Overvaluation and earnings management

Journal of Banking & Finance 33 (2009) 1652–1663

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Overvaluation and earnings management Jianxin (Daniel) Chi a, Manu Gupta b,* a b

W.P. Carey School of Business, Morrison School of Management and Agribusiness, Arizona State University, Mesa, AZ 85212, United States Department of Finance, Insurance and Real Estate, School of Business, Virginia Commonwealth University, 301 W Main St., Richmond, VA 23284-4000, United States

a r t i c l e

i n f o

Article history: Received 28 October 2008 Accepted 28 March 2009 Available online 5 April 2009 JEL classification: G14 G34 M41 Keywords: Overvaluation Earnings management Agency costs Overvalued equity Discretionary accruals

a b s t r a c t Consistent with Jensen’s [Jensen, M., 2005. Agency costs of overvalued equity. Financial Management 34, 5–19] agency-costs-of-overvalued-equity prediction, we find that overvaluation is statistically and economically related to subsequent income-increasing earnings management. This relation is robust to a series of tests that address potential endogeneity concerns, including omitted variable bias and reverse causality. The agency costs of overvalued equity are substantial. Overvaluation-induced income-increasing earnings management is negatively related to future abnormal stock returns and operating performance, and this negative relation becomes more pronounced as prior overvaluation intensifies. Among the most overvalued firms, those with high discretionary accruals underperform those with low discretionary accruals during the following year by 11.88% as measured by the three-factor alphas, and by 12.87% points as measured by industry-adjusted unmanaged EBITDA-to-assets ratio. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction The finance literature has widely documented that financial market’s valuation of a firm affects managerial behavior and corporate actions. Recently, Jensen (2005) theorizes that overvaluation can induce a new type of agency costs: the agency costs of overvalued equity.1 In Jensen’s argument, the manager of an overvalued firm faces two options. First, the manager can communicate to the market that he cannot deliver the expected operating performance to justify the inflated stock price either by telling the market outright or by waiting until the next reporting date and report a negative performance surprise. This option has potential to negatively affect the manager’s compensation and career. Facing potentially negative outcomes, what else can the manager do, given that by definition, an overvalued firm does not have sufficient positive-NPV investment opportunities to justify the market valuation? His second option thus includes actions to inflate reported performance to try to justify the inflated stock price. Such actions could be overinvesting through

acquisitions or expansions, committing frauds, and managing earnings.2 By doing so, the manager hopes to delay the negative compensation and career consequences but can destroy substantial shareholder value in the long run. In this study, we examine empirically the significance of the agency costs of overvalued equity by focusing on one of those actions, earnings management. Our first main research question is whether equity overvaluation leads to more income-increasing earnings management. Using a sample of US firm-year observations from 1964 to 2003, an earnings management measure based on a modified version of the Jones (1991) model, and a measure of overvaluation by RhodesKropf et al. (RKRV, 2005), we find that overvaluation is significantly related to subsequent income-increasing earnings management, i.e., higher discretionary accruals (henceforth DACC). The effect is large economically: a one-standard-deviation increase in total valuation error generates a 15%-standard-deviation increase in DACC. This relation is robust to controlling for a host of firm attributes and governance and managerial incentive attributes. The results

* Corresponding author. Tel.: +1 804 828 7175; fax: +1 804 828 3972. E-mail addresses: [email protected] (J. Chi), [email protected], mgupta00@ gmail.com (M. Gupta). 1 Although not the focus of our study, an obviously important question to our research premises is ‘‘does misvaluation exist?” This question is being intensively debated in the literature. We have no ambition to settle this debate here, but we do attempt to address this question in Section 2.

2 As earnings management becomes a common practice, some researchers argue that earnings management benefits shareholders in some ways, for example, see Arya et al. (2003) and Subramanyam (1996). Theoretically, the net effect of earnings management to shareholders could be negative because it increases the information asymmetry between managers and shareholders. Empirically, we provide evidence in later sections that higher discretionary accruals are related to lower future abnormal stock returns and operating performance.

0378-4266/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2009.03.014

J. Chi, M. Gupta / Journal of Banking & Finance 33 (2009) 1652–1663

also hold when we use alternative measures of DACC and when we employ a host of robustness tests to mitigate endogeneity concerns. Our second main research question is how the overvaluationinduced income-increasing earnings management affects longrun shareholder wealth. Consistent with the accrual anomaly literature, we find that higher DACC is associated with lower future abnormal stock returns. Our new finding is that this association becomes stronger as prior overvaluation intensifies. Among the most overvalued firms, those with high DACC underperform those with low DACC during the following year by 11.88% after adjusting for the Fama and French (1993) risk factors. We also find that higher DACC is associated with lower future operating performance, and that this association becomes stronger as prior overvaluation intensifies. Among the most overvalued-firms, those with high DACC underperform those with low DACC during the following year by 12.87% points as measured by industry-adjusted unmanaged EBITDA-to-assets ratio. To our knowledge, we are the first to document a systematic relation between overvaluation and earnings management and the effect of overvaluation-induced earnings management on future firm performance. Our findings have important implications for how financial market valuation affects managerial behavior and corporate actions3, specifically managerial incentive to manipulate reported earnings (see Abarbanell and Lehavy, 2003; Fischer and Stocken, 2004; Yu et al., 2006; Gupta et al., 2008; Kao et al., 2009). M/B has become an important control variable when explaining earnings management. Skinner and Sloan (2002) and McNichols (2002) predict that growth firms are more likely to have higher income-increasing earnings management. As most would agree that M/B is a noisy measure of growth opportunities (e.g., see Penman, 1996), the method that we use decomposes M/B into valuation errors and long-run value to book. We show that while growth opportunities as measured by long-run value to book are positively related to income-increasing earnings management, overvaluation is also positively related to income-increasing earnings management but for different reasons. These findings highlight the importance of differentiating between the effects of long-run valuation and overvaluation on earnings management. Our results also complement Efendi et al.’s (2007) findings that CEO holdings of in-the-money stock options are significantly related to financial restatements. Our study adds to their results based on 95 restatements by examining a much larger sample of firms with DACC. Our use of the RKRV M/B decomposition permits us to measure valuation errors more directly. We also provide evidence on the wealth effect of the agency costs of overvalued equity. In the next section, we address four questions that are important to our research premises and develop our main hypotheses. We describe our data and methodology in Section 3, present and discuss our results in Section 4, and conclude in Section 5. 2. The agency theory of overvalued equity and its testable hypotheses 2.1. Does misvaluation exist? An obviously important premise to our research is that misvaluation exists. Misvaluation can arise when there is information asymmetry between insiders and outsiders (assuming the market is only semi-strong form informationally efficient). Misvaluation,

3 See Baker and Wurgler (2002), Shleifer and Vishny (2003), Polk and Sapienza (2004), RKRV (2005), and Povel et al. (2007).

