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Erschienen in: Review of Accounting Studies 2/2014

01.06.2014

Why do managers avoid EPS dilution? Evidence from debt–equity choice

verfasst von: Rong Huang, Carol A. Marquardt, Bo Zhang

Erschienen in: Review of Accounting Studies | Ausgabe 2/2014

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Abstract

Survey evidence reveals that managers prefer to avoid dilution of earnings per share (EPS), though financial theory suggests it is irrelevant in firm valuation. We explore contracting and behavioral explanations for this apparent paradox using a large sample of debt–equity issuers. We first provide evidence that firms with greater agency conflicts between managers and shareholders are more likely to use EPS as a performance measure in bonus contracts. After controlling for possible endogeneity related to compensation contract design, we find that managers are more likely to avoid earnings dilution when their bonus compensation explicitly depends upon EPS performance. This effect is increasing in the magnitude of bonus compensation for this subset of firms; we document no such associations for the firms that do not use EPS in setting bonus pay. Additional tests of firms’ speed of adjustment to target leverage ratios and firms’ debt conservatism levels indicate that explicitly rewarding executives on EPS performance helps to resolve underleveraging problems. We also find that clientele effects are associated with managers’ aversion to earnings dilution. Our findings provide a deeper understanding of the factors that underlie the use of accounting performance in compensation contracts and new evidence on the implications of the contracting role of accounting in firm decision-making.

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Fußnoten
1
“Earnings dilution” typically refers to the reduction in reported EPS that occurs through the issuance of additional common shares or the conversion of convertible securities. We use the terms “earnings dilution” and “EPS dilution” interchangeably throughout the paper.
 
2
See “SEC Approves Tougher Rules on Executive Pay,” The New York Times, Dec. 17, 2009.
 
3
The Office of Federal Housing Enterprise Oversight’s (OFHEO) now-famous 2006 report sharply criticized Fannie Mae’s over-reliance on EPS in determining executive pay. According to the report, “Fannie Mae tied major portions of executive compensation to EPS, a metric easily manipulated by management.” The report also states, “Fannie Mae’s executives were precisely managing earnings to the one-hundredth of a penny to maximize their bonuses while neglecting investments in systems internal controls and risk management,” and Fannie Mae’s reaching of announced targets for EPS each quarter “were illusions deliberately and systematically created by senior management with the aid of inappropriate accounting and improper earnings management.”
 
4
Bushman and Smith (2001, p. 284) make a similar argument, observing that “executives likely understand the mapping from their actions to accounting numbers better than the mapping from actions to equity value.”
 
5
Graham and Harvey (2001) note that avoiding EPS dilution seems to be particularly important to CFOs working in regulated industries. We omit financial institutions to ensure consistency with prior research; however, we may be reducing the power of our tests if these firms are more likely than other firms to reward executives on EPS performance.
 
6
These definitions of net debt and equity issued may include convertible debt or preferred stock, which may introduce error into our model of debt–equity choice. As a sensitivity test, we omit observations where either preferred stock (Compustat data item #130) or convertible debt (data item #79) increases by more than 5 % of total assets during the fiscal year. We find that eliminating these observations (12 preferred stock issues and 74 convertible debt issues) does not qualitatively change our findings.
 
7
As a sensitivity test, we add back the dual issuers and repeat our analysis; our results are robust to their inclusion.
 
8
Many of these firms were ADRs. We also note that proxy statements are increasingly available via SEC EDGAR during the later years of our sample period.
 
9
We note that Ittner et al. (1997) find that both utilities and firms that follow innovative organizational strategies are more likely to choose nonfinancial performance measures in their bonus contracts, consistent with our descriptive evidence from Table 1, where we find an inverse relation between the use of EPS and nonfinancial measures.
 
10
We use simulated marginal tax rates as calculated by Graham and Mills (2008) using financial statement data, which are highly correlated with marginal rates based on actual tax returns. We thank John Graham for providing data on marginal tax rates. When these data are not available, we use Graham and Mill’s (2008) “PseudoStatutory” variable, which they show is a second-best alternative to their simulated rates.
 
11
We assume a pure cubic function in relating credit ratings to yields because it appears to effectively capture the convex relationship between credit ratings and yields documented by John et al. (2003) and can be fitted using with only two data points. To illustrate, John et al. (2003) show that the average spread between AAA and BBB rated debt is 50–60 basis points, while the spread between BBB and CCC is over 500 points. If we assume a pure cubic function where x is the numerical credit rating from Compustat, ranging from 1 to 29, and y is the spread between AAA and BBB rated debt, the spread will equal the rating cubed times a coefficient—we subtract 2 from each rating so that the rating for AAA rated debt will equal 0 and run through the origin—or spread = ax3. Using 60 basis points as the spread and an x value of 9 (11 − 2), the coefficient equals 60/729 or 0.0823. Applying this relation to CCC-rated debt results in an estimated yield of 0.0823 times (20–2)3, or 480 basis points, which is close to the spreads that John et al. (2003) report for CCC rated debt. We also applied a linear model to estimate yields; our results are insensitive to this design choice.
 
