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Secured debt and managerial incentives

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Abstract

Financial theory holds that firms can control agency costs through the use of short-term and secured debt. We examine the relation between the use of secured debt and the incentive of the manager to increase the risk of the firm, as measured by vega. We find that firms utilize secured debt to a lesser extent when managerial volatility sensitivity is higher. Our results suggest that these same firms employ short-term debt as the primary tool to control risk-shifting. Managers with a high risk appetite avoid secured debt, but appear to do so without compromising the interests of the shareholders.

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Notes

  1. This literature is vast and will not be reviewed here, but see, for example, Knopf et al. (2002), Coles et al. (2006), Sundaram and Yermack (2007), Billett et al. (2010), and Shaw (2012) and the many references contained in these papers.

  2. http://research.stlouisfed.org/fred2/.

  3. As in Brockman et al. (2010), financial firms are excluded because Compustat does not report the variables required to compute debt maturity structure of these firms.

  4. Core and Guay (2002) develop a method of computing sensitivities of a option portfolio to stock price and stock return volatility that is easily implemented using data from only the current year’s proxy statement or annual report. Other papers that use this method to compute option sensitivities include Brockman et al. (2010), Cao and Laksmana (2010), Coles et al. (2006), Knopf et al. (2002), and Wang (2012).

  5. The adoption of FAS123R changed the reporting requirements for executive compensation for fiscal years 2006 onwards. Among other things, the details of option grants now appear in two additional tables (the plan-based award and outstanding equity award tables) as opposed to being presented in a single separate table previously. We take due care to define the compensation variables consistently across the two disclosure regimes. The details about the calculation of each compensation variable under both the old and new formats can be found in ‘‘Appendix’’ of Hayes et al. (2012) and Appendix A3 of Coles et al. (2010).

  6. We do not winsorize the incentive measures, delta and vega, at the 1st percentile because these variables are truncated at zero.

  7. As in Brockman et al. (2010), we define LVEGA as ln(1 + VEGA*1,000) and LDELTA as ln(1 + DELTA*1,000), adding one to each measure since DELTA and VEGA can be zero.

  8. Censored regression applications fall into two categories: (a) censored regression analysis and (b) corner solution models (Woolridge 2002, pp. 517–520). Though both lead to the same standard censored Tobit model (or type I Tobit model) estimation, there is a difference in interpretation. In the former model, the dependent variable is censored from above and/or below, i.e. it is not observed for some part of the population. That is not the case with secured debt. In the latter model, the dependent variable takes on value 0 with positive probability but is a continuous random variable over strictly positive values, as is the case with secure debt. As Wooldridge points out, it is problematic to use OLS in this setting.

  9. In a recent review article, Roberts and Whited (2012) discuss how research in corporate finance has addressed endogeneity issues.

  10. An alternative approach to control for endogeneity would be to estimate simultaneous equations system using three stage least squares (3SLS), but this approach has the drawback that if one equation in the system is misspecified it can cause bias in estimates of other (correctly specified) equations. Our main focus is on getting the right specification for the secured debt equation estimation, so we choose to use 2SLS. That said, we did re-estimate the models using 3SLS and obtained very similar results.

  11. For brevity’s sake we do not report the results of the first-stage regressions, but it should be noted that, consistent with Brockman et al. (2010), we find that short term debt and vega are positively related, while short-term debt and delta are negatively related.

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Authors and Affiliations

  1. Department of Finance, Saint Louis University, 3674 Lindell Boulevard, St. Louis, MO, 63108, USA

    Michael J. Alderson, Naresh Bansal & Brian L. Betker

Authors
  1. Michael J. Alderson
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  2. Naresh Bansal
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  3. Brian L. Betker
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Corresponding author

Correspondence to Brian L. Betker.

Appendix: Variable description and data source

Appendix: Variable description and data source

This appendix defines the variables used in the study. CEO compensation data is from Execucomp, accounting data is from Compustat, stock return information is from CRSP. The Compustat data items are in italics.

