How Does Earnings Quality Affect the Equity Market? an Alternative Measure and a New Perspective

How does earnings quality affect the equity market? An alternative measure and a new perspective

Chad Larson Olin School of Business – Washington University in St. Louis [email protected]

Robert J. Resutek † Tuck School of Business – Dartmouth [email protected]

First Draft – July 2011 Current Draft – November 2011

Preliminary – do not cite without permission

Abstract

We propose and empirically validate forward-looking measures of earnings quality. Our earnings quality measures directly relate to how precisely future earnings can be estimated and have two components: an accrual component and a cash flow component. Our empirical tests produce three results: (i) The forward-looking earnings quality measures are more strongly associated with the absolute magnitude of earnings forecast errors relative to time-series based earnings quality measures. (ii) There is a strong negative relation between future returns and our measures of earnings quality. This relation is strongest in the accrual component of our earnings quality measure, but also exists in the cash flow component. (iii) Variation in earnings quality is unrelated to variation in conventional measures of risk.

JEL Codes: G14, M41

Key Words: Earnings quality, earnings volatility, accrual quality, earnings management

Data Availability: Data is available from public sources as identified in the text.

† Contact author. We thank Weili Ge and workshop participants at Dartmouth, BYU’s Accounting Research Symposium, and Washington University for helpful comments.

How does earnings quality affect the equity market? An alternative measure and a new perspective

Abstract

1 Introduction

This study proposes new, forward-looking estimates of earnings quality and examines the relation between these measures and future returns over the period 1974-2010. We limit our definition of earnings quality and define it only in terms of uncertainty, namely, how precisely future earnings can be estimated. This view is consistent with SFAC No. 1 which states one of the objectives of financial reporting is to provide information that is relevant to assessing the amount, timing, and uncertainty of future cash flows. This view is also consistent with theoretical studies that model earnings quality as a reduction of the market’s assessment of variance in the terminal value due to earnings (Ewert and Wagenhofer 2010) and empirical studies that define earnings quality in terms of precision (Ecker et al. 2006; Francis et al. 2008).1

We argue that the precision with which future earnings can be estimated is a joint function of two elements of uncertainty. The first element is uncertainty in the future financial performance as measured by GAAP (economic uncertainty). The second element is uncertainty in the financial reporting and disclosure choices managers will make in reporting future performance (reporting uncertainty). Prior studies often use time-series volatility measures to proxy for earnings quality. These time-series measures jointly capture both aspects of earnings uncertainty. Our empirical approach provides a direct estimate of the economic uncertainty element of earnings quality, an element that has been largely ignored by prior studies despite its significance to variation in earnings quality (Dechow et al. 2010).

The motivation for our earnings quality measures is both theoretical and practical. From a theoretical perspective, earnings quality is often proxied as a noise term that informs on how precisely future earnings can be estimated. Since change in equity value is a function of future earnings, not realized earnings, understanding how precisely future earnings can be estimated is important when assessing a firm’s earnings quality. Empirical studies to date have proxied for the noise term using time-series variation in realizations of earnings and cash flows. This empirical design choice could lead to flawed earnings quality inferences if time-series variation is not a reasonable proxy for how precisely future earnings can be estimated.

From a practical perspective, we note several limitations with respect to earnings quality variables derived from a time-series of realizations. First, time-series measures of earnings quality assume stationary earnings and cash flow processes. Given extensive evidence on earnings momentum and

1 We note that most empirical proxies for earnings quality are direct or derivative functions of earnings volatility. For example, earnings predictability (e.g., Francis et al. 2005), earnings smoothness (Leuz et al. 2003), and residual accrual volatility (Dechow and Dichev 2002) are each estimated from empirical proxies of volatility.

1 earnings reversals (Kothari 2001), the reasonableness of this assumption is questionable. Second, time-series based earnings quality variables often require a minimum 5-10 year time-series of earnings, cash flow, and accrual realizations, thereby limiting empirical inferences to those firms that have ‘survived’ for at least 5-10 years. Given these limitations, we develop an empirical measure of earnings quality that requires a minimal time-series and does not impose a stationarity assumption.

Our empirical design is based on the premise that a reasonable proxy for the uncertainty of future earnings and cash flows is the distribution (or volatility) surrounding expected future earnings and cash flows. In this spirit, we employ a matched-firm empirical design that matches the firm of interest to firms with similar characteristics from prior periods. From this matching process, we obtain an expectation of future earnings and cash flows. We then use the standard deviation of the matched-firm realizations as proxies for the uncertainty surrounding future earnings and cash flows. Because our uncertainty estimates are functions of the average financial performance of comparable firms as measured by GAAP, we argue that these estimates capture uncertainty in future financial performance (economic uncertainty). Likewise, we argue that the element of earnings uncertainty associated with idiosyncratic reporting choices of individual managers is not captured since these choices tend to cancel out against the choices of the other matched-firms. Given our two independently-generated estimates of earnings and cash flow uncertainty, we derive an estimate of uncertainty that is attributable to the accrual process.

We then examine whether the equity market places differential weights on the two components of earnings quality: cash flow uncertainty and accrual uncertainty. An extensive literature in accounting has developed over the past ten years that examines the equity market consequences of variation in earnings quality. Given that our measures of earnings quality are forward-looking and more directly map into the precision in investor expectations of future earnings, our measures have the potential to capture value-relevant pricing dynamics that may not be evident from time-series based measures of earnings quality.

Our results contribute to the earnings quality literature in four ways. First, we argue that our measures of earnings quality, the accrual and cash flow components of earnings uncertainty, capture the economic element of earnings quality. Recent reviews of the earnings quality literatures observe that although the quality of a firm’s earnings is a joint function of both the firm’s economic performance and the reporting choices of managers, the earnings quality literature often inadequately distinguishes between these two elements (Dechow et al. 2010). To the best of our knowledge, our study represents the first attempt to propose empirical variables that only capture the earnings quality dynamics associated with the firm’s expected economic performance. This advance allows us to make stronger inferences with respect

2 to whether the reporting choices of mangers or the underlying economic fundamentals of the firm lead to certain empirical associations.

Second, we empirically validate and compare our earnings quality variables against the conventional time-series measures used in prior studies. Across both the cross-sectional and time-series analyses, our estimates of earnings quality are more strongly associated with how precisely future earnings can be estimated compared to the conventional, time-series measures of earnings quality.

Third, in contrast to recent studies which find no relation between volatility-based measures of earnings quality and realized one-year returns (Core et al. 2008; McInnis 2010), we find a strong negative relation between future returns and our forward-looking measures of earnings quality. Our results do not contradict Core et al. (2008) or McInnis (2010). Rather, we show that when the components of earnings quality are estimated on a forward-looking basis and capture only the economic elements of a firm, there is a strong negative relation between future returns and accrual components of earnings quality.

