel and Lee (1998). There is strong empirical evidence in support of the residual income valuation, V/P, as an indicator of mispricing. Lee et al. (1999) report that V/P predicts one-month-ahead returns on the Dow 30 stocks better than aggregate book-to-market. Frankel and Lee (1998) also show that the residual income value is a better predictor than book value of the cross-section of contemporaneous stock prices, and that V/P is a predictor of the one-year-ahead cross-section of returns. In addition, Ali et al. (2003) show that after controlling for several possible risk factors, V/P continues to significantly predict future returns. D'Mello and Shroff (2000) apply V/P to measure mispricing of equity repurchases, and Dong et al. (2006) to takeovers.

The second measure of mispricing is the absolute value of excess value computed at the end of June of each year as the natural log of the ratio between a firm's capital and its imputed value, based on the Berger and Ofek (1995) approach. Mispricing2 = Ln[Capital/I(Capital)], where Capital is total capital that is the market value of equity plus the book value of debt, I(Capital) is the imputed value derived as the product of firm sales and the median capital to size ratio in the firm's industry. The industry classification here is based on the Fama–French 48 sectors. This measure of mispricing is constructed in a similar fashion as the first one, but uses the firm's total capital instead of price and computes imputed value based on the Fama–French 48 industry classification.

The third measure of mispricing is the absolute value of the firm-specific component of the difference between market value and fundamental value, based on Rhodes-Kropf et al., 2005. This procedure differs from the residual income valuation approach in the sense that it does not rely on analysts’ earnings forecasts. According to Rhodes-Kropf et al. (2005), fundamental value, V, is estimated by decomposing the market-to-book into two components: a measure of price to fundamentals (Ln(M/V)), and a measure of fundamentals to book value (Ln(V/B)). The first component captures the part of book-to-market associated with mispricing. In extreme cases where markets perfectly anticipate, this component would be equal to zero, otherwise positive (over-valuation) or negative (under-valuation). This component is further decomposed into firm-specific and industry-specific mispricing. In our tests, we use the firm-specific mispricing component based on Model III of Rhodes-Kropf et al., 2005 that also accounts for net income and leverage effects.

  • display math(1)

where M is firm value, B is book value, NI+ is the absolute value of net income, I(<0) is an indicator function for negative net income observations, and LEV is the leverage ratio.

The fourth measure of mispricing is the absolute value of the industry-adjusted market-to-book ratio. Mispricing4 = Ln[MB/Median(MB)], where MB is the market-to-book ratio and Median(MB) is the industry median MB. Several empirical studies have utilized MB as a mispricing measure (Walkling and Edmister, 1985; Ikenberry et al., 1995; Rau and Vermaelen, 1998).

The fifth measure of mispricing is the absolute value of a firm's average monthly abnormal return for each year. The expected return of month t is computed using the factor coefficients obtained from the Fama–French three-factor model estimated over the five-year period immediately preceding month t. Abnormal returns are computed as differences of actual returns from the expected returns, and the absolute differences are monthly mispricing values.

Finally, the mispricing index combines all five mispricing measures described above. The mispricing index is constructed each year for each observation i = 1,…,N as:

  • display math(2)

where Rankk(Mispricingi,k) is the rank function which assigns a rank for each observation from least misvalued (rank of 1) to most misvalued (rank of N). Mispricingi,k is the kth measure of mispricing for firm i in the sample, and λ represents the dimensions of mispricing measures. The denominator, λ, averages the ranks by the number of mispricing values available for each firm in the sample in a particular year. For example, the sum of the Rankk(Mispricingi,k) values of a firm that has only three mispricing measures is divided by λ = 3. Finally, dividing by N, we scale the index from 0 (least mispriced) to 1 (most mispriced). By computing the average of all ranks from five different mispricing measures, the index has the advantage that it balances out the effects and shortcomings of all other mispricing measures while aggregating their informativeness, and thereby provides a more complete picture of mispricing.

Detailed descriptions for all variables used to construct the mispricing index and their summary statistics can be found in Tables 1 and 2, respectively. We check out the coefficients of correlations between the different mispricing measures (not tabulated here). As expected, all mispricing measures are significantly positively correlated at the 1% level, or better, even though these valuation measures are based on widely different theoretical concepts and their measurements rely on a variety of accounting and/or financial variables. All individual mispricing measures are more significantly and positively correlated with the mispricing index than with the other individual measures, suggesting that the index is an appropriate aggregate measure of mispricing for use in the tests.