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specifically overvaluation, can also arise if investors disagree on a financial asset’s payoffs and if short sale constraints limit pessimistic investors’ participation in the price formation process (Miller, 1977). Even, if there are plenty of shares available for the short sellers to borrow, they may still hesitate to do so given the substantial risks involved in short selling (e.g., Shleifer and Vishny, 1997). Fama and French (2007, p. 683) even conclude that ‘‘lower costs for short sales can make prices less rational when short-selling is driven by the erroneous beliefs of misinformed investors”. As we have already mentioned, our objective is not to identify the exact causes of misvaluation, but rather to examine that once misvaluation arises, what the impacts are on managers’ earnings management behavior. As argued by Jensen (2005), the occurrence of overvaluation-induced agency costs does not rest on what exactly causes the overvaluation.4 2.2. Why would managers try to prolong an overvaluation? Regardless of the exact causes of overvaluation, over time the price of overvalued equity will drop towards the underlying value. This price drop is inevitable because information about the firm’s fundamentals will be revealed over time, and investors’ opinions about valuation will converge towards the underlying value. However, a drop in equity price for any reason is rarely desirable to any manager.5 In contrast, a manager has a lot to gain when equity price increases. First, the manager’s wealth and compensation increase with the stock price through stock-performance-based incentives (e.g., Bergstresser and Philippon, 2006; Burns and Kedia, 2008). Second, the manager’s job security increases with the stock price. A manager is less likely to lose his job when the stock is performing well (e.g., Weisbach, 1988). Third, a strong stock performance increases the manager’s value in the executive labor market. The opposite of all the above could happen if equity price drops. Motivated by these incentives, a manager naturally strives for higher stock price.6 2.3. What actions can managers take to prolong the overvaluation? To prolong the overvaluation, a manager can resort to overinvesting through acquisitions or expansions, committing frauds, or managing earnings. Overinvesting and acquisitions can occur even when the manager does not realize the overvaluation, either because he has strong hubris (Roll, 1986), he is overconfident (Malmendier and Tate, 2005), or he is overly optimistic (Heaton, 2002). In this situation, the manager believes that the high stock 4 Evidence of misvaluation is voluminous although much of the evidence is from large sample studies whose results are often subject to debate because of methodological issues (e.g., see Fama, 1998; Loughran and Ritter, 2000; and Mitchell and Stafford, 2000). However, case studies have yielded evidence of misvaluation that is less disputable (e.g., Lamont and Thaler, 2003). A complete review on market efficiency or inefficiency is not our objective here. Some recent and excellent review articles include Fama (1998), Shiller (2003) and Bloomfield and Michaely (2004) provide evidence that Wall Street professionals generally believe that mispricing exists. 5 The practice of taking a ‘‘big bath” may push the stock price below its fair value. However, the manager’s motivation in this practice is still to achieve a larger price increase in the next period by (artificially) depressing the current price. 6 We can see how much managers dislike any equity price drop from their efforts to meet or beat market expectations (e.g., Skinner and Sloan, 2002; Graham et al., 2005). Many of the recent accounting scandals can be attributed at least partly to the mangers’ efforts to meet and beat earnings expectations. One example is that of Computer Associates (CA), which was a Wall Street darling during the late 1990s and saw its stock price rose 65% in 1999. During 2000, top executives at CA engaged in a practice of falsely recording and reporting future revenues by extending fiscal quarters by a ‘‘three-day window,” also known within CA as the ‘‘35-day month.” Several executives were later indicted and pleaded guilty. According to the indictment, ‘‘the goal of the 35-day month was to permit CA to report that it met or exceeded its projected quarterly revenue and earnings when, in truth, it had not” (DOJ press release, September 22, 2004, http://www.usdoj.gov/opa/pr/2004/September/04_crm_642.htm).

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valuation is justified because he has superior ability or because there are abundant positive-NPV investment opportunities. In contrast, overvaluation-induced income-increasing earnings management or financial frauds can be more clearly attributed to the agency conflicts outlined by Jensen (2005). Earnings management is more prevalent than financial frauds and is often the precursor of financial frauds. Graham et al. (2006) provide evidence that the aggregate shareholder value destroyed by earnings management far exceeds that by high-profile fraud cases. Therefore, we focus on the relation between overvaluation and earnings management.

levels of firm performance (Cohen et al., 2005). We calculate total accruals, TAit, as the change in current assets (item 4) plus the change in debt in current liabilities (item 34) and the change in income tax payable (item 71), and then minus the change in current liabilities (item 5), the change in cash (item 1), and depreciation and amortization expenses (item 14). We obtain DACC as the residuals from Eq. (1). 3.2. Overvaluation measures

Hypothesis 1. Overvaluation is positively related to subsequent income-increasing earnings management.

A common valuation measure is the ratio of market value of assets to book value of assets (M/B). The literature has used M/B as proxies for both misvaluation and growth opportunities. As RKRV (2005) show, if there exists a perfect measure of the firm’s true value, V, we can first think of M/B as:

2.4. Why would efforts to prolong overvaluation lead to lower longrun shareholder wealth?

M=B ¼ M=V  V=B;

Managing earnings upwards to sustain an inflated stock price can destroy shareholder value, because of the following four reasons. First, managing earnings distracts the manager from his core responsibilities and thus wastes managerial efforts. If the manager decides to sacrifice long-run economic value to meet short-run earnings targets, the value destruction can be substantial (Graham et al., 2006). Second, since over time overvaluation has to dissipate and managed earnings has to reverse, managing earnings upwards to prolong the overvaluation could lead to a sharper reversal in reported earnings and stock price. This increased volatility can increase the firm’s perceived risk and discount rate and lower the valuation. Third, managing earnings reduces the firm’s credibility and can lead to a loss in shareholder value because of lost reputation. Karpoff et al. (2008) estimate that for each dollar of market value that a firm gains through misrepresenting its financial information, when the misrepresentation is revealed, the firm will lose that dollar plus an additional $3.08. Of that $3.08 additional loss, 88% can be attributed to lost reputation. Fourth, as argued by Kedia and Philippon (forthcoming), earnings manipulation is a necessary condition for overinvestment and is usually accompanied by overinvestment, which by definition leads to lower shareholder wealth and poorer operating performance. All the above arguments and evidence suggest that there are significant agency costs of the overvaluation-induced income-increasing earnings management. Hypothesis 2. More overvaluation-induced income-increasing earnings management is related to lower long-run shareholder wealth.