Fourth, we propose an alternative equity market consequence associated with variation in earnings quality. Prior studies have posited that if earnings quality is relevant to the equity market, the consequences of this quality would manifest through variation in the firm’s cost of capital (Francis et al. 2004; Easley and O’Hara 2004; Lambert et al. 2007). Our evidence suggests that if earnings quality is a function of earnings uncertainty, a proposition supported by many prior studies, variation in earnings quality has an economically significant effect on investor expectations of future earnings. Our evidence does not refute or rebuff the contention that earnings quality is inversely related to cost of equity. In fact, our results say little on the effect that disclosure quality has on a firm’s cost of equity, an effect that has been posited in both empirical (Botosan 1997; Botosan and Plumlee 2002) and theoretical studies (Lambert et al. 2007). Rather, our empirical evidence highlights the need to consider the effect of economic uncertainty on a firm’s earnings quality when examining the relation between disclosure quality and cost of equity capital. This consideration is especially important since the financial reporting and disclosure policies of a manager are most likely correlated to uncertainty in the firm’s future earnings.

Our study proceeds as follows. Section 2 describes prior literature and motivates our empirical design. Section 3 describes our cross-sectional measures of uncertainty and discusses summary statistics. Section 4 empirically compares our cross-sectional measures of uncertainty against the time-series equivalents. Section 5 notes how we decompose earnings quality into an accrual and cash flow component and compares these earnings quality elements against the conventional time-series earnings quality variables. Section 6 documents the relation between our cross-sectional earnings quality variables

3 and future returns. Section 7 discusses robustness tests and time-series variation in our measure. Section 8 concludes.

2. Prior literature and motivation 2.1 Prior literature An underlying principle of accrual accounting is that accruals eliminate random fluctuations in cash flows resulting from the timing of cash receipts and disbursements. The elimination of random fluctuations in cash flows leads to earnings that are generally less volatile than cash flows, thereby making earnings more informative about current and future economic performance (Dechow 1994; Dechow et al. 2010). However, because the accrual process requires management discretion and estimation, it is subject to both intentional and unintentional measurement error thereby reducing the reliability of accruals relative to cash flows (Richardson et al. 2005). Herein is the tension motivating recent studies examining whether earnings quality affects firm value through cost of capital. Recent asset pricing models such as Easley and O’Hara (2004), Lambert et al. (2007) suggest that firms with more precise information about future cash flows should enjoy lower costs of equity. While the mechanisms vary across these and other estimation risk models, the general empirical implications are the same. That is, to the extent that the precision that market agents can estimate a firm’s future dividend stream is positively associated with the firm’s earnings quality, a firm’s cost of capital will negatively covary with the firm’s earnings quality. Despite an extensive set of empirical studies in this area over the past 10 years, strong debate still exists as to whether a firm’s earnings quality is associated with its cost of capital. Much of the debate centers on the fact that despite relations between measures of earnings quality and implied cost of equity measures that suggest that earnings quality is inversely related to systematic risk (Francis et al. 2004; 2005), realized returns are unrelated to these measures of earnings quality (Core et al. 2008; McInnis 2010). Since realized future returns are viewed as an unbiased, albeit potentially noisy, estimate of cost of equity (Lewellen 2010), the lack of a significant relation with current period measures of earnings quality leaves doubt in the minds of some researchers as to the value-relevance of earnings quality to the equity market.

Perhaps overlooked in the debate on the equity market consequences of earnings quality is whether earnings quality affects investor expectations, and thereby asset price. There is a relatively extensive asset pricing literature that suggests if certain assumptions regarding the existence of homogeneous expectations, frictionless markets, influential market makers with unlimited arbitraging ability are relaxed, predictable cross-sectional variation in future returns not attributable to risk can be noted. Alternatively, even if classic asset pricing assumptions bind, if the precision of accounting

4 information is associated with uncertainty in future earnings or parameter uncertainty, predictable variation in future returns will be noted (Pastor and Veronesi 2003; Lewellen and Shanken 2002).

Early empirical evidence suggestive that differences in investor expectations can lead to predictable future return patterns is noted by Diether et al (2002). The differences in opinion (DO) hypothesis builds on an idea put forth in Miller (1977). Specifically, Diether et al. contend that analyst forecast dispersion proxies for investor disagreement. They suggest that the negative future return pattern associated with analyst forecast dispersion is consistent with Miller’s hypothesis that stock price will reflect a more optimistic valuation if pessimistic investors are kept out of the market by high short-sale costs or high arbitrage risk. Subsequent studies confirm the relation between future returns and analyst forecast dispersion, although debate Diether et al.’s explanation (Johnson 2004; Barron et al. 2009).

In a related stream of literature, Zhang (2006) and Jiang et al. (2005) suggest that the earnings processes of firms operating in uncertain environments are less “knowable” to even sophisticated investors (information uncertainty (IU) hypothesis). The IU hypothesis also focuses on variation in investor expectations of future earnings, but frames the argument from the perspective of ambiguity. Jiang et al. and Zhang each suggest that ambiguity relating to the level and distribution of future earnings amplifies behavioral pricing biases like those posited in Daniel et al. (1998; 2001). Specifically, investor overconfidence in private information leads to greater deviation in price from intrinsic value in high IU stocks since verifiable, public information is less prevalent. Consequently, high IU firms tend to realized stronger price continuation anomalies such as return momentum and post-announcement drift since price discovery takes longer.

Prior studies in both the DO and IU literatures do not directly specify a primitive economic mechanism describing the cause of the disagreement or ambiguity. For example, the empirical proxies for information uncertainty of Jiang et al. (2005) include firm age, implied equity duration, and return volatility. Other proxies of value ambiguity examined in prior studies include firm size, return volatility, variation in institutional ownership, and many non-accounting based variables. Interestingly, despite theory suggesting earnings quality is positively associated with how precisely future earnings can be estimated (Ewert and Wagenhofer 2010), direct accounting-based measures of earnings quality are rarely examined in the DO and IU literature.2 In these instances, empirical evidence suggesting that accounting-based measures of information uncertainty are associated with future returns is much weaker

2 Zhang (2006) examines cash flow volatility and analyst earnings forecast dispersion (table 2, pp. 114) and fails to find a significant relation between these measures of information uncertainty and future returns.

(or insignificant) in comparison to the non-accounting based measures such as return volatility, market capitalization, and firm age.

2.2 Motivation for forward-looking earnings quality measure As examined in prior studies, empirical estimates of earnings quality are generally derived in one of two ways. The first way uses a 5-10 year time-series of earnings, cash flow, and accrual realizations to estimate accounting-based measures of earnings quality. Common time-series based measures include derivatives of firm-specific OLS regressions (e.g., earnings persistence, earnings predictability, residual accrual volatility) as well as derivatives of firm-specific volatility estimates (e.g., earnings smoothness). The second way is to use firm characteristics not directly related to accounting realizations, but posited to affect investors’ perspective of future earnings. These characteristics include such things as firm age, firm size, R&D intensity, propensity to meet or beat earnings targets. Shortcomings of empirical proxies of earnings quality have been discussed at length in the literature (McNichols 2002; Dechow et al. 2010; DeFond 2010). Criticisms generally center on the fact that these measures jointly capture uncertainty attributable to financial performance and uncertainty attributable to the idiosyncratic reporting choices of managers. For example, Dechow et al. note that prior studies often inadequately distinguishes the effect on earnings quality of fundamental performance from the effect of the reporting choices managers make.3 A less discussed, but possibly equally important criticism, relates to the specification of the earnings quality measures derived from a time-series of accounting realizations. As noted above, many earnings quality measures are either direct or derivative functions of time-series variation in the accounting realizations. For example, Francis et al. (2005) examine earnings characteristics such as earnings predictability, earnings smoothness, accruals quality, cash flow volatility, and sales volatility. Each of these characteristics is computed over rolling 10 year windows using time-series variation in accounting realizations. If these measures of earnings quality are to be relevant to the equity market, they must be informative of qualities of future earnings realizations. In other words, if historical variation is a poor proxy for future uncertainty, then earnings quality will appear empirically to be irrelevant to the equity market even if the equity market is systematically affected by earnings quality.

