Differential risk effect of inside debt, CEO compensation diversification, and firm investment

Abstract

The main purposes of this paper are to study (1) a differential effect of inside debts on components of the firm risk, and (2) how it relates to the diversification of CEOs’ portfolios to reduce exposures to the firm risk. We find that compensating CEOs with inside debts (e.g., pensions and other deferred compensation plans) leads to reductions in firms’ systematic risk and idiosyncratic risk, but to disproportionate degrees. CEOs with larger inside debts draft and implement policies, which lead to a significantly larger reduction in the idiosyncratic firm risk and investment. We then show that the differential effect is the result of an asymmetry in CEOs’ perceived benefits of diversifying exposures to individual firm risk components. We further show that granting excessive debt-based pay may divert executives from firm specific but productive activities (e.g., R&D investments), therefore may compromise the long-run corporate success.

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Notes

  1. 1.

    The only exception is Cassell et al. (2012), who examine the association between inside debt and volatility of residual return from the market model regression (i.e. a measure of idiosyncratic risk). However, Cassell et al. (2012) do not examine the impact of inside debt on the systematic risk component of firm risk, which is directly linked to the values of both equity and debt components of executive compensation (e.g., Hall and Murphy, 2002); nor do Cassell et al. (2012) explore the differential effect of inside debt on these two risk components, which is the focus of this study.

  2. 2.

    The term “inside debt” is used by Jensen and Meckling (1976) to capture the fraction of the firm’s debts granted to executives. Prior studies (e.g., Sundaram and Yermack 2007; etc.) point out certain forms of compensation such as pension and deferred compensation plans have debt-type payments and could potentially be viewed as inside debt. In this study, we use “debt-based compensation”, “debt-based pay”, and “inside debt” interchangeably.

  3. 3.

    For example, Sundaram and Yermack (2007) find firms with CEOs of significant inside debt holdings exhibit low default risk. Similarly, Wei and Yermack (2011) find that volatilities of debt and equity securities decrease upon the initial public disclosure of significant CEO inside debt holdings. Chen et al. (2011) and Wang et al. (2017) find a substitution relation between CEO inside debt and conservative accounting policies in preempting risk-taking (Kanagaretnam et al. 2013; Kim and Zhang 2016). More recently, Cassell et al. (2012) provide evidence that CEOs of sizeable inside debt holdings prefer investment and financing policies that reduce future stock return volatility.

  4. 4.

    For example, Wei and Yermack (2011) find that the disclosure of large CEO inside debt holdings as a response to the 2007 SEC regulation change is associated with an increase in bond prices but a decrease in equity prices. Edmans and Liu (2011) demonstrate that increased debt-based compensation leads to greater conservatism in project selection and thus higher (lower) the debt (equity) value. Anderson and Core (2018) also demonstrate that debt value always decreases when the volatility of asset returns rises, with the reduction in debt value shared by stocks and options (Table 1).

  5. 5.

    We acknowledge that the equity instruments in CEOs’ portfolios also expose them to excessive amount of market risk exposure; therefore, there exists a diversification benefit to reduce market risk as well. The observed risk differential should relate to the difference between the diversification benefits from reducing the two risk components. We focus on the benefit related to reducing idiosyncratic risk in formulating Hypothesis 3 for two reasons. First, to the extent that the undesirable effect of market-wide uncertainty on a CEO’s portfolio is hedgeable, the associated diversification benefit will be smaller, leaving it secondary in generating the cross-sectional variations in the difference between the diversification benefits related to the two risk components. Secondly, prior studies (e.g., Lewellen 2006) find that exposure to firm-specific risk is more important in shaping actions by under-diversified risk-averse CEOs. We, therefore, focus on the benefit associated with reducing firm-specific risk as the primary driving force behind the cross-sectional variations in the divergence between diversification benefits of risk components. Our results (e.g., Tables 5, 6) are consistent with our argument.

  6. 6.

    Evidence that overconfident CEOs are less averse to firm-specific risk can be found in Cooper et al. (1988) and Landier et al. (2004) that majority of entrepreneurs underestimate the difficulty in starting up a firm; in Malmendier and Tate (2005, 2008), that overconfident managers exhibit below average risk-aversion by holding options beyond rational thresholds; and in Hirshleifer et al. (2012), that overconfident CEOs invest heavily in R&D, which generates more innovations but greater firm-specific uncertainty.

  7. 7.

