Journal of Business Finance & Accounting
Journal of Business Finance & Accounting, 40(7) & (8), 869–900, September/October 2013, 0306-686X doi: 10.1111/jbfa.12038
Does Investor Sentiment Affect Earnings
Management?
A
NAS
IMPSON∗Abstract: I hypothesize and find that earnings management via accruals is driven partially by
the prevailing market-wide investor sentiment. Managers inflate earnings in periods of higher sentiment, but report more conservatively during periods of low sentiment. Moreover, the likelihood of income-increasing earnings management to avoid negative earnings surprises is also positively associated with investor sentiment. These results are robust to: (i) controls for time-varying firm characteristics such as growth, investment opportunity sets, future profitability, leverage and size; (ii) macroeconomic variables such as future inflation, GDP growth, and growth in industrial production; (iii) multiple proxies for investor sentiment; and (iv) discre-tionary revenues as alternative measure of earnings management. Cross-sectional analyses reveal that firms whose stock returns co-move more with investor sentiment are more (less) likely to manage earnings upward via abnormal accruals in quarters of higher (lower) sentiment. The findings of managers’ strategic use of abnormal accruals show the need for increased attention from boards of directors, auditors and regulators to heightened managerial incentives to overstate earnings and to report optimistic earnings numbers during periods of high investor sentiment.
Keywords:investor sentiment, earnings management, discretionary accruals
1. INTRODUCTION
In this paper, I examine the relationship between investor sentiment and firms’ attempts to manage earnings during the time period 1976–2005. I follow Baker and Wurgler’s (2007) definition of investor sentiment “as a belief about future cash flows and investment risks that is not justified by the facts at hand”. Baker et al. (2007) posit that, in the presence of time-varying investor sentiment, managers will respond to investors’ sentiment-driven expectations by “packaging the firm and its securities in a way that maximizes appeal to investors”. Recent work in finance has studied the impact of such sentiment on corporate actions, such as equity issues (Baker and Wurgler, 2002), dividend payouts (Baker and Wurgler, 2004; and Li and Lie, 2006),
∗The author is at the London School of Economics and Political Science, London, UK. The author thanks
Andrew W. Stark (Editor), an anonymous referee, Lakshmanan Shivakumar, Shivaram Rajgopal, Martin Walker, Baljit Sidhu, and workshop participants at the London Business School Accounting Symposium for helpful comments and Ankit Agarwal for excellent research assistance. (Paper received July, 2012; revised version accepted May, 2013).
Address for correspondence: Ana Simpson, London School of Economics and Political Science, De-partment of Accounting, Office OLD3.31, Houghton Street, London WC2A 2AE, United Kingdom. e-mail: A.Simpson1@lse.ac.uk
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investment (Gilchrist et al., 2005; Polk and Sapienza, 2009) and acquisitions (Dong et al., 2006). However, as noted by Baker et al. (2007), there is scant empirical evidence on how managers respond strategically to investor sentiment via corporate reporting decisions. The nascent accounting literature on investor sentiment has examined the influence of investor sentiment on management forecast disclosures (Bergman and Roychowdhury, 2008) and disclosure of proforma earnings metrics (Brown et al., 2012). Surprisingly, there is little evidence on the effect of investor sentiment on earnings management, given the wide attention in the literature to managers’ attempts at interfering with the financial reporting process. This study fills the literature gap by investigating the strategic use of abnormal accruals in response to market-wide investor sentiment.
The most closely related study to this one is Ali and Gurun (2009), which examines whether managers inflate accruals of small firms to exploit overvaluation per unit of accruals in periods of high investor sentiment. Ali and Gurun, however, do not investigate whether firms’ incentives to manage earnings upwards or downwards vary strategically with sentiment. Managers’ motives to overstate earnings by taking income-increasing accruals are likely to be higher in periods of high investor sentiment (e.g., due to pressure to meet optimistic investor and analyst expectations), whereas in periods of low sentiment, managers may have incentives to understate earnings (e.g., because of reputational risk arising from heightened investor scrutiny). Ali and Gurun’s (2009) use of total accruals as a measure of earnings management, however, does not allow for making inferences on whether accrual-related managerial discretion has an income-increasing or income-decreasing effect in periods of low investor sentiment. Furthermore, Ali and Gurun’s study is silent on whether investor sentiment influences managerial involvement in earnings surprise games. Given the evidence that managers seek to avoid negative earnings surprises (Brown and Caylor, 2005; Graham et al., 2005), and that investors’ reaction to negative earnings surprises becomes stronger with higher sentiment (Seybert and Yang, 2012), it is of interest to establish whether managers’ documented reporting of inflated accruals in periods of high investor sentiment is driven by a desire to avoid negative earnings surprises.
I hypothesize that, during periods of high market-wide investor sentiment, man-agers portray the firm in a manner that maximizes its appeal to sentiment-driven investors. Hence, during periods of high investor sentiment, managers will boost earnings via positive abnormal accruals to meet investors’ optimistic expectations of future firm performance. During low-sentiment periods, managers may report conservatively (by not overstating accruals, or by taking income-decreasing accruals) to mitigate the higher disclosure costs arising from increased scrutiny by pessimistic investors. These arguments suggest a positive association between market-wide investor sentiment and managers’ use ofabnormal accruals. I further expect that in periods of high sentiment, firms will manage earnings upwards to avoid negative earnings surprises: hence managers’ incentives to report positive abnormal accruals to meet or beat analysts’ forecasts should increase with investor sentiment.
I use signed abnormal accruals to measure earnings management. Consistent with prior literature (Bergman and Roychowdhury, 2008; Walther and Willis, 2013), the primary proxy for investor sentiment used is the Michigan Consumer Sentiment Index. In the empirical work, I document that managers’ use of abnormal accruals is positively associated with investor sentiment. Furthermore, the likelihood of income-increasing
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earnings management to avoid negative earnings surprises is also positively associated with investor sentiment. Thus managers appear to recognize that investor sentiment is time-varying, and they respond to such time-varying market-wide sentiment via earn-ings management: they boost accruals to avoid negative earnearn-ings surprises (proxied by meeting or beating analysts’ forecasts) when investor sentiment is relatively high, and report more conservatively in periods of relatively low investor sentiment. The conservative reporting in periods of low investor sentiment is described by a higher likelihood of taking negative abnormal accruals.
The findings relating earnings management to investor sentiment are robust to several alternative explanations. As the regressions control for lagged abnormal accruals and several other firm characteristics, alternative explanations based on time variation in growth, investment opportunity sets (proxy for growth prospects), future profitability (proxy for future operating performance), leverage and size (proxy for debt and political visibility type contracting incentives to manage earnings) do not subsume the results. These robustness tests provide some confidence that the findings are empirically distinct from and incremental to the proxies for firm-specific incentives traditionally proposed in the literature by Watts and Zimmerman (1978). Additional analyses reveal that the results are not subsumed by macroeconomic variables such as future inflation, GDP growth, or growth in industrial production, which suggests that abnormal accruals are likely to respond to the portion of investor sentiment not attributable to fundamental economic factors. I further check the robustness of the empirical analysis by examining discretionary revenues as an alternative measure of earnings management. The results are qualitatively similar to the earlier ones, which alleviates concerns about accruals being an inadequate measure of earnings management. Furthermore, the documented link between discretionary revenues and sentiment provides additional evidence of how firms manage earnings in response to investor sentiment.
I probe further to understand what drives the documented positive association between earnings management and investor sentiment. If the time variation in abnor-mal accruals is at least partly driven by managers’ responding to investor sentiment, then I would expect firms whose stock returns co-move more with changes in investor sentiment (or with higher sentiment betas), in the cross-section, to boost earnings by a larger extent. Consistent with this intuition, I find that the positive association between abnormal accruals is stronger for firms with greater investor sentiment betas.