3.2 Agency Costs

Financial economists have attempted to measure firms’ propensity for agency conflicts by using measures of internal and external agency problem resolution mechanisms. Agrawal and Knoeber (1996) address the empirical implications of the interdependence among such mechanisms. They examine seven mechanisms that potentially can control agency problems and present evidence of interdependence, suggesting that results obtained from cross-sectional OLS regressions of firm performance on several single mechanisms may be misleading. Therefore, to avoid this problem, we utilize a number of measures used in past studies, and combine them into an agency costs index for each firm. These measures include various firm characteristics, governance mechanisms, and measures of analysts’ coverage, and are described below.

First, the free cash flows: agency conflicts involving free cash flows are likely to be prevalent in low growth firms because they generally have substantial free cash flow, which managers could decide to overinvest. In contrast, high growth firms are not as likely to suffer from the free cash flow problem because they are usually short of cash after using internal funds for funding new projects and often need to rely on external financing to cover their financing needs. Therefore, following Doukas et al. (2000) we proxy agency costs of free cash flow using the interaction of a poor growth opportunities indicator with free cash flows standardized by total assets, where free cash flows are measured as operating income before depreciation minus the sum of taxes, interest expense, and dividends paid (Lehn and Poulsen, 1989). A growth dummy takes the value of 1 if the firm's Tobin's q is less than 1 (indicating a poorly managed firm) and 0 otherwise.

Second, the expense ratio, which is computed as operating expense divided by sales, and measures managers’ inefficiency in terms of controlling operating costs. Therefore, high ratios represent high agency costs.

Third, the asset utilization ratio, which is sales over total assets and proxies the effectiveness of firm's management in deploying assets. The idea behind the asset utilization ratio as a measure of agency costs is that when a firm has a low sales-to-asset ratio, it is likely that managers act in inefficient ways by making poor investment decisions, consuming executive perquisites, etc. Therefore, the ratio should be inversely related to agency costs. Both the expense ratio and the asset utilization ratio have been used in Ang et al. (2000).

Fourth, the proportion of independent directors on a corporate board: a smaller proportion is an indicator of higher potential for agency conflicts. Cotter et al. (1997) show that target shareholder gains from tender offers are higher when the target's board is more independent, suggesting that independent directors are more likely to use resistance strategies to enhance shareholder wealth. This notion is also supported by the findings of Uzun et al. (2004). They show that the likelihood of corporate fraud declines as the fraction of independent directors increases.

Fifth, institutional ownership, which is the percentage of shares that are owned by institutional investors. Given the monitoring role of institutional investors, institutional ownership should be inversely related to agency costs. Brickley et al. (1988) show that institutional investors and other blockholders vote more actively on anti-takeover amendments than non-blockholders, and that institutional opposition is greater when the proposal seems to harm stockholders. McConnell and Servaes (1990) find a significant and positive relation between Tobin's q and the fraction of shares owned by institutional investors. Jiambalvo et al. (2002) find that the extent to which stock prices lead earnings is positively associated with the level of institutional ownership. Hartzell and Starks (2003) find that institutional ownership concentration is positively related to the pay-for-performance sensitivity of managerial compensation and negatively related to the level of compensation. They suggest that institutional investors serve a monitoring role in mitigating agency problems between shareholders and managers. Therefore, the higher the percentage ownership by institutions, the lower should be the agency costs.

Sixth, the corporate governance index, which is constructed by Gompers et al. (2003) to proxy for the level of shareholder rights. The governance index is constructed by counting 28 provisions related to shareholder protection and listed in five categories: Delay, Protection, Voting, Other, and State. Among the 28 provisions, 24 are unique and enter the index with equal weight. Gompers et al. (2003) construct the governance index without requiring any judgment about the efficacy or wealth effects of any of these provisions but consider their impact on the balance of power between managers and outside shareholders. Based on Jensen's (1986) argument that threat of takeover is a strong form of managerial discipline, a firm with a high governance index (i.e. many anti-takeover provisions) is expected to have a high level of agency problems.

Seventh, product market competition: the competition in the product markets drives prices towards minimum average cost in an activity, thereby motivating managers to increase firm efficiency. Hart (1983), in a theoretical model, shows that the competition in the product market reduces the amount of managerial slack. Some studies have empirically tested the relation between product market competition and corporate agency costs. For example, Jagannathan and Srinivasan (1999) show that competition in the product market reduces agency costs. Our proxy for the competition in the product market is computed as the inverse value of the Herfindahl concentration index. The competition measure should be negatively related to agency costs.