3. Data and methods 3.1. Earnings management measures We measure earnings management using DACC obtained from a modified version of the Jones (1991) model. We run annual crosssectional regressions of the following model for each of the Fama and French (1997) 48-industry groups:

TAit 1 DSalesit  DARit ¼a þ b1 Assetsit1 Assetsit1 Assetsit1 PPEit CFOit þ b2 þ b3 þ eit ; Assetsit1 Assetsit1

ð1Þ

where DSalesit is the change in sales (Compustat item 12), DARit the change in accounts receivables (item 2), PPEit property, plant and equipment (item 7), and CFOit cash flow from operations excluding extraordinary items (item 18 – TAit). We include cash flow from operations in the modified Jones’ model to control for extreme

ð2Þ

where M/V captures misevaluation and V/B captures growth opportunities. Rewrite (2) into logarithm form, we obtain:

m  b ¼ ðm  v Þ þ ðv  bÞ;

ð3Þ

where the lowercase letters denote logarithm values. (m  v), the deviation of the firm’s market value from its true value, can arise from industry-wide misvaluation or firm-specific misvaluation. Therefore, for any firm i at year t, we can further decompose (m  v) into two components and rewrite (m  b) as following:

mit  bit ¼ mit  v ðhit ; ajt Þ þ v ðhit ; ajt Þ  v ðhit ; aj Þ þ v ðhit ; aj Þ  bit ; |fflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflffl} firm-specific error

industry-level error

long-run valuation

ð4Þ where we use j to denote industry. We express v as a linear function that multiplies some firm-specific accounting information hit and a vector of estimated accounting valuation multiples a. v ðhit ; ajt Þ is the estimated firm value based on contemporaneous industry-level valuation multiples ajt. Thus, the first component in Eq. (4) captures the valuation error caused by firm-specific deviation from contemporaneous industry-level valuation. v ðhit ; aj Þ is the estimated firm value based on long-run industry-level valuation multiples aj. Thus, the second component in Eq. (4) captures the valuation error caused by the deviation of current industry valuation from the long-run industry valuation. The third component in Eq. (4) is the difference between long-run value and book value, i.e., the logarithm of the true value-to-book ratio, capturing growth opportunities. Note that each of the three components varies across firms and years because each component utilizes hit, which is firm i’s accounting information at year t. To operationalize Eq. (4), we need to estimate the valuation models v ðhit ; ajt Þ and v ðhit ; aj Þ. Again, we follow RKRV and estimate the valuation models as a function of book value, net income, and financial leverage. A detailed description on the estimation method is available from the authors upon request. RKRV (2005) provide evidence that supports the validity of the valuation errors. They show that valuation errors based on M/B decomposition can better explain merger waves than M/B can, consistent with their theory of misvaluation-driven merger waves. Hertzel and Li (2008) study investment and financing behaviors of firms issuing seasoned equity. They find that the pre-issue value-to-book ratio from the RKRV M/B decomposition is positively related to post-issue R&D expenses and capital investments. However, the pre-issue total valuation error is not related to post-issue R&D and capital investments, but positively related to post-issue debt reduction. They interpret these findings as evidence that the valuation errors capture misvaluation, not growth opportunities captured by the long-run value-to-book ratio.

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J. Chi, M. Gupta / Journal of Banking & Finance 33 (2009) 1652–1663 Table 1 Misvaluation and subsequent abnormal stock returns. Ranks by total valuation error (1 is lowest)

Three-factor model

Four-factor model

1–12 month

13–24 month

25–36 month

1–12 month

13–24 month

25–36 month

1 2 3 4 5 1st–5th (p-Value)

0.0037 0.0006 0.0009 0.0023 0.0020 0.0057 (0.00)

0.0055 0.0018 0.0003 0.0023 0.0042 0.0097 (0.00)

0.0039 0.0016 0.0002 0.0012 0.0024 0.0063 (0.00)

0.0060 0.0024 0.0006 0.0003 0.0001 0.0059 (0.00)

0.0069 0.0029 0.0013 0.0001 0.0009 0.0078 (0.00)

0.0051 0.0028 0.0016 0.0010 0.0005 0.0046 (0.00)

Reported are monthly calendar-time portfolio alphas from the Fama and French (1993) three-factor model and the four-factor model regressions. For the period 1965–2003, we start with all CRSP/Compustat firms for which we can compute the valuation errors of RKRV (2005). We sort all observations monthly into misvaluation quintiles based on total valuation error and form five monthly calendar-time portfolios. Rank 1 means lowest total valuation error. Portfolio alphas for the 1–12 month, 13–24 month, and 25– 36 month holding periods are estimated as follows:

Rit  Rft ¼ ai þ b1i ðRmt  Rft Þ þ b2i ðSMBt Þ þ b3i ðHMLt Þ þ b4i ðUMDt Þ þ ei ; where Rit is the equal-weighted portfolio monthly returns of portfolio i for months 1–12, 13–24, and 25–36 after the portfolio formation, Rft is the return on one-month T-bills, (Rmt  Rft) is the excess return on the value-weighted market portfolio, SMBt is the return on the size factor mimicking portfolio, HMLt is the return on the book-to-market factor mimicking portfolio, and UMDt is the return on the momentum factor mimicking portfolio. UMDt is excluded from the three-factor model. We obtain the three-factor and the momentum factor returns from Ken French’s website. Alphas that are significant at the 5% level are in boldface.

We conduct a test that further sheds light on whether the valuation errors effectively capture misvaluation. We test an investment strategy that buys undervalued stocks and sells overvalued stocks. If the valuation errors effectively capture true misvaluation, such a strategy should generate significant profit. Otherwise, such a strategy will generate zero profit. To test this strategy, we start with all CRSP/Compustat firms for which we can compute the valuation errors. We sort these firms every month (to accommodate different fiscal yearends) into misvaluation quintiles based on total valuation error and form equal-weighted monthly calendar-time portfolios. Because fiscal yearend data are not available to the market right away, we skip two months after portfolio formation before we start calculating portfolio returns. Although we can calculate the valuation errors starting in 1963, some of the monthly portfolios have too few observations before 1965. Therefore, we start this strategy in 1965 and end in 2003. For each portfolio, we run a time-series regression of the portfolio monthly excess returns on the Fama and French (1993) three or four factor returns and estimate the alpha for each portfolio for the 1– 12 month, 13–24 month, and 25–36 month holding periods. The results are in Table 1. The three-factor alpha is decreasing nearly monotonically from the least overvalued (or most undervalued) portfolio (quintile 1) to the most overvalued portfolio (quintile 5). For the 1–12 month horizon, an arbitrage strategy that buys the least overvalued portfolio and sells the most overvalued portfolio generates a monthly alpha of 0.57% (or 6.84% per year). For the 13–24 month and 25– 36 month horizons, the arbitrage strategy generates annualized alphas of 11.64% and 7.56%. We obtain similar results when we add a momentum factor (the UMD factor) to the three-factor model. We are mindful that the calendar-time portfolio test itself is subject to the joint-hypothesis problem. However, it is beyond the scope of this study to debate whether the three- or four-factor model is the true asset pricing model. Our evidence is merely that the valuation errors seem to capture a significant portion of misvaluation when measured against the widely used asset pricing benchmarks in the literature.

management literature. Watts and Zimmerman (1990) argue that large firms face higher political costs and therefore could have a stronger incentive to use accounting discretions to reduce the political costs. We include the logarithm of lagged book assets to control for firm size. Firms with more volatile business have a greater incentive to manage earnings to reduce their appeared risk level. We include in our regressions the standard deviation of sales for the last five years (requiring a minimum of three years of data). Firms with high growth rates are likely to have high accruals. The value-to-book ratio controls for growth opportunities. Firms can also build up large inventories in anticipation of high future sales and thus have higher accruals. Therefore, we use inventory-to-total-assets ratio as another control for growth. Francis et al. (2004) recognize the effects of differences in measurement and recognition of tangible and intangible assets on accrual quality. We control for the differences in asset structure through three variables: the ratio of intangible assets (R&D and advertising) to total assets, a dummy variable for firms that have no reported intangibles, and the ratio of net fixed assets to total assets. Hribar and Collins (2002) identify potential problems around ‘‘non-articulation” dates, such as mergers and acquisitions when earnings management measures are created using successive years of balance sheet data. We include an M&A dummy that takes on the value of one if a firm engaged in a merger or acquisition over the past three years. We also use an alternative DACC measure based on total accruals from cash flow statement items. We control for lagged financial leverage. Higher financial leverage increases the volatility of net income and gives the manager a stronger incentive to manage earnings to avoid covenant violations and preserve their credit ratings. On the other hand, higher leverage is a proxy for closer monitoring from debt-holders and could be related to less earnings management. Based on Kothari et al. (2005), we control for firm performance with the return-on-assets ratio.