Variable

Definition and source

ABNEARN

(Earnings in yeart+1 (ibadj) − Earnings in yeart)/(share price (prcc_f) x outstanding shares (cshpri) in year t). Source: Compustat.

ASSET_MAT

Book value-weighted average of the maturities of property plant and equipment and current assets, computed as (gross property, plant, and equipment (ppegt)/total assets (at)) * (gross property, plant, and equipment (ppegt)/depreciation expense (dp)) + (current assets (act)/total assets (at)) *(current assets (act)/cost of goods sold (cogs)). Source: Compustat.

AVG_RET

Average of daily stock returns over the preceding 180 days. Source: CRSP daily file.

CAPEX

Net capital expenditures (capx − sppe) scaled by assets (at). Source: Compustat.

CASHCOMP

Sum of the CEO’s salary and bonus (in 100 thousands). Source: ExecuComp.

DELTA

Change in the value of the CEO’s stock and option portfolio due to a 1 % increase in the value of the firm’s common stock price. Source: Compustat.

FIX_ASSET

Ratio of net property, plant, and equipment (ppent) to total assets (at). Source: Compustat.

ITC_DUM

Equals one if the firm has an investment tax credit (itci), and zero otherwise. Source: Compustat.

LEVERAGE

Long-term debt (dltt) divided by the market value of the firm (prcc_f * cshpri + at − ceq). Source: Compustat.

LSIZE

Market value of equity (prcc_f * cshpri) plus the book value of total assets (at) minus the book value of equity (ceq), in logs. Source: Compustat.

LSIZE2

Square of LSIZE

LTENURE

Years since CEO, difference between current year minus the date became CEO, in logs. Source: ExecuComp

MTB

Market value of the firm (prcc_f * cshpri + at − ceq) divided by the book value of total assets (at). Source: Compustat.

NOL_DUM

Equals one if the firm has operating loss carryforwards (tlcf), and zero otherwise. Source: Compustat.

OWN

Number of shares owned by the CEO (shrown) scaled by total shares outstanding (shrsout). Source: ExecuComp.

REG_DUM

Equals one if the firm’s SIC code is between 4900 and 4939 and zero otherwise. Source: Compustat.

ROA

Ratio of operating income before depreciation (oibdp) to total assets (at). Source: Compustat.

SECURE

Secured Debt (dm) scaled by total debt (dltt + dlc). Source: Compustat.

SGR

Sales growth rate computed as ln(sale t/sale t−1). Source: Compustat.

STD_DEV

Monthly stock return standard deviation during the fiscal year multiplied by the ratio of the market value of equity (prcc_f * cshpri) to the market value of assets (prcc_f *cshpri + at–ceq). Source: Monthly returns from CRSP monthly file and financial accounting information from Compustat.

STOCKRET

Buy-and-hold return during the fiscal year. Source: CRSP monthly file.

SURCASH

Cash from assets-in-place (oancf − dpc + xrd) scaled by assets (at). Source: Compustat.

TERM

Yield on 10-year government bonds subtracted from the yield on 6-month government bonds at the fiscal year end. Source: St. Louis Fed.

VEGA

Change in the value of the CEO’s option portfolio due to a 1 % change in the annualized standard deviation of stock returns. Source: ExecuComp.

ZSCORE_DUM

Equals one if Altman’s Z-score is greater than 1.81, and zero otherwise. Altman’s Z-score is computed as: 3.3*oiadp/at + 1.2*(act − lct)/at + sale/at + 0.6*prcc_f*csho/(dltt + dlc) + 1.4*re/at. Source: Compustat.

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Alderson, M.J., Bansal, N. & Betker, B.L. Secured debt and managerial incentives. Rev Quant Finan Acc 43, 423–440 (2014). https://doi.org/10.1007/s11156-013-0380-x

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