The study makes several contributions to the literature. First, I extend the existing research on earnings management in response to investor sentiment by investigating whether managers strategically vary their accrual reporting choices in periods of high and low investor sentiment, and whether investor sentiment affects managers’ involvement in earnings surprise games. I do so by documenting that in periods of higher investor sentiment managers are more likely to boost accruals to avoid negative earnings surprises, and that in periods of low investor sentiment they are likely to report more conservatively. Second, the study adds to the limited literature on revenue management by documenting the time-varying use of discretionary revenues as an earnings management tool. Third, the study complements work on firm-specific incentives to manage earnings by examining whether earnings management at the firm level responds to aggregate market-level conditions. Fourth, the results question the common assumption that reported earnings surprises cause stock price changes, but not vice versa (e.g., Beaver et al., 1980). I show that the sensitivity of a firm’s stock
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returns to market-wide investor sentiment might affect future earnings for that firm via earnings management.
The remainder of the paper is organized as follows. Section 2 reviews the existing literature, and develops the basis for the predictions. Section 3 discusses the method-ology and data, and section 4 presents the main results and robustness tests. Section 5 concludes.
2. HYPOTHESIS DEVELOPMENT
In this section I review the literature and develop the hypothesized positive association between investor sentiment and managers’ attempts to manage earnings via accruals.
(i) Related Literature
The literature defines investor sentiment as optimism or pessimism about stocks in general, or as a state where investor beliefs about future firm value deviate from fundamental information (DeLong et al., 1990; Morck et al., 1990; Chang and Fong, 2004; Baker and Wurgler, 2006, 2007; Sabherwal et al., 2011). This literature has shown that investor sentiment varies over time. Sentiment changes over time because biases associated with information processing, such as the effect of overconfidence (Daniel et al., 1998) or representativeness and conservatism (Barberis et al., 1998), tend to change over time for exogenous reasons, leading to differential appetites for traders to speculate. Baker et al. (2007) posit that managers are likely to respond to investors’ sentiment-driven expectations by maximizing the firm’s appeal to investors. Managerial responses could take the form of corporate actions (e.g., stock splits, name changes, dividend policy changes, capital investments) and corporate reporting decisions (Baker et al., 2007).
The limited empirical evidence on corporate reporting decisions in response to sentiment includes Bergman and Roychowdhury (2008) and Brown et al. (2012). Bergman and Roychowdhury (2008) examine the relationship between investor sentiment and voluntary disclosures, proxied by management earnings forecasts. They show that during periods of high (low) sentiment managers reduce (increase) the frequency of their disclosures in an attempt to maintain (increase) investors’ and analysts’ optimistic (pessimistic) earnings estimates. Brown et al. (2012) investigate managers’ propensity to disclose proforma earnings metrics in response to investor sentiment, and also document a positive relationship between the two. The focus of these studies on managers’ attempts to achieve higher stock prices by influencing sentiment-driven expectations via disclosures is linked to the more general evidence from behavioral finance that investor sentiment biases expectations of future firm performance by both investors (e.g., Brown and Cliff, 2005; Baker and Wurgler, 2006; Lemmon and Portniaguina, 2006) and analysts (Hribar and McInnis, 2012; Walther and Willis, 2013). I extend this evidence by investigating whether managers face pressure to meet investors’ and analysts’ optimistic forecasts in high-sentiment periods and respond to it by managing earnings upwards, and whether they respond to pessimistic expectations and higher scrutiny in low-sentiment periods by reporting more conservatively.
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My study is most closely linked to Ali and Gurun (2009), who find that investors price the accrual component of earnings more aggressively during high-sentiment periods than in low-sentiment periods for small stocks that are vulnerable to investor sentiment, and that managers tend to increase total and current accruals for such stocks in periods of high sentiment. My incremental contribution to Ali and Gurun (2009) is that I examine whether managers have incentives to report conservatively in periods of low sentiment, and whether managers boost accruals in periods of high sentiment in order to meet or beat analysts’ forecasts. Furthermore, I employ a more comprehensive approach to modeling opportunistic managerial behavior in response to sentiment by: (1) using abnormal accruals, which, unlike total accruals, exclude adjustments correlated with fundamental performance (normal accruals) (Dechow et al., 2010); (2) using discretionary (abnormal) revenues, which provide evidence on how firms manage earnings in response to market-wide sentiment; and (3) controlling for macroeconomic factors to ensure that the link between sentiment and firm performance is not at least partly attributed to variation of both sentiment and accruals with macroeconomic conditions.
(ii) Investor Sentiment and Attempts to Boost Accruals
My hypothesis rests on two key arguments: (1) the pay-off to managing earnings upwards is higher in high-sentiment periods than in low-sentiment periods; and (2) upward earnings management in low-sentiment periods is difficult and more costly because of increased investor scrutiny during periods of low sentiment.
Several prior studies have shown that earnings management benefits firms in a variety of ways, including avoiding debt-covenant violation, generating a return premium in equity markets, lowering firms’ cost of debt, and aiding relationships with suppliers and customers. These inducements provide incentives for managers to typically overstate earnings.1I expect that managers’ incentives to engage in earnings
management are likely to be higher during high-sentiment periods. One reason for this heightened incentive is the finding in Bergman and Roychowdhury (2008) and Hribar and McInnis (2012) that the optimistic bias in analyst forecasts increases with sentiment. Hence the pressure on managers to generate positive accruals to meet such an optimistic analyst estimate benchmark is also greater in high-sentiment periods. Another reason is the evidence by Ali and Gurun (2009) of a greater price effect per unit of accruals in high-sentiment periods than in low-sentiment periods, which probably heightens incentives for managers seeking a short-term boost to stock prices to inflate accruals during high-sentiment periods relative to low-sentiment periods. More generally, investors are likely to overvalue stocks during high-sentiment periods (e.g., Brown and Cliff, 2005; Baker and Wurgler, 2006; Lemmon and Portniaguina, 2006). Managers aiming for higher stock prices might take advantage of investors’ inclination to overvalue stocks by inflating earnings in high-sentiment periods.
I hypothesize that during periods of low investor sentiment, managers may report more conservatively, and are hence less likely to use income-increasing accruals to
1 For example, Bartov, Givoly and Hayn (2002) document that firms that meet or beat expectations, even if achieved through earnings management, enjoy a premium in their stock returns. Jiang (2008) shows that firms meeting or beating earnings expectations enjoy lower cost of debt, even if the meet/beat was achieved through earnings management. Raman and Shahrur (2008) show that earnings management helps a firm attract more relationship-specific investments from suppliers and customers.
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boost earnings or use income-decreasing accruals, for four reasons. First, Bergman and Roychowdhury (2008) and Hribar and McInnis (2012) find that analysts are less optimistic in low-sentiment periods: hence managers are more likely to meet earnings benchmarks without having to resort to income-increasing accruals during such times. Second, it is also possible that the income-increasing accruals used during periods of higher investor sentiment reverse during periods of lower sentiment. Third, behavioral research finds that, when individuals hold pessimistic beliefs, they process information in a more systematic, detail-oriented manner than when they hold optimistic beliefs (Schwarz, 1990; Taylor, 1991; Bless et al., 1996). Thus pessimistic investors are likely to exercise more scrutiny and take less information at face value than optimistic investors. Fourth, there is evidence that regulators and policymakers increase scrutiny and intervention during low-sentiment periods (e.g., Nofsinger and Kim, 2003; Nofsinger, 2005; Hirshleifer, 2008). Given this heightened level of scrutiny, managers are likely to report conservatively during low-sentiment periods in order to mitigate higher regulatory and reputational risks.2
These arguments lead to the following hypothesis:
H1: Managers’ use of income-increasing abnormal accruals to boost earnings is positively associated with the level of investor sentiment.
