On the persistence and pricing of industry-wide and firm-specific earnings, cash flows, and accruals

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On the persistence and pricing of industrywide and firm-specific earnings, cash flows, and accruals Kai Wai Hui, Karen K. Nelson, P. Eric Yeung

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S0165-4101(15)00047-6 http://dx.doi.org/10.1016/j.jacceco.2015.06.003 JAE1065

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Journal of Accounting and Economics

Received date: 23 May 2013 Revised date: 17 June 2015 Accepted date: 26 June 2015 Cite this article as: Kai Wai Hui, Karen K. Nelson, P. Eric Yeung, On the persistence and pricing of industry-wide and firm-specific earnings, cash flows, and accruals, Journal of Accounting and Economics, http://dx.doi.org/10.1016/ j.jacceco.2015.06.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

On the persistence and pricing of industry-wide and firm-specific earnings, cash flows, and accruals 1. Introduction This paper examines the relation between industry-wide and firm-specific information contained in earnings and the extent to which this information is reflected in stock prices. Economic theory suggests that firm performance determined by industry fundamentals (e.g., consumer taste, production technology, and regulatory environment) is relatively longlasting. On the other hand, performance that deviates from industry norms tends to dissipate more quickly because learning and imitation improve industry losers’ performance but erode industry winners’ competitive edge (e.g., Mueller, 1977, 1986, 1990; Waring, 1996). To the extent that accounting earnings are a (noisy) measure of economic profits, we expect the industry-wide component of earnings to be more persistent than the firm-specific component. Prior research suggests that investors tend to fixate on reported earnings, however, not fully recognizing differences in the persistence of its components (e.g., Sloan 1996). As a result, we expect the market to underreact to the higher persistence of the industry-wide component of earnings. Predicting greater persistence of the industry-wide component of earnings inherently assumes industry homogeneity and stationarity. Firms in homogeneous industries face relatively similar economic forces, and thus industry-wide earnings should be more persistent than in heterogeneous industries. Earnings components should also be more persistent in stable industries than in those disrupted by business shocks. The effect of business shocks, however, should be smaller on industry-wide earnings than on firmspecific earnings if, as expected, industry-wide earnings are more stationary in general. Investors that underestimate the persistence of industry-wide earnings are thus more

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likely to do so for homogeneous industries and in the presence of a large industry-wide business shock. Our focus on economic fundamentals represents a departure from Sloan’s (1996) work on accruals and cash flows, which are components of earnings defined by accounting systems (Dechow, 1994; Subramanyan, 1996). He shows that even though the accrual component is less persistent than the cash flow component, stock prices act as if investors fail to fully appreciate this difference. Thus, we also gauge the differential persistence and pricing of earnings components classified by both economic forces and accounting constructs. We decompose industry-wide and firm-specific earnings into their respective cash flow and accrual components. Considering both economic fundamentals and accounting constructs suggests that industry-wide cash flows is the most persistent component of earnings, while at the other extreme firm-specific accruals is the least persistent. The relative persistence of the other two components – industry-wide accruals and firm-specific cash flows – is less clear. On the one hand, there is robust prior evidence that cash flows are more persistent than accruals. On the other hand, economic theory suggests that industry-wide performance is more persistent than firm-specific performance. We test our empirical predictions over the period 1988-2011. Following Bhojraj et al. (2003), we use Global Industry Classification Standard (GICS) industry codes and define industry-wide earnings as average earnings (scaled by assets) of all firms in the same eightdigit GICS industry. We define firm-specific earnings as the difference between a firm’s reported earnings (scaled by assets) and the industry-wide earnings. Consistent with predictions, we find that industry-wide earnings are significantly more persistent than firm-specific earnings. However, investors fail to fully distinguish this differential persistence as stock prices place similar weights on these two earnings components in forecasting one-year-ahead earnings. In other words, the market 2

underreacts to the persistence of industry-wide earnings and overreacts to the persistence of firm-specific earnings. To further test the implications of industry fundamentals on the persistence and pricing of earnings components, we partition the sample using three alternative proxies for industry homogeneity, i.e., within-industry similarity in size, operating activities, or the number of business segments. As expected, industry-wide earnings are significantly more persistent in homogenous industries compared to heterogeneous industries. In contrast, the persistence of firm-specific earnings does not vary with industry homogeneity. Further, the market fails to understand the consequences of industry homogeneity for the relative persistence of industry-wide earnings. In fact, the underreaction to industry-wide earnings is significant only in homogeneous industries. In a similar vein, we find that the market fails to understand the consequences of business model shocks on the persistence of earnings components. We identify large industry-wide business shocks following Guay et al. (2014) and firm-specific business shocks following Owens et al. (2014). Although these business shocks reduce the persistence of industry-wide and firm-specific earnings, respectively, the effect is significantly lower for industry-wide earnings, consistent with this being the more persistent earnings component in general. The market, however, appears to overestimate the effect of the business shock on the persistence of industry-wide earnings, and thus overreacts to its effect on industrywide earnings. Finally, consistent with predictions, we find that industry-wide cash flows is the most persistent component of earnings while firm-specific accruals is the least persistent. Thus, the higher (lower) persistence of cash flows (accruals) documented in prior research is attributable primarily to the industry-wide (firm-specific) component and it is these specific components that we expect to drive mispricing. The results confirm that stock prices 3

significantly underweight the persistence of industry-wide cash flows and significantly overweight the persistence of firm-specific accruals. Our results are robust to several alternative industry classifications, including Standard Industry Classification (SIC) and North America Industry Classification System (NAICS) codes, and industry-size portfolios. Importantly, our results do not replicate in a pseudo-industry classification, suggesting that our findings cannot be explained as a mechanical artifact attributable to the aggregation of data across firms. Our results are also robust to defining industry-wide earnings as the sales- or market-value-weighted average of firms in the industry. Additional tests show that industry-wide earnings are a significant predictor of future stock returns, consistent with investor underreaction. The spread between the annualized returns of the highest and lowest deciles of industry-wide earnings is 9.5% (4.9%) in the first (second) year after portfolio formation, both statistically and economically significant.1 Moreover, the spread is negative in only one year of our twenty four-year sample period, indicating that the results are stable over time. Findings from regression analyses show that these results are robust to controlling for a variety of other factors that may be correlated with industry-wide earnings and future abnormal stock returns. This paper contributes to the literature on earnings persistence and its role in equity valuation. Over the past two decades there has been a substantial amount of research focusing on the persistence of total earnings and its accounting components (i.e., cash flows versus accruals), leading Dechow et al. (2010) to call for more extensive study on how economic fundamentals as well as their interaction with accounting rules affect earnings 1 These results do not necessarily imply that a profitable trading strategy exists. Although the mean (median) share price in the lowest decile is $24.83 ($15.79), compared to $31.32 ($38.88) in the highest decile, market frictions besides illiquidity could prevent taking a short position in the stocks in the lowest decile. Moreover, it is not clear whether firms in the extreme deciles represent a perfect hedge, and thus we draw no conclusions regarding the risk-adjusted profitability of a trading strategy.

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persistence. This paper provides an initial step in this important direction. Our findings also have implications for the literature on the determinants of earnings quality more specifically. Prior research in this area focuses almost exclusively on firm-level determinants of earnings quality. More recently, however, accounting academics have recognized the importance of studying the role of economic fundamentals in the earnings generating process and its effects on earnings quality (e.g., Owens et al., 2014). Thus, our findings more generally illustrate the importance of considering economic fundamentals in modeling earnings quality. We focus our attention on industry fundamentals as a key driver of earnings persistence. Financial statement analysis texts typically emphasize the importance of industry analysis in assessing firm performance (e.g., Wahlen et al., 2015), and survey data indicates that financial professionals view industry fundamentals as one of the most important factors affecting the sustainability of earnings at their company (Dichev et al., 2013). However, as yet there is limited empirical evidence on the role of industry fundamentals, and innate factors in general, on the persistence of reported earnings. Early work by Brown and Ball (1967) and Magee (1974) find that a significant portion of the variability of a firm’s earnings can be explained by industry-level news. Lev (1983) documents a significant association between earnings persistence and economic factors including product type and industry competition. We provide new evidence documenting the differential persistence of industry-wide and firm-specific earnings, and how these persistence parameters vary predictably with several economic fundamentals and interact with the accounting constructs of accruals and cash flows. Finally, we contribute to the literature examining market participants’ use of industryrelated earnings information in evaluating performance and valuing the firm. Prior work examines how quickly industry-wide earnings information is impounded in stock prices, 5

with mixed results (e.g., Ayers et al. 1997; Elgers et al. 2008). Other research shows that analysts increase the relative amount of industry-wide earnings news incorporated into stock prices (Piotroski and Roulstone 2004), even though investors’ underreact to the industry-wide earnings news contained in analysts’ industry reports (Hui and Yeung, 2013). Extending our analysis of earnings persistence, we contribute to this research by showing that investors not only underreact to the higher persistence of industry-wide earnings in general but also overreact to the lower persistence of firm-specific earnings. Moreover, these effects are exacerbated by other economic fundamentals (e.g., industry homogeneity, business model shocks) that investors also fail to fully understand. The remainder of our paper proceeds as follows. Section 2 outlines our sample and the measurement of our test variables. Section 3 reports our primary tests regarding industrywide and firm-specific earnings. Section 4 extend this analysis to examine the cash flow and accrual components of industry-wide and firm-specific earnings, while section 5 reports the results of several additional analyses. Section 6 concludes.