Our study represents the first attempt to estimate only one component of earnings quality, the component associated with economic uncertainty, and do so on a forward looking basis. As noted above, we define earnings quality in terms of precision, namely how precisely can future earnings be estimated. This perspective is similar to Easley and O’Hara (2004), Lambert et al. (2007), Ecker et al. (2006) who

3 A similar criticism is often leveled against the empirical models used to distinguish discretionary from non-discretionary accruals in the earnings management literature – see McNichols 2000.

6 each interpret precision as a proxy for earnings (or information) quality. Further, to the extent that earnings precision is relevant to equity investors and informative of the future earnings process of the firm, our definition of earnings quality also dovetails into the broad definition for earnings quality of Dechow et al. (2010).

Our empirical design rests on the premise that an estimate of the second moment of a random variable (e.g., earnings volatility or precision) is only as valid as the estimate of the first moment of that random variable (e.g., the earnings expectation). Accordingly, we build from the empirical designs of Barber and Lyon (1996), Blouin et al. (2010) and Donelson and Resutek (2011) and form expectations of future earnings and cash flows based on a matched-firm approach. We then use the distributions of the matched firms earnings and cash flow realizations to derive our estimates of earnings quality which, by default, relate directly to how precisely future earnings can be estimated. Further, because the idiosyncratic financial reporting choices of managers tend to cancel out when aggregated across multiple firms, we argue that variation in our earnings quality measures is attributable to economic uncertainty, not uncertainty in how managers will choose to report in the future.

3 Sample, variable measurement, and descriptive statistics 3.1 Cross-sectional volatility measurement (CSEV and CSCFV) Our earnings quality measures are based on the uncertainties (or volatilities) surrounding the expectations of future earnings and cash flows. To derive empirical estimates of these volatilities, we begin by first estimating future earnings and cash flows in the spirit of Barber and Lyon (1996).4 We match each firm i, at time t, on three characteristics. First, we match by firm size based on NYSE average total asset deciles. Each year, firms are allocated to one of three asset-based portfolios based on prior year breakpoints. The first portfolio comprises the smallest NYSE size decile (this is roughly 50% of the sample). The remaining two portfolios are constructed by combining the first through fourth NYSE deciles and the fifth through tenth NYSE deciles. We then match each firm i to firms within the respective size portfolio with comparable earnings characteristics in year t-1. Specifically, for a firm i in fiscal year t, we utilize as matches all firms within the same size portfolio whose earnings level and one year earnings change at fiscal year end t-1 (Earningst-1-Earningst-2) are between 70% and 130% of firm i's earnings level and one-year earnings change at fiscal year end t. This matching process yields, for each firm i, a set of firms with comparable expected performance that is observable at the time t.

For each matched-firm, we then compute the change in earnings between t-1 and t. To reduce the

4 For brevity, we discuss the construction of our cross-sectional measure of earnings volatility in section 3.1. The construction of our cross-sectional measure of cash flow volatility (CSCFV) follows the exact same steps as CSEV, but we substitute cash flow from operations for earnings.

7 mechanical effect that an extreme earnings change in a matched-firm has on estimates of earnings volatility, we discard matched-firms with extreme performance, defined as earnings changes less than -50% or greater than 50%. We use the median change in earnings across matched-firms (between t-1 and t) as an expectation of firm i's expected earnings change between t and t+1.

We use the standard deviation in the expected earnings of firm i as a measure of firm i's earnings volatility around its t+1 earnings expectation. We require at least two matches for each firm to compute this characteristic. When at least two matches are not available in year t-1 (the minimum number of observations to derive a standard deviation), we extend the matching process back to year t-2. If at least two matches are not available across t-1 and t-2, we extend it back to t-3. We repeat this process for unmatched-firms back 5 years.5

To summarize, for each firm i at time t, we require an earnings realization (Earni,t) and a change in earnings over the last fiscal year (Earni,t-Earni,t-1). We then group all firms j with earnings realizations

(Earnj,t) and change in earnings (Earnj,t-1-Earnj,t-2) between 70% and 130% of firm i. That is, to be included in firm i’s matched-firms, firm j must meet the following criteria:

 0.70(Earni,t)≤( Earnj,t-τ) ≤1.30(Earni,t) and

 0.70(Earni,t-Earni,t-1)≤( Earnj,t- τ -Earnj,t- τ-1) ≤1.30(Earni,t-Earni,t-1), where |Earnj,t- τ -Earnj,t- τ-1|<0.50

Formally, we define the earnings expectation and earnings volatility as follows:

Ei,t[Earni,t+1] = median(Earni,t + δEarnj,t)

n n 2 2 CSEVi,t = 1 1 n-1 (Earn i,t1 - Earn i,t1 )  n-1 (δEarn j ) i1 i1 To construct our cross-sectional cash flow volatility measure (CSCFV), we follow the same empirical matching process as discussed above for CSEV, but substitute cash flow from operations for earnings. That is, firms are grouped into portfolios based on size, level of cash flows, and change in cash flows and a cash flow expectation and volatility estimate are derived.

5 We account for fiscal year end month in our matching process to ensure that the matched firms (firms j) earnings realizations are available to form the earnings estimate and volatility estimate of firm i at time t.

Table 1 Variable Construction and Summary Statistics This table reports derivation of the cross-sectional volatility estimates (Panel A) and cross-sectional summary statistics (Panel B). Specifically, Panel B reports the time-series average of the annual cross-sectional mean (Avg.), standard deviation (Std.), 1st percentile (Min.), 50th percentile (Med.), 99th percentile (Max.), and number of observations (Obs.). The sample spans firms with fiscal year ends between 1973:06 and 2010:12. All variables are scaled by average total assets st th with the exception of LogB/Mt and are annually winsorized at the 1 and 99 percentile.

Panel A: Derivation of cross-sectional volatility estimates

Earnt CFOt Full Compustat sample 1/1/1950 – 12/31/2010 371,763 371,763 Compustat sample where -1.0 ≤ Earnt (CFOt) ≤ 1.0 and Earnt-1 (CFOt-1) is not missing 301,683 199,323 Firms with at least 2 matches going back: 1 year 266,380 176,020 2 years 10,084 5,948 3 years 2,226 1,224 4 years 887 506 5 years 437 238 280,014 183,396 NYSE/AMEX/Nasdaq firms (CRSP exchange code 1, 2, or 3), fiscal years ending 6/73 – 12/10 and CRSP share code 10 or 11: w/non-missing CSEV or TSEV (CSCFV or TSCFV) 166,658 129,086 w/non-missing Earnt and CFOt and ≥ 1 volatility vars. (CSEV or TSEV or CSCFV or TSCFV) 141,608 141,608

Panel B: Summary Statistics

Variable Description Avg. Std. Min. Med. Max. Obs.