(iii) Underlying Assumptions Behind the Hypothesis
To derive Hypothesis 1, the previous subsection implicitly relies on two specific assumptions: (1) managers are able to identify instances of mispricing (sentiment) for their own firm, and these perceptions of mispricing could impact on their accrual decisions; and (2) in the presence of investor sentiment, managers tend to emphasize short-term stock price as opposed to long-term fundamental value.3I discuss each of
these assumptions in turn.
The assumption that managers can identify periods of high and low sentiment, and exploit these periods of mispricing via accruals, is relatively easy to justify, because decades of research has shown that managers know more about the firm than outside investors do. Consistent with this notion, Graham et al. (2005) provide survey evidence that managers’ perception of stock mispricing is an important factor in firms’ reporting decisions.
The second assumption is that managers care more about short-term stock price than about long-run fundamental value in the presence of investor sentiment. In perfect capital markets with fully rational investors, the two objectives of maximizing short-run price and maximizing long-run value are the same as a definition of market efficiency that stock prices equal fundamental value. However, if the assumption of investor rationality is relaxed, these two objectives may become distinct. In this case, I
2 On the other hand, in light of Mian and Sankaraguruswamy’s (2012) findings, upward earnings management to attain good news is potentially more beneficial in high-sentiment situations than in low-sentiment situations (i.e., upward earnings management to attain good news is less likely in low-low-sentiment situations). Conversely, upward earnings management to avoid bad news is potentially less beneficial in high-sentiment situations than in low-high-sentiment situations (i.e., upward earnings management to avoid bad news is more likely to take place in low-sentiment situations). Hence the link between earnings management and low investor sentiment may be an empirical issue. I specifically address this issue in section 4(v) below. 3 These are in addition to the standard assumption in the literature that investors do not completely see through earnings management.
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suggest that the objective of maximizing current stock price becomes more important, thereby creating incentives for managers to respond to the short-term demands of sentiment-driven investors using various channels, one of which could be financial reporting decisions. At least three factors point towards managers’ preferences for the short term over the long term: (1) they have a perceived limited horizon of employment with the firm; (2) managerial compensation, such as vested options or the impending vesting of stock options, is tied to short-run share prices; and (3) Hirshleifer et al. (2006) argue that overpriced stocks can increase investment opportunities for the firm by decreasing their cost of capital, and managers might want to exploit these opportunities by maintaining or increasing current share prices.
3. METHODOLOGY AND SAMPLE DATA
(i) Computing Abnormal Accruals
To examine the hypothesis empirically, I regress signed quarterly abnormal accruals, a commonly used proxy for earnings management, on lagged quarterly measures of investor sentiment. The investor sentiment measure, as mentioned before, is the Michigan Consumer Sentiment Index. This is a consumer confidence index published monthly by the University of Michigan and Thomson Reuters. The index is publicly released in the same month as that for which the survey is conducted. The index is normalized to have a value of 100 in December 1964. At least 500 telephone interviews are conducted each month on a US sample that excludes Alaska and Hawaii. The index focuses on three broad areas: (1) how consumers view the prospects for their own financial situation; (2) how they view the prospects for the general economy over the near term; and (3) how they view the prospects for the economy over the long term. For each calendar quarter, the Michigan index is computed by averaging its monthly values.
The data for estimating accruals and other control variables are derived from the quarterly Compustat files and daily files from the Center for Research and Security Prices (CRSP) for the period 1976–2005. I retain in my sample firms that are non-financial and non-utility (SIC codes 6000–6999 and 4900–4999 excluded) and have December fiscal year-ends. I restrict the sample to December year-end firms to avoid overlapping quarterly observations, and to avoid co-mingling observations from different fiscal quarters. I exclude firm-years in which a firm was involved in mergers and acquisitions as per Compustat, because Hribar and Collins (2002) caution that accruals computed using the balance-sheet method for such firms are prone to measurement error. I include only firms with common ordinary shares (CRSP share codes 10 and 11, which excludes American Depository Receipts (ADRs) and closed-end mutual funds from the sample) that are listed on the NYSE, AMEX or NASDAQ.
Abnormal accruals for firmiand quartert(ABACCit) are measured by subtracting
“normal” accruals from total accruals. Total accruals represent the difference between income before extraordinary items reported in the cash flow statement [EBEI (CF) (data 76)] and net cash flow from operating activities (NCF, data 106).4 Accruals
4 Quarterly data items 76 and 106 are reported cumulatively in Compustat (i.e., data item 76 for quarter 2 includes income for quarter 1 plus income for quarter 2), because of which the figures for quarters 2 to 4 are obtained as changes from the previous quarter.
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are estimated from changes in consecutive balance sheet items whenever NCF is unavailable. That is, I compute total accruals as the change in current assets (CA, data item 40) minus the change in cash and short-term investments (Cash, data item 36) minus the change in current liabilities (CL, data item 49) other than the change in the current portion of long-term debt (STD, data item 45) minus depreciation and amortization (DEPN, data item 5).
I compute “normal” accruals (E(ACCit)) for firmiand quartertusing a variety of
accrual models that are often used in the earnings management literature:
Jones (1991) model:
E(ACCit)=α0+α1REVit+α2PPEit. (1)
Dechow et al. (1998) (CFO) model:
E(ACCit)=α0+α1REVit+α2PPEit+α3CFOit, (2)
whereREVitis change in revenues; PPEitis property, plant and equipment; and CFOit
is the cash from operations. All variables and the intercepts are standardized by the firm’s average total assets for that quarter.
Additionally, I consider two non-linear models proposed by Ball and Shivakumar (2006), which allow accruals to reflect asymmetric loss recognition. These models use either negative annual stock returns (DRET) or negative cash flows (DCFO) to proxy for economic losses:
CFO DRET model:
E(ACCit)=α0+α1REVit+α2PPEit+α3CFOit+α4DRETit+α5CFOit∗DRETit.
(3) CFO DCFO model:
E(ACCit)=α0+α1REVit+α2PPEit+α3CFOit+α4DCFOit+α5CFOit∗DCFOit.
(4) Finally, because prior studies have documented a systematic relationship between accruals and firm characteristics, such as market-to-book and performance and growth (e.g., Dechow et al., 1995; Desai et al., 2004; Cheng et al., 2012; Collins et al., 2013), I also employ an accrual model that extends the CFO model in equations (3) and (4) by controlling for firm characteristics that are potentially related to performance and growth, such as return on assets (ROA), market-to-book ratio (MB), logarithm of market capitalization (SIZE), and leverage (LEV). This is consistent with the recommendation of Cheng et al. (2012), who show that adjusting the traditional accrual models for ROA and performing the estimation at the industry level leads to estimates that are better suited for investigating earnings management. I augment their model by adding MB, SIZE and LEV.
Modified CFO model:
E (ACCit)=α0+α1REVit+α2PPEit+α3CFOit+α4MBit
+α5ROAit+α6SIZEit+α7LEVit. (5)
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The parameters for computing “normal” accruals for firm i and quarter t are estimated from quarterly cross-sectional regressions of the above accruals models. The regressions are estimated separately for each quarter and each industry in the 48 Fama and French (1997) industry classifications, with at least 15 observations. I use prediction errors based on the model parameters in quartert−1 to compute abnormal accruals in quartert.5All variables are winsorized at the top and bottom 1% level of the
distribution for each industry and quarter. I test the main hypothesis of the paper using a cross-sectional regression of firm-quarter abnormal accruals (ABACCit) on lagged
quarterly values of the Michigan Consumer Sentiment Index.6
(ii) Regression Model
In this section, I discuss the empirical specification where I correlate abnormal accru-als (my proxy for earnings management) with the proxy for market-wide sentiment (the Michigan Consumer Sentiment Index) in the previous quarter by estimating the following pooled cross-sectional regression:
ABACCit=λ0+λ1MICHINDEXt−1+ 4
j=1
λ2jABACCit−j +λ3QTR4t+uit, (6)
where MICHINDEXt−1is the Michigan Consumer Sentiment Index as of quartert−1.