2. Data and variable measurement 2.1. Sample Our sample selection strategy proceeds in two steps. First, we impose only those data requirements necessary to calculate industry-wide earnings (as well as industry-wide accruals and cash flows) to ensure that we have the most representative measures of these variables for our analyses. Thus, our sample selection starts with the 127,178 firms-years in the Compustat universe that have common shares listed on one of the three major U.S. stock exchanges (NYSE, AMEX, or NASDAQ) and a GICS code during the period 1988-

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2011.2 From this initial sample, we exclude financial institutions (two-digit GICS code = 40) because the nature of accruals for financial institutions differs from that for industrial firms, leaving 101,688 observations. Next, we require each eight-digit GICS industry to have at least two firms with Compustat data on earnings and cash flows from the Statement of Cash Flows. We use the remaining 81,415 observations to calculate the industry-wide measures of earnings, cash flows, and accruals defined in Section 2.2 below. In the second step of our sample selection strategy we obtain the final sample of observations for our empirical tests by merging the preliminary sample with the CRSP stock files, leaving 71,843 observations. We then drop observations without the requisite data to calculate the measures of business model shocks, reducing our sample to 68,368 firm-years. Lastly, to implement the Mishkin tests for market rationality, we further require one-year-ahead earnings, accruals, and stock returns data, leaving 60,259 firm-year observations in our final sample.3 Panel A of Table 1 shows the average number of firms in our final sample for each fiveyear window in our sample period (except for the last window which is only four years). There are fewer firms, on average, in the first window of the sample period, consistent with the general trend observed in Compustat. Panel B shows the number of firms across NYSE size deciles. As expected, the sample leans slightly toward small firms (e.g., Fama and French 2008). Panel C shows the number and frequency of sample firm-years in each major industry sector defined by the two-digit GICS code, as well as the frequency of all firms in the Compustat-CRSP population. This comparison shows that the industry composition of our sample is similar to that of the Compustat-CRSP population. The largest sectors in the 2 We focus on the analyst-based GICS industry classification because it provides a better grouping of firms for capital markets-based research (Ramnath, 2002; Bhojraj et al., 2003; Hui and Yeung, 2013). As discussed in section 5.3, our results are robust using two alternative industry classification systems, the four-digit SIC and the six-digit NAICS. 3 Our results are not sensitive to the exclusion of firms with stock prices below $5 or $1.

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sample are information technology (20.00%) industrials (19.94%), consumer discretionary (19.27%), and health care (13.24%). 2.2. Computation of main variables and descriptive statistics Our

tests

require

partitioning

earnings

into

industry-wide

and

firm-specific

components. Following prior studies (e.g., Brown and Ball, 1967; Ayers and Freeman, 1997; Hui and Yeung, 2013), industry-wide earnings represent the common component of earnings for all firms in the same industry, while firm-specific earnings are the deviations of individual firms’ earnings from the industry average.4 Specifically, let Earni,j,t denote the earnings of firm i in industry j for year t, measured as operating income after depreciation scaled by average assets (Sloan, 1996). 5 Assuming there are N firms in industry j, the industry-wide earnings of industry j for year t is defined as: N

IndE j , t = 1 / N ∑ i =1 Earni , j , t

(1)

and firm-specific earnings of firm i in industry j for year t is defined as

FirmE i , j , t = Earni , j , t − IndE j ,t

(2)

Following the same approach, we also decompose industry-wide earnings (IndEt) into industry-wide operating cash flows (IndCFt) and industry-wide accruals (IndAcct): IndEt = IndCFt + IndAcct

(3)

where IndCFt is the average of operating cash flows scaled by average assets (CFt) for all firms in the same industry, and IndAcct is the difference between IndEt and IndCFt. Similarly, we decompose firm-specific earnings (FirmEt) into firm-specific operating cash flows (FirmCFt) and firm-specific accruals (FirmAcct): 4 Our measure of industry-wide earnings thus contains the impact of market-wide forces on each industry. Untabulated results are similar when we subtract average earnings of all firms in a given year from IndE, our measure of industry-wide earnings. 5 We pull our data from Compustat using the DATAFMT = STD flag to ensure that we are using the original “as reported” and unrestated data. Using “as reported” data is appropriate in our context because it gives us the set of financial information available for investors during the event year.

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FirmEt = FirmCFt + FirmAcct

(4)

where FirmCFt is the difference between CFt and IndCFt, and FirmAcct is the difference between FirmEt and FirmCFt. Table 2 provides descriptive statistics for the key variables of interest. The median of Earn is 0.044, indicating that return on assets for the typical firm in our sample is approximately 4.4%. The standard deviation of FirmE is almost twice that of IndE, consistent with our expectation that firm-specific profitability is more volatile than industry-wide profitability. Turning to the accounting components of earnings, cash flows are positive (0.073), on average, and accruals are negative (-0.058), consistent with prior work. The statistics also indicate that the variation in cash flows and accruals is mainly driven by their firm-specific components. The standard deviations of FirmCF and FirmAcc are approximately twice as large as the standard deviations of IndCF and IndAcc, respectively.

3. Analysis of industry-wide and firm-specific earnings 3.1. Univariate results of differential persistence The basic premise of our paper is that the industry-wide component of earnings is more persistent than the firm-specific component. To provide initial evidence on this issue, Figure 1A (1B) displays time-series plots of average earnings for the top and bottom deciles of IndEt (FirmEt) over a [t-3, t+3] window, where year zero is the year in which firms are ranked into the extreme deciles.6 Figure 1A shows that earnings performance is generally quite persistent for IndE, although there is some mean reversion for negative IndE. In contrast, Figure 1B shows a conspicuous pattern of mean reversion in earnings for both

6

Because we require year t+3 data, the sample period for this analysis is 1988-2009. In addition, firms are required to survive during the seven-year window. 9

positive and negative FirmE, consistent with our expectation that the firm-specific component of earnings dissipates more rapidly than the industry-wide component. More specifically, we find that earnings of firms in the bottom (top) decile of IndE are significantly more persistent than earnings of firms in the bottom (top) decile of FirmE (p < 0.01).7 3.2. Mispricing of industry-wide and firm-specific earnings components To test the joint proposition that the industry-wide component of earnings is more persistent than firm-specific earnings but investors fail to fully appreciate this difference, we follow prior research (e.g., Abarbanell and Bernard, 1992; Sloan, 1996) and use the Mishkin (1983) approach to estimate the implicit weights placed on these earnings components in prices.8 We first test whether the industry-wide component of earnings is more persistent than the firm-specific component by estimating the following ordinary least square (OLS) regression: Earnt+1 = a0 + a1 IndEt + a2 FirmEt + ε1t+1

(5a)

where Earnt+1 is reported earnings for year t+1, and IndEt and FirmEt are the industrywide and firm-specific components of earnings for year t, respectively. We expect that the association between industry-wide earnings and future earnings is greater than the association between the firm-specific earnings and future earnings (i.e., a1 > a2). To estimate the weights implicit in stock prices on the two earnings components in predicting one-year-ahead earnings, we use the following Mishkin non-linear generalized least square pricing regression model: CARt+1 = Multiple × (Earnt+1 – α0 – α1 IndEt – α2FirmEt) + ε2t, 7

(5b)

In this test, earnings persistence is defined as the coefficient for earnings in year t (scaled by average assets) in the regression of earnings in year t+1. 8 Note that inferences from Mishkin tests rely on comparing the magnitude of estimated coefficients which contain estimation errors as the regression specification is imperfect. We explicitly control for other variables that are correlated with returns in our analysis in section 5.5. 10

where CARt+1 is the (size-adjusted) abnormal return for year t+1 (i.e., starting the fourth month after the end of fiscal year t), and Multiple is the earnings response coefficient.9 If stock prices behave as if investors fail to fully appreciate the differential persistence of the earnings components, we expect α1 = α2 in model (5b). Further, comparing coefficient estimates across models (5a) and (5b), we expect a1 > α1 and a2 < α2. Before we discuss the results of these estimations, panel A of Table 3 shows baseline regressions that forecast one-year-ahead earnings with current total earnings in column (1) and the forecasting equation implicit in stock returns in column (2). We find in column (1) that the estimated coefficient on current earnings (Earnt) is 0.536. Consistent with the market on average correctly pricing the persistence of total earnings, we find in column (2) that the implied weight on Earnt is 0.534, which is statistically indistinguishable from the estimated persistence of earnings (p = 0.95). Turning to the main findings, column (1) of panel B presents the results of estimating equation (5a). The estimated coefficient on industry-wide earnings (IndE) is 0.760, significantly greater (p < 0.01) than the 0.487 estimated coefficient on firm-specific earnings (FirmE). Thus, as predicted, the evidence indicates that the industry-wide component of earnings is significantly more persistent than the firm-specific component.10 Column (2) of panel B shows the estimated weights on industry-wide and firm-specific earnings implicit in stock prices. The estimated coefficients on IndE (0.587) and FirmE (0.523) are not significantly different from each other (p = 0.60). Furthermore, the χ2 tests 9 The Mishkin test is conducted using non-linear least square estimation (i.e., the nlsur command in STATA). The cross-equation restrictions are tested using a Wald-based test with robust Wald statistics. We use the robust variance estimator that considers the clustered standard errors and is robust to simultaneous correlation along both firm and year dimensions (Konstantinidi et al. 2012). 10 An alternative approach to the levels specification in equation (5a) is to examine earnings reversals by regressing earnings changes at t+1 on lagged earnings changes. Consistent with the findings in Table 3, the results of these untabulated tests reveal that earnings shocks exhibit a significant reversal effect and that the change in firm-specific earnings reverses at a significantly faster rate than the change in industry-wide earnings. These findings provide additional support consistent with our prediction that the industry-wide component of earnings performance is more persistent than the firm-specific component.