Earnt Earnings before X-ordinary -0.003 0.157 -0.642 0.038 0.262 3,726.5 CFOt Cash flow from operations 0.054 0.154 -0.530 0.077 0.400 3,726.5 dNWCt Change in NWC 0.013 0.093 -0.304 0.010 0.319 3,726.5 OthAcct Other operating accruals 0.070 0.069 -0.096 0.055 0.386 3,726.5 CSEVt Cross-sectional earnings volatility 0.077 0.058 0.004 0.061 0.258 3,563.1 TSEVt Time-series earnings volatility 0.068 0.081 0.003 0.039 0.459 2,989.6 CSCFVt Cross-sectional cash flow volatility 0.096 0.050 0.013 0.088 0.261 3,242.3 TSCFVt Time-series cash flow volatility 0.097 0.084 0.010 0.072 0.479 2,594.3 dSalest One year change in Sales 0.122 0.309 -0.919 0.094 1.253 3,723.7 LogB/Mt Book-to-market -0.458 0.823 -2.985 -0.374 1.335 3,584.0

Earnt Earnings before extraordinary items (IB) scaled by average total assets (AT)

CFOt Earnt + OthAcct - dNWCt dNWCt One year change net working capital. Net working capital is defined as current assets (ACT) minus cash (CHE) minus current liabilities (LCT) plus short term debt (DLCC). Short term debt is set to zero if missing.

OthAcct Non-working capital accruals from the Statement of Cash Flow to adjust Earnt to cash flow from operations. CSEVt Cross-sectional earnings volatility as defined in section 3.1 TSEVt Time-series earnings volatility computed as the standard deviation of Earnt between t-4 and t. CSCFVt Cross-sectional cash flow volatility as defined in section 3.1 TSCFVt Time-series cash flow volatility computed as the standard deviation of CFOt between t-4 and t. dSalest One year change in sales (Sale) between t-1 and t

LogB/Mt Natural log of book to market. To compute book value of equity we take, when not missing, shareholder’s equity (SEQ), or common equity plus preferred stock (CEQ+PSTK), or total assets minus total liabilities (AT-LT) in that order. From shareholders equity, we subtract preferred stock value, where we use either redemption value (PSTKRV) or liquidating value (PSTKL), in that order. Finally, if not missing we add to book value the balance sheet value of deferred taxes (TXDITC). Market equity is per CRSP as of the last day of fiscal year t.

3.2 Sample Selection Our primary sample is drawn from the population of all firms listed in the Compustat Annual Industrial and Research files with fiscal year ends between 1973:06 and 2010:12. The sample begins in 1973 because the first year Compustat reports flow of funds data, which we use to compute cash flow from operations, is 1971 and we need at least three years of data to perform our matching process (two years for firm i, one year to match against). Since we are interested in examining how the earnings estimates of analysts and investors are affected by earnings and cash flow volatility, we require a cross-sectional or time-series volatility estimate for either earnings or cash flows. We further reduce the sample size to those domestic firms traded on the NYSE, AMEX, and NASDAQ (CRSP exchange code 1, 2, 3 and CRSP share codes 10 and 11). Our primary sample is comprised of all firms that meet the above criteria, yielding 141,608 observations – see panel A of table 1 for sample construction detail. We annually winsorize all current-year summary statistics. To prevent summary statistics from being overly influenced by the increase in observations in the latter years of the sample and to report results that more closely correlate to the average time-series regression slopes examined in later tests, summary statistics are time series means (an average of each annual statistic).

3.3 Descriptive Statistics Panel B of table 1 provides descriptive statistics for all firms in our primary sample. Summary statistics are largely in line with those reported in prior studies. However, several characteristics are worth noting. First, our cross-sectional measures of earnings and cash flow volatility capture roughly 20% more observations than the respective time-series variables. Second, there is considerably less dispersion in the cross-sectional volatility estimates compared to the time-series volatility estimates. For example, the standard deviation of CSEVt (0.058) is roughly 30% lower than the standard deviation of

TSEVt (0.081). Finally, the cross-sectional earnings volatility estimate (0.077) is marginally higher than the time-series estimate of earnings volatility (0.068), although considerably less right skewed.

Table 2 provides a correlation matrix for key variables, and results again are largely in line with prior studies. Volatility measures derived using both time-series and cross-sectional empirical designs are negatively associated with firm size and book-to-market. Also worth noting is the fact that correlations between the time-series and cross-sectional volatility variables are positive, but are not highly collinear (0.40<ρ<0.60). This suggests that the volatility variables capture similar economic variation, but are distinct enough that we should be able to attribute unique effects to the time-series and cross-sectional volatility characteristics.

Table 2 Time-series means of annual correlations This table reports the time-series average of the annual cross-sectional correlations, with Pearson product moment correlations reported above the diagonal, and Spearman rank correlations reported below the diagonal. The sample spans firms with fiscal year ends between 1973:06 and 2010:12. LogSizet is the natural log of the market value of equity (in $millions) per CRSP as of fiscal year end t. All other variables are defined in table 1.

Earnt CFOt dNWCt CSEVt TSEVt CSCFVt TSCFVt dSalest LogSizet LogBMt

Earnt - 0.61 0.26 -0.59 -0.43 -0.33 -0.28 0.28 0.35 0.01 CFOt 0.54 - -0.43 -0.40 -0.28 -0.35 -0.25 0.03 0.27 0.05 dNWCt 0.25 -0.44 - -0.13 -0.05 0.03 0.00 0.34 0.06 -0.08 CSEVt -0.37 -0.28 -0.07 - 0.55 0.47 0.41 -0.12 -0.47 -0.17 TSEVt -0.32 -0.20 -0.06 0.60 - 0.39 0.64 -0.06 -0.34 -0.25 CSCFVt -0.19 -0.18 0.00 0.49 0.43 - 0.48 -0.01 -0.45 -0.16 TSCFVt -0.19 -0.19 0.01 0.45 0.62 0.52 - 0.00 -0.37 -0.20 dSalest 0.38 0.08 0.32 -0.09 -0.09 0.00 0.00 - 0.12 -0.21 LogSizet 0.40 0.30 0.05 -0.54 -0.44 -0.51 -0.44 0.14 - -0.25 LogB/Mt -0.25 -0.09 -0.10 -0.12 -0.15 -0.11 -0.13 -0.26 -0.28 -

3.4 Time-series patterns the cross-sectional volatility measures

Academic research has noted an increase in stock return and earnings volatility since the early 1960’s (Campbell et al. 2001; Wei and Zhang 2006). Further, recent accounting studies have noted a decline in earnings quality over the same time period (Rajgopal and Venkatachalam 2011). As a final descriptive exercise, we plot the median firm’s earnings and cash flow volatility for each year in our sample. Firms are classified as ‘tiny’, ‘small’, or ‘large’, based on the total asset portfolios in which they are classified in the matching process. Several interesting patterns are noted.