To control for potential serial correlation in abnormal accruals (ABACCit), I introduce
four lags of the dependent variable in the model. I also include an indicator variable to identify the fourth quarter, QTR4t, as the abnormal accruals in this quarter are
potentially distinct from those related to the first three quarters.7I account for
cross-correlation by clustering standard errors by firm and year. The main coefficient of interest isλ1. Hypothesis 1 predicts that the coefficientλ1will be positive. Empirical
results related to equation (6) are discussed in Section 4(ii).
(iii) Control Variables
In this section, I consider whether the association between investor sentiment and abnormal accruals posited in the previous sub-section can be explained by other confounding factors. I discuss the various potential confounding factors, and then present empirical results in Section 4(ii) that attempt to control for these factors.
(a) Investment Opportunities
One potential explanation for a significant positive coefficient λ1 is that it merely
reflects time variation in investment opportunities. That is, firms with higher abnormal
5 Using model parameters from four quarters ago rather than from the previous quarter could account for potential seasonality in normal accruals. However, I choose the previous quarter’s parameters to use the most recent data for estimating abnormal accruals. In any case, the regressions include up to four lags of abnormal accruals as control variables, and such lagged terms ought to account for seasonality in accruals. 6 I use the lagged value of the Michigan index, as I expect that managers first find out about the sentiment of investors in the month/quarter before the earnings announcement and then manage earnings, which is released at the next month/quarter.
7 I include indicator variables for each quarter in a year and obtain qualitatively similar results.
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accruals might have greater investment opportunities than firms with lower abnormal accruals in periods of higher investor sentiment, and the association I document might simply reflect such omitted investment opportunities. Another reason to control for future investment opportunities is the hypothesis that investors and managers could both hold overly optimistic growth forecasts (they embody optimistic “sentiment” about the world). These common growth assumptions would potentially impose an upward tendency in accruals, but not because the manager seeks to take advantage of favorable pricing conditions, as argued in the development of Hypothesis 1. Recall that the hypothesis relies primarily on managers responding to stock mispricing (due to investor sentiment), as opposed to other “rational” explanations for why managers increase abnormal accruals in high-sentiment periods, and decrease them in low-sentiment periods. To investigate these potential confounds, I include the market-to-book ratio, a proxy for the level of a firm’s investment opportunity set, as an additional control variable in the regressions.
(b) Leverage, Size and Future Operating Performance
Next, I account for whether any potential association between income-increasing accruals and sentiment has explanatory power beyond the traditional debt covenant, political visibility and bonus motivations to manage earnings, as proposed by Watts and Zimmerman (1978). I do not explicitly test for the bonus hypothesis, because it is difficult to obtain data on bonus plans in a machine-readable format for a large sample of firms. I rely instead on ROA as a proxy for managers’ incentives to manage earnings and hence maximize the present value of their bonuses. To rule out the potential confounding explanations related to the debt covenant and political visibility hypotheses, I employ the debt-to-equity ratio and firm market value as control variables. My objective is to assess whether the initial association I document still holds after accounting for these debt covenant and political visibility hypotheses proxies (see Fields et al., 2001). To mitigate concerns that the proxies for earnings management capture omitted correlated variables pertaining to future improvements in a firm’s operating performance, I include improvement in future operating performance, measured as the change in one-year-ahead ROA.8
4. DATA AND RESULTS
(i) Descriptive Statistics
Panel A of Table 1 reports descriptive data on the key dependent and independent variables for the sample period. Both ABACC and the Michigan index show significant variation over time. The average ABACC is 0.04% of total assets (0.0004) with a range from −0.05% in 1981 to 0.12% in 1985. The MICHINDEX varies from a peak of 109.2 in 2000, right before the dot-com crash, to a trough of 65.5 in 1980. Table 1 also reports descriptive data on MEETBEAT (a measure of firms’ propensity to meet or beat analysts’ forecasts) and discretionary revenues (an alternative earnings management metric), to be discussed in greater depth in Sections 4(vi) and 4(vii)
8 I also considered the quarterly return on CRSP value-weighted market index as a proxy for performance. The qualitative results reported here are insensitive to the inclusion of this additional explanatory variable.
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Table 1
Descriptive Data
Panel A: Mean, Median and Standard Deviation of Abnormal Accruals (ABACCit), Michigan Consumer Index (MICHINDEXt), Propensity to
Meet or Beat Analysts’ Forecasts (MEETBEATit) and Discretionary Revenues (DISC REVit)
ABACCit(Modified CFO model) MICHINDEXt MEETBEATit DISC REVit(QTR REV model)
Mean Median Std. dev. Mean Median Std. dev. Mean Median Std. dev. Mean Median Std. dev.
1976–2005 0.0004 0.0002 0.0147 91.93 92.60 10.29 0.55 1 0.50 0.0088 0.0047 0.0493
Min. yearly value −0.0005 −0.0002 0.0034 65.50 58.7 0.86 0.37 0 0.48 0.0012 0.0005 0.0259
Max. yearly value 0.0012 0.0006 0.0317 109.2 107.2 10.66 0.65 1 0.50 0.0135 0.0077 0.0697
No. of obs. 167,176 116 107,334 232,762
Panel B: Spearman/Pearson Correlations between Abnormal Accruals Estimated from Jones Model, CFO Model, Modified CFO Model, CFO DCFO Model and CFO DRET Model
ABACCit ABACCit ABACCit ABACCit ABACCit
(Jones model) (CFO model) (Modified CFO model) (CFO DCFO model) (CFO DRET model)
ABACCit 0.76 0.38 0.65 0.66
(Jones model) <0.0001 <0.0001 <0.0001 <0.0001
ABACCit 0.83 0.48 0.86 0.86
(CFO model) <0.0001 <0.0001 <0.0001 <0.0001
ABACCit 0.28 0.30 0.44 0.42
(Modified CFO model) <0.0001 <0.0001 <0.0001 <0.0001
ABACCit 0.70 0.82 0.29 0.78
(CFO DCFO model) <0.0001 <0.0001 <0.0001 <0.0001
ABACCit 0.68 0.82 0.28 0.84
(CFO DRET model) <0.0001 <0.0001 <0.0001 <0.0001
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Table 1 (Continued)
Note:
Panel A of this table presents the mean, median and standard deviation of firm-level abnormal accruals (ABACCit) from the modified CFO model, Michigan Consumer Sentiment Index (MICHINDEXit), a measure of firms’ propensity to meet or beat analysts’ forecasts (MEETBEATit), and discretionary revenues (DISC REVit) from the QTR REV model. Panel A also presents the minimum and maximum values of the yearly means, medians and standard deviations of these four variables. Panel B presents the Spearman and Pearson correlations between abnormal accruals from the Jones model, CFO model, modified CFO model, CFO DCFO model and CFO DRET model. Abnormal accruals for firmiin quartertare computed as the difference between firmi’s total accruals in quartertand its normal accruals obtained from the following models:
Jones model: E (ACCit)=α0+α1REVit+α2PPEit
CFO model: E (ACCit)=α0+α1REVit+α2PPEit+α3CFOit
Modified CFO model: E (ACCit)=α0+α1REVit+α2PPEit+α3CFOit+α4MBit+α5ROAit+α6SIZEit+α7LEVit
CFO DRET model: E (ACCit)=α0+α1REVit+α2PPEit+α3CFOit+α4DRETit+α5CFOit∗DRETit CFO DCFO model: E (ACCit)=α0+α1REVit+α2PPEit+α3CFOit+α4DCFOit+α5CFOit∗DCFOit
whereREVitis the change in revenues in quartert; PPEitis the property, plant and equipment at the end of quartert; CFOitis the cash from operations in quarter t; MBitis the market-to-book ratio at the end of quartert; ROAitis the return on assets in quartert;ROAitis the four-quarter-ahead change in ROA, i.e., ROA in quartert+4 minus the ROA in quartert; SIZEitis the logarithm of market capitalization at the end of quartert; LEVitis the debt-to-equity ratio at the end of quartert, where debt is measured as long-term debt plus debt in current liabilities; DRETitis an indicator variable equal to 1 if the firm’s stock return from 90 days before the earnings announcement to 45 days after the earnings announcement is negative, and 0 otherwise; and DCFOitis an indicator variable equal to 1 if CFOit is negative, and 0 otherwise. All variables and intercepts are standardized by the average total assets in that quarter. The parametersα0toα7are estimated separately
for each of the Fama and French (1997) industry classifications with at least 15 observations, from a regression of accruals on the above variables in quartert−1. MICHINDEXtis the quarterly average of the monthly Michigan Consumer Sentiment Index published by the University of Michigan and Thomson Reuters. QTR4t is an indicator variable that takes the value 1 for the fourth quarter, and 0 for all other quarters. MEETBEATitis an indicator variable equal to 1 for firms meeting or beating the latest analyst consensus earnings forecast, and 0 otherwise. Discretionary revenues (DISC REVit) from QTR REV model for firmiin quartertare computed astARit−a−btREVit, whereARitis the change in accounts receivable in quartert, and a and b are estimated fromtARit=α+βtREVit+εit. The sample period covers every quarter in the period 1976 to 2005.