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comparing estimated coefficients in columns (1) and (2) indicate that stock prices significantly underweight IndE (i.e., 0.587 < 0.760, p < 0.01) but overweight FirmE (i.e., 0.523 > 0.487, p = 0.04). 11 Taken together, these results support our prediction that investors fixate on reported earnings without recognizing the higher (lower) persistence of performance attributable to the industry-wide (firm-specific) component of earnings.12 3.3. Cross-sectional tests In this section, we report the results of two additional tests exploring potentially important sources of cross-sectional variation in the persistence of industry-wide and firmspecific earnings. Specifically, we consider whether industry homogeneity (section 3.3.1) or business model shocks (section 3.3.2) affect the persistence of earnings components and their pricing by investors. 3.3.1 Industry homogeneity When firms in an industry are relatively homogeneous (i.e., they transact in similar factor and product markets and employ similar production technologies), their performance is more likely to be influenced by the same set of underlying economic forces. As a result, industry-wide earnings should be more informative about firms’ future earnings than in industries where firms are relatively heterogeneous. If investors fail to distinguish the higher persistence of the industry-wide component of earnings, and if this effect is

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The adjusted R2 in the forecasting equation is substantially higher than in the return equation. This is to be expected given findings in prior literature showing the greater explanatory power of accounting information for forecasting future earnings vis-à-vis abnormal returns (e.g., Allen et al. 2013). 12 Inferences are similar when we conduct the Mishkin tests using rank regressions, where the dependent and independent variables are their respective within-sample ranks. Inferences are also similar when we conduct the Mishkin tests following Elgers et al.’s (2008) approach. Specifically, we first add additional control variables (size and book-to-market) to both forecasting and return equations. Second, in the return equation we remove the Multiple variable, and the weights on industry-wide (firm-specific) earnings are derived by taking the negative ratio of the coefficient for IndE (FirmE) to the coefficient for Earnt+1. Additionally, inferences are similar if we use the approach suggested by Konstantinidi et al. (2012), which allows for different multiples for earnings components in the first-stage regression (i.e., Equation 5a). 12

exacerbated by industry homogeneity, then we expect the results reported above to be stronger for more homogeneous industries.13 To examine the effects of industry homogeneity, in Table 4 we repeat our primary tests partitioning the sample using three alternative proxies for industry homogeneity. In panel A, an industry is regarded as homogeneous if the firms in the industry are of similar size (e.g., Albuquerque, 2009; Ecker et al. 2013). We define an industry as homogeneous (heterogeneous) if the standard deviation of market value of firms in the industry in a given year is below (above) the median standard deviation of all industries in our sample in that year. The results reveal three notable differences between homogeneous and heterogeneous industries. First, the forecasting equation has significantly greater explanatory power in homogeneous industries (adjusted R2 = 49.01%) compared to heterogeneous industries (adjusted R2 = 37.96%) (p < 0.01). Second, the estimated persistence of IndE in homogeneous industries (0.789) is significantly greater than in heterogeneous industries (0.664) (p = 0.03). In contrast, there is relatively little difference in the estimated persistence of FirmE across homogeneous (0.498) and heterogeneous industries (0.462) (p = 0.36). Taken together, these effects result in a larger differential persistence (p = 0.09) between industry-wide earnings and firm-specific earnings in homogeneous industries than in heterogeneous industries. Third, building on these results, the market underreaction to industry-wide earnings is significant only in homogeneous industries (p < 0.01). The results reported in panel A thus support our contention that industry homogeneity contributes not only to greater persistence of the industry-wide component of earnings, but also to greater mispricing of this component by investors.

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The classification of industries across all three proxies is relatively stable. Among the most homogeneous industries are Airport Services, Oil and Gas Drilling, and Homebuilding, while heterogeneous industries include IT Consulting and Services, Tobacco, and Semiconductors. 13

Our second proxy classifies an industry as homogeneous if the operating activities of firms in that industry have similar characteristics. In panel B of Table 4, an industry is homogeneous (heterogeneous) if the dispersion of firms’ operating activities in that industry in a given year is below (above) the median dispersion of all industries in our sample in that year. 14 Our third proxy for industry homogeneity is the dispersion in the number of business segments. In panel C of Table 4, an industry is homogeneous (heterogeneous) if the average number of business segments of firms in the industry in a given year is below (above) the median of all industries in our sample in that year. The results using either of these alternative proxies for industry homogeneity are consistent with those reported in panel A. Specifically, the earnings components have greater explanatory power for forecasting future earnings in homogenous industries, driven by the relatively high persistence of industry-wide earnings relative to firm-specific earnings for firms in these industries. Moreover, the market underreaction to industry-wide earnings is significant only in homogeneous industries. In sum, the results in Table 4 show that our main finding that industry-wide earnings are more persistent and investors fail to understand this property is stronger for homogeneous industries, as expected. 3.3.2 Business model shocks The persistence of earnings should be highly influenced by the economic environment in which firms operate. When there is a shock to that environment, earnings persistence is likely to diminish. Industry-wide business shocks, such as changing customer tastes, likely affect the persistence of industry-wide earnings. Firms in the same industry, however, may

14 Following Dopuch et al. (2012), dispersion of firms’ operating activities is defined as the average of the following four standard deviations in the industry: profit margin (net income scaled by sales), receivables turnover (average accounts receivable divided by sales), payable turnover (average accounts payable divided by sales, multiplied by one minus profit margin), and inventory turnover (g scaled by one minus profit margin, truncated above at 1 and below at -1 where g is the estimated coefficient for sales in the regression of inventory on sales and changes in sales). To ensure comparability, these four measures are converted to standardized values.

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react differently to business shocks or experience a firm-specific shock, such as a technological innovation, thus affecting the persistence of firm-specific earnings. We test whether industry-wide (firm-specific) shocks reduce the persistence of industrywide (firm-specific) earnings by estimating the following OLS regression: Earnt+1 = b0 + b1IndEt + b2FirmEt + b3IndEt × IndShockt + b4FirmEt × FirmShockt + b5IndShockt + b6 FirmShockt + ε1t ,

(6a)

where IndShockt (FirmShockt) is an indicator variable representing the presence of a large industry (firm) business shock in year t. We expect negative coefficient estimates for the interaction variables IndEt × IndShockt and FirmEt × FirmShockt (i.e., b3 < 0 and b4 < 0). We follow Guay et al.’s (2014) approach to measuring industry-wide shocks. Specifically, an industry is viewed as experiencing a large shock if the average absolute value of the percentage changes of the following variables in year t-1 is greater than the sample median: Industry Assets (sum of all firms’ assets in the industry), Industry Sales (sum of sales in the industry), US Sales (average U.S. sales-to-total sales ratio in the industry), Industry Growth (average market-to-book ratio in the industry), Industry Investment (sum of capital expenditure in the industry), Industry Research (sum of research and development expense in the industry), and Industry Advertising (sum of advertising expense in the industry). Similarly, we follow Owens et al.’s (2014) approach to identify large firm-specific shocks. A firm is classified as experiencing a large shock in year t if the average of the following five indicators is above the median value of all firms in that year: Merger (equals one if Compustat footnote data item “sale_fn” = "AB",

i.e., sales have been "restated

for/reflects a major merger or reorganization resulting in the formation of a new company”), Discontinued Operation (equals one if the magnitude of the income effect of discontinued operations is greater than five percent of sales), Industry Membership Change (equals one if

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firm’s eight digit GICS differs in years t-1 and t), Restructuring (equals one if the magnitude of restructuring charges is greater than five percent of sales), and Large Special Items (equals one if the magnitude of special items is greater than five percent of sales). Column (1) of Table 5 presents the results of estimating equation (6a). For firms not experiencing a large business shock, we continue to find that industry-wide earnings are more persistent than firm-specific earnings (i.e., 0.832 > 0.561, p < 0.01). As expected, the coefficient estimates on both interaction terms, IndEt × IndShockt and FirmEt × FirmShockt, are negative and significant, and indicate that industry-wide shocks have less of an effect on earnings persistence than firm-specific shocks (i.e., –0.113 < –0.200, p < 0.01). This result is consistent with our prediction that industry-wide earnings are more persistent in general, and thus shocks tend to have a smaller effect. To test whether investors fail to fully recognize the impact of business shocks on the persistence of earnings components, we estimate the following regression: CARt+1 = Multiple × (Earnt+1 – β0 – β1IndEt – β2FirmEt – β3IndEt × IndShockt – β4FirmEt × FirmShockt – β5IndShockt – β6FirmShockt) + ε2t

(6b)

If investors correctly assess the impact of business shocks on the persistence of earnings components, the coefficients on the interaction variables, IndEt × IndShockt and FirmEt × FirmShockt, will be negative (i.e., β3 < 0 and β4 < 0) and not significantly different from those estimated in the forecasting regression (6a) (i.e., β3 = b3 and β4 = b4). However, if investors underestimate the persistence of industry-wide earnings, as documented above, we expect them to assume that the industry business shock reduces persistence to a greater extent than it actually does, leading to an overreaction to the effect of the industry shock on industry-wide earnings (i.e., β3 is more negative than b3). Similarly, investors that over-

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estimate the persistence of firm-specific earnings will underreact to the negative impact of the firm shock on firm-specific earnings (i.e., β4 is less negative than b3). Column (2) of Table 5 reports the results of estimating regression (6b). Comparing the estimated regression coefficients across the forecasting equation (column 1) and return equation (column 2), shows that the market correctly weights the persistence of IndE and FirmE in the absence of a large business shock (i.e., when IndShock = 0 and FirmShock = 0). However, a large shock to the industry or firm significantly impairs investors’ ability to assess earnings persistence. Specifically, the market overreacts to the lower persistence of industry-wide earnings arising from an industry business shock (i.e., –0.306 < –0.113, p < 0.01) but underreacts to the lower persistence of firm-specific earnings arising from a firmspecific shock (–0.147 > –0.200, p = 0.07). Overall, the results in Table 5 provide supporting evidence that (i) industry-wide earnings are more persistent than firm-specific earnings, as the impact of a business shock on industry-wide earnings is smaller than that on firmspecific earnings, and (ii) the market does not appear to recognize the higher persistence of industry-wide earnings even in the presence of a large business shock.