Eighth, analysts’ coverage: security analysis can act as a monitoring mechanism in reducing agency costs (Doukas et al., 2000), and coverage is expected to be negatively related to agency costs. Hong et al. (2000) point out that there is a strong firm-size effect on analyst coverage. Therefore, our analyst coverage measure is based on the residuals from the regression of analyst coverage on firm size.

Ninth, analysts’ earnings forecast error: the forecast error captures the forecasting ability of security analysts covering the firm. The absolute forecast error has also been used by several studies as a proxy of information asymmetry (Christie, 1987; Atiase and Bamber, 1994). If a firm is transparent, the considerable amount of information about future earnings is available to market participants, and so analysts make accurate earnings forecasts. Therefore, the forecast error should be positively related to agency costs.

Tenth, analysts’ earnings forecast dispersion: Barron et al. (1998) show that analyst forecast dispersion reflects both diversity of analyst beliefs and the lack of precision in analyst forecasts. Prior studies have also used the dispersion of analyst forecasts as an information asymmetry proxy (e.g. Krishnaswami and Subramaniam, 1999). The forecast dispersion is therefore supposed to be positively related to agency costs.

Finally, an agency cost index combines all ten agency cost measures described above. We construct an index for a firm's agency costs by combining ranks (inverse ranks) of measures that are positively (negatively) related to agency costs. The methodology used in the construction is the same as the one used for the mispricing index.

4 Agency Costs and Equity Mispricing

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

In this section, we present analysis based on univariate tests, the design of our multi-factor regressions, and empirical evidence on the relation between agency costs and equity mispricing.

4.1 Mean Comparisons

Table 3 illustrates how high agency cost firms differ from low agency cost firms in terms of firm characteristics. It reports the mean values of all variables used in the study for the quintile groups classified based on the level of the agency cost index. Also reported are the mean differences across the two extreme groups (highest versus lowest quintiles) and the corresponding t-statistics for the mean difference tests. In line with the first hypothesis, the mispricing index shows a positive relation with the level of agency costs. The mean difference of mispricing between the highest and lowest quintile groups is 0.038 with a t-statistic of 13.92. The dollar amount of the different CEO compensation components, in most cases, is on average lower for firms in the highest quintile compared to firms in the lowest quintile. The evidence from the remaining firm-specific variables is consistent with prior studies examining the relationship of agency costs and firm characteristics. Firms with high levels of agency costs are generally younger, smaller, more levered, and less profitable than firms with low levels of agency costs. They are also more likely to be diversified across many industries, and less likely to pay dividends.

Table 3. Mean comparisons: Univariate tests
 Sorted by agency cost indexMean difference: High–Lowt-statistic: difference = 0
Quintile1 (Low)Quintile2Quintile3Quintile4Quintile5 (High)
  1. Reported are mean values of variables for the quartile subsamples sorted on agency cost index that combines the yearly ranks of the individual measures of agency costs. Also reported are the differences in mean values between high- and low-index groups and the corresponding t-statistics. Refer to Table 1 for variable definitions. *** and * denote statistical significance at the 1% and 10% levels, respectively.

Mispricing
Mispricing index0.5040.4790.4820.5020.5410.038***13.92
CEO compensation
Total compensation48993926320233882367−2531***−4.30
Salary613.4611.9577.3573.8553.1−60.34***−5.28
Bonus660.9651.8534.2526.2403.5−257.4***−7.97
Restricted stock grants829.5266.8327.3255.5244.0−585.4−1.18
Stock options2518206113931712874.1−1644***−5.78
Long-term incentive plan109.7190.8152.3121.9101.8−7.949−0.32
Other compensation45.1940.8840.5743.2548.273.0790.38
All other compensation122.3102.7177.3155.0142.420.120.93
Proportion of stock options0.2380.2030.1620.1460.094−0.144***−14.08
Proportion of restricted stock grants0.0540.0470.0570.0480.0550.0010.28
Other firm characteristics
Size20.0419.9819.7819.5819.42−0.614***−23.57
Leverage0.1040.1390.1650.1880.2170.114***38.96
Profitability0.0840.0590.0370.006−0.031−0.114***−61.95
Firm age2.3992.4402.3832.3012.150−0.250***−18.96
Business diversification0.2990.3390.3460.3400.3140.015*1.80
Dividend payer0.4710.4720.4180.3320.202−0.269***−36.15

4.2 Regression Analysis

Univariate tests can only provide limited insight into whether the positive impact of agency costs on equity mispricing is driven by other firm variables. That is, the pattern presented in the previous test could disappear after controlling for other factors that affect mispricing. Therefore, more tests in a regression setting are necessary to uncover the true relationship between agency costs and mispricing. Based on the literature on equity mispricing (Doukas et al., 2005), we use a number of different control variables. These variables also account for the fact that many of our mispricing measures aggregated into the mispricing index are relative valuation measures that are often related to firm characteristics. They are firm size, leverage, profitability, firm age, a business diversification, and a dividend payer indicator.