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below. MEETBEAT varies from 46.64% in 1984 (when data are first available) to 61.87% in 2005, consistent with prior evidence that the frequency of benchmark beating has grown over time (e.g., Matsumoto, 2002; Brown and Caylor, 2005). In line with the hypothesized strategic reporting in response to investor sentiment, MEETBEAT peaks in 2000 (64.68%) when investor optimism is also at its highest. Discretionary revenues are considerably higher than ABACC, with a mean of 0.0088 and a median of 0.0047 (0.88% and 0.47% of total assets, respectively).
Panel B of Table 1 presents correlations between the abnormal accruals estimated from various models. The modified CFO model estimates exhibit the lowest correla-tion with the rest of the estimates, most likely because the inclusion of size, market-to-book, leverage, etc. in the modified CFO model removes accrual variation related to these underlying firm characteristics. This is consistent with Collins et al. (2013), who show that controlling for performance and growth dampens the variation in accruals related to size, market-to-book, earnings-to-price, etc. Based on this I use the modified CFO model as the main model in the paper.
(ii) Regression of Abnormal Accruals
Table 2 presents the results of estimating equation (6). Panels A, B and C report results based on abnormal accruals estimated from a variety of accrual models. The results in Panel A, which uses Jones’s model to estimate abnormal accruals, indicate that the coefficient on the lagged Michigan Consumer Sentiment Index, MICHINDEX, is positive and significant, which is consistent with the hypothesis that managers respond to market-wide sentiment by boosting abnormal accruals in high-sentiment periods and reporting conservatively in low-sentiment periods.
The positive association between abnormal accruals and sentiment is robust to the introduction of control variables such as market-to-book ratio, leverage, size, ROA, and improvement in annual ROA. Further, the fourth-quarter indicator is significantly negative, suggesting that abnormal accruals are more conservative in quarters when financial statements are audited, which is consistent with the findings of Brown and Pinello (2007). The statistical significance of MICHINDEX remains robust to the introduction of these control variables. In fact, the inclusion of the control variables has little effect on the magnitude of the coefficient on MICHINDEX, suggesting that the relationship between investor sentiment and abnormal accruals is unlikely to be caused by time-based variation in growth or investment opportunity sets, or contracting variables.
The significant positive coefficient on MICHINDEX continues to be observed also in Panels B and C, which report results for abnormal accruals estimated from the CFO model, the modified CFO model, the CFO DCFO model and the CFO DRET model. Thus, irrespective of whether I include CFO as an explanatory variable or allow non-linearity in the accrual model, I consistently observe a significantly positive relationship between abnormal accruals and the Michigan Consumer Sentiment Index.9 Moreover, these coefficients imply an economically significant relationship
between MICHINDEX and abnormal accruals. For instance, the coefficient of 0.010 on
9 In untabulated analyses I investigate whether the results are sensitive to the exclusion of extreme observations such as dropping the top and bottom 1% of abnormal accrual observations. Excluding the extreme observations leaves the qualitative results unchanged.
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Table 2
Abnormal Accruals and Investor Sentiment
Panel A: Jones Model
Coeff. t-value Coeff. t-value
Jones Model Accruals (1) (2) (3) (4)
MICHINDEXt−1 0.009 1.73 0.009 1.96
MBit−1 −0.000 −0.01 0.000 0.28
ROAit−1 0.084 9.55 0.098 8.32
SIZEit−1 −0.000 −1.89 −0.000 −2.43
LEVit−1 0.000 0.16 −0.000 −0.08
ROAit+1 −0.019 −4.08 −0.009 −3.03
QTR4t −0.003 −3.98 −0.003 −4.43
ABACCit−1 −0.084 −7.60
ABACCit−2 0.009 0.60
ABACCit−3 0.002 0.23
ABACCit−4 0.168 12.18
INTERCEPT −0.007 −1.40 −0.007 −1.56
No. of obs. 169,691 169,691
Adj.R2% 4.25 7.80
Panel B: Linear Models with Cash Flow from Operations
CFO Model Coeff. t-value Coeff. t-value Modified CFO Coeff. t-value Coeff. t-value
Accruals (1) (2) (3) (4) Model Accruals (5) (6) (7) (8)
MICHINDEXt−1 0.010 2.95 0.009 2.81 MICHINDEXt−1 0.005 2.88 0.005 2.95
MBit−1 −0.000 −12.68 −0.000 −10.72 MBit−1 0.000 4.11 0.000 0.54
ROAit−1 0.159 16.39 0.139 7.81 ROAit−1 0.044 3.71 0.044 3.78
SIZEit−1 −0.001 −4.03 −0.001 −3.98 SIZEit−1 −0.000 −1.59 −0.000 −1.60
LEVit−1 0.000 0.52 0.000 0.49 LEVit−1 0.000 0.20 −0.000 −1.46
ROAit+1 −0.144 −3.16 −0.142 −3.17 ROAit+1 0.036 1.61 0.036 1.62
QTR4t −0.001 −2.06 −0.002 −2.92 QTR4t −0.000 −1.09 −0.000 −1.33
ABACCit−1 −0.043 −1.73 ABACCit−1 −0.027 −1.24
ABACCit−2 0.083 6.35 ABACCit−2 0.003 0.73
ABACCit−3 0.059 7.43 ABACCit−3 0.001 0.54
ABACCit−4 0.114 7.95 ABACCit−4 0.003 1.01
INTERCEPT −0.005 −1.46 −0.003 −1.28 INTERCEPT −0.002 −0.95 −0.002 −0.94
No. of obs. 170,174 170,174 No. of obs. 167,176 167,176
Adj.R2% 10.37 12.94 Adj.R2% 0.25 0.30
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Table 2 (Continued)
Panel C: Non-linear Models
CFO DRET Coeff. t-value Coeff. t-value CFO DCFO Coeff. t-value Coeff. t-value Model Accruals (1) (2) (3) (4) Model Accruals (5) (6) (7) (8)
MICHINDEXt−1 0.010 3.13 0.008 2.54 MICHINDEXt−1 0.013 3.84 0.010 3.18
MBit−1 −0.000 −11.04 −0.000 −13.43 MBit−1 −0.000 −4.30 −0.000 −4.82
ROAit−1 0.127 11.24 0.088 7.70 ROAit−1 0.125 14.55 0.087 12.35
SIZEit−1 −0.000 −3.10 −0.000 −2.09 SIZEit−1 −0.000 −0.02 −0.000 −0.61
LEVit−1 0.000 0.96 0.000 0.95 LEVit−1 −0.000 −0.07 0.000 0.34
ROAit+1 −0.079 −2.89 −0.078 −2.88 ROAit+1 −0.082 −3.44 −0.080 −3.42
QTR4t −0.002 −2.81 −0.002 −3.37 QTR4t −0.001 −2.16 −0.002 −4.00
ABACCit−1 0.010 1.27 ABACCit−1 0.006 0.99
ABACCit−2 0.094 21.80 ABACCit−2 0.105 31.51
ABACCit−3 0.048 9.08 ABACCit−3 0.044 7.49
ABACCit−4 0.148 22.45 ABACCit−4 0.161 25.83
INTERCEPT −0.004 −1.18 −0.003 −1.18 INTERCEPT −0.008 −2.68 −0.006 −2.25