3.3. Control variables

4.1. Sample description

Our main analysis is based on regressions of DACC on lagged overvaluation measures. We include in these regressions important control variables that have been identified by the earnings

Jensen (2005) suggests that the agency costs of overvalued equity could have existed for a longer period of time. He states that ‘‘in part, the massive overvaluation of equity. . .is consistent with

4. Empirical results

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J. Chi, M. Gupta / Journal of Banking & Finance 33 (2009) 1652–1663

Table 2 Variable definitions and summary statistics. Variable Earnings management Discretionary accruals (DACC)

Valuation measures Firm-specific valuation error

Industry-level valuation error

Value-to-book ratio

Total valuation error Control variables Log (total assets) Sales_std Inventory Intangibles No_intangible dummy Net fixed assets M&A dummy Book leverage

ROA

Definition

Mean

Std Dev

Q1

Median

Q3

Discretionary accruals are the difference between total accruals (from balance sheet) and predicted accruals from the modified Jones model, both scaled by lagged total assets. The modified Jones model is estimated for each Fama and French (1997) 48industry and year group

0.006

0.086

0.045

0.005

0.032

The difference between the market valuation and the valuation implied by contemporaneous industry-level valuation multiples. In Eq. (4), it is the mit  v(hit;ajt) component The difference between the valuation implied by contemporaneous industry-level valuation multiples and the valuation implied by long-run industry-level valuation multiples. In Eq. (4), it is the v(hit;ajt)  v(hit;aj) component The difference between the valuation implied by long-run industry-level valuation multiples and the book value. In Eq. (4), it is the v(hit;aj)  bit component Sum of the firm-specific valuation error and the industry-level valuation error

0.022

0.418

0.255

0.047

0.168

0.023

0.478

0.155

0.019

0.111

0.349

0.557

0.104

0.315

0.563

0.046

0.626

0.318

0.094

0.180

5.004 0.184

1.928 0.172

3.542 0.072

4.743 0.132

6.235 0.233

0.176 0.475

0.157 25.635

0.032 0.000

0.148 0.010

0.280 0.045

0.394 0.352

0.489 0.236

0.000 0.167

0.000 0.298

1.000 0.503

0.276

0.447

0.000

0.000

1.000

0.512

0.205

0.365

0.524

0.664

0.013

0.154

0.008

0.044

0.078

Natural log of total book assets (item #6) Standard deviation of sales dividend by total assets (items #12/#6) over last 3 years Inventory divided by total assets (items #3/#6) Ratio of intangibles assets (R&D and advertising) to total assets (items (#45 + #46)/#6) =1 if a firm has no reported intangibles, 0 otherwise Net property, plant and equipment divided by total assets (items #8/#6) =1 if the firm made any acquisition over past 3 years, 0 otherwise (from annual file footnote #1) Financial leverage measured as one minus the ratio of book value of common equity over the book value of total assets. (1 – (items #60/#6)) Return on assets (items #18/#6)

what we have seen in the past.”7 Therefore, we construct a data set starting in the 1960s and examine whether the agency costs of overvalued equity arose only recently or have existed for a longer period but were only recently recognized. Starting with the Compustat universe, we calculate overvaluation measures from 1963 to 2002 and DACC measures from 1964 to 2003. We then match overvaluation with the following year’s DACC. We obtain 91,742 firm-year observations that have all the requisite variables. Table 2 presents the summary statistics. We winsorize all continuous variables at the 1st and the 99th percentiles to reduce the influence of outliers. As expected from our construction method, the means of the DACC measure is close to zero. The quartile values indicate that the distribution of DACC is close to normal. For the two valuation error measures, the means are expected to be zero because by construction, when pooled across time and firms, approximately half of the observations will be undervalued and half will be overvalued. The reported means in Table 2 are different from zero because we calculate the valuation errors using the largest possible Compustat sample, but our final sample is smaller after requiring the availability of the variables to construct DACC and the control variables. For the larger sample, the two valuation error means are zero. The standard deviation statistics show that there is substantial variation in these two valuation error

7 Malkiel (2007, Chapter 3) vividly recounts past high valuation waves from the 1960s through the 1980s.

measures. The log value-to-book ratio has a mean of 0.349 and a median of 0.315, which translate to value-to-book ratio of 1.42 and 1.37. The summary statistics of the control variables are comparable to those reported in the literature. 4.2. Regression results Table 3 has our baseline regression results. The dependent variable is DACC. The OLS regression models include firm and year dummies to control for unobservable firm and year fixed effects. All t-statistics are adjusted for heteroskedasticity and firm-level clustering. Column (1) shows that the coefficient on value to book is positive, consistent with Skinner and Sloan (2002). More important to our research question, total valuation error is positive and highly significant, consistent with Jensen’s hypothesis that as firms become more overvalued, their income-increasing earnings management activity intensifies. Economically, total valuation error and value-to-book ratio have comparable effects on earnings management. For a one-standard-deviation increase in value to book, DACC increases by 1.28% points. From total valuation error, this effect is 1.25% points. In Column (2), we further decompose the total valuation error into firm-specific error and industry-level error. Both of them have positive and significant coefficients. For the control variables, we see that larger firm size is related to lower DACC. Higher financial leverage is also related to lower DACC, potentially consistent with debt-holders providing some monitoring function.