The regression results appear in Table 4. Columns [1] and [2] display the models where the mispricing index is the dependent variable, while columns [3] and [4] show results for models where the logistic mispricing index, log of one plus the mispricing index, is used as dependent variable. The results show a significant positive relation between agency costs and mispricing, suggesting that higher agency costs are strongly associated with higher levels of equity mispricing. In regressions [2] and [4], the estimated coefficient of the agency cost index is 0.046 with a t-statistic of 5.44 and 0.040 with a t-statistic of 4.75, respectively. The coefficients of the control variables suggest that equity mispricing is especially high for firms that are small, less leveraged, less profitable, young, and less likely to pay dividends. Overall, the results from Table 4 indicate that the level of agency costs is a strong determinant of equity mispricing in support of the first hypothesis. It should be noted that the results we obtained using the individual mispricing measures are qualitatively similar to the ones reported here. They are left out of the paper for the sake of brevity, but are available upon request.

Table 4. Agency costs and equity mispricing
 Dependent variable: Mispricing indexDependent variable: Logistic mispricing index
[1][2][3][4]
  1. This table shows the cross-sectional regressions of equity mispricing on agency costs and other firm characteristics. Columns [1] and [2] report results using the index levels of mispricing and agency costs, while columns [3] and [4] report results using the logistic index values. Refer to Table 1 for variable definitions. t-statistics are shown in parentheses. *** denotes statistical significance at the 1% level.

Agency costs index0.031***0.046***  
(4.15)(5.44)  
Logistic agency costs index  0.024***0.040***
  (3.26)(4.75)
Size−0.008***−0.008***−0.006***−0.005***
(−12.38)(−10.79)(−13.07)(−11.36)
Leverage−0.227***−0.205***−0.146***−0.131***
(−42.44)(−34.85)(−40.88)(−33.51)
Profitability−0.140***−0.120***−0.091***−0.078***
(−17.43)(−14.64)(−17.10)(−14.33)
Firm age−0.029***−0.028***−0.019***−0.018***
(−23.19)(−21.92)(−22.72)(−21.46)
Business diversification−0.026***−0.019***−0.018***−0.013***
(−12.84)(−9.12)(−13.06)(−9.03)
Dividend payer−0.061***−0.050***−0.042***−0.034***
(−28.29)(−21.15)(−29.02)(−21.74)
Intercept0.789***0.741***0.596***0.564***
(59.58)(31.83)(66.17)(36.09)
Industry dummiesNoYesNoYes
Year dummiesNoYesNoYes
N 30 71630 71630 71630 716
Adjusted R2 (%)20.8523.5120.7223.59

5 Equity-based Compensation and the Effect of Agency Costs on Mispricing

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

Next, we turn to the question of whether incentive compensation alleviates the conflict of interest thereby reducing the impact of agency conflicts on mispricing. To directly test this, we perform two tests: (i) a univariate comparison of mean levels of mispricing for firms that use versus firms that do not use restricted stock and option grants as components of their CEO compensation packages; (ii) a multivariate regression test where we control for the interaction of the agency costs index with two variables that capture the percentage of the CEO's total compensation that consists of options grants and restricted stock grants, respectively.

To perform the univariate test, we create two dummy variables, which take the value of 1 if a firm uses restricted stock grant (alternatively, stock options) for CEO compensation, and 0 otherwise. The relationship between equity-based compensation components and the different mispricing measures is reported in Table 5. The first set of rows shows how the five individual mispricing measures differ for firms that use versus firms that do not use restricted stock grants, while the second set of rows shows the corresponding comparison between firms that use versus firms that do not use stock options. Table 5 clearly shows that firms providing restricted stock grants to their CEOs are substantially less mispriced than firms that do not. In particular, we find a large drop in the industry-adjusted market-to-book (Mispricing4): the average value for firms that do not provide restricted stock grants is 0.412 but it reduces by 0.099 (24%) when firms use the grants to motivate their CEO. All other measures present a consistent pattern, and differences are significant at a 1% level. However, the use of stock option grants for CEOs is positively associated with mispricing. This evidence provides support for the notion that CEOs may want to induce stock mispricing when their compensation relies heavily on stock options.