    It is possible that a firm’s tax loss carry forwards will continue and apply to future years (Sarkar 2014), causing them to correlate with future firm risk measures. To address the concern, we drop TAX from the instrumental variable list and re-run all the regressions. Our results continue to hold.

  8. 8.

    Armstrong and Vashishtha (2012) use the market-based profitability measure (i.e. previous years’ stock returns) as the instrument in examining CEO equity incentives and risk-takings. We use the previous year’s earning-based profitability measure (i.e. ROA) because it more relevant to debt contracting and valuations than the market-based performance measure (Basu 1997; Watts 2003a, b; etc.); therefore suits the current study better. We repeat all our analysis using the same instruments adopted by Armstrong and Vashishtha (2012), and all our results continue to hold.

  9. 9.

    Irvine and Pontiff (2008) find idiosyncratic volatility decreases with market power. Because firms with market power will generate higher profit, it is possible that firm’s performance (either ROA or stock returns) will be negatively associated with idiosyncratic volatility. To address the concern that measures of firm’s past performance are likely to be related to firm risk, we drop ROA (and stock returns), and use the annual median inside debt of the firms (excluding the sample firm) in the same industry (defined by 2-digit SIC) as the last instrument. All main results continue to hold. These results are available upon request.

  10. 10.

    Another benefit of 3SLS is that estimated coefficients will be more efficient than those using 2SLS, provided that regression relations are properly identified. In Sect. 4.2.1, we establish the validity of the model specification and identification with a battery of tests.

  11. 11.

    Both the “dynamic” and “static” inside debt measures are subject to measurement errors. By considering only the pension and deferred compensations, these measures explicitly ignore other compensation components (e.g., salary, bonus, etc.) that also carry debt features. In addition, Anantharaman et al. (2013) find that the alignment effect of inside debt is driven largely by the supplemental executive retirement plans (SERP) in the pensions, while other debt-based components, such as rank-and-file (RAF), do not provide similar effect. Wang et al. (2017) find the substitution relation between inside debt and accounting conservatism is primarily from executive pension. Chen et al. (2015) show that measurement error of the explanatory variable will affect estimations in the regression model by reducing the multiple correlations and increasing the residual variance, and demonstrate that the instrumental variable approach can be used to reduce the bias in regression estimates resulting from the measurement errors.

  12. 12.

    Both the systematic and idiosyncratic components of firm risk are unobservable; consequently, empirical estimations of the two components are model dependent. We also use the market model to disaggregate total firm risk, and the results are quantitatively similar.

  13. 13.

    We measure firm-specific risk as the volatility of unexpected daily stock returns. Measuring firm systematic risk as the volatility of expected daily stock returns instead of Beta coefficient allows for a direct comparison of inside debt’s effects on the two risk components.

  14. 14.

    Imputed firm returns is computed as \( R_{i,t} = \left( {\sum\nolimits_{1}^{n} {V_{k} *R_{i,t} } } \right)/\sum\nolimits_{1}^{n} {V_{k} } \), where n is the number of a firm’s industry segments at the beginning of the fiscal year; Ri,t is segment k’s realized return on date t, which equals to date t average return of all single-segment firms in the segment’s industry; and Vk is the book value of segment k's assets at the beginning of the fiscal year from the Compustat Industry Segment Database. Following Berger and Ofek (1995), we require the sum of firm segment sales to be within 1% of the total sales reported by COMPUSTAT to ensure segment data integrity. We also require all firm segments to have at least five firms in the same two-digit SIC segment industry.

  15. 15.

    Using imputed risk measures is subject to two caveats. First, data restrictions significantly reduce the sample size (reduces sample size from 5148 to 3148), which makes the test results unreliable and hypotheses difficult to confirm. Second, by construct, the imputed return is the return on a portfolio of all the single-segment firms from industries where the firm has businesses. Portfolio diversification reduces idiosyncratic fluctuations but maintains market-wide variability in imputed returns (as evidenced by results reported in Table One). Because Hypotheses 2 and 3 posit a larger reduction in the firm-specific risk, the downward biased firm-specific risk estimate will work against these hypotheses. Since both caveats make hypotheses more difficult to confirm, consistent results using imputed risk measures will add to the validity of the conclusions drawn from results using realized return based risk measures.

  16. 16.

    Wang and Hanna (1997) and Grable (2000) find a positive association between risk tolerance and age. More recently, Serfling (2014) documents a negative association between CEO age and stock return volatility.

  17. 17.