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J. Chi, M. Gupta / Journal of Banking & Finance 33 (2009) 1652–1663 Table 3 Regression results of discretionary accruals (DACC) on valuation errors. (1) 1964–2003

(2) 1964–2003

(3) Subperiod 1 1964–1973

(4) Subperiod 2 1974–1983

(5) Subperiod 3 1984–1993

(6) Subperiod 4 1994–2003

0.017 (12.51) 0.026 (15.67)

0.020 (2.57) 0.025 (3.07)

0.019 (5.65) 0.026 (6.30)

0.024 (7.32) 0.031 (5.46)

0.018 (8.62) 0.015 (4.21)

0.020 (15.92) 0.023 (15.65)

0.028 (17.11)

0.031 (3.64)

0.025 (6.15)

0.044 (10.53)

0.038 (12.00)

0.008 (9.42) 0.006 (1.91) 0.166 (19.57) 0.000 (0.12) 0.000 (0.07) 0.024 (5.43) 0.000 (0.26) 0.030 (8.12) 0.012 (2.23) 0.003 (0.74) Included Included 91,742 0.09

0.007 (8.97) 0.006 (1.90) 0.166 (19.67) 0.000 (0.08) 0.000 (0.10) 0.025 (5.61) 0.000 (0.35) 0.028 (7.51) 0.011 (2.20) 0.009 (2.02) Included Included 91,742 0.09

0.031 (3.68) 0.018 (0.9) 0.286 (4.62) 0.077 (1.01) 0.007 (1.44) 0.021 (0.58) 0.002 (0.71) 0.086 (2.78) 0.141 (2.12) 0.103 (2.6) Included Included 5092 0.17

0.017 (6.25) 0.02 (2.79) 0.282 (14.32) 0.012 (0.65) 0.000 (0.12) 0.071 (6.19) 0.002 (1.30) 0.081 (7.29) 0.126 (5.70) 0.033 (2.16) Included Included 24,925 0.12

0.017 (6.21) 0.005 (0.59) 0.212 (10.55) 0.000 (0.05) 0.004 (0.98) 0.005 (0.49) 0.001 (0.50) 0.046 (4.76) 0.026 (2.13) 0.049 (2.94) Included Included 26,591 0.11

0.011 (5.84) 0.013 (1.98) 0.177 (9.02) 0.000 (0.11) 0.001 (0.18) 0.007 (0.72) 0.000 (0.3) 0.008 (1.17) 0.003 (0.42) 0.024 (1.95) Included Included 35,134 0.08

Valuation measures Firm-specific valuation error Industry-level valuation error Total valuation error Value-to-book ratio Control variables Log(total assets) Sales_std Inventory Intangibles No_intangible dummy Net fixed assets M&A dummy Book leverage ROA Intercept Firm fixed effects Year fixed effects N Adjusted R2

The dependent variable is discretionary accruals. All variable definitions are in Table 2. The sample period is 1964–2003. Columns 3 through 6 report regression results for the four subperiods: 1964–1973, 1974–1983, 1984–1993, and 1994–2003. In parentheses are t-statistics adjusted for heteroskedasticity (White, 1980) and firm-level clustering.

ROA, standard deviation of sales, and the levels of inventory and fixed assets are all positively related to DACC. In columns (3) through (6), we split our sample into four tenyear subperiods. There is a significantly positive relation between both valuation errors and DACC in each subperiod. Therefore, although the agency costs of overvalued equity are only recently brought to attention by Jensen (2005), they have existed since at least the 1960s. Corroborating Jensen’s (2005) statement that ‘‘we cannot manage things that we cannot distinguish,” the rather striking contrast between the lack of awareness of this agency problem and its long existence signifies the importance of research on this topic. The coefficient estimates on both valuation errors, but especially for the industry-level error, drop during the last subperiod. There are two probably explanations for this drop in the last period. First, more advances in information technology and more stringent disclosure requirements over time could have reduced the information asymmetry between managers and investors, and could have reduced the effectiveness of using accruals to manage earnings. Second, DACC is a bounded variable (e.g., see Barton and Simko, 2002). That is, managers can use DACC to manage earnings only to a certain extent before the reported earnings lose credibility or the managers commit accounting frauds. Over the last 40 years, the use of DACC has increased substantially (Rajgopal and Venkatachalam, 2008). We also find evidence indicating increased use of DACC. For our sample, from the first to the last subperiod the 95th percentile value of DACC increases from 11% of total assets to 14% of total assets. Thus, when the DACC level is al-

ready high, the marginal effect of overvaluation on DACC will diminish. The greater drop in the coefficient of industry-level error indicates that managers are particularly concerned about performing poorly relative to the industry. That is, the economic significance of firm-specific valuation error on DACC is greater than that of industry-level valuation error. Consistent with this explanation, we find that for the last subperiod, a one-standard-deviation increase in firm-specific valuation error implies a 9.9%-standard-devivation increase in DACC. A one-standard-deviation increase in industry-level valuation error implies a 3.5%-standard-deviation increase in DACC. To further investigate the diminishing marginal effect of overvaluation on DACC, we include squared terms of the two valuation errors in our baseline regression. The two squared valuation errors have negative coefficient estimates, consistent with our prediction that the relation between overvaluation and DACC is concave because DACC is a bounded variable. The detailed regression result is available from the authors upon request. The diminishing marginal effect of valuation errors on DACC does not necessarily mean that the ill effects of overvaluation-induced agency problem will also diminish. When a manager cannot use DACC to inflate reported income but still feel the need to do so, he is more likely to cross the line and engage in other activities that are more likely to be fraudulent. We have seen the number of restatements increase over recent years (e.g, General Accounting Office, 2002; Glater, 2005; Burns and Kedia, 2006). We have also seen unprecedented number and scale of financial frauds in recent years.

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Previous research has shown that governance and managerial incentive attributes are related to earnings management and firm valuation. To ensure that our findings are not driven by omitted governance variables, we control for the following eleven governance and incentive attributes in regression (2) of Table 3: institutional ownership, managerial ownership, equity-based incentives, classified board, annul number of board meetings, CEO tenure, CEO power (Bebchuk et al., 2008), the strengths of shareholder rights, auditor tenure, whether the auditor is one of the Big Six accounting firms, and auditor opinion. We find that our main results are robust to controlling for important governance attributes. To further understand how governance affects the relation between overvaluation and DACC, we interact each governance variable with the two valuation errors. We find little evidence that the governance variables affect the relation between overvaluation and DACC. Conventional governance mechanisms do not seem to be able to alleviate this agency problem effectively probably because of the unawareness of the potential harms of overvalued equity, as argued by Jensen (2005). As the awareness of this agency problem increases, we may observe some of the governance mechanisms have greater effectiveness in mitigating this agency problem. Detailed regression results are available from the authors upon request. Free cash flow level could affect the potential severity of agency conflicts (e.g., see Jensen, 1986; Chi and Lee, 2008). We construct a free cash flow proxy following Lehn and Poulsen (1989) and control for free cash flow in our regressions. Our results do not change. 4.3. Endogeneity concern and two-stage least squares (2SLS) results So far we have addressed the endogeneity concern by: (1) lagging valuation errors in the regressions to minimize the possibility of reverse causality running from DACC to valuation errors, and (2) controlling for probable determinants of DACC in the regressions to avoid the omitted variable bias.8 Despite these two remedies, we recognize that the endogeneity concern is not completely resolved. Specifically, if past DACC is related to future valuation errors, the firm fixed effects estimators on valuation errors will be biased.9 Past DACC could be positively related to future misvaluation because after all, the literature has documented that an important incentive for managers to engage in earnings management is to influence reported earnings and the current stock price. However, evidence from the accrual anomaly literature suggests that managerial efforts to pump up stock price through accrual management is futile, or at least short lived, because past (discretionary) accruals are negatively related to future stock returns. We follow the method in Chi (2005) and conduct an intertemporal correlation analysis between DACC and total valuation error. Consistent with our main hypothesis that overvaluation induces income-increasing earnings management, we find that DACC of year t is positively related to total valuation error in years t and t  1. Consistent with the accruals anomaly literature, we find that DACC of year t is negatively related to total valuation error in year t + 1. The tabulated result is available from the authors upon request. Below, we employ three methods to examine whether the correlation between past DACC and future misvaluation has significantly biased our findings. First, we orthogonalize total valuation error to DACC and use the orthogonalized total valuation error as our main explanatory variable. Operationally, we regress total valuation error of year t on DACC of year t, and use the regression residual to replace the total valuation error in model (1) of Table 3. As shown in column (1) 8

So far, we have controlled for firm characteristics, governance characteristics, firm fixed effects, and year fixed effects. 9 For a complete discussion of this bias in a dynamic panel data set, see Wooldridge (2002).