Table 5. Comparisons of equity mispricing levels
 Non-usersUsersNon-users–Users
  1. This table presents averages of mispricing measures for users and non-users of restricted stock grants or stock options. It also reports the differences between the two groups. Refer to Table 1 for variable definitions. *** indicates significance at the 1% level.

Comparisons of mispricing levels between users and non-users of restricted stock grants
Mispricing10.8640.8240.040***
Mispricing20.6310.5450.085***
Mispricing30.4170.3270.090***
Mispricing40.4120.3220.099***
Mispricing50.0960.0790.017***
Mispricing index0.4780.4200.057***
Comparisons of mispricing levels between users and non-users of stock options
Mispricing10.8500.868−0.018
Mispricing20.5970.645−0.048***
Mispricing30.3740.443−0.068***
Mispricing40.3850.415−0.060***
Mispricing50.0950.0910.004***
Mispricing index0.4630.474−0.011***

The results that correspond to Table 5 are in line with the second hypothesis, which suggests that the impact of agency costs on mispricing gets stronger (weaker) when the proportion of the CEO's compensation that comes from options (restricted stocks) increases. This implies that the coefficient (β1) of the agency cost index can be expressed as:

  • display math(3)

δ1 and δ2 capture the effect of option grants and restricted stock grants as percentages of total compensation respectively, on the impact of agency costs on mispricing. These two coefficients represent the effect on mispricing from the firm's choice of equity-based compensation, which should affect the degree of conflicts of interest in an agency problem. Here, δ0 represents the effect of agency costs on mispricing that exists in the absence of any equity-based incentive compensation. Subsequently, we plug equation (3) into the model and re-write it as:

  • display math(4)

If our second hypothesis is supported, the coefficient of the interaction term between agency cost and stock options (z2) will be positive, and the coefficient of the interaction term between agency cost and restricted stock grant (z3) will be negative.

Table 6 documents the coefficients of the above regression model. Our results show that the coefficient of the interaction term of agency cost index with stock option grants (z3) is positive and the largest and most significant among z1, z2, and z3. Column [1] presents the estimated coefficient for the first interaction, which is 0.128 with a t-statistic of 10.96, which is four times larger than the pure effect of agency costs on mispricing. We do not find that restricted stock grants firms provide lead to high equity mispricing. This evidence suggests that the lion's share of the effect of agency costs on mispricing comes from conflicts of interest worsened by stock option grants. We find that the coefficient of the interaction term between option grants and agency costs is significant and positive in all models. This result is consistent with the notion that mispricing increases as the use of stock options exaggerates the agency problem between managers and shareholders. Unlike the univariate test results, the coefficient of the interaction between restricted stock grants and agency costs is not significant, implying that restricted stocks grants do not affect the impact of agency conflicts on mispricing after accounting for other effects.

Table 6. Agency costs, equity-based compensation, and mispricing
 Dependent variable: Mispricing indexDependent variable: Logistic mispricing index
[1][2][3][4]
  1. This table reports the coefficient estimates of the cross-sectional regressions of equity mispricing. Columns [1] and [2] report results using the index levels of mispricing and agency costs, while columns [3] and [4] report results using the logistic index values. Refer to Table 1 for variable definitions. t-statistics are shown in parentheses. *** and * denote statistical significance at the 1% and 10% levels, respectively.

Agency costs index0.031*0.068***  
(1.91)(3.84)  
Agency costs index × Proportion of stock options0.128***0.121***  
(10.96)(10.36)  
Agency costs index × Proportion of restricted stock grants0.0010.006  
(0.04)(0.26)  
Logistic agency costs index  0.029*0.067***
  (1.80)(3.84)
Logistic agency costs index × Proportion of stock options  0.101***0.095***
  (10.46)(9.89)
Logistic agency costs index × Proportion of restricted stock grants  0.00050.005
  (0.02)(0.25)
Size−0.106***−0.078***−0.074***−0.054***
(−5.61)(−4.06)(−5.87)(−4.26)
Leverage−0.005***−0.006***−0.003***−0.004***
(−3.50)(−4.23)(−3.48)(−4.24)
Profitability−0.035***−0.027***−0.024***−0.018***
(−9.88)(−7.08)(−10.00)(−6.97)
Firm age−0.075***−0.059***−0.050***−0.039***
(−19.14)(−13.90)(−19.28)(−13.90)
Business diversification−0.031***−0.029***−0.020***−0.019***
(−11.87)(−11.20)(−11.48)(−10.78)
Dividend payer−0.273***−0.235***−0.177***−0.150***
(−22.86)(−18.23)(−22.31)(−17.59)
Intercept0.758***0.790***0.566***0.586***
(26.09)(15.72)(28.79)(17.43)
Industry dummiesNoYesNoYes
Year dummiesNoYesNoYes
N 8657865786578657
Adjusted R2 (%)24.0627.1023.6026.86