No. of obs. 148,990 148,990 No. of obs. 170,114 170,114
Adj.R2% 7.08 10.78 Adj.R2% 7.34 11.55
Note:
This table reports the estimates from the following regression of firm-level abnormal accruals (ABACCit) in quarterton lagged MICHINDEXt−1and lagged firm
characteristics:
ABACCit=λ0+λ1MICHINDEXt−1+λ2MBit−1+λ3ROAit−1+λ4SIZEit−1+λ5LEVit−1+λ6ROAit+1 4
j=1
λ7jABACCit−j+λ8Qtr4t+ut
The variable definitions are provided in Table 1. Thet-statistics are estimated with clustering by firm and year. The coefficient on MICHINDEXitis multiplied by 100 for expositional convenience. Panel A reports results for the Jones model, Panel B reports results for linear models employing CFO, and Panel C reports results for non-linear models.
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MICHINDEX in regression of abnormal accruals from the CFO model implies that an increase in investor sentiment from the first to the third quartile leads to an increase in abnormal accruals in the following quarter by 0.2% of total assets. This increase, which approximates to an increase in annual earnings of the average firm by about 1% of total assets, is economically significant and at the same time plausible.
Overall, the t-statistics on MICHINDEX are significantly positive across all five regressions in Panels A to C. In the remainder of the paper, I present results based only on the Dechow et al. (1998) model for accruals, which additionally controls for firm characteristics (the modified CFO model), as the results based on the other models are qualitatively similar to those reported.
(iii) Alternative Proxies for Investor Sentiment
To examine the robustness of the results to the definition of investor sentiment, I consider alternative proxies for investor sentiment discussed in the literature (e.g., Lee et al., 1991; Baker and Wurgler, 2006; Glushkov, 2006; Qiu and Welch, 2006). The alternative proxies for investor sentiment are: (1) NYSE share turnover (NYSETURN), as used by Baker and Wurgler (2006) as a proxy for market liquidity, where greater liquidity is a sign of activity by irrational traders; (2) the de-trended level of margin borrowing (MARGIN DEBT), which is related to trading activity, and is often cited as a bullish sign as it represents the changes in relative demand of investors for additional investment funds; (3) bull–bear spread, measured as the difference between percentage of bulls and percentage of bears (BMB II); and (4) the percentage of bulls from Investors Intelligence Survey (BULLS). Following Brown and Cliff (2005), I use the difference between percentage of bullish and bearish letters (“bull–bear spread”) as a forward-looking sentiment indicator. Since many of the writers of these newsletters are current or past market professionals, this difference can be considered a proxy for institutional investors’ sentiment, and represents a direct measure of sentiment.
For each calendar quarter, the proxies for investor sentiment are computed by averaging the monthly investor sentiment proxies.10 To examine the relationship
between abnormal accruals and the alternative proxies for investor sentiment, I re-estimate equation (6) after replacing MICHINDEX with alternative proxies for investor sentiment. The results reported in Table 3 suggest that abnormal accruals are positively and significantly related to three out of the four alternative proxies for investor sentiment: NYSE turnover (t-statistic on NYSETURN= 3.47 in column 2), MARGIN DEBT (t-statistic on MARGIN DEBT=2.90 in column 4) and BMB II (t-statistic on BMB II=1.77 in column 3). The association between abnormal accruals and BULLS appears insignificant, with the t-statistic on BULLS being 1.46. In the remainder of the paper I use the Michigan index as a proxy for investor sentiment, for the purpose of consistency with other accounting papers (e.g., Bergman and Roychowdhury, 2008).
(iv) Macroeconomic Factors
One simple explanation for the relationship between abnormal accruals and in-vestor sentiment I document above is that inin-vestor sentiment merely captures
10 I thank Denys Glushkov for providing these data.
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Table 3
Abnormal Accruals and Alternative Investor Sentiment Proxies
Investor Sentiment Investor Sentiment Investor Sentiment Investor Sentiment Proxy: NYSETURNt Proxy: MARGIN DEBTt Proxy: BMB IIt Proxy: BULLSt
Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
(1) (2) (3) (4) (5) (6) (7) (8)
Investor Sentiment Proxyt−1 0.178 3.47 0.000 2.90 0.003 1.77 0.004 1.46
MBit−1 0.000 0.48 0.000 0.57 0.000 0.50 0.000 0.49
ROAit−1 0.047 3.63 0.046 3.78 0.047 3.65 0.046 3.67
SIZEit−1 −0.000 −1.54 −0.000 −1.64 −0.000 −1.43 −0.000 −1.44
LEVit−1 −0.000 −1.43 −0.000 −1.45 −0.000 −1.43 −0.000 −1.43
ROAit+1 0.036 1.58 0.036 1.59 0.035 1.54 0.035 1.54
QTR4t −0.001 −1.93 −0.001 −1.85 −0.001 −2.18 −0.001 −2.31
ABACCit−1 −0.027 −1.22 −0.027 −1.23 −0.027 −1.24 −0.027 −1.24
ABACCit−2 0.003 0.73 0.003 0.73 0.004 0.77 0.004 0.77
ABACCit−3 0.001 0.50 0.001 0.53 0.000 0.21 0.000 0.20
ABACCit−4 0.003 0.98 0.003 0.99 0.003 0.99 0.003 0.99
CONSTANT 0.008 2.97 0.002 1.41 0.002 1.81 0.001 0.46
No. of obs. 157,372 159,827 154,884 154,884
Adj. R2% 0.30 0.30 0.30 0.30
Note:
This table presents estimates from regressions of firm-level abnormal accruals in quartert(ABACCit) from the modified CFO model on lagged investor sentiment proxies and lagged firm characteristics. The sentiment proxies are the quarterly averages of monthly investment sentiment indicators. The investor sentiment proxies considered are NYSETURNt, log of the aggregate NYSE turnover; MARGIN DEBTt, the level of margin borrowing; BMB IIi, bull–bear spread measured as the difference between percentage of bulls and percentage of bears; and BULLSt, the percentage of bulls from Investors Intelligence Survey. Thet-statistics are estimated with clustering by firm and year. The sample period covers every quarter in the period 1976 to 2005. The variable definitions are provided in Table 1. All coefficients on the investor sentiment proxies are multiplied by 100 for expositional convenience.