Table 4 Controlling for intertemporal correlation between discretionary accruals and valuation errors. (1) Orthogonalized (Total valuation error) Total valuation error Lag (DACC) Value-to-book ratio Log (total assets) Sales_std Inventory Intangibles No_intangible dummy Net fixed assets M&A dummy Book leverage ROA Intercept Firm fixed effects Year fixed effects N Adjusted R2

(2)

0.021 (15.57)

0.024 (14.32) 0.007 (8.45) 0.010 (2.90) 0.170 (18.50) 0.000 (0.16) 0.001 (0.47) 0.029 (6.26) 0.000 (0.18) 0.033 (8.48) 0.014 (2.33) 0.072 (10.10) Included Included 78,422 0.10

0.021 (15.03) 0.148 (25.36) 0.023 (13.57) 0.006 (6.52) 0.011 (3.07) 0.197 (20.16) 0.000 (0.07) 0.001 (0.59) 0.030 (6.17) 0.002 (2.63) 0.032 (7.90) 0.023 (3.66) 0.078 (10.46) Included Included 78,422 0.12

The dependent variable is discretionary accruals. All variable definitions are in Table 2. In column (1), the orthogonalized total valuation error is the residual of regressing total valuation error on contemporaneous DACC. In column (2), lagged DACC is included as a control variable. In parentheses are t-statistics adjusted for heteroskedasticity (White, 1980) and firm-level clustering.

of Table 4. The orthogonalized total valuation error remains large and highly significant.10 The regression of total valuation error on DACC has an R2 of less than 0.5%, suggesting that only a small part of misvaluation can be explained by DACC. When we orthogonalize total valuation error to DACCs of years t, t  1, and t  2, we obtain very similar results. Second, we control for the effect of past DACC on total valuation error by including lagged DACC as a control variable in the regression. As shown in column (2) of Table 4, after controlling for lagged DACC, total valuation error remains large and highly significant. We obtain very similar results when including up to three lags of DACC. Although our research interest is not in estimating the coefficient on lagged DACC, the fixed effects estimator on lagged DACC is biased because we are running a dynamic panel. We try to employ the Arellano–Bond procedure to identify instruments for lagged DACC from within the system.11 However, we are not able to find a set of instruments that do not reject the null in the Sargan test that the instruments are exogenous, as required by Arellano– Bond procedure. Thus, although the coefficient estimate on total valuation error is probably not seriously biased to our favor even though the coefficient estimate on lagged DACC is biased, we are cautious in drawing a strong conclusion from this regression.

10 As in the regressions of Table 3, this and following regressions all employ valuation error that is lagged one year with respect to DACC. 11 The Arellano–Bond procedure is developed by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). See Roodman (2006) for a pedagogic introduction to this procedure.

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J. Chi, M. Gupta / Journal of Banking & Finance 33 (2009) 1652–1663 Table 5 Two-stage least squares (2SLS) regressions. The excluded instrument=

Idiosyncratic volatility (1)

Analyst forecast dispersion (2)

Idiosyncratic volatility (3)

Analyst forecast dispersion (4)

Predicted (Total valuation error)

0.014 (4.36) 0.014 (5.78) 0.000 (0.30) 0.012 (6.01) 0.071 (19.31) 0.000 (0.44) 0.003 (4.17) 0.038 (18.54) 0.001 (1.30) 0.004 (1.95) 0.016 (7.71) 0.032 (11.82) No No 86,148

0.057 (4.30) 0.026 (7.25) 0.001 (2.14) 0.014 (3.99) 0.100 (10.97) 0.000 (1.19) 0.007 (5.07) 0.057 (10.18) 0.002 (2.03) 0.011 (2.70) 0.046 (7.59) 0.055 (10.02) No No 31,695

0.033 (7.59) 0.035 (8.66) 0.007 (8.83) 0.003 (0.99) 0.172 (29.12) 0.000 (0.20) 0.000 (0.24) 0.028 (6.97) 0.000 (0.31) 0.030 (11.01) 0.002 (0.58) 0.018 (0.22) Included Included 86,148

0.072 (2.75) 0.070 (4.33) 0.002 (0.69) 0.004 (0.75) 0.197 (13.94) 0.000 (0.78) 0.002 (1.00) 0.022 (2.29) 0.003 (2.00) 0.015 (3.15) 0.054 (2.18) 0.080 (1.03) Included Included 31,695

Value-to-book ratio Log (total assets) Sales_std Inventory Intangibles No_intangible dummy Net fixed assets M&A dummy Book leverage ROA Intercept Firm fixed effects Year fixed effects N

Reported are the second stage regression results of discretionary accruals (DACC) on predicted total valuation error and the controls. We treat total valuation error as endogenous. In the first-stage, we regress total valuation error on the included control variables as well as one of the two excluded instruments: idiosyncratic volatility and  2 . We first run a regression of daily firm excess returns on the CRSP value-weighted market analyst earnings forecast dispersion. Idiosyncratic volatility is defined as: ln 1R R2 index excess returns for each month, and then average the monthly R2 to obtain the annual R2. We calculate analyst forecast dispersion as the standard deviation of annual earnings forecasts scaled by the stock price. All other variable definitions are in Table 2. t-Statistics are in parentheses.