    We also use sample median values of age and CEO non-firm wealth (see Sect. 3.5) to classify the sample and obtain qualitatively similar results.

  18. 18.

    Available from http://people.few.eur.nl/dittmann/data.htm. We set non-firm wealth to missing when the estimate is negative.

  19. 19.

    We compute the average value of exercisable unexercised options by scaling their year-end estimated aggregate value (Execucomp data item OPT_UNEX_EXER_EST_VAL) by the year-end aggregate number of exercisable unexercised options held by the executive (Execucomp data item/OPT_UNEX_EXER_NUM). CEO is defined as overconfident if the average option value exceeds the year end stock price by 67%.

  20. 20.

    In alternative specifications, we follow Grant et al. (2009) and Cassell et al. (2012) and use the ratio of option Vega to Delta. All main results do not change.

  21. 21.

    Institutional ownership data is retrieved from Thomson Reuters institutional holdings (13f) database.

  22. 22.

    Over the same period, firms included in COMPUSTAT database have average total assets of $6114 million (median = $324 million), net sales of $2515 (median = $131 million), debt ratio of 0.390 (0.165) and profitability (i.e. ROA) of -0.473 (0.008. respectively). Differences between measures on COMPUSTAT firms and those reported in Table 1 are all significant at the 0.01 level.

  23. 23.

    Average (median) debt financing by sample firms is $2983 million ($992 million). Average (median) inside debt to external debt ratio (i.e. CEO inside debt holdings scaled by sum of short- and long-term debts) is 4.82% (0.36%).

  24. 24.

    Cassell et al. (2012) use the same inside debt definition and report the mean and median values of 2.472 and 0.407, respectively. They measure firm risk using the variance of future stock returns, and report mean (median) logarithm values on total risk and idiosyncratic risk of ‒ 6.9494 and ‒ 2.4647 (‒ 6.9668 and ‒ 2.4870) (Table 2, p. 597). We measures risk using standard deviations of stock returns. Our measures of total risk and firm-specific risk, when converted to variances and log-transformed, have mean (median) values of ‒ 5.7489 and ‒ 2.2961 (‒ 6.2668 and ‒ 2.5328), close to Cassell et al.'s results.

  25. 25.

    Notice that imputed total risk is significantly lower than that using realized returns (difference = ‒ 0.005, t-value = ‒ 18.62). Further analysis indicates a significant difference in unsystematic risk (difference =   − 0.008, t-value = − 33.11). By contrast, imputed systematic risk is not different from that using realized returns (difference = 0.0005, t-value = 2.77). In Sect. 3.3, we argue that imputed firm-specific risk measure may be biased downward. Table 1 provides supporting evidence.

  26. 26.

    Shea (1997) suggests that instrumental variables are strong if the part of instruments important to one endogenous variable is linearly independent of the part important to other endogenous variables.

  27. 27.

    Differences in reductions on systematic risk from 1% increase in inside debt between the two groups are 0.107% (Panel A), 0.013% (Panel B) and 0.099% (Panel C), respectively. Corresponding reductions on firm-specific risk are larger at 0.189% (Panel A), 0.129% (Panel B), and 0.321% (Panel C), respectively.

  28. 28.

    Data requirements for the construction of imputed returns reduces sample size by 39% (from 5148 firm-year observations down to 3148 observations). We examine the impact of the significant reduction in sample size on the strength of instrumental variables and model specification. Test results (untabulated) confirm that selected instruments continue to be strong and our regressions are well specified.

  29. 29.

    Following Cen and Doukas (2017), we require at least three observations to compute DCP volatility. We exclude observations with correlation between DCP returns and company stock returns above 0.9 (i.e. CEOs who invest DCP exclusively in their company’s stock), and those with correlation between DCP returns and the interest rate of 5-year treasury note above 0.9 (i.e. CEOs whose firms offer no investment options to CEOs but directly pay an above-market interest rate). Because DCP data is available from 2006, and four-year of DCP returns are required to derive DCP volatility, the sample using the Cen and Doukas (2017) proxy starts from year 2009. A total of 2488 firm-year observations with DCP return volatility information are included in the analysis.

  30. 30.

    Information on the trading activities of the CEO is obtained from Thomson Reuters.

  31. 31.

    In a certain year, a firm experiences a material R&D increase if (1) its ratio of R&D to firm assets increases, (2) its R&D intensity level is at least 5%, and (3) its dollar amount of R&D increases from the prior year, and the increase in R&D to assets ratio must be at least 5%. A code of 1 is assigned to firm-year with a material R&D increase, and 0 otherwise.