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Table 4
Abnormal Accruals and Investor Sentiment with Macroeconomic Control Variables
Coeff. t-value
(1) (2)
MICHINDEXt−1 0.006 2.22
MBit−1 0.000 0.55
ROAit−1 0.045 3.83
SIZEit−1 −0.000 −1.64
LEVit−1 −0.000 −1.47
ROAit+1 0.036 1.62
QTR4t−1 −0.002 −2.20
INFt−1 −0.005 −2.34
IPGt−1 −0.001 −1.00
RGDPGt−1 0.002 1.32
ABACCt−1 −0.027 −1.24
ABACCt−2 0.003 0.73
ABACCt−3 0.001 0.56
ABACCt−4 0.003 0.99
CONSTANT −0.002 −0.93
No. of obs. 167,176
Adj. R2% 0.31
Note:
This table presents estimates from regression of firm-level abnormal accruals in quartert(ABACCit) from
the modified CFO model on lagged MICHINDEXt−1and lagged firm characteristics after controlling for
macroeconomic activities: growth in industrial production in quartert−1 (IPGt−1); inflation measured as
the growth in seasonally adjusted consumer price index in quartert−1 (INFt−1); and growth in real gross
domestic product in quartert–1 (RGDPGt−1). Thet-statistics are estimated with clustering by firm and year.
The sample period covers every quarter in the period 1976 to 2005. The rest of the variable definitions are provided in Table 1. The coefficient on MICHINDEXt−1is multiplied by 100 for expositional convenience.
macroeconomic factors, such as inflation or anticipated macroeconomic growth, with which market-wide sentiment and perhaps abnormal accruals are correlated. Following the approach in Lemmon and Portniaguina (2006), I investigate both the aggregate consumer expectations measure embodied in the Michigan Consumer Sentiment Index and its two components: the portion that is related to the underlying economic factors, and the portion unrelated to the underlying economic factors. That is, I re-estimate equation (6) after controlling for the following lagged quarterly macroeconomic activities in the regression to proxy for underlying economic factors that may be correlated with investor sentiment: (i) inflation; (ii) industrial production growth; and (iii) growth in real GDP.11
The results, reported in Table 4, indicate that industrial production growth and growth in real GDP are not significantly related to future abnormal accruals. Only inflation exhibits a negative and significant association with future abnormal accruals. However, the coefficient on MICHINDEX remains positive (coeff.= 0.006;t-stat=
2.22), and similar in magnitude to those documented in Panel B of Table 2. Similar
11 The data for all macroeconomic activities are drawn from the FRED database provided by Federal Reserve Bank of St. Louis. In addition to these macroeconomic variables, I also consider year-ahead increase in average ROA across firms, quarterly growth in real consumption and quarterly growth in nominal GDP, with no significant change in inferences. A combined reading of these patterns suggests that the time-based variation observed in the proxies is unlikely to be driven by systematic macroeconomic forces.
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Table 5
Positive and Negative Abnormal Accruals and Investor Sentiment
Coeff. z-value Coeff. z-value
POS ABACCit (1) (2) NEG ABACCit (1) (2)
MICHINDEXt−1 0.018 1.82 MICHINDEXt−1(×100) −0.010 −1.86
MBit−1 0.000 1.81 MBit−1 −0.000 −0.45
ROAit−1 0.059 1.06 ROAit−1 −0.089 −12.16
SIZEit−1 −0.001 −2.62 SIZEit−1 −0.001 −3.52
LEVit−1 0.000 1.00 LEVit−1 0.000 0.26
ROAit+1 −0.001 −0.22 ROAit+1 0.015 5.53
QTR4t−1 −0.003 −1.58 QTR4t−1 0.000 0.09
INFt−1 0.020 1.96 INFt−1 −0.007 −2.72
IPGt−1 −0.002 −0.79 IPGt−1 0.001 1.10
RGDPGt−1 −0.011 −2.49 RGDPGt−1 −0.000 −0.10
POS ABACCt−1 0.010 1.81 NEG ABACCt−1 0.004 0.40
POS ABACCt−2 0.009 1.17 NEG ABACCt−2 0.066 2.76
POS ABACCt−3 0.010 1.91 NEG ABACCt−3 0.032 2.27
POS ABACCt−4 0.009 1.28 NEG ABACCt−4 0.050 13.09
CONSTANT −0.058 −1.86 CONSTANT −0.003 −0.67
ln sigma −2.085 −4.78 ln sigma −3.176 −25.03
No. of obs. 170,174 No. of obs. 170,174
Note:
This table presents estimates from a tobit regression of POS ABACCit (NEG ABACCit) on lagged
MICHINDEXt−1and lagged firm characteristics after controlling for macroeconomic activities: growth in
industrial production in quartert−1 (IPGt−1); inflation measured as the growth in seasonally adjusted
consumer price index in quartert−1 (INFt−1); and growth in real gross domestic product in quartert–
1 (RGDPGt−1). POS ABACCit(NEG ABACCit) takes the value of abnormal accruals (ABACCit) from the
modified CFO model when they are positive (negative), and 0 otherwise. NEG ABACCithas been multiplied
by−1 for ease of interpretation of the results. Thet-statistics are estimated with clustering by firm and year. The sample period covers every quarter in the period 1976 to 2005.The rest of the variable definitions are provided in Table 1. The coefficient on MICHINDEXt−1is multiplied by 100 for expositional convenience.
inferences obtain when I include contemporaneous macroeconomic activities instead of their lagged values. In sum, these results suggest that the relationship between abnormal accruals and lagged investor sentiment is not attributable to omitted growth or other fundamental macroeconomic factors.
(v) Positive versus Negative Abnormal Accruals and Investor Sentiment
The above documented positive association between abnormal accruals and investor sentiment supports the hypothesis that managers respond to relatively high investor sentiment by taking income-increasing accruals, and report more conservatively in periods of relatively low investor sentiment. In order to investigate further whether in periods of low investor sentiment, managers are less likely to boost earnings by not overstating earnings or by reporting conservatively via downward earnings management, I examine the association between investor sentiment and income-increasing abnormal accruals versus income-decreasing abnormal accruals.
Table 5 presents the results of estimating regressions to test the association of investor sentiment with positive abnormal accruals, and separately its association with negative abnormal accruals. The dependent variable in each tobit regression, POS ABACC (NEG ABACC) is equal to positive (negative) abnormal accruals; and 0
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otherwise. Negative abnormal accruals are multiplied by−1 for ease of interpretation of the regression results. The results confirm the hypothesis that the likelihood of managing earnings upwards increases with the level of investor sentiment, and the likelihood of taking negative abnormal accruals decreases with investor sentiment. The coefficient on MICHINDEX remains significant in both specifications, although with a lower t-statistic than in the regressions with both positive and negative abnormal accruals, most likely due to lower estimation power.
(vi) Abnormal Accruals and Propensity to Exceed Analyst Forecasts
The findings so far show that managers’ use of income-increasing accruals is positively related to investor sentiment. Several recent papers document that firms’ attempts at managing earnings are driven by incentives to meet or beat earnings benchmarks, especially the analyst consensus immediately before the earnings announcement (e.g., Burgstahler and Dichev, 1997; Skinner and Sloan, 2002; Brown and Caylor, 2005; Graham et al., 2005; Degeorge et al., 2007; Mande and Son, 2011). Evidence also exists that investors’ reactions to negative earnings surprises become stronger with higher sentiment (Seybert and Yang, 2012). This would suggest that managers may have stronger incentives to avoid negative earnings surprises in periods of high investor sentiment. In order to test whether managers interfere with the financial reporting process for the purpose of avoiding negative earnings surprises, I simultaneously examine whether managers boost accruals, and whether they meet or beat analysts’ forecasts in periods of higher investor sentiment.12,13I adopt a similar approach to the
one employed by Brown and Pinello (2007) where I model the probabilities of positive abnormal accruals conditional on firms’ meeting or beating analysts’ forecasts.