4. Analysis of industry-wide and firm-specific cash flows and accruals Our assertion regarding the effects of industry fundamentals suggests that the persistence of industry-wide cash flows and accruals (IndCF and IndAcc) is greater than the persistence of firm-specific cash flows and accruals (FirmCF and FirmAcc). Extending the argument of Sloan (1996), however, suggests that the persistence of IndCF (FirmCF) is greater than the persistence of IndAcc (FirmAcc). Considering both industry fundamentals and accounting constructs therefore leads to the prediction that industry-wide cash flows (IndCF) is the most persistent component of earnings and firm-specific accruals (FirmAcc)

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is the least persistent. The relative persistence of the other two components – industry-wide accruals (IndAcc) and firm-specific cash flows (FirmCF) – is less obvious as the industry and accounting forces affecting persistence are not acting in concert. In this section, we present results analyzing the persistence and pricing of these four earnings components. We use the following Mishkin generalized least square regressions to test the persistence and pricing of the industry-wide and firm-specific cash flows and accruals: Earnt+1 = c0 +c1IndCFt +c2FirmCFt +c3IndAcct +c4FirmAcc +ε5t+1 Rett+1 = Multiple × (Earnt+1 –γ0 –γ1IndCFt –γ2FirmCFt –γ3IndAcct –γ4FirmAcc) +ε6t+1

(7a) (7b)

In regression model (7a), we expect that the persistence of industry-wide cash flows (i.e., c1) is highest while the persistence of firm-specific accruals (i.e., c4) is lowest. Moreover, in regression model (7b), we expect that the underreaction associated with cash flows documented by Sloan (1996) is mainly driven by industry-wide cash flows because the higher persistence of cash flows is attributable to the industry-wide component. If our prediction is correct, we expect c1 > γ1 (underreaction to industry-wide cash flows). Likewise, we predict that Sloan’s (1996) evidence of an overreaction associated with accruals is mainly driven by firm-specific accruals because the lower persistence of accruals is attributable to the firm-specific component. In other words, we expect c4 < γ4 (overreaction to firm-specific accruals). We do not make explicit predictions on the relation between c2 and γ2 and between c3 and γ3. Under the earnings fixation hypothesis, investors are likely to apply one average persistence parameter to all earnings components (as suggested by Table 3, panel A). Thus, there may be no difference between c2 and γ2 (and between c3 and γ3), not because investors understand their persistence but because the average persistence parameter used by investors roughly equals the persistence of FirmCF and IndAcc by chance.

18

Panel A of Table 6 reports our replication of prior findings (e.g., Sloan, 1996) showing that the market underreacts to cash flows and overreacts to accruals. We find in our sample that the implied weight on CF in prices is significantly lower than the estimated persistence of CF (i.e., 0.518 < 0.583, p < 0.01). We also find that the implied weight on Acc in prices is significantly higher than the estimated persistence of Acc (i.e., 0.550 > 0.489, p <0.01) and insignificantly different from the implied weight on CF in prices (p = 0.25). The first column of panel B of Table 6 shows the results of estimating model (7a). Consistent with our predictions, we find that industry-wide cash flows are the most persistent component of earnings and firm-specific accruals are the least persistent. Specifically, the estimated coefficient on IndCF (= 0.826) is significantly higher than the coefficients on any of the other components of earnings. In addition, the estimated coefficient on FirmAcc (= 0.449) is the lowest of all the earnings components. We also find that the estimated coefficient on FirmCF (= 0.528) is significantly lower than that on IndAcc (= 0.666), indicating that industry-wide accruals are more persistent than firmspecific cash flows. The second column of panel B presents the results of estimating model (7b). Consistent with our expectations, we find that the estimated coefficient on IndCF in the return regression (= 0.529) is significantly lower than that in the forecasting regression (= 0.826) and that the estimated coefficient on FirmAcc in the return regression (= 0.534) is significantly higher than that in the forecasting regression (= 0.449). These results indicate that the underreaction to cash flows is mainly attributable to its industry-wide component while the overreaction to accruals is mainly attributable to its firm-specific component.15

15

Decomposing total accruals into discretionary and nondiscretionary components, Xie (2001) finds that discretionary accruals are less persistent than non-discretionary accruals and that future returns are stronger for the discretionary component. Following on this theme, we further decompose firm-specific accruals into discretionary (FirmDA) and nondiscretionary (FirmNDA) components, where FirmDA is estimated as the 19

Finally, the insignificant results of the F-tests in the return regression further suggest that the market does not differentiate the persistence of the four main components of earnings partitioned by economic fundamentals and accounting conventions. In sum, we find that industry fundamentals play an important role in determining the persistence of earnings that is distinct from the accounting constructs of accruals and cash flows investigated in prior research. Our analysis serves to pinpoint the source of differential persistence, and hence market mispricing, as attributable primarily to the relatively high persistence of industry-wide cash flows at one extreme and the low persistence of firm-specific accruals at the other. However, cash flows are not uniformly more persistent than accruals, as firm-specific cash flows are less persistent than industrywide accruals.

5. Additional analyses 5.1. Good news vs. bad news Prior research shows that bad earnings news is less persistent than good earnings news (i.e., Basu, 1997). Our finding that firm-specific earnings are less persistent than industrywide earnings indicates the possibility that bad news is largely firm-specific (e.g., writeoffs). To shed some light on this issue, we test i) whether the differential persistence of industry-wide and firm-specific earnings is more pronounced when firms report bad news and ii) whether the market understands the firm-specific nature of bad news. Table 7 presents the results of Mishkin tests which include two BadNews interaction terms in our main model in equations (5a) and (5b). BadNews is an indicator variable set

residual of the Jones’ model and FirmNDA is the difference between FirmAcc and FirmDA. Substituting these two components for FirmAcc in Panel B of Table 6, untabulated results show that the persistence and pricing of FirmNDA and FirmDA is similar to FirmAcc. In other words, FirmNDA and FirmDA are the least persistent of the earnings components, and are over-weighted by the market. Moreover, there is no significant difference in the persistence and pricing of these two components. 20

equal to one if the change in reported earnings is negative, and zero otherwise. As expected, the results in column (1) indicate that the lower persistence of firm-specific earnings is primarily driven by bad news. In particular, we observe economically and statistically significant differential persistence between IndE × BadNews and FirmE × BadNews. In contrast, the difference between the estimated coefficients for IndE and FirmE is much smaller. Column (2) presents the implied differential persistence of earnings components in stock prices. The results indicate that while the market understands that bad news is less persistent (i.e., the coefficient estimates are significantly negative for both BadNews interaction terms), it does not seem to understand that bad news is largely firm-specific. The coefficient estimates on the two BadNews interaction terms are not significantly different from each other (p = 0.74), suggesting that the market perceives bad news as equally less persistent for industry-wide and firm-specific earnings. This result reinforces our main finding that the market does not seem to understand the differential persistence of industry-wide and firm-specific components of earnings, either in general or in the presence of bad news. 5.2 Pre- versus post-2004 Green et al. (2011) finds that the accruals anomaly first documented by Sloan (1996) appears to have decayed significantly after 2004. In this section we discuss untabulated tests examining whether the markets’ mispricing of the industry-wide and firm-specific components of earnings also disappears after 2004. We first confirm in our data that investors do not appear to significantly misprice the differential persistence of the accrual and cash flow components of earnings after 2004, consistent with the findings in Green et al. (2011). In contrast, we find that investors continue to significantly misprice the differential persistence of industry-wide and firm-specific earnings (i.e., the results reported in Table 3, Panel B are robust both before and after 2004). 21

To reconcile these findings, we partition industry-wide and firm-specific earnings into their respective accrual and cash flow components. We find a noticeable shift in the weighting of the industry-wide components after 2004. Specifically, there is an increase in investors’ weighting on the persistence of industry-wide cash flows after 2004 and a decrease in the weighting on industry-wide accruals.16 Because these shifts both occur in the weighting of the accounting components of industry-wide earnings, there is no overall change in the weighting of industry-wide earnings over time. However, the substantial decrease in the weighting on industry-wide accruals does decrease the overweighting of total accruals, and hence diminishes the accruals anomaly effect. Thus, we not only reconcile our findings to those of Green et al. (2011) but also further our understanding of the persistence of earnings and its components as defined by industry economic fundamentals and accounting constructs. We leave for future research to explore why investors’ weighting of the industry-wide cash flow and accrual components of earnings shifted in recent years. 5.3. Alternative industry definitions We examine the sensitivity of our main results to two alternative industry definitions, four-digit SIC code and six-digit NAICS code. In both cases, we find that industry-wide earnings are significantly more persistent than firm-specific earnings, consistent with the findings reported above. However, the coefficient estimate on IndE using the GICS classification (= 0.760, Table 3) is significantly higher than the estimates using either SIC or NAICS, consistent with prior research suggesting that GICS is a better industry definition for capital markets-based research. In contrast, the coefficient estimates on 16

A difference-in-difference comparison of the estimated coefficient estimates in the forecasting and return equations in the periods before and after 2004 reveals an increase in the weighting on industry-wide cash flows of 0.102 and a decrease in the weighting on industry-wide accruals of -0.333. The weighting changes on the firm-specific components are marginal in comparison; investors’ increased their weighting on firm-specific cash flows (accruals) by 0.074 (0.067). 22

FirmE are similar across all three industry classifications. Nevertheless, inferences regarding the underreaction (overreaction) to industry-wide (firm-specific) earnings are unchanged using either SIC or NAICS. We further examine the robustness of our results using a combination of both industry and firm size as an alternative definition of industry (e.g., Albuquerque, 2009). Specifically, we re-define industry-wide earnings as the average earnings (scaled by average assets) of all firms in the same eight-digit GICS code and in the same size decile. We continue to find that industry-wide earnings are significantly more persistent than firm-specific earnings. However, the estimated coefficient on IndE in this analysis is significantly less than in our primary GICS-based analysis (p < 0.01), suggesting that this approach does not provide a superior industry classification in our particular context. We continue to find underreaction (overreaction) to industry-wide (firm-specific) earnings using the combination of both industry and firm size as an alternative definition of industry. Finally, we repeat our main analyses using industry-wide and firm-specific earnings defined by a pseudo- industry classification. One potential concern with our findings is that the persistence of industry-wide earnings is higher than firm-specific earnings because of the aggregation of data across firms rather than because of industry economic fundamentals. To the extent that aggregating earnings reduces noise in the data, we could obtain a higher estimate of persistence even if the firms are not from the same industry. Our analysis of industry homogeneity in section 3.3.1 suggests that this does not explain our results, but to further rule out any mechanical relation introduced by aggregation, we form 136 pseudo-industries, equal to the total number of eight-digit GICS industries in our sample. Observations are assigned to each pseudo-industry according to the alphabetic order of the sample firms’ names. The number of observations in each pseudo-industry