  32. 32.

    Industry dummies are not adopted because several industry dummy variables are perfectly correlated with the dichotomous dependent variable.

  33. 33.

    We also examine association between R&D level and inside debt using our sample. The results are consistent with those by Cassell et al. (2012).

  34. 34.

    Most of the coefficients have the same signs and significance levels as those by Campbell et al. (2016) (model 3, Table 3 on p. 343). To save space, we only report stage two results. Stage one results are available upon request.

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Appendix: Variable definition

Appendix: Variable definition

Risk measures
RISK_TOT Total risk, computed as the annualized standard deviation of daily realized (imputed) returns over the estimation window (i.e., fiscal year t + 1)
RISK_SYS Systematic risk, computed as the annualized standard deviation of the expected portion of the daily realized (imputed) returns, estimated using Fama–French 3-factor models, over the estimation window
RISK_UNSYS Unsystematic risk, computed as the annualized standard deviation of the unexpected portion of daily realized (imputed) returns over the estimation window
Inside debt measures
ID/DE “Static” CEO inside debt ratio, defined as the ratio of CEO inside debts to equity holdings divided by firm’s debt-equity ratio. CEO inside debts are the sum of present value of accumulated pension (ExecuComp data item PENSION_VALUE_TOT) and deferred compensations (ExecuComp data item DEFER_BALANCE_TOT); equity holdings are the total values of stocks (including restricted shares) and options, calculated using methodology by Core and Guay (2002). Firm debt to equity ratio is sum of long-term debts (data 9) and debts in current liabilities (data34) divided by the market value of equity (data 25 * data 199)
RLTV ID/DE “Dynamic” CEO inside debt ratio, defined as the marginal change in inside debts over marginal change in the equity holdings given a unit change in the firm value, scaled by a ratio of the marginal change in the firm’s external debts over a marginal change in the firm’s external equity from the same unit change in overall value of the company
Firm specific characteristics
SIZE Natural logarithm of annual sales (data 12)
FIRMAGE Natural logarithm of firm age, measured as the current year minus the first year firm has non-missing stock price on Compustat
MTB Market to book ratio, computed as market value of equity (data 25 * data 199), divided by book value of equity (data 60)
SALEGRO Growth rate of annual sales, defined as percentage change in annual sales (data 12)
LEV Firm leverage as total debt (data 9 + data 34) divided by book value of assets (data 6)
TAX Dichotomous variable that equals to 1 if firm has tax loss carry-forward; 0 otherwise
ROA Return on assets, defined as income before extraordinary item (data 18) scaled by lagged total asset
WAGETAX_ST maximum tax rate for wage of the state where the firm is headquartered
CEO specific measures
CEO_AGE CEO age
LNTENURE Natural logarithm of number of years CEO serves in the office
LNCASHCOMP Natural logarithm of CEO cash compensation, defined as sum of salary and bonus
DELTA CEO wealth incentive, defined as change in the value of CEO’s equity portfolio with a 1% change in stock price, using methodology by Core and Guay (2002)
VEGA CEO risk incentive, defined as change in the value of CEO’s equity portfolio with a 1% change in stock return volatility, using methodology by Core and Guay (2002)
OPTIMISM Dichotomous variable that equals to 1 between period when CEO holds options that is 67% or more in the money for at least twice; 0 otherwise
NON_FIRM_WEALTH Ratio of non-firm wealth in CEO total wealth, where CEO non-firm wealth is calculated according to Dittmann and Maug (2007). CEO total wealth is the sum of CEO non-firm wealth and firm-related wealth, computed as the sum of values of CEO stock holdings, options and inside debt
Corporate governance measures
E-INDEX Entrenchment index of Bebchuk et al. (2009)
INSTHLDGTOP5 Percentage of firm shares held by the top five institutional investors by fiscal year end

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Lee, CF., Hu, C. & Foley, M. Differential risk effect of inside debt, CEO compensation diversification, and firm investment. Rev Quant Finan Acc 56, 505–543 (2021). https://doi.org/10.1007/s11156-020-00901-0

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Keywords

  • Executive compensation
  • Inside debt
  • Risk-taking incentive
  • Systematic and idiosyncratic risk
  • R&D investment
  • Diversification

JEL Classification

  • G32
  • J33
  • L25
  • M12