Cross-sectional earnings volatility (CSEV) Time-series earnings volatility (TSEV) Tiny (∙∙∙); Small (---); Large (─) Tiny (∙∙∙); Small (---); Large (─)

Cross-sectional cash flow volatility (CSCFV) Time-series cash flow volatility (TSCFV) Tiny (∙∙∙); Small (---); Large (─) Tiny (∙∙∙); Small (---); Large (─)

Fig. 1: Time-series variation in the median volatility measures, 1973-2010. This figure plots the median earnings and cash flow volatility estimates by year. Volatilities are reported by size portfolios, where portfolio classification is based on NYSE total asset breakpoints. Tiny firms (∙∙∙∙) have average total assets less than the 10th NYSE total asset percentile; Small firms (---) have average total assets between the 10th and 40th NYSE total asset percentile; Large firms (─) have average total assets greater than the 40th NYSE total asset percentile.

First, both cross-sectional earnings volatility and time-series earnings volatility have increased over time. However, the increase in volatility is much sharper in the cross-sectional estimates compared to the time-series estimates. Second, the cross-sectional earnings volatility estimates are highest at the peak of the dotcom bull market (2000) and during the financial crisis (2009). Both years correspond to periods of high uncertainty in the market as measured by either return volatility or the VIX index. Third, cash flow volatility has been relatively constant over time whereas earnings volatility has increased. This suggests that earnings uncertainty attributable to the accrual process has increased significantly over the time, consistent with the tenor of prior studies (Rajgopal and Venkatachalam 2011).

4 Empirical tests of the cross-sectional volatility measures

In this section, we empirically compare our cross-sectional volatility measures against the more conventional time-series volatility measures. There are two purposes for this analysis. First, since the empirical validity of our earnings quality measures depends on how well our volatility estimates proxy for uncertainty, we want to empirically confirm whether our cross-sectional estimates of earnings and cash flow volatility better proxy for actual earnings and cash flow volatility on average across the full sample. Second, we want to examine time-series trends in the volatility estimates to determine if the volatility estimates are getting better or worse over the sample period. 4.1 Cross-sectional comparison of the volatility measures

To assess how well our cross-sectional estimates of volatility proxy for actual volatility at time t, we annually regress realized volatility against the volatility estimates. Specifically, in table 3 we report the time-series average slopes from annual regressions of realized volatility regressed on the empirical

12 volatility estimates. We define realized volatility as the absolute value of the difference between realized earnings (cash flows) and expected earnings (expected cash flows).6 Formally, Match Real_CSEV = |Earnt+1 - Et [Earnt+1]| (1a) Real_TSEV = |Earnt+1 - Earnt| (1b) Match Real_CSCFV = |CFOt+1 - Et [CFOt+1]| (1c) Real_TSCFV = |CFOt+1 - CFOt| (1d) In panel A of table 3, we report the results of annual regressions of the realized volatility variables (1a-1d) on their respective volatility estimates. The closer the slope (γi) is to 1.0, the better the respective variable proxies for actual volatility at time t.

Real_CSEVt+1 = α1 + γ1 CSEVt + e (2a) Real_TSEVt+1 = α2 + γ2 TSEVt + e (2b) Real_CSCFVt+1 = α3 + γ3 CSCFVt + e (2c) Real_TSCFVt+1 = α4 + γ4 TSCFVt + e (2d) In addition, since estimates of earnings and cash flow volatility are direct functions of expected future earnings and expected future cash flows, we also report the time-series average of the annual cross-sectional slopes (βi) of actual earnings and cash flows regressed on expected earnings and cash flows in panel B of table 3. Match Earnt+1 = a1 + β1 Et [Earnt+1] + e (3a) Earnt+1 = a2 + β2 Earnt + e (3b) CFO = a + β E Match[CFO ] + e (3c) t+1 3 3 t t+1 CFOt+1 = a4 + β4 CFOt + e (3d) Both sets of t-statistics are based on the variability in the time-series slope estimates and incorporate a Newey-West (1987) correction with five lags to control for possible autocorrelation in the slope estimates. Panel A of table 3 notes the average slopes (γ) on both cross-sectional volatility measures (models 1 and 3) are significantly higher than the time-series volatility measures (models 2 and 4). This pattern holds in the full sample and in the overlapping sample and suggests that the cross-sectional volatility estimates better proxy for actual volatility compared to the conventional time-series measures. Second, the average slopes (β) on the earnings and cash flow expectations in panel B suggest that expectations derived from the matched-firm approach yields better estimates of future earnings and cash flows compared to the random-walk expectation model. Thus, our matched-firm expectation model appears to produce better estimates of future earnings and cash flows compared to the random-walk expectation model.

6 Note that if the earnings expectation model is well-specified, unexpected earnings should be approximately zero and the expected volatility of earnings (proxied by TSEVt-4,t or CSEVt) will equal 2 2 2 E[(Earn t1 - E t [Earn t1 ] ) ]  E[((Earn t1 - E t [Earn t1 ] ) 0) ]  (unexpected earnings)  Earn t1 - E t Earn t1 

Table 3 Analysis of predictive volatility slopes, 1973-2009 This table reports the time-series average slopes of annual cross-sectional regressions (intercepts are included in all regressions but omitted from the panels). Panel A reports the average annual slopes from regressing realized earnings volatility and realized cash flow volatility from t+1on estimates of earnings and cash flow volatility at time t. Panel B reports the average annual slopes from regressing earnings and cash flow realizations from t+1 on their respective expectations at time t. All variables are scaled by average total assets and annually winsorized at the 1st and 99th percentile. The t-statistics are based on the time-series variation in the annual slopes and incorporate a Newey-West adjustment (5- lags) to adjust for possible autocorrelation in the slope estimates. The cross-sectional earnings and cash flow estimates

(Et[Earnt+1] and Et[CFOt+1] are described in section 3.1. All other variables are described in table 1.

Panel A: Annual regressions of realized volatility regressed on expected volatility

(1) Real_CSEVi,t+1 = α1 + γ1 CSEVi,t + ei,t ; where Real_CSEVt+1 = |Earnt+1 – Et[Earnt+1]|

(2) Real_TSEVi,t+1 = α2 + γ2 TSEVi,t + ei,t ; where Real_TSEVt+1 = |Earnt+1 – Earnt|

(3) Real_CSCFVi,t+1 = α3 + γ3 CSCFVi,t + ei,t ; where Real_CSCFVt+1 = |CFOt+1 – Et[CFOt+1]|

(4) Real_TSCFVi,t+1 = α4 + γ4 TSCFVi,t + ei,t ; where Real_TSCFVt+1 = |CFOt+1 – CFOt+1|

Earnings Cash Flows (1) (2) (3) (4)

FM Slope 0.812 0.651 0.662 0.534 Full Sample FMNW t-stat (13.66) (15.19) (11.54) (24.48) Obs. 3,363.9 2,826.4 3,041.4 2,572.2

FM Slope 0.766 0.655 0.654 0.534 Overlapping FMNW t-stat (12.49) (15.36) (13.72) (24.61) Sample Obs. 2,691.5 2,691.5 2,442.2 2,442.2

Panel B: Annual regressions of realized earnings and cash flow regressed on expected earnings and cash flows

(1) Earni,t+1 = a1 + β1 Et[Earni,t+1] + ei,t

(2) Earni,t+1 = a2 + β2 Earni,t + ei,t

(3) CFOi,t+1 = a3 + β3 Et[CFOi,t+1] + ei,t

(4) CFOi,t+1 = a4 + β4 CFOi,t + ei,t

Earnings Cash Flows (1) (2) (3) (4)