23

equals the number of observations in each eight-digit GICS industry in our sample. 17 Industry-wide and firm-specific earnings are then calculated within each pseudo-industry. Untabulated results show that the persistence of pseudo-IndE is not significantly different from pseudo-FirmE (p = 0.79). Thus, the results from this analysis indicate that our findings cannot be explained as a simple function of aggregation. 5.4. Alternative definitions of industry-wide earnings It is possible that industry leaders’ earnings are more representative of industry-wide earnings than the average earnings of all firms in the industry. We thus test our predictions defining industry-wide earnings as the sales- or market value-weighted average earnings of all firms in the same industry. We continue to find that industry-wide earnings are significantly more persistent than firm-specific earnings with either definition, and stock prices behave as if investors do not understand these differences. 5.5. Analysis of future stock returns One implication of our prediction that investors underreact to industry-wide earnings is that we should observe a predictable drift in future stock prices in the direction of industrywide earnings. To test for this effect, we examine future average abnormal stock returns for each decile portfolio of IndE. In this analysis, we impose two additional filters on our sample, leaving 53,960 observations. First, we require that firms have a December fiscal year-end and that the dominant fiscal year-end in the industry is December. 18 This requirement avoids look-ahead bias in the data because the distribution of industry-wide 17

For example, there are ten observations in the first eight-digit GICS industry (10101010) for 2008, so we assign the first ten firms sorted in alphabetic order to that industry. 18 For example, assume that an industry has five firms, four of which have a November fiscal year-end and the fifth a December fiscal year-end. Because the dominant fiscal year-end is November, all firms in this industry are excluded from the analysis. In contrast, an industry containing four firms with a December fiscal year-end and one with a November fiscal year-end has December as the dominant fiscal year-end, and we thus include the four December fiscal year-end firms in calculating industry-wide earnings and future stock returns. The proportion of the 4,372 non-December year-end observations removed from the tests in this section is somewhat higher than that of the Compustat population in the consumer products, industrial, and information technology sectors. 24

earnings for a year is known to investors (e.g., Sloan 1996). Second, we require stock price at the end of the year to be above $1 to avoid the noise in CRSP stock prices due to a variety of microstructure issues (infrequent trading, bid-ask spread bounces, etc.). We calculate the cumulative abnormal return (CAR) as the size-adjusted 12-month buyand-hold stock return starting the fourth month after the end of the fiscal year t (i.e., April of year t+1), adjusted for the 12-month stock return for the firm’s corresponding NYSE and AMEX size decile return.19 As a robustness check, we also calculate the DGTW three-factor (size, book-to-market, and momentum) adjusted returns (CAR3f) (Daniel et al., 1997; Wermers, 2004).20 Table 8 reports average future abnormal stock returns for the IndE decile portfolios. The results in column (3) show a monotonic increase in CARt+1 from the Low IndE decile (−0.060) to the High IndE decile (0.035). The difference between the average CARt+1 in the Low and High deciles is 9.5%, an economically and statistically significant result.21 We report similar results in column (4) where the average CARt+2 generally increases from the Low decile (−0.036) to the High decile (0.012), producing a statistically significant difference of 4.9% (p < 0.01). 22 The evidence therefore suggests that the ability of industry-wide

19

In our sample, 96.1% of the earnings announcements occur before April of year t+1. Our results are robust to eliminating observations that have not announced earnings by April of year t+1. If a stock is de-listed during the return accumulation period, we obtain delisting returns following Shumway (1997) and Shumway and Warther (1999) and assume the proceeds are reinvested to earn the average return of the matching size decile portfolio. Specifically, for firms that are delisted during the future return period, we calculate the remaining return by taking CRSP’s delisting return and then reinvesting the proceeds in the equally-weighted reference portfolio. For firms delisted due to poor performance (delisting codes 500 and 520–584), we use a −35 percent delisting return for NYSE/AMEX firms and a −55 percent delisting return for NASDAQ firms. Results are robust to using alternative de-listing return adjustments (i.e., using actual delisting return from CRSP and −100 percent for delisted firms due to poor performance). 20 Specifically, we subtract the buy-and-hold return of a portfolio of firms that have similar characteristics (i.e., size, book-to-market ratio, prior return momentum) from the buy-and-hold return of the sample firm. The DGTW benchmarks are available via http://www.smith.umd.edu/faculty/rwermers/ftpsite/Dgtw/coverpage.htm 21 Untabulated analysis reveals that one-year-ahead hedge portfolio returns are positive in all but one year of our 24-year sample period. Finding consistently positive returns over time suggest that the results are unlikely explained by omitted risk factors. 22 We assume portfolio rebalancing at the end of year t+1 and drop firms that are delisted during year t+1 when calculating the holding period return for year t+2, reducing the sample size to 48,379. Likewise, we drop firms 25

earnings to predict future returns is relatively long-lasting, although the magnitude of the hedge portfolio return decreases by 50% from the first year to the second year after portfolio formation. Finally, the evidence in column (5) indicates that industry-wide earnings do not predict three-years-ahead abnormal returns (CARt+3). Results in Columns (6) to (9) are similar when we use the three-factor adjusted returns.23 To test whether the observed univariate return predictability is robust, we estimate the following OLS regression model to control for other variables that may be correlated with both industry-wide earnings and future abnormal stock returns: Abnormal Return = d0 +d1IndE10t + d2Acc10t + d3NOA10t + d4Surp10t + d5Betat + d6Log(MV)t + d7BMt + d8IndMomt + d9FMomt +d10Arbitraget+d11SalesGt + εt+1, (8) where IndE10t represents decile-ranked industry-wide earnings converted to [0,1].24 Our primary focus is on industry-wide earnings (IndE10) which we predict is positively associated with one-year-ahead abnormal stock returns (i.e., b1 > 0). We include controls for several other accounting-based market anomalies documented in prior literature. Specifically, we include accounting accruals and net operating assets, as Sloan (1996) and Hirshleifer et al. (2004) find that accruals and cumulative accruals predict negative future stock returns. Similarly, we include earnings surprise (Surp10) to control for the effects of

that are delisted during either year t+1 or year t+2 when we calculate holding period return for year t+3, reducing the sample size to 45,977. 23 We also examine whether future abnormal returns cluster around earnings announcements. Specifically, we calculate the average of size-adjusted abnormal returns of hedge portfolios over three-day windows centered on the next one- to four-quarterly earnings announcements. The hedge portfolios are formed by taking a long position in the firms in the top decile of industry-wide earnings (IndE) and a short position in the firms in the bottom decile of IndE. The results show that the total hedge returns are positive around four subsequent earnings announcements (=4.52%), almost half of the total hedge returns. Under the null that abnormal returns do not cluster around earnings announcements, we expect hedge returns of 0.452% around these earnings announcements. The difference between observed hedge returns and expected hedge returns are statistically significant (p < 0.01) as well as economically significant (i.e., 9.41 times the “expected hedge” returns). Results for two-year-ahead hedge returns are similar. The observed higher concentration of abnormal returns around subsequent earnings announcements suggests that at least a portion of the delayed price response is due to misperception about future earnings, which is corrected around subsequent earnings release dates. 24 The control variables are defined in Table 9. We winsorize all continuous variables at the extreme one percentile. Inferences are unchanged if risk factors (i.e., beta, size, market-to-book ratio) and momentum variables are not winsorized. Results are also similar when we further exclude influential observations that have absolute Studentized residual greater than two. 26

post-earnings announcement drift (e.g., Bernard and Thomas, 1989, 1990; Collins and Hribar 2000). We also include proxies for firm risk (LogMV, Beta, BM and SalesG), industry and firm momentum in stock prices (IndMom and FMom) (Moskowitz and Grinblatt, 1999, Jegadeesh and Titman, 1993), and arbitrage costs (Arbitrage). Columns (1) and (2) of Table 9 present the results of estimating equation (8) with oneand two-years ahead size-adjusted returns as the dependent variable, respectively. As expected, the coefficient estimates on IndE10 are significantly positive in both regressions and their magnitudes indicate that the univariate hedge returns reported in Table 8 are robust to controlling for other factors.25 Consistent with prior findings, we find that Acc10 and NOA10 predict future negative stock returns, while Surp10 predicts future positive stock returns, and these effects decline over longer horizons. Finally, we find significantly positive coefficients on BM and IndMom, consistent with the book-to-market and industry momentum effects. Columns (3) and (4) of Table 9 present the results using three-factor adjusted abnormal stock returns. The coefficient estimates on IndE10 remain significant in both estimations (t ≥ 1.80). Interestingly, the estimated coefficients for IndMom are no longer significant, indicating that the industry return momentum results are sensitive to risk-adjustments. Overall, we find that the univariate results are robust to controlling for several other important factors that are correlated with future stock returns.26

25 All t-statistics are based on firm and year double clustered standard errors. Because we are explaining CAR t+2 in column (2), the control variables are measured at the end of year t+1 with the exception of Acc10 and NOA10 which are measured at the end of year t for a fair comparison to IndE10. Inferences are unchanged when we run Fama-MacBeth regressions, median regressions, and weighted least square regressions, where the weight is the market value of a firm, suggesting that the abnormal returns are unlikely driven by a small portion of the firms in the sample. 26 Balakrishnan et al. (2010) find that reporting a profit/loss predicts future stock returns. Inferences for IndE are unchanged when we include a dummy variable equal to one if total earnings is negative and zero otherwise. We also find similar results after including cash flow-to-price ratio in the regression (Desai et al., 2004). Finally, the findings are also very similar if we use Fama-MacBeth regressions.