12
Credit ratings are available for 809 of our 1,493 observations. Our findings are robust to omitting observations with missing ratings.
 
13
EPSDILUTION may also capture firms’ growth prospects or market timing effects. However, we explicitly control for these effects by including firms’ market-to-book ratios and prior stock returns in Eq. (2). Another possibility is that EPSDILUTION reflects the relative cost of debt to equity, if one views E/P as a rough proxy for the cost of equity capital. To address this issue, we redefine EPSDILUTION by replacing E/P with Easton’s (2004) cost of capital measure, which Botosan and Plumlee (2005) document as being most highly associated with known valuation risk factors. If EPSDILUTION is proxying for relative financing costs, we expect this new variable to be more strongly associated with financing choice than our original measure. In untabulated analysis, we find that this variable is not a significant determinant of debt–equity choice, which suggests that EPSDILUTION is unlikely to be proxying for relative financing costs in this setting.
 
14
Ai and Norton (2003) have demonstrated difficulties in interpreting the estimated coefficients on interaction terms in nonlinear models, and Norton et al. (2004) present a methodology for adjusting the marginal effects on interaction terms. However, Greene (2010) and Kolasinksi and Siegel (2010) recently have concluded that these adjustments are inappropriate. We therefore present our main results without the Norton et al. (2004) adjustments.
 
15
We thank Brian Bushee for providing transient institutional ownership data. The Baker and Wurgler (2006) index captures six investor sentiment proxies, including the closed-end fund discount, share turnover, average first day initial public offering returns, number of initial public offerings, share of equity issues in total debt and equity issues, and the dividend premium. SENTIMENT is the first principal component of the six sentiment proxies that have been orthogonalized with respect to a set of macroeconomic variables. We obtain this data from the following website: http://​pages.​stern.​nyu.​edu/​~jwurgler/​.
 
16
Using a sample of 5,980 firm-years over 1993–2005, we estimate target leverage as a function of lagged market-to-book ratios, stock returns, return on assets, net operating loss carryforwards, tangible asset intensity, R&D expenditures, selling expenses, and industry median debt ratios. We generally find that these variables are associated with leverage ratios in the predicted fashion—MB, RET, ROA, SG&A, and R&D are negatively related to leverage ratios, while SIZE, PPE, NOLC, and INDLEV are positively related to leverage ratios. Results of our target leverage estimation are available upon request.
 
17
As an alternative to ISSUESIZE, we include the variable %STOCK, defined as the number of shares that would be issued for a given amount of financing, divided by the number of shares outstanding at the beginning of the year. To obtain the number of shares issued, we divide the dollar amount of the debt or equity issue by the average share price over the year. While these two variables are very highly positively correlated (ρ = 0.83), the use of the former variable helps to more clearly distinguish the effects of EPS dilution from other effects related to the issuance of new common shares. In untabulated analysis, we report a significantly negative estimated coefficient on %STOCK, but our inferences regarding H1–H3 remain unchanged.
 
18
While dual issuers are omitted from the sample, only observations in which there is both a debt and an equity offering that each exceeds 5 % of total assets are eliminated. For example, a firm that in the same fiscal year issues debt (equity) that exceeds 5 % of assets and equity (debt) that is <5 % of total assets would be retained in the sample. This calculation results in a continuously distributed dependent variable with a mean (median) of 0.481 (0.586), which allows the use of 2SLS.
 
19
Our results are robust to using CEO compensation instead of the average of the top five executives’ compensation.
 
20
Managers’ existing stock and option holdings may also influence financing decisions, as they help to align executives’ interests with those of shareholders. In addition, option holdings could affect our results if their exercise is used as an alternative tax shield to debt financing (see Graham et al. 2004). As a robustness test, we add executives’ existing holdings of stock and options, estimated using the methodology outlined in Core and Guay (1999), as a control variable in our main analysis; our results are robust to its inclusion.
 
21
We thank John Graham for providing kink data.
 
22
The example can be easily generalized to allow the financing event to occur at any time during the fiscal year.
 
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Metadaten
Titel
Why do managers avoid EPS dilution? Evidence from debt–equity choice
verfasst von
Rong Huang
Carol A. Marquardt
Bo Zhang
Publikationsdatum
01.06.2014
Verlag
Springer US
Erschienen in
Review of Accounting Studies / Ausgabe 2/2014
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-013-9266-3

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