FM Slope 0.932 0.751 0.728 0.492 Full Sample FMNW t-stat (84.10) (48.15) (12.31) (6.99) Obs. 3,363.9 2,826.4 3,041.4 2,572.2

FM Slope 0.908 0.754 0.717 0.495 Overlapping FMNW t-stat (62.12) (46.04) (12.33) (6.89) Sample Obs. 2,691.5 2,691.5 2,442.2 2,442.2

4.2 Time-series comparison of the volatility estimates The t-statistics on the volatility estimates reported in panel A of table 3 tend to be lower for the cross-sectional estimates compared to the time-series estimates. Since the t-statistics capture time-series variation in the slopes over the entire 38 year sample, extreme slope outliers could inflate the standard errors. Of particular interest is where the slope outliers reside. That is, are the slopes trending toward or

14 away from 1.0 in the latter part of the sample? In figure 2, we plot the five year rolling average of the annual slopes (γ) for regressions 2a – 2d. The time-series variation in slopes noted in Figure 2 shows why the cross-sectional volatility estimates tend to have lower t-statistics relative to the time-series measures. Early period estimates tend to be poorly specified, leading to lower average slopes (γ) and hence larger standard errors. However, by the mid-1980s, the average predictive slopes on the cross-sectional volatility estimates are significantly larger than those on the time-series volatility estimates. Further, the cross-sectional estimates tend to be more stable in the post-1980 period.7 Earnings Volatility Cash flow volatility

Fig. 2: Rolling 5 year average predictive slopes, 1977-2009. This figure reports the 5 year rolling average slopes (γ) from the annual cross-sectional regressions specified in equations 2a – 2d.

In sum, the evidence reported in table 3 and figures 2a and 2b suggests that our matching process produces reasonable estimates of earnings and cash flow volatility that appear to dominate those estimates derived from a time-series variation. Further, our cross-sectional estimates appear to perform considerably better in the latter part of the sample. 5. The relative importance of the accrual and cash flow component of earnings quality

In this section, we build from the analysis in table 3 and investigate the extent to which our cross-sectional measures of earnings quality explain the precision with which future earnings can be estimated incremental to other accounting-based and market-based measures of earnings quality. The purpose of this analysis is twofold. First, there is an extensive literature positing different characteristics as proxies for earnings quality – see Dechow et al. 2010. We control for the accounting-based measures of earnings quality examined by Francis et al. (2005) which represent the modal measures of earnings quality examined in the accounting literature. The measures are earnings predictability (Pred.), earnings persistence (Pers.), earnings smoothness (Smooth), and residual accrual volatility (AQ). Using these variables as established measures of earnings quality, we investigate whether our cross-sectional

7 In unreported analysis, we examine the second half of the sample (i.e., we exclude years 1973-1990). Results from this 19 year subsample yields average slopes on CSEVt and CSCFVt of 0.910 (NW t-stat ≈ 24.0) and 0.771 (NW t-stat ≈ 51.3), respectively.

15 measures of earnings quality are incrementally informative. Second, other firm characteristics such as firm size, book-to-market, accruals, and return momentum are known to be associated with future returns and earnings quality. Accordingly, since our ultimate interest is to understand if future returns are associated with our cross-sectional measures of earnings quality, we also examine whether our cross-sectional measures of earnings quality explain variation in earnings precision incremental to these characteristics.

5.1 The accrual-components of cross-sectional earnings quality

We propose two measures of earnings quality derived from our cross-sectional estimates of earnings and cash flow volatility. Our first measure is simply the difference between earnings volatility and cash flow volatility. Formally,

CSAccV = CSEV - CSCFV (4a)

The intuition supporting this measure follows from the fact that earnings are comprised of a cash flow component and an accrual component. Cash flow volatility captures fundamental uncertainty plus uncertainty attributable to the timing of cash receipts and disbursements. The accrual process should dampen uncertainty attributable to timing issues; thus, CSAccV should be negative in the average firm (evidence in table 1 confirms this hypothesis). However, CSAccV also captures elements of fundamental uncertainty unrelated to uncertainty in future economic benefits captured by cash flows. Indeed, positive values of CSAccV imply that the uncertainty in accrual-based earnings leads to earnings uncertainty estimates greater than that captured by cash flow uncertainty.

Assuming the accrual process is homogenous across all firms in a given year and using CSCFV as the baseline fundamental uncertainty estimate prior to the accrual process, variation in CSAccV can be interpreted as the extent to which accruals amplify or attenuate earnings uncertainty relative to the baseline uncertainty attributable to cash flows.

Prior studies in the discretionary accrual literature acknowledge that variation in accrual processes across firms is likely correlated to industry-specific dynamics. To account for this possibility, our second measure relaxes the assumption that the accrual and cash flow processes are homogeneous across time and across industries. Specifically, we account for possible differences in the accrual processes across industries via annual cross-sectional industry regressions. Our empirical implementation and intuition is consistent with that applied in the discretionary accrual literature (Xie 2001) and the

16 residual accrual volatility literature (Dechow and Dichev 2002).8 Formally, Ind CSEVi,t = aj,t + b1CSCFVj,t +et, where et ≡ CSAccVt (4b) Similar to 4a, we take the perspective that cash flow volatility represents a baseline measure of earnings uncertainty (prior to the effect of accrual accounting). We interpret variation in the residual as informative of how much the accrual process increases or decreases earnings uncertainty relative to the average effect the accrual process has in the industry. 5.2 The relation between the accrual and cash flow components of earnings quality and realized expectation errors Table 4 reports the results from annual cross-sectional regressions of realized earnings volatility

(Real_CSEVt+1) and the absolute value of realized analyst forecast errors (|FEt+1|) regressed on our cross-sectional measures of earnings quality (CSCFV, CSAccV, and CSAccVInd) and other firm characteristics. The goal of these regressions is to establish whether variation in our measures of earnings quality explain realized deviations from expected earnings incremental to other firm characteristics associated with earnings quality (models 2 and 4) and firm characteristics associated with future returns (models 3 and 5). Remember, we view the precision that future earnings can be estimated as a summary Ind indicator of earnings quality. Thus, significant positive slopes on CSCFVt, CSAccVt, and CSAccVt would suggest that our cross-sectional measures are incrementally contributive to existing earnings quality measures.

Results in table 4 highlight several key results. First, both components of earnings quality, the cash flow component (CSCFV) and the accrual components (CSAccV and CSAccVInd) are strongly associated with both measures of realized forecast errors. The slopes on the components of earnings quality are positive and between four and fifteen standard errors from zero. Further, the magnitudes of their respective slopes suggest that both the accrual component and cash flow components are incrementally associated with how precisely future earnings can be estimated.

Second, the cross-sectional estimates of earnings quality are much more strongly associated with the measures of realized volatility than the time-series estimates of earnings quality. Indeed, the time-series earnings quality characteristics are only marginally associated with the absolute value of analyst forecast error (|FEt+1|). Further, with the exception of earnings predictability (Pred.), the other measures of earnings quality (AQ, Pers., and Smooth) are much noisier estimates of earnings quality Ind compared to CSCFVt, CSAccVt, and CSAccVt .

8 We classify firms into industry using Ken French’s 49 industry classification system, available on his website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). Consistent with prior studies, we require at least 20 firms per industry-year to be included in the industry-adjusted sample. Results are not sensitive to this threshold.