Third, we address the reverse causality related endogeneity concern by identifying exogenous instrumental variables for the valuation error and employing a two-stage least squares (2SLS) model. Exogenous instruments that can explain misvaluation, but are uncorrelated with past DACC should minimize endogeneity caused by reverse causality running from DACC to misvaluation. An obvious challenge to 2SLS is to identify strong and truly exogenous instruments. We rely on theoretical guidance as well as several well accepted statistical tests to assess the validity of our instruments. We treat all the control variables as exogenous instruments because they likely explain a nontrivial portion of misvaluation. We identify two excluded exogenous instruments: analyst earnings forecast dispersion and idiosyncratic volatility. Analyst forecast dispersion is a proxy for differences of opinion and could lead to mispricing (e.g., Anderson et al., 2005). Pontiff (2006) surveys the literature and argues that idiosyncratic risk is the single largest cost faced by arbitrageurs and thus is related to mispricing. We follow Ferreira and Laux (2007), among others, and calculate idiosyncratic volatility as ln ((1  R2)/R2). To calculate R2, we run a regression of daily firm excess returns on the CRSP valueweighted market index excess returns for each month, and average the monthly R2 to obtain the annual R2. We calculate analyst forecast dispersion as the standard deviation of annual earnings forecasts scaled by the stock price. Analyst forecast data are from IBES and start in 1976 although the data are sparse before 1983. We use the instruments to predict total valuation error from the first-stage regression and use the predicted value in the second stage regression. The results are in Table 5. In column (1) we use idiosyncratic volatility as the excluded instrument. The estimate on total valuation error is highly significant. The first-stage regres-

sion has an R2 of 41%. The Anderson (1951) canonical correlation test rejects the null that the equation is underidentified (i.e., the excluded instrument is not a relevant instrument) at the 0.1% significance level. The Cragg and Donald (1993) test also strongly rejects the null that the excluded instrument is a weak instrument. In column (2) we use earnings forecast dispersion as the excluded instrument. Total valuation error is highly significant. Both the Anderson test and the Cragg–Donald test strongly reject the null that earnings forecast dispersion is an irrelevant or weak instrument. In columns (3) and (4), we further control for firm and year fixed effects in both the first stage and the second stage regressions. The estimates on total valuation error become larger and remain highly significant. 4.4. The wealth effect of overvaluation-induced earnings management To measure the long-run wealth effect of overvaluation-induced earnings management, we sort firms into misvaluation quintiles based on total valuation error (measured at year t  1) and then into DACC quintiles (measured at year t) and form 25 equalweighted portfolios. We then measure the portfolios’ subsequent abnormal stock returns and operating performance during each of the three years after the portfolio formation (years t + 1 to t + 3). Similar to our procedure in Table 1, for stock returns, we sort firms every month and form monthly calendar-time portfolios. We skip two months between portfolio formation and calculation of portfolio returns to account for the time lag between fiscal yearend and 10-k filings. For each portfolio, we run a time-series regression of the portfolio monthly excess returns on the Fama and French (1993) three factors and estimate the alpha for each portfolio for the 1–12 month, 13–24 month, and 25–36 month

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Table 6 Misvaluation, discretionary accruals (DACC), and stock performance. Ranks by DACC (1 is lowest)

Panel A. Monthly alphas for the 1–12 month holding period Ranks by total valuation error (1 is lowest) 1 2 3 4 5 1st–5th (p-Value) Panel B. Monthly alphas for the 13–24 month holding period Ranks by total valuation error 1 2 3 4 5 1st–5th (p-Value) Panel C. Monthly alphas for the 25–36 month holding period Ranks by total valuation error 1 2 3 4 5 1st–5th (p-Value)

1st–5th

p-Value

1

2

3

4

5

0.0107 0.0039 0.0040 0.0015 0.0003 0.0110 (0.00)

0.0063 0.0029 0.0011 0.0004 0.0016 0.0079 (0.00)

0.0063 0.0020 0.0005 0.0019 0.0031 0.0094 (0.00)

0.0053 0.0019 0.0001 0.0010 0.0052 0.0104 (0.00)

0.0029 0.0011 0.0020 0.0063 0.0102 0.0131 (0.00)

0.0078 0.0028 0.0059 0.0077 0.0099

(0.00) (0.03) (0.00) (0.00) (0.00)

0.0084 0.0045 0.0034 0.0003 0.0003 0.0081 (0.00)

0.0050 0.0034 0.0020 0.0012 0.0003 0.0047 (0.01)

0.0038 0.0033 0.0008 0.0007 0.0011 0.0050 (0.00)

0.0051 0.0024 0.0015 0.0022 0.0026 0.0077 (0.00)

0.0029 0.0008 0.0001 0.0038 0.0065 0.0094 (0.00)

0.0055 0.0053 0.0035 0.0041 0.0068

(0.00) (0.00) (0.02) (0.01) (0.00)

0.0053 0.0015 0.0019 0.0020 0.0009 0.0062 (0.01)

0.0055 0.0033 0.0005 0.0006 0.0005 0.0059 (0.00)

0.0046 0.0014 0.0012 0.0014 0.0011 0.0057 (0.00)

0.0053 0.0015 0.0010 0.0006 0.0012 0.0065 (0.00)

0.0040 0.0014 0.0018 0.0007 0.0027 0.0068 (0.00)

0.0013 0.0001 0.0037 0.0013 0.0019

(0.45) (0.93) (0.01) (0.42) (0.37)

Reported are monthly calendar-time portfolio alphas from the Fama and French (1993) three-factor model. For the period 1970–2003, we sort all observations monthly into misvaluation quintiles (based on total valuation error measured at year t  1) and then into DACC quintiles (measured at year t) and form 25 monthly calendar-time portfolios. Rank 1 means lowest total valuation error or lowest DACC. Portfolio alphas for the 1–12 month, 13–24 month, and 25–36 month holding periods are estimated as follows:

Rit  Rft ¼ ai þ b1i ðRmt  Rft Þ þ b2i ðSMBt Þ þ b3i ðHMLt Þ þ ei ; where Rit is the equal-weighted portfolio monthly returns of portfolio i for months 1–12, 13–24, and 25–36 after the portfolio formation (i.e., years t + 1 to t + 3), Rft is the return on one-month T-bills, (Rmt  Rft) is the excess return on the value-weighted market portfolio, SMBt is the return on the size factor mimicking portfolio, and HMLt is the return on the book-to-market factor mimicking portfolio. We obtain the three-factor and the momentum factor returns from Ken French’s website. Alphas that are significant at the 5% level are in boldface.

holding periods. We calculate alphas only using portfolios formed from 1970 onward as many portfolios before 1970 have very few observations. In Table 6, we show the alphas from the calendar-time portfolio regressions. Alphas that are significant at the 5% level are in boldface. Examining the 1–12 month three-factor alphas, we see that within each DACC rank (i.e., within each column), the alphas decrease monotonically as the misvaluation rank increases (i.e., moving down the rows), consistent with the evidence presented in Table 1 that provides validity to the misvaluation measure. Within each misvaluation rank (i.e., within each row), the alphas decrease nearly monotonically as the DACC rank increases. The differences in alphas between the lowest-DACC and the highest-DACC portfolios are highly significant and range from 0.28% per month to 0.99% per month. Except for the lowest valuation rank, the DACC-based arbitrage portfolio alphas increase monotonically as overvaluation increases. That is, managing earnings upwards is associated with more negative future abnormal returns when prior overvaluation is more severe. As for the 0.78% arbitrage portfolio alpha for the lowest valuation rank, one potential explanation is that those firms have the lowest valuation because they are in some type of distress. Trying to manage earnings upwards for a distressed firm can waste more managerial efforts, signal more severe information asymmetry risk, and cause more reputation loss. The positive alphas from DACC-based arbitrage portfolios are consistent with the evidence in the accrual anomaly literature (e.g., Sloan, 1996; Xie, 2001; Kothari et al., 2006; Huang et al., 2009). Our new evidence, which is the focus of this study, is that the accrual anomaly becomes more pronounced as overvaluation