27

6. Summary and conclusion Economic theory suggests that the component of firm performance determined by industry fundamentals is more persistent than the component unique to the firm. Based on this foundation, we examine the relative persistence of industry-wide and firm-specific earnings and whether investors correctly anticipate how economic fundamentals affect the persistence of these earnings components. To test our predictions, we group firms into industries using the Global Industry Classification Standard (GICS) and parse reported earnings into an industry-wide component, equal to the average earnings of all firms in the industry, and a firm-specific component, equal to the residual. Consistent with predictions, we find that industry-wide earnings are significantly more persistent than firm-specific earnings. Investors place similar weights on the two components, however, causing equity prices to underweight (overweight) the persistence of industry-wide (firm-specific) earnings. We further identify two economic forces – industry homogeneity and business model shocks – that we expect to systematically affect industrywide earnings persistence. Using three different proxies for industry homogeneity, we find that industry-wide earnings are significantly more persistent in homogenous industries than heterogeneous industries, as expected. The market does not appear to recognize the effects of homogeneity on earnings persistence, however, and thus underreacts to the persistence of industry-wide earnings to a greater extent in homogenous industries. Also consistent with expectations, we find that business model shocks reduce the persistence of both industry-wide and firm-specific earnings, although as the more persistent of the two components, the effect is significantly less pronounced for industry-wide earnings. Investors do not appear to recognize the higher persistence of industry-wide earnings even in the face of a large business shock, and thus overreact to its effects. Our findings are robust to alternative definitions of industries and industry-wide earnings. 28

Our focus on economic fundamentals and the persistence of industry-wide and firmspecific earnings differs from the prior research based on the accounting classification of earnings into cash flow and accruals (e.g., Sloan 1996). To examine these two non-mutually exclusive perspectives on earnings components, we decompose industry-wide and firmspecific earnings into their respective cash flow and accrual components. Consistent with predictions, we find that industry-wide cash flow is the most persistent component of earnings while firm-specific accruals is the least persistent, and accordingly market mispricing is attributable to these components. Our results highlight the importance of industry economics in the earnings generating process and investors’ naïve expectations about this fundamental valuation attribute. Our results also have implications for the earnings quality literature in which earnings persistence is a commonly used metric (e.g. Dechow et al. 2010). Research in this area focuses on firm-specific characteristics as determinants of earnings quality. Consideration of how broader industry fundamentals contribute to the earnings generating process could lead to better specified models and a richer understanding of the factors influencing innate earnings quality.

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FIGURE 1A Average Earnings for Firms in the Top and Bottom Deciles of Industry-Wide Earnings

0.40 0.30

IndE Decile 10

0.20 0.10 0.00 t-3

t-2

t-1

t

t+1

t+2

t+3

-0.10

IndE Decile 1 -0.20 -0.30 -0.40

FIGURE 1B Average Earnings for Firms in the Top and Bottom Deciles of Firm-Specific Earnings

0.40 0.30

FirmE Decile 10 0.20 0.10 0.00 t-3

t-2

t-1

t

t+1

t+2

t+3

-0.10

FirmE Decile 1 -0.20 -0.30 -0.40 Figure 1A presents the average earnings for firms in the top and bottom deciles of industry-wide earnings (IndE) of year t. Earnings (Earn) is operating income after depreciation for year t deflated by average assets. Industry-wide earnings (IndE) for a firm are defined as average Earn across all firms in the same eight-digit GICS industry. Figure 1B presents the average earnings for firms in the top and bottom deciles of firmspecific earnings (FirmE) of year t. Firm-specific earnings are the difference between Earn and IndE.

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TABLE 1 Sample Description Panel A. Average Number of Firms per Year Year 1988 1992 1993 1997 1998 2002 2003 2007 2008 2011 All years

Average Number of Firms 1,924 2,732 2,608 2,638 2,687 2,511

Panel B: Number of Firms by Size Decile NYSE Size Decile 1 (smallest) 2 3 4 5 6 7 8 9 10 (largest) Total

Number of Firms 5,929 7,008 6,761 6,454 6,412 6,074 5,791 5,279 5,339 5,212 60,259

Panel C: Industry Composition Two-digit GICS Industry sector name code 10 Energy 15 Materials 20 Industrials 25 Consumer Discretionary 30 Consumer Staples 35 Health Care 45 Information Technology 50 Telecommunication Services 55 Utilities Total

% of Total Firms 9.84% 11.63% 11.22% 10.71% 10.64% 10.08% 9.61% 8.76% 8.86% 8.65% 100%

No. of firm-years 4,190 4,736 12,014 11,612 3,554 7,980 12,051 1,290 2,832 60,259

% of sample 6.95 7.86 19.94 19.27 5.90 13.24 20.00 2.14 4.70 100.00

% of CRSP & Compustat 6.92 7.11 16.68 20.38 5.29 14.60 21.43 3.04 4.54 100.00

Panel A shows the average number of firms for each five-year window in our sample period (except for the last window which is only four years). Panel B shows the distribution of sample firms across NYSE size deciles. The threshold values for NYSE size deciles are based on beginning-of-year market capitalization for all NYSE firms. Panel C shows the distribution of firms in our sample and in the CRSP & Compustat population across two-digit GICS industry groups.

34

TABLE 2 Descriptive Statistics Earnt

Mean 0.015

Std 0.168

P10 –0.124

Q1 0.002

Median 0.044

Q3 0.088

P90 0.141

IndE t

0.006

0.082

–0.077

–0.009

0.028

0.051

0.069

FirmEt

0.009

0.151

–0.106

–0.026

0.017

0.070

0.145

CFt

0.073

0.152

–0.063

0.032

0.089

0.146

0.210

Acct

–0.058

0.111

–0.165

–0.097

–0.050

–0.009

0.048

IndCFt

0.066

0.072

0.001

0.048

0.078

0.103

0.132

FirmCFt

0.007

0.137

–0.116

–0.043

0.010

0.069

0.144

IndAcct

–0.060

0.042

–0.112

–0.083

–0.055

–0.033

–0.015

0.002

0.103

–0.096

–0.036

0.006

0.049

0.104

FirmAcct

Earnt is earnings for year t, defined as operating income after depreciation, deflated by average assets. IndEt is industry-wide earnings for year t, defined as average Earnt across sample firms within the same eight-digit GICS industry. FirmEt is the difference between Earnt and IndEt. CFt is operating cash flows for year t, defined as net cash flow from operating activities less the accrual portion of extraordinary items and discontinued operations reported on the statement of cash flows, deflated by average total assets. Acct is the total operating accruals for year t, defined as Earnt minus CFt. IndCFt is industry-wide cash flows for year t, defined as the average of income before extraordinary items (deflated by average assets) across sample firms in the same eight-digit GICS industry. FirmCFt is firm-specific cash flows for year t, defined as CFt minus IndCFt. IndAcct is industry-wide accruals for year t, defined as the average of Acct of all firms in an industry.

35

TABLE 3 Differential Persistence and Pricing of Industry-Wide Earnings and Firm-Specific Earnings Panel A: Baseline regressions (1) Forecasting Equation Earnt+1 Multiple

(2) Return Equation CARt+1 1.017*** (17.64)

0.008*** (2.84)

–0.025*** (–2.97)

Earnt

0.536*** (30.81)

0.534*** (8.13)

Observations Adj. R2

60,259 43.78%

60,259 4.74%

Intercept

Panel B: Mispricing of industry-wide and firm-specific earnings (1) (2) Forecasting Equation Return Equation Earnt+1 CARt+1 Multiple 1.026*** (18.15)

χ2 Test

0.00 (p = 0.95)

χ2 Test

Error in Returns

Insignificant

Error in Returns

0.008*** (2.84)

–0.025*** (–2.89)

IndEt

0.760*** (28.17)

0.587*** (3.91)

6.93 (p < 0.01)

Under-

FirmEt

0.487*** (27.13)

0.523*** (8.69)

4.29 (p = 0.04)

Over-

Intercept

Observations Adj. R2

60,259 46.03%

60,259 4.75%

F-Tests: IndEt = FirmEt

p < 0.01

p = 0.60

This table presents the results of Mishkin tests of differential earnings persistence and whether stock prices behave as if investors underreact to industry-wide earnings. In Panel A, we present the results of the following two regressions: Earnt+1 = a0 + a1Earnt + et+1 CARt+1 = Multiple × (Earnt+1 - α0 - α1Earnt) + εt+1

In Panel B, we present the results of the following two regressions: Earnt+1 = a0 + a1IndEt + a2FirmEt + ε1t+1 CARt+1 = Multiple × (Earnt+1 - α0 - α1IndEt - α2FirmEt) + ε2t+1 Earnt is earnings for year t, defined as operating income after depreciation, deflated by average assets. Earnt+1 is Earn for year t+1. CARt+1 is abnormal return for year t+1, defined as the size-adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t. IndEt is industry-wide earnings for year t, defined as the average of Earnt across sample firms within the same eight-digit GICS industry. FirmEt is the difference between Earnt and IndEt. Multiple is the earnings multiple implicated in the stock returns. *, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively. All t-statistics are based on firm and year double clustered standard errors.

36

Panel A: Size

TABLE 4 Industry Homogeneity and the Differential Persistence and Pricing of Earnings Components

Insig.

Error in Returns

0.31 (p = 0.58)

Over-

Forecasting Equation Earnt+1

0.601*** (4.48)

3.23 (p = 0.07)

Error in Returns

0.664*** (12.37)

0.513*** (7.16)

Forecasting Equation Earnt+1

Under-

0.462*** (12.71)

Heterogeneous Industries Return χ2 Test Equation CARt+1 1.140*** (11.81)

–0.037** (–4.08) 9.63 (p < 0.01)

Over-

Homogeneous Industries Return χ2 Test Equation CARt+1 0.963*** (13.25)

0.008*** (2.47) 0.534*** (2.94) 3.13 (p = 0.08)

31,539 4.49%

Multiple

0.789*** (27.90) 0.527*** (6.93)

31,539 37.96%

–0.014 (–0.99)

IndEt 0.498*** (25.08) 28,720 5.22%

p = 0.49

0.012*** (3.57)

FirmEt 28,720 49.01%

p < 0.01

Intercept

Observations Adj. R2 p = 0.96

0.012*** (4.07)

0.664*** (3.07)

–0.028** (–3.30)

3.85 (p = 0.05)

0.48 (p = 0.49)

Over-

Insig.