Table 4 Cross-sectional regressions of forecast errors on earnings quality characteristics, 1973 – 2010 This table reports the time-series average of annual cross-sectional regressions of realized forecast errors regressed on the components of earnings volatility and other firm characteristics. All variables are scaled by average total assets and annually winsorized at the 1st and 99th percentile. AQ, Pers., Pred., and Smooth are defined below; all other variables are defined in previous tables. The t-statistics are based on the time-series variation in the annual slopes and incorporate a Newey-West adjustment (5-lags) to adjust for possible autocorrelation in the slope estimates.

Dependent Variable: Real_CSEVt+1 Dependent Variable: |FEt+1|

(1) (2) (3) (4) (5) (1) (2) (3) (4) (5)

Intercept -0.009 -0.007 0.027 0.001 0.049 0.017 0.009 0.076 0.014 0.088 (-2.27) (-2.15) (6.60) (0.28) (10.15) (2.49) (2.67) (10.37) (4.09) (8.68)

CSCFVt 0.939 0.554 0.813 0.352 0.535 0.434 0.312 0.261 0.190 0.138 (10.91) (8.74) (9.02) (4.89) (5.11) (5.57) (6.52) (4.42) (7.13) (6.63)

CSAccVt 0.693 0.433 0.617 0.364 0.267 0.262 (14.65) (11.94) (12.84) (4.85) (6.64) (4.81)

Ind CSAccVt 0.382 0.560 0.230 0.212 (13.21) (13.56) (7.65) (7.19)

AQ1 0.176 0.180 0.159 0.173 (5.64) (5.48) (1.51) (1.59)

Pers.2 -0.003 -0.003 -0.003 -0.003 (-4.20) (-3.70) (-1.65) (-1.50)

Pred.3 0.369 0.407 0.242 0.299 (12.56) (20.98) (2.11) (2.01)

Smooth4 0.003 0.005 0.008 0.008 (3.99) (5.69) (1.95) (2.22)

LogB/Mt -0.011 -0.015 0.023 0.022 (-4.23) (-4.68) (6.44) (6.23)

LogSizet -0.004 -0.005 -0.010 -0.011 (-9.95) (-8.93) (-11.88) (-10.03)

dNWCt -0.030 -0.040 -0.034 -0.038 (-4.08) (-4.63) (-4.43) (-4.49)

Avg. Obs. 2,924 2,152 2,915 2,038 2,767 1,716 1,284 1,716 1,227 1,644 Avg. R2 0.213 0.257 0.224 0.252 0.211 0.058 0.082 0.13 0.083 0.127

1AQ Std. dev. of regression residuals over 5 year period, lagged 1 year – see Dechow and Dichev (2002) Acct = α + β1CFOt+1 + β2CFOt+ β3CFOt-1+ μ 2 Pers. Slope (β) from regression over trailing 5 year period, lagged 1 year. Earnt+1 = α + βEarnt + μ 3 Pred. Std. dev. of regression residuals (μ) over trailing 5 year period, lagged 1 year. Earnt+1 = α + β Earnt + μ 4Smooth Time-series earnings volatility (TSEV) divided by time-series cash flow volatility (TSCFV)

Third, models 3 and 5 note that book-to-market is negatively associated with Real_CSEVt+1 (-0.015) but positively associated with the absolute value of analyst forecast error (0.022), both with t-statistics greater than 4.50 in absolute terms. The positive relation between book-to-market and absolute forecast errors is consistent with that noted in Diether et al. (2002) and Johnson (2004). The negative

18 relation we document between Real_CSEVt+1 and book-to-market is due primarily to the inclusion of many small growth firms that do not have the requisite analyst following to compute a consensus forecast.

Indeed, the slope from regressing Real_CSEVt+1 on book-to-market attenuates to close to zero (although marginally significant) in unreported regressions that only examine observations with analyst forecast errors.

In sum, our cross-sectional estimates of earnings quality are significantly associated with how precisely future earnings can be estimated. The strength of the relations is much stronger than those of conventional, time-series measures of earnings quality. To the extent that earnings quality, as proxied by precision, is value-relevant to equity investors, a significant relation with future returns may be noted. If so, this finding would be unique to the earnings quality literature as, to date, studies examining the relation between future returns and current period measures of earnings quality have not found a significant relation. We examine this formally in the next section.

6. The relation between the components of earnings volatility and future returns

Inferences from table 4 and those from Figure 1 suggest the accrual process has a significant effect on the time-series and cross-sectional characteristics of earnings quality. In this section, we examine whether the earnings quality characteristics captured by our cross-sectional measures are associated with future returns.

As noted in section 2, the debate in the extant earnings quality literature centers on whether earnings quality proxies for information risk and is priced by equity investors. Consequently, if our cross-sectional measures of earnings quality capture information risk, then we may find positive slopes on the earnings quality variables. Alternatively, if our cross-sectional earnings quality measures are negatively associated to investor expectations of future earnings, then we may find a negative relation between our earnings quality variables and future returns. Finally, if our cross-sectional measures of earnings quality capture idiosyncratic noise that does not systematically affect equity price, we may find no relation between future returns and our earnings quality measures. The no relation result would be consistent in tenor to that of Core et al. (2008) and McInnis (2010) who both find no relation between future returns and the accounting-based measures of earnings quality they examine.

6.1 Return sample

The sample of firms we examine in our return tests closely mirrors the primary sample we describe in section 3.2 with a few minor differences. First, because we need a future return series to match against current period firm characteristics, our primary return sample spans 1974:04 – 2010:03.

Second, we exclude firms with share prices below $1 share to reduce the influence of very thinly traded stocks which are more sensitive to certain trading frictions (short-sale constraints, lack of institutional attention, bid-ask bounce). The future return period begins in the fourth month of fiscal year t+1 and extends twelve months (through the end of month 3, t+2). All variables with the exception of future returns are winsorized monthly at the 1st and 99th percentile. All explanatory variables are updated once per fiscal year with the exception of return momentum, which is updated monthly and represents the natural log of the cumulative 11 month buy-and-hold return between months m-12 and m-1.9 Our primary return sample has an average of 2,591 firms per month and spans 432 months. We also examine other subsamples of firms which are derived directly from the primary return sample (i.e., we only winsorize the primary sample of firms). The varying subsamples differ slightly in the average number of firms per monthly cross-section.

6.2 The relation between earnings quality and future returns – full sample

Table 5 reports the time-series average of monthly cross-sectional regressions of future returns Ind regressed on our cross-sectional earnings quality variables (CSCFVt, CSAccVt, CSAccVt ) and firm characteristics known to be associated with future returns. Specifically, we include firm market value

(LogSizet), book-to-market (LogB/Mt), return momentum (LogMomt), and change in net working capital

(dNWCt). These variables respectively control for the size anomaly (Banz 1981), value/growth anomaly (Fama and French 1992), return momentum (Jegadeesh and Titman 1993), and the accrual anomaly (Sloan 1996). The t-statistics are computed from the time-series variation in the monthly slopes over the 432 month sample (models 1-4) and 408 month sample (models 5-8).