intensifies. This is consistent with the prediction of the agency theory of overvalued equity. In Panels B and C of Table 6 we report the alphas for the 13– 24 month and 25–36 month holding periods. As expected, the patterns we observe in Panel A become less clean over time, and the magnitude and significance of the alphas dissipate over time, but some of the alphas remain large and significant even for the 25– 36 month holding period. We find similar results when using the Fama–French four-factor model to estimate alphas. To measure operating performance, we use the EBITDA-to-assets ratio (Compustat item #13 / #6). We rank and sort firms each year into 25 portfolios and compute each portfolio’s EBITDA-to-assets ratio for each of the three years after the portfolio formation. Following Cornett et al. (2008), we subtract DACC from the reported EBITDA to get the unmanaged EBITDA, and we adjust the unmanaged EBITDA-to-assets ratio by annual industry medians of unmanaged EBITDA-to-assets ratio, where we use the Fama and French (1997) 48-industry classifications to define industries. Thus, we obtain an industry-adjusted operating performance measure that is relatively free of managerial manipulation. In Table 7, we report the means of the annual industry-adjusted unmanaged EBITDA-to-assets ratio for each portfolio in each of the three years after the portfolio formation. Examining the Year 1 result in Panel A, we see that within each misvaluation rank, future operating performance decreases nearly monotonically as DACC increases. The pivotal result of the operating performance analysis is in the rightmost column, where we see that the performance difference between the lowest and the highest-DACC portfolios increases as overvaluation intensifies. This is consistent with the

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1

2

3

4

5

1st–5th

p-Value

Panel A. Year 1 Ranks by total valuation error (down, 1 is lowest) 1 0.0034 2 0.0249 3 0.0321 4 0.0511 5 0.0342

0.0171 0.0269 0.0314 0.0446 0.0536

0.0038 0.0117 0.0143 0.0311 0.0339

0.0210 0.0062 0.0045 0.0071 0.0042

0.0507 0.0292 0.0296 0.0323 0.0945

0.0540 0.0541 0.0617 0.0834 0.1287

(0.00) (0.00) (0.00) (0.00) (0.00)

Panel B: Year 2 Ranks by total valuation error (down, 1 is lowest) 1 0.0110 2 0.0150 3 0.0184 4 0.0313 5 0.0201

0.0123 0.0211 0.0294 0.0453 0.0459

0.0003 0.0135 0.0144 0.0314 0.0329

0.0109 0.0030 0.0084 0.0108 0.0029

0.0240 0.0146 0.0120 0.0160 0.0653

0.0130 0.0296 0.0304 0.0473 0.0855

(0.03) (0.00) (0.00) (0.00) (0.00)

Panel C: Year 3 Ranks by total valuation error (down, 1 is lowest) 1 0.0070 2 0.0147 3 0.0192 4 0.0338 5 0.0317

0.0138 0.0212 0.0294 0.0462 0.0493

0.0037 0.0160 0.0128 0.0291 0.0299

0.0083 0.0018 0.0111 0.0129 0.0006

0.0218 0.0110 0.0114 0.0132 0.0570

0.0148 0.0256 0.0306 0.0469 0.0887

(0.02) (0.00) (0.00) (0.00) (0.00)

Reported are the means of portfolio industry-median adjusted EBITDA-to-assets ratio, where EBITDA is the difference between reported EBITDA and DACC. For the period 1964–2003, we sort all firm-year observations annually into misvaluation quintiles (measured at year t  1) and then into DACC quintiles (measured at year t) and form 25 annual portfolios. Rank 1 means lowest total valuation error or lowest DACC. Panels A through C report the operating performance for each of the three years after the portfolio formation (i.e., year t + 1 to t + 3).

prediction that overvaluation-induced income-increasing earnings management becomes more harmful as overvaluation intensifies. When we use reported EBITDA-to-assets ratio as the performance measure, we find qualitatively similar results. Panels B and C show similar results as those in Panel A. In summary, the stock return and operating performance analyses jointly provide consistent evidence that the agency costs of overvalued equity are real and substantial, and become more substantial as overvaluation increases. During the first year after the portfolio formation and for the most overvalued quintile, firms in the highest DACC quintile underperform firms in the lowest DACC quintile by 11.88% as measured by the three-factor alphas, and by 12.87% points as measured by industry-adjusted unmanaged EBITDA-to-assets ratio. Both the abnormal stock return and operating performance results are consistent with Kedia and Phillipon’s (forthcoming) argument that earnings manipulation leads to overinvestment, and overinvestment by definition leads to poorer operating and stock performance. 4.5. Additional robustness checks In addition to the robustness checks already mentioned, we also perform the following robustness checks. We exclude financial firms and utility firms (Fama and French (1997) industries 31 and 44–47) from our sample and obtain similar results. We also use 2-digit-SIC-code classification to estimate DACC and the Fama–French 12-industry classification to estimate valuation errors and obtain qualitatively similar results. We use two other versions of the modified Jones model. One version excludes CFO/ Assets from Eq. (1). The other version includes B/M in Eq. (1). Our results are robust to using these two alternative versions. Following Hribar and Collins (2002), we compute DACC using cash flow statement data and find qualitatively similar results. Our sample includes both income-increasing (positive) DACC and income-decreasing (negative) DACC. When we split our sample into positive and negative DACC groups, as expected we find that the documented positive relation between overvaluation and DACC is much more pronounced in the positive DACC group. We

choose to report the results with the full sample because censoring likely introduces sample selection bias and because we view DACC as a relative measure of earnings management, largely consistent with how the literature has used it. If using the full sample reduces power and therefore is a more conservative test design, then we take comfort from the fact that we find the reported results even with a conservative test design. As another robustness check, we run a logistic regression where the dependent variable is one for positive DACC and zero for negative DACC. Using the set of control variables of Table 3, we find that the valuation errors have significantly positive coefficients (all p-values < 0.01), indicating that overvalued firms are more likely to use incoming increasing DACC. When we use the absolute value of DACC as the dependent variable and run firm fixed effects using the same specification as in Table 3, we find that the valuation errors have significantly positive coefficients (all p-values < 0.01). However, based on previous results, the positive coefficients are likely driven by overvalued firms more aggressively managing earnings upwards rather than downwards. Our arbitrage portfolio strategy in Table 1 imposes no constraint on firm size. In a robustness test, we exclude firms with stock price less than $5 at calendar yearend because these firms may be subject to infrequent trading and bid-ask bounce. As expected, the arbitrage portfolio alphas are smaller. For example, the annualized three-factor alphas for each of the three years are 1.50%, 6.22%, and 4.31%. Alternatively, we exclude those firms whose market capitalization is below the NYSE 5% breakpoint, and the results are very similar to those when we impose the $5 stock price constraint. It is not surprising to find that mispricing is more concentrated among smaller and lower-priced stocks. When we impose the $5 constraint and re-run the tests, we obtain qualitatively similar results. 5. Conclusion Jensen (2004, 2005) predicts that equity overvaluation could induce managers to engage in activities that can sustain the inflated stock price in the short run but can destroy shareholder value in the long run. One such activity is earnings management. We pro-

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