Our evidence that the firms providing stock option grants to their CEOs suffer from exaggerated agency problems and thus greater mispricing may be interpreted as an indication that stock option grants can increase the value of overvalued firms, but can decrease the value of undervalued firms. If the latter were the case, then the question that arises is why the shareholders of undervalued (i.e. poor performing) firms would agree to stock options grants. Accordingly, we must carefully examine whether the effect of equity-based compensation, and options grants in particular, is different for overvalued and undervalued firms. To do so, we retest the mispricing models separately for the two groups. We start by using the five alternative excess value variables whose absolute values were used to measure mispricing and create an excess valuation index following the method used to create the mispricing index. The sample firms are classified into the overvalued (undervalued) group if their yearly excess value index is above (below) the median.

The results of separate regressions for over- and undervalued firms are presented in Table 7. Columns [1] through [4] report results for the overvalued group of firms, while columns [5] and [8] report results for the undervalued firms. For the overvalued sample firms, we find that the coefficient of the agency costs index is insignificant, while that of the interaction with the proportion of stock options remains positive and highly significant. In column [1] we find that z1 is −0.007 (t-statistic of −0.35) and z2 increases to 0.138 from 0.124 in the test of the whole sample. This result implies that for overvalued firms mispricing is mainly caused by the relative amount of stock option grants to total compensation. This finding is consistent with the evidence of myopia-inducing option grants uncovered in the literature. The estimated coefficient z1 is insignificant, indicating that the agency conflicts per se do not explain the upward deviation from the fundamental value. Thus, our results further show that in the absence of options grants agency conflicts do not result in more overvaluation.

Table 7. Agency costs, equity-based compensation, and mispricing: Separate regressions for overvalued and undervalued firms
 Regressions for overvalued firmsRegressions for undervalued firms
Dependent variable: Mispricing indexDependent variable: Logistic mispricing indexDependent variable: Mispricing indexDependent variable: Logistic mispricing index
[1][2][3][4][5][6][7][8]
  1. This table reports the coefficient estimates of the cross-sectional regressions of equity mispricing for overvalued and undervalued firms. The sample firms are classified into the overvalued (undervalued) group if their yearly excess value index is above (below) the median. Columns [1] through [4] report test results for overvalued firms, while columns [5] through [8] report test results for undervalued firms. Refer to Table 1 for variable definitions. t-statistics are shown in parentheses. ***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Agency costs index−0.0070.031  0.233***0.299***  
(−0.35)(1.28)  (11.86)(14.67)  
Agency costs index× Proportion of stock options0.138***0.137***  −0.014−0.025  
(9.84)(9.69)  (−0.87)(−1.65)  
Agency costs index× Proportion of restricted stock grants−0.026−0.031  0.0050.008  
(−0.78)(−0.91)  (0.18)(0.30)  
Logistic agency costs index  −0.0060.030  0.233***0.306***
  (−0.30)(1.32)  (11.31)(14.40)
Logistic agency costs index× Proportion of stock options  0.106***0.105***  −0.015−0.024*
  (9.50)(9.39)  (−1.05)(−1.85)
Logistic agency costs index× Proportion of restricted stock grants  −0.022−0.025  0.0050.006
  (−0.81)(−0.92)  (0.21)(0.26)
Size−0.002−0.002−0.001−0.001−0.010***−0.014***−0.007***−0.009***
(−0.92)(−1.00)(−0.72)(−0.82)(−5.86)(−7.73)(−5.97)(−7.81)
Leverage−0.458***−0.415***−0.294***−0.267***−0.050***0.053***−0.030***0.042***
(−21.87)(−17.96)(−21.94)(−18.04)(−3.96)(3.93)(−3.44)(4.51)
Profitability−0.023−0.0003−0.017−0.002−0.356***−0.313***−0.237***−0.206***
(−0.94)(−0.01)(−1.06)(−0.14)(−15.32)(−13.99)(−14.67)(−13.28)
Firm age−0.035***−0.033***−0.022***−0.021***−0.015***−0.014***−0.010***−0.010***
(−9.97)(−9.12)(−9.75)(−8.88)(−5.10)(−4.87)(−5.01)(−4.80)
Business diversification−0.042***−0.036***−0.027***−0.023***−0.027***−0.010**−0.019***−0.007**
(−8.85)(−6.94)(−8.96)(−6.92)(−6.34)(−2.26)(−6.47)(−2.30)
Dividend payer−0.078***−0.067***−0.050***−0.043***−0.067***−0.035***−0.047***−0.024***
(−14.42)(−11.43)(−14.59)(−11.53)(−14.78)(−7.52)(−14.85)(−7.54)
Intercept0.784***0.807***0.577***0.591***0.635***0.522***0.484***0.402***
(21.31)(13.97)(23.99)(15.79)(17.42)(6.55)(18.65)(7.24)
Industry dummiesNoYesNoYesNoYesNoYes
Year dummiesNoYesNoYesNoYesNoYes
N 44124412441244124245424542454245
Adjusted R2 (%)32.1734.9932.0034.8824.5434.8023.8434.69