Error in Returns

0.628*** (19.03)

0.530*** (7.59)

Forecasting Equation Earnt+1

Under-

0.494*** (16.30)

Heterogeneous Industries Return χ2 Test Equation CARt+1 1.010*** (16.44)

Over-

p = 0.45

30,022 4.27% p < 0.01

30,022 42.36%

Error in Returns

p < 0.01

IndEt = FirmEt

F-Tests:

Panel B: Operating Activities

–0.023** (–2.28) 13.62 (p < 0.01)

Forecasting Equation Earnt+1

0.005* (1.70)

0.557*** (4.07)

3.50 (p = 0.06)

Homogeneous Industries Return χ2 Test Equation CARt+1 1.035*** (16.44)

0.812*** (28.94)

0.517*** (7.06)

Multiple

IndEt

0.477*** (24.58)

Intercept

FirmEt

30,237 5.22% p = 0.72

30,237 49.32% p < 0.01

Observations Adj. R2 F-Tests: IndEt = FirmEt

37

Panel C: Number of Business Segments

Insig.

Error in Returns

0.21 (p = 0.64)

Over-

Forecasting Equation Earnt+1

0.658*** (3.89)

4.58 (p = 0.03)

Error in Returns

0.620*** (18.20)

0.508*** (4.85)

Forecasting Equation Earnt+1

Under-

0.455*** (9.68)

Heterogeneous Industries Return χ2 Test Equation CARt+1 1.244*** (11.87)

–0.042** (–3.93) 11.67 (p < 0.01)

Over-

Homogeneous Industries Return χ2 Test Equation CARt+1 0.950*** (16.46)

0.007** (2.05) 0.503*** (3.36) 3.52 (p = 0.06)

29,322 4.54%

Multiple

0.775*** (28.98) 0.527*** (9.24)

29,322 34.66%

Intercept IndEt 0.496*** (24.17) 30,937 5.10%

p = 0.19

–0.010 (–0.76)

FirmEt 30,937 48.19%

p < 0.01

0.014*** (5.14)

Observations Adj. R2 p = 0.85

F-Tests: p < 0.01

IndEt = FirmEt

This table presents the results of Mishkin tests of differential earnings persistence and whether stock prices behave as if investors underreact to industry-wide earnings separately for homogeneous industries and heterogeneous industries. The specifications of the Mishkin tests are described in the notes to Table 3. In panel A, an industry is homogeneous (heterogeneous) if the standard deviation of market value of firms in this industry in a given year is below (above) the median standard deviation of all industries in our sample in that year. In panel B, an industry is homogeneous (heterogeneous) if the dispersion of firms’ operating activities in this industry in a given year is below (above) the median dispersion of all industries in our sample in that year. Dispersion of firms’ operating activities in an industry is defined as the average of the following four standard deviations in the industry: Profit Margin (net income scaled by sales), Receivables Turnover (average accounts receivable divided by sales), Payable Turnover (average accounts payable divided by sales, multiplied by one minus profit margin), and Inventory Turnover (g scaled by one minus profit margin, truncated above at 1 and below at -1; g is the estimated coefficient for sales in the regression of inventory on sales and changes in sales). To ensure comparability, these four measures are converted to standardized values. In panel C, an industry is homogeneous (heterogeneous) if the average number of business segments of firms in this industry in the given year is below (above) the median value of number of business segments of all industries in our sample in that year. *, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively. All t-statistics are based on firm and year double clustered standard errors.

38

TABLE 5 Business Shocks and the Differential Persistence and Pricing of Industry-Wide and Firm-Specific Earnings (1) (2) Forecasting Return Equation Equation Earnt+1 CARt+1 χ2 Test 1.049*** (18.97)

Multiple

0.006* (1.92)

Intercept

Error in Returns

–0.028** (–2.14)

IndEt

0.832*** (32.12)

0.747*** (5.58)

2.55 (p = 0.11)

Insig.

FirmEt

0.561*** (19.42)

0.567*** (10.05)

0.36 (p = 0.55)

Insig.

IndEt × IndShockt

–0.113*** (–4.55)

–0.306*** (–3.39)

9.12 (p < 0.01)

Over-

FirmEt× FirmShockt

–0.200*** (–4.59)

–0.147*** (–2.41)

3.18 (p = 0.07)

Under-

IndShockt

–0.001 (–0.58)

0.009 (0.78)

FirmShockt

–0.003 (–0.72)

–0.022* (–1.80)

60,259 47.32%

60,259 4.89%

p < 0.01

p = 0.09

p < 0.01

p = 0.21

Observations Adj. R2 F-Tests: IndEt =

FirmEt

IndEt × IndShockt = FirmEt× FirmShockt

This table presents the results of Mishkin test of whether stock prices behave as if investors underreact to the impact of business shocks on industry-wide earnings and firm-specific earnings. In columns (1) and (2), we present the results of the following two regressions: Earnt+1 = b0 + b1IndEt + b2FirmEt + b3IndEt × IndShockt + b4FirmEt × FirmShockt + b5IndShockt + b6 FirmShockt + ε3t+1 CARt+1 = Multiple × (Earnt+1 -β0 - β1IndEt - β2FirmEt - β3IndEt × IndShockt - β4FirmEt × FirmShockt - β 5IndShockt - β6 FirmShockt) + ε4t+1 Earnt is earnings for year t, defined as operating income after depreciation, deflated by average assets. Earnt+1 is Earn for year t+1. CARt+1 is abnormal return for year t+1, defined as the size-adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t. IndEt is industry-wide earnings for year t, defined as the average of Earnt across sample firms within the same eight-digit GICS industry. FirmEt is the difference between Earnt and IndEt. Multiple is the earnings multiple implicated in the stock returns. IndShockt is an indicator variable, defined as one if there are large industry-wide shocks in an industry-year and zero otherwise. An industry is defined as having a large shock if the average absolute value of the percentage changes of the following variables from the prior year is greater than the sample median: IndAssets (sum of all firms’ assets in the industry), IndSales (sum of sales), USSales (average U.S. sales-to-total sales ratio), IndGrowth (average market-to-book ratio in the industry), IndInvestment (sum of capital expenditure), IndResearch (sum of research and development expense), and IndAdvertising (sum of advertising expense). FirmShockt is an indicator variable, defined as one if there are large firm-specific shocks for the firm-year and zero otherwise. A firm is defined as having a large shock in a year if the average of the following five indicators is above the median value of all firms: Merger (Compustat footnote data item “sale_fn” = "AB", sales have been "restated for/reflects a major merger or reorganization resulting in the formation of a new company), Discontinued Operation (equals one if the magnitude of the income effect of discontinued operations is greater than five percent of sales ), Industry Membership Change 39

(firm’s eight digit GICS differs in years t-1 and t), Restructuring (the magnitude of restructuring charges is greater than five percent of sales), and Large Special Items (the magnitude of special items is greater than five percent of sales). *, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively. All t-statistics are based on firm and year double clustered standard errors.

40

TABLE 6 Persistence and Pricing of Industry-wide and Firm-Specific Accruals and Cash Flows Panel A: Baseline regressions (1) Forecasting Equation Earnt+1 Multiple

(2) Return Equation CARt+1 1.010*** (17.66)

χ2 Test

Error in Returns

0.010*** (3.47)

–0.026*** (–3.02)

CFt

0.583*** (36.25)

0.518*** (7.16)

6.81 (p < 0.01)

Under-

Acct

0.489*** (26.06)

0.550*** (8.60)

7.02 (p < 0.01)

Over-

Observations Adj. R2

60,259 44.85%

60,259 4.75%

(p < 0.01)

p = 0.25

Intercept

F-Test: CFt = Acct

Panel B: Mispricing of industry-wide and firm-specific cash flows and accruals (1) (2) Forecasting Equation Return Equation Earnt+1 CARt+1 χ2 Test Multiple 1.018*** (18.14) Intercept

0.012 (3.45)

Error in Returns

–0.028 (2.73)

IndCFt

0.826*** (37.02)

0.529*** (3.04)

19.70 (p < 0.01)

Under-

FirmCFt

0.528*** (30.77)

0.511*** (7.83)

0.75 (p = 0.39)

Insignificant

IndAcct

0.666*** (17.58)

0.669*** (4.29)

0.01 (p = 0.91)

Insignificant

FirmAcct

0.449*** (24.00)

0.534*** (8.99)

21.85 (p < 0.01)

Over-

Observations Adj. R2

60,259 47.01%

60,259 4.77%

F-Tests: IndCFt = FirmCFt

p < 0.01

p = 0.90

FirmCFt = IndAcct

p < 0.01

p = 0.25

IndAcct = FirmAcct

p < 0.01

p = 0.31

This table presents Mishkin generalized least square regression results. In panel A, the models estimated are: Earnt+1 = a0 + a1CFt + a2Acct + et+1 CARt+1 = Multiple × (Earnt+1 - α0 - α1CFt - α2Acct) + εt+1 In panel B, the models estimated are: Earnt+1 = c0 + c1IndCFt + c2FirmCFt + c3IndAcct + c4FirmAcct + ε5t+1 CARt+1 = Multiple × (Earnt+1 - γ0 - γ1IndCFt - γ2FirmCFt - γ3IndAcct - γ4FirmAcct) + ε6t+1 Multiple is the earnings multiple implied in stock prices. Earnt+1 is earnings for year t+1, defined as operating income after depreciation, deflated by average assets. CARt+1 is abnormal return for year t+1, defined as the size-adjusted 12month buy-and-hold stock return starting the fourth month after the end of fiscal year t. CFt is operating cash flows for year t, defined as net cash flow from operating activities less the accrual portion of extraordinary items and 41

discontinued operations reported on the statement of cash flows, deflated by average total assets. Acct is the total operating accruals for year t, defined as Earnt minus CFt. IndCFt is industry-wide cash flows for year t, defined as the average of income before extraordinary items (deflated by average assets) across sample firms in the same eight-digit GICS industry. FirmCFt is firm-specific cash flows for year t, defined as CFt minus IndCFt. IndAcct is industry-wide accruals for year t, defined as the average of Acct of all firms in an industry.*, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively. All t-statistics are based on firm and year double clustered standard errors.