This test will help in examining whether the earlier documented relationship be-tween abnormal accruals and investor sentiment arises simply because both managers and analysts are optimistic or pessimistic about the future. If the earlier results are driven by such a commonality in bias between managers and analysts, then there is little reason to expect a positive relationship when one focuses on the propensity to meet or beat analysts’ forecasts, as analysts’ forecasts and managers’ accrual estimates will be both upwardly or downwardly biased. Hence I would not expect the propensity of managers to meet or beat analysts’ forecasts by taking positive abnormal accruals to co-vary with investor sentiment. On the other hand, if earnings management is at least partly in response to analyst optimism, I would expect to observe the likelihood of meeting or beating analysts’ forecasts via positive abnormal accruals to increase in periods of high sentiment.
I replace the dependent variable in equation (6), ABACC, with an indicator variable, POS DA, equal to 1 if abnormal accruals are positive, and 0 otherwise. In estimating the logit regressions I condition the probability of POS DA=1 on firms’ having met or exceeded analysts’ expectations (MEETBEAT=1). MEETBEAT is an indicator variable that is set to 1 if the firm meets or exceeds its latest analyst consensus
12 I recognize that managers potentially rely on both earnings management (through real activities or accruals) and expectations management to meet or beat analyst forecasts (Athanasakou et al., 2011). Ascertaining whether managers rely on either earnings or expectations management in this context falls beyond the scope of this robustness test.
13 I use only analyst forecasts as a benchmark to measure earnings surprises, in line with the evidence by Herrmann et al. (2011) of insignificant incremental threshold effects beyond meeting analyst forecasts.
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Table 6
Probabilities of Positive Abnormal Accruals, Conditional on Meeting or Beating (Not Meeting or Beating) Analysts’ Forecasts and Investor Sentiment
Coeff. p-value Coeff. p-value
POS DAit (1) (2) (3) (4)
MICHINDEXt−1 0.398 0.00 0.380 0.00
MBit−1 0.005 0.00 0.005 0.00
ROAit−1 4.549 0.00 3.621 0.00
SIZEit−1 −0.035 0.00 −0.034 0.00
LEVit−1 −0.007 0.00 −0.007 0.01
ROAit+1 −1.199 0.00 −0.864 0.00
QTR4t−1 −0.081 0.00 −0.083 0.00
INFt−1 0.415 0.00 0.406 0.00
IPGt−1 −0.051 0.00 −0.042 0.01
RGDPGt−1 −0.102 0.02 −0.110 0.01
POS DAit−1 0.166 0.00
POS DAit−2 0.204 0.00
POS DAit−3 0.107 0.00
POS DAit−4 0.285 0.00
CONSTANT −0.010 0.92 −0.366 0.00
Chi-square 612.26 0.00 1,342.54 0.00
No. of obs. 58,967 58,967
POS DAit=1 31,358 31,358
POS DAit=0 27,609 27,609
Notes:
This table reports results from the following logit regression:
Prob (POS DAit=1|MEETBEATit=1)=β0+β1MICHINDEXt−1+β2MBit−1+β3ROAit−1
+β4SIZEit−1+β5LEVit−1+β6ROAit−1+β7QTR4it−1+β8j 4
j=1
POS DAit−j
+β9INFt−1+β10IPGti1+β11RGDPGti1+ςt
The logit regression is estimated for a sample of firm-quarters with non-negative earnings surprises (MEETBEATit =1), where the probabilities of positive discretionary accruals (POS DAit) are regressed
on lagged MICHINDEXt−1and lagged firm characteristics after controlling for macroeconomic activities:
growth in industrial production in quartert−1 (IPGt−1); inflation measured as the growth in seasonally
adjusted consumer price index in quartert−1 (INFt−1); and growth in real gross domestic product in
quartert(RGDPGt−1). POS DAitis an indicator equal to 1 if abnormal accruals (ABACCit) are positive, and
0 otherwise. MEETBEATitis an indicator variable equal to 1 for firms meeting or beating the latest analyst
consensus earnings forecast, and 0 otherwise. Thet-statistics are estimated with clustering by firm and year. The sample period covers every quarter in the period 1984 to 2005. The rest of the variable definitions are provided in Table 1.
earnings forecast.14 I also control for macroeconomic variables for the purpose of
completeness.
The results from a logit regression of Prob(POS DA| MEETBEAT=1), reported in Table 6, suggest that managers’ propensity to boost accruals in order to meet or exceed analyst forecasts is more pronounced when the Michigan Consumer Sentiment Index is higher. The coefficient on MICHINDEX is 0.398 in column 1 with a
p-value < 0.0001 and 0.380 with a p-value < 0.0001 in column 2, which includes
14 I measure MEETBEAT based on the latest analyst consensus forecast, in line with the evidence by Eames and Kim (2012) that analysts’ forecasts issued earlier in the year are not a good proxy for market expectations.
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four lags of POS DA.15 To provide some insight into the economic significance of
these coefficients I calculate the marginal effect of MICHINDEX on the dependent variable, with all the independent variables in the model set at their mean values (untabulated). The marginal effect implies that, as the Michigan Consumer Sentiment Index increases from the first quartile to the third quartile, the probability that a firm will report positive abnormal accruals to meet or beat analysts’ forecast increases by 9.9% (9.4% when POS DA lags are included). This finding provides some reassurance that the main results are not due to potential correlated omitted variables discussed above. Furthermore, this result shows that managers’ incentive to report positive abnormal accruals to avoid negative earnings surprises increases with sentiment.
(vii) Discretionary Revenues and Investor Sentiment
Several studies have raised concerns over the use of abnormal accruals to proxy for earnings management (e.g., Dechow et al., 1995). Stubben (2010) shows that traditional accrual models are more biased, less well specified and less powerful in detecting earnings management than discretionary revenue models. Hence I check the robustness of the empirical analysis by examining an alternative measure of earnings management, namely discretionary revenues. I replace the abnormal earnings measure in equation (6), ABACC, with a measure of discretionary revenues, DISC REV. Discretionary revenues are measured by subtracting “normal” change in accounts receivable from total change in accounts receivable (AR). The “normal” change in accounts receivable is estimated from each of the following quarterly models, proposed by Stubben (2006):
Quarterly revenue (QTR REV) model:
E(tARit)=α+βtREVit. (7)
Modified quarterly revenue (MOD QTR REV) model:
E(tARit)=a+b(tREVit−tARit), (8)
whereaandbare estimated from:tARit =α+βtREVit+εit.
Lagged revenue (LAG REV) model:
E(tARit)=α+β1tREVit+β2tREVit−1+β3iREVit−2+β4tREVit−3. (9)
Conditional quarterly revenue (COND QTR REV) model:
E(tARit)=α+β1tREVit+β2tREVit∗SIZE TAit+β3tREVit∗AGEit
+β4tREVit∗AGE SQit+β5tREVit∗GRR Pit+β6tREVit∗GRR Nit
+β7tREVit∗GRMit+β8tREVit∗GRM SQit (10)
The conditional quarterly model controls for firm characteristics related to per-formance and growth (Stubben, 2006). These include: logarithm of total assets
15 Qualitatively identical results are obtained when the MEETBEAT regressions are estimated using an ordinary least squares approach rather than logit.
C