We find different results for the undervalued group of firms. The estimated mispricing is strongly related to the degree of agency conflicts. The estimated coefficient of agency costs index is fout to seven times greater than the one in Table 6. The coefficient of the interaction term between the agency costs index and the proportion of stock options is negative, implying that options grants may alleviate the undervaluation caused by agency conflicts. However, both the magnitude and the significance of the negative effect of options grants on the impact of agency conflicts is much weaker for undervalued firms than that of the corresponding positive effect observed for overvalued firms. These results are in contrast to popular belief that option grants should be in greater demand when firms are valued lower than their fundamental value.

Overall, our results are consistent with the notion that agency conflicts lead to undervaluation. In addition, if agency conflicts are coupled with significant option grants that can induce managerial myopia, they may cause overvaluation as well.

6 Robustness

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

In this section we present several robustness checks aimed at ensuring that the previous findings are not due to the particular estimation methodology used. According to Petersen (2009), the most common methods are the Fama and MacBeth (1973) procedure, fixed-effect regressions, and cluster-correcting models in recently published finance papers. He shows in technical ways that the chosen methods can be incorrect and yield different results in many cases. Therefore, we examine if our evidence holds in the following methods.

First, since our study relies on cross-sectional/time-series data, we use a fixed-effects panel regression, which regards differences between firms as parametric shifts of the regression function and controls for possible differences across firms.1 Second, one may argue that prices may generally be high or low relative to their fundamental values in a given year. In pooled cross-sectional tests, a lack of independence across observations may cause t-statistics to be biased. To fix such a plausible problem, we follow Fama and MacBeth (1973) by estimating separate annual regressions. Statistical significance of the estimated coefficients is computed as: inline image, where inline image is the mean coefficient over the sample years, inline image is the standard deviation of the yearly estimates, and n is the number of years. Third, we compute statistical significance using the regressions with clustering at the firm level and standard errors that are robust to heteroskedasticity (White, 1980). This is because, within a firm, unexplained deviations from fundamental values are likely to persist. Clustering at firm level helps to avoid this problem. Finally, we estimate a model using only the first-year observation of each firm. This robustness check with the first-year data allows us to assess whether or not previous results are driven by the existence of multiple observations on the same firms. The results of these robustness checks are reported in Table 8. We find that all regressions show a consistent pattern of coefficients on the agency costs index. They all remain positive and statistically significant. Therefore, the previous results presented in this paper are confirmed by these alternative regression models.