42

TABLE 7 Bad News and the Differential Persistence and Pricing of Industry-Wide and Firm-Specific Earnings (1) (2) Forecasting Return Equation Equation Earnt+1 CARt+1 χ2 Test 1.056*** (18.06)

Multiple

Error in Returns

–0.036** (–3.59)

Intercept

–0.002 (–0.54)

IndEt

0.851*** (29.29)

0.797*** (4.54)

0.42 (p = 0.51)

Insig.

FirmEt

0.759*** (39.16)

0.748*** (9.65)

0.10 (p = 0.76)

Insig.

IndEt × BadNewst

–0.070** (–3.90)

–0.243*** (–2.29)

5.42 (p = 0.02)

Over-

FirmEt× BadNewst

–0.333*** (–16.08)

–0.267*** (–4.99)

3.08 (p = 0.08)

Under-

BadNewst

0.009*** (5.59)

0.013 (1.38)

Observations Adj. R2

60,259 48.07%

60,259 4.86%

p < 0.01

p = 0.71

p < 0.01

p = 0.74

F-Tests: IndEt =

FirmEt

IndEt × BadNewst = FirmEt× BadNewst

This table presents the results of Mishkin test of whether stock prices behave as if investors underreact to the impact of bad news on industry-wide earnings and firm-specific earnings. In columns (1) and (2), we present the results of the following two regressions: Earnt+1 = d0 + d1IndEt + d2FirmEt + d3IndEt × BadNewst + d4FirmEt × BadNewst + d5BadNewst + ε7t+1 CARt+1 = Multiple × (Earnt+1 -ϕ0 - ϕ1IndEt - ϕ2FirmEt - ϕ3IndEt × BadNewst - ϕ4FirmEt × BadNewst - ϕ 5BadNewst) + ε8t+1 Earnt is earnings for year t, defined as operating income after depreciation, deflated by average assets. Earnt+1 is Earn for year t+1. CARt+1 is abnormal return for year t+1, defined as the size-adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t. IndEt is industry-wide earnings for year t, defined as the average of Earnt across sample firms within the same eight-digit GICS industry. FirmEt is the difference between Earnt and IndEt. Multiple is the earnings multiple implicated in the stock returns. BadNewst is an indicator variable, defined as one if the change in earnings (i.e., Earnt Earnt-1 ) is less than zero, and zero otherwise. *, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively. All t-statistics are based on firm and year double clustered standard errors.

43

TABLE 8 Future Stock Returns by Decile Portfolios of Industry-Wide Earnings IndEt Decile

(1) N

(2) IndE

(3) CARt+1

(4) CARt+2

(5) CARt+3

(6) CAR3ft+1

(7) CAR3ft+2

(8) CAR3ft+3

Low

5,884

-0.146

–0.060

–0.036

0.021

–0.055

–0.040

–0.010

2

5,429

-0.033

–0.041

–0.033

–0.014

–0.028

–0.029

–0.014

3

5,441

-0.004

–0.040

0.008

0.036

–0.011

0.004

0.004

4

5,490

0.008

–0.018

–0.011

0.004

–0.013

–0.024

0.007

5

5,176

0.021

–0.010

–0.009

0.020

–0.009

–0.001

0.024

6

5,443

0.024

0.007

–0.019

0.012

0.004

–0.022

0.008

7

5,312

0.031

0.008

0.012

0.012

0.008

0.002

0.028

8

5,267

0.041

0.015

0.006

–0.001

0.017

0.001

0.012

9

5,438

0.052

0.019

0.016

–0.000

0.020

–0.007

–0.014

High

5,080

0.070

0.035

0.012

0.034

0.021

0.002

0.004

High – Low N

53,960

53,960

0.095*** (8.98)

0.049*** (4.19)

0.013 (1.00)

0.076*** (9.44)

0.042*** (5.12)

0.014 (1.35)

53,960

48,379

45,977

53,960

48,379

45,977

IndEt is industry-wide earnings for year t, defined as the average of operating income after depreciation (deflated by average assets) across sample firms within the same eight-digit GICS industry. CARt+1 is abnormal return for year t+1, defined as the size-adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t. CARt+2 is abnormal return for year t+2, defined as the size-adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t+1. CARt+3 is abnormal return for year t+3, defined as the size-adjusted 12month buy-and-hold stock return starting the fourth month after the end of fiscal year t+2. CAR3ft+1 is the GDTW threefactor (size, book-to-market, and momentum) adjusted abnormal return. CARt+2 and CAR3ft+2 are abnormal return for year t+2, defined as the size- or three-factor adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t+1.*, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively.

44

TABLE 9 Regressions of Future Abnormal Stock Returns Size-adjusted Returns (1) (2) CARt+1 CARt+2 Intercept IndE10t

-0.013 (-0.23) 0.090*** (3.20)

-0.049 (-0.66) 0.048** (2.32)

Three-Factor adjusted Returns (3) (4) CAR3ft+1 CAR3ft+2 0.041 (1.04)

0.035 (0.86)

0.063*** (3.08)

0.036* (1.82)

Acc10t

-0.047** (-2.29)

-0.041* (-1.95)

-0.053** (-2.96)

-0.036* (-1.92)

NOA10t

-0.081*** (-3.70)

-0.035* (-1.89)

-0.056*** (-4.10)

-0.022** (-2.34)

Surp10t

0.031** (2.49)

0.027** (2.05)

0.021** (2.73)

0.013* (1.72)

Betat or t+1

0.006 (0.36)

0.011 (0.57)

0.002 (0.30)

0.004 (0.50)

-0.005 (-1.15)

-0.004 (-0.90)

-0.003 (-0.69)

-0.003 (-0.71)

LogMVt or t+1 BMt or t+1

0.085*** (2.97)

0.088*** (2.91)

0.058*** (2.97)

0.056*** (3.32)

IndMomt or t+1

0.138*** (3.12)

0.171*** (4.35)

FMomt or t+1

0.005 (1.27)

0.003 (1.10)

Arbitraget or t+1

-0.424 (-1.43)

-0.243 (-0.80)

-0.682*** (-3.20)

-0.533*** (-2.73)

SalesGt or t+1

-0.015* (-1.89)

-0.021** (-2.11)

-0.026*** (-3.66)

-0.024** (-2.59)

Observations Adj. R2

53,960 2.36%

48,379 2.29%

53,960 2.61%

48,379 1.55%

-0.019 (-0.48)

-0.011 (-0.26)

0.002 (0.65)

0.001 (0.46)

This table presents results of multiple regressions of one-year-ahead abnormal stock returns (CARt+1 and CAR3ft+1). CARt+1 is abnormal return for year t+1, defined as the size-adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t. CAR3ft+1 is the GDTW three-factor (size, bookto-market, and momentum) adjusted abnormal return. CARt+2 and CAR3ft+2 are abnormal return for year t+2, defined as the size- or three-factor adjusted 12-month buy-and-hold stock return starting the fourth month after the end of fiscal year t+1. IndE10t is decile ranked (converted to [0, 1]) industry-wide earnings for year t, defined as the average of operating income after depreciation (deflated by average assets) across sample firms within the same eight-digit GICS industry. Acc10t is decile ranked (converted to [0, 1]) operating accruals for year t, defined as earnings minus operating cash flows deflated by average assets. NOA10t is decile ranked (converted to [0, 1]) net operating asset deflated by lagged total assets in at the end of fiscal year t. Surp10t is decile ranked (converted to [0, 1]) fourth-quarter seasonal random walk earnings surprise deflated by stock price at the beginning of the fourth quarter. LogMVt is the natural log of the market value of equity at the end of year t. BMt is the ratio of book value plus deferred tax to market value at the end of year t. IndMomt is the six-month size-adjusted buy-and-hold stock return ending the third month after the end of fiscal year t, averaged across sample firms within the same eight-digit GICS industry. FMomt is the six-month size-adjusted buy-and-hold stock return ending the third month after the end of fiscal year t. Arbitraget is the arbitrage cost, measured as the standard deviation of residuals from market model in the 48 months before the end of year t. SalesGt is the average sales growth from year t-2 to year t. All continuous variables are winsorized at 1% and 99%. *, **, and *** indicate statistical significance at the two-tailed 10%, 5%, and 1%, respectively. All t-statistics are based on firm and year double clustered standard errors.

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On the persistence and pricing of industry-wide and firm-specific earnings, cash flows, and accruals* Kai Wai Hui Department of Accounting Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong [email protected] Karen K. Nelson Jones Graduate School Business Rice University Houston, TX 77005 [email protected] P. Eric Yeung Samuel Curtis Johnson Graduate School of Management Cornell University Ithaca, NY 14850 [email protected] June 2015

Abstract Economic theory suggests that the industry-wide component of firm performance is more persistent than the firm-specific component. This paper provides evidence on this assertion and tests whether investors misprice these components of earnings. Consistent with predictions, we find greater persistence in the industry-wide component of earnings that is not fully recognized in stock prices. We show that these effects are partially driven by the market’s inability to recognize the differential persistence of industry-wide earnings in homogenous industries or in the presence of a large business shock. Finally, we show that industry-wide cash flows is the most persistent component of earnings while firm-specific accruals is the least persistent, suggesting that economic fundamentals and accounting constructs are jointly informative about firms’ future earnings. The market predictably misprices these components, however, significantly underweighting the persistence of industry-wide cash flows and overweighting the persistence of firm-specific accruals.

* We appreciate helpful comments and suggestions by Jerry Zimmerman (the editor), Arthur Kraft (the referee), and workshop participants at the University of Arkansas, University of California at Irvine, University of Colorado, University of Kentucky, University of North Carolina at Chapel Hill, Temple University, and the 2013 Lone Star Accounting Research Conference.

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