Table 8. Robustness checks of regression of equity mispricing on agency cost
 Panel regressionTime-series average of cross-sectional regressionHeteroskedasticity correction modelFirst-year regression
Dependent variable: Mispricing indexDependent variable: Logistic mispricing indexDependent variable: Mispricing indexDependent variable: Logistic mispricing indexDependent variable: Mispricing indexDependent variable: Logistic mispricing indexDependent variable: Mispricing indexDependent variable: Logistic mispricing index
[1][2][3][4][5][6][7][8]
  1. This table reports robustness checks of regressions of equity mispricing on agency cost and other firm characteristics. Columns [1] and [2] report results using panel regressions. Columns [3] and [4] report results using the time-series average of cross-sectional annual regressions as outlined in Fama and MacBeth (1973). Columns [5] and [6] report results using White's (1980) heteroskedasticity correction model. Columns [7] and [8] report results only using the first-year data of each firm. Refer to Table 1 for variable definitions. t-statistics are shown in parentheses. ***, ** and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Agency costs index0.035*** 0.031*** 0.031** 0.051*** 
(4.19) (2.87) (2.52) (2.95) 
Logistic agency costs index 0.030*** 0.024** 0.024* 0.043**
 (3.65) (2.25) (1.94) (2.55)
Size−0.018***−0.012***−0.008***−0.005***−0.008***−0.006***−0.007***−0.005***
(−19.01)(−18.90)(−4.48)(−4.62)(−6.23)(−6.50)(−3.60)(−3.98)
Leverage−0.139***−0.090***−0.198***−0.126***−0.227***−0.146***−0.270***−0.169***
(−21.79)(−21.28)(−10.81)(−10.18)(−25.54)(−24.42)(−19.47)(−18.98)
Profitability−0.104***−0.070***−0.069**−0.047**−0.140***−0.091***−0.153***−0.097***
(−13.29)(−13.36)(−2.47)(−2.61)(−11.72)(−12.03)(−9.25)(−9.14)
Firm age−0.033***−0.022***−0.029***−0.019***−0.029***−0.019***−0.017***−0.011***
(−20.21)(−19.96)(−21.04)(−20.00)(−13.58)(−13.20)(−5.50)(−5.42)
Business diversification−0.010***−0.007***−0.030***−0.020***−0.026***−0.018***−0.035***−0.023***
(−4.40)(−4.72)(−10.06)(−9.64)(−7.46)(−7.46)(−6.11)(−6.23)
Dividend payer−0.041***−0.029***−0.065***−0.044***−0.061***−0.042***−0.063***−0.042***
(−14.29)(−15.05)(−16.59)(16.40)(−14.73)(−14.90)(−10.05)(−10.37)
Intercept0.961***0.700***0.771***0.584***0.789***0.596***0.749***0.567***
(54.42)(59.44)(24.94)(27.35)(31.44)(34.72)(20.77)(24.15)
N 30 71630 71630 71630 17630 71630 71648834883
R2 (%)19.5619.5721.6121.5720.8620.7419.3919.37

7 Summary and Conclusions

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References

Recently, the finance literature has emphasized the importance of equity mispricing. Previous studies have found that there is a strong positive relation between information asymmetry and equity mispricing, but these have not provided direct evidence on the relationship between agency conflicts and equity mispricing. This is the main contribution of this paper. In addition, this paper also provides evidence with regards to the role of managerial equity-based compensation in the aforementioned relationship. We extend previous studies by treating all stock-based incentives equally and relate them to both lowered agency costs and enhanced firm stock value. In light of both academic evidence and the recent skepticism about the effectiveness of equity-based compensation fueled from recent financial scandals, we separately analyze two different components of equity-based compensation, stock options grants, and restricted stock grants.

We utilize ten agency costs proxies and provide evidence that the level of agency costs is significantly and positively related to equity mispricing. Our findings extend the existing literature, in that we find that the options grants component of equity-based compensation exacerbates the link between agency conflicts and equity mispricing. This phenomenon is more pronounced when firms are valued higher than their fundamental value. The evidence suggests that the use of stock options as incentive compensation may induce managerial myopia and exaggerate agency problems, thus resulting in more mispricing (overvaluation). On the other hand, options grants do not affect the negative impact of agency costs on valuation of poor-performing firms (undervalued firms). Finally, we find some weak evidence of a beneficial effect of restricted stock grants.

Note
  1. 1

    There is a possibility of endogenous relation between agency costs and mispricing. Some firm characteristics may drive both the estimated mispricing and firms’ agency costs. Alternatively, the agency cost variable may be determined by the degree of mispricing, rather than the other way around. Since the literature has struggled to find good instrumental variables, we present the fixed-effects panel regressions as a potential solution to particular sources of endogeneity (see Himmelberg et al., 1999).

References

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Related Literature and Hypothesis Development
  5. 3 Data and Measures
  6. 4 Agency Costs and Equity Mispricing
  7. 5 Equity-based Compensation and the Effect of Agency Costs on Mispricing
  8. 6 Robustness
  9. 7 Summary and Conclusions
  10. References