Three key results emerge from table 5. First, across all specifications, the accrual component of earnings quality is negatively associated with future returns. Controlling for size and the cash flow component of earnings quality in models 1 and 3, the average slope on CSAccVt is -3.177 (t-stat -2.67) Ind while the average slope on CSAccVt is -3.817 (t-stat -3.89). In models 2 and 4 we control for book-to-market (LogB/M), return momentum (LogMom), accruals (dNWC). The slopes on the cash flow component of earnings quality (CSCFVt) become essentially insignificant. However, slopes on both the accrual elements of earnings quality stay significantly negative, although the absolute magnitudes of their slopes attenuate some toward zero.

9 For example, for a regression in April of 2002, return momentum would be computed over the 11 month span between April, 2001 and February 2002.

Table 5 Predicting future monthly stock returns This table reports the average slopes and their t-statistics from monthly cross-sectional regressions of future returns on current period earnings quality variables on other firm characteristics. The future monthly return series is 12 months and begins in the fourth month after fiscal year end (4/t+1 – 3/t+2). The full sample (models 1-4) spans 432 months between 1974:04 – 2010:03; the overlapping sample (models 5-8) spans 408 months between 1976:04 – 2010:03. We exclude all financial firms (6000-6999) and utility firms (4900-4999). LogMomt is equal to the natural log of return momentum and is updated monthly. For month j, LogMom is equal to the natural log of the cumulative return from month j-12 to j-2. All other variables are defined in previous tables and updated at the beginning of month 4 of year t+1. Explanatory variables are winsorized monthly. Slopes are multiplied by 100 and t-statistics are computed from the time-series variation in the monthly slopes (Fama and MacBeth 1973).

Full Sample Overlapping Sample (1) (2) (3) (4) (5) (6) (7) (8)

Intercept 2.654 2.149 2.679 2.221 2.080 2.169 2.171 2.239 FM t-stat (8.00) (7.28) (8.03) (7.31) (7.30) (7.96) (7.36) (8.07)

CSCFVt -5.331 -2.369 -3.673 -1.534 -2.861 -2.259 -2.053 -1.657 FM t-stat (-2.92) (-1.72) (-2.64) (-1.55) (-2.09) (-2.03) (-2.15) (-2.11)

CSAccVt -3.177 -1.881 -2.358 -2.094 FM t-stat (-2.67) (-2.02) (-2.47) (-2.67)

Ind CSAccVt -3.817 -2.824 -3.175 -2.880 FM t-stat (-3.89) (-3.79) (-4.22) (-4.38)

TSCFVt -1.481 -0.822 FM t-stat (-1.73) (-1.43)

TSAccVt -0.579 FM t-stat (-0.73)

Ind TSAccVt -0.846 FM t-stat (-1.16)

LogSizet -0.189 -0.138 -0.198 -0.151 -0.129 -0.138 -0.143 -0.151 FM t-stat (-4.71) (-3.71) (-4.94) (-3.94) (-3.61) (-3.99) (-3.88) (-4.30)

LogB/Mt 0.290 0.278 0.226 0.199 0.213 0.187 FM t-stat (4.87) (4.35) (3.81) (3.55) (3.30) (3.11)

LogMomt 0.587 0.578 0.619 0.605 0.648 0.637 FM t-stat (3.07) (3.00) (3.23) (3.20) (3.35) (3.34)

dNWCt -2.175 -2.281 -1.969 -1.968 -2.011 -2.028 FM t-stat (-7.49) (-7.57) (-6.53) (-6.56) (-6.48) (-6.54)

Avg. Obs. 2,590.8 2,402.2 2,461.3 2,284.9 1,934.0 1,934.0 1,814.9 1,814.9 Avg.. R2 0.018 0.033 0.017 0.033 0.033 0.037 0.033 0.036

Ind Second, the relation between future returns and the CSAccVt is much stronger than that Ind between future returns and CSAccVt. Models 3 and 4 note slopes of -3.817 and -2.824 on CSAccVt

(each with a t-statistic ≈ 3.8 in absolute terms) whereas the slopes on CSAccVt in models 1 an2 are -3.177 and –1.881 (with t-statistics of 2.67 and 2.02). By industry-adjusting the accrual component of earnings

Ind quality, variation in CSAccVt is more directly attributable to firm-specific differences in the accrual process. By contrast, some of the variation in CSAccVt is due to accrual process differences attributable to industry differences.

Third, the negative relation between our cross-sectional measures of earnings quality and future returns is not simply attributable to the young, unseasoned firms captured by our cross-sectional measures and not by the time-series measures. Specifically, models 5-8 show that in seasoned firms (those with at least five years of data on Compustat) the slopes on the accrual components of earnings quality are significantly negative. Further, in models 6 and 8 which report slopes for both the cross-sectional and time-series measures of earnings quality, the slopes on each of the cross-sectional measures of earnings quality are between 2.0 and 4.5 standard errors from zero whereas the time-series measures are essentially insignificant.

6.3 The relation between earnings quality and future stock returns – larger firms

There is extensive evidence that firms with low market capitalizations and/or low stock prices tend to contribute disproportionately to anomalous return patterns (e.g., Bhardwaj and Brooks 1992; Pontiff 1996). Accordingly, table 6 examines the same regression specifications reported models 2 and 4 of table 5, but excludes subsets of smaller firms with share prices under $5/share. The premise of these tests is to establish whether the negative relations we document on our earnings quality variables in table 5 are due to small, economically insignificant firms subject to stronger trading frictions and/or having little institutional ownership or if the negative relation extends to larger, more economically significant firms.

Table 6 reports average monthly slopes from four sets of firms with share prices above $5 share. The five dollar per share threshold is an important screen since most stocks cannot be short sold if share price is less than five dollars. Further, many institutions are prohibited from owning shares trading under $5 per share. Models 1 and 2 report regression slopes for the full sample with share price above $5/share as of the beginning of the future return period. Models 3 and 4 exclude firms with market capitalizations below the 20th percentile of all NYSE, AMEX, and NASDAQ stocks while model 5 and 6 exclude firms below the 20th percentile of NYSE stocks. These screens reduce the influence of small, economically insignificant firms that comprise an economically significant percentage of publicly traded firms. Finally, models 7 and 8 exclude firms trading on the NASDAQ exchange. Due to less stringent listing requirements, the average firm on the NASDAQ tends to be earlier in its economic life relative to firms trading on the NYSE or AMEX and smaller in size. Accordingly, models 7 and 8 are alternative screens for firm size and economic age.

Table 6 Predicting future returns, 1974:04 – 2010:03 This table reports the average slopes and their t-statistics from monthly cross-sectional regressions. The future monthly return series is 12 months, begins in the fourth month after fiscal year end (4/t+1 – 3/t+2) and spans 432 months between 1974:04 – 2010:03. Share price and market capitalization classifications are determined as of the beginning of the future return period. All variables are defined in previous tables, updated at the beginning of month 4 of year t+1 and winsorized monthly at the 1st and 99th percentile. Slopes are multiplied by 100 and t-statistics are computed from the time-series variation in the monthly slopes (Fama and MacBeth 1973).

Models 1,2: All firms with share prices ≥ $5 Models 3,4: All firms with share prices ≥ $5 and market capitalizations ≥ 20th percentile of all firms Models 5,6: All firms with share prices ≥ $5 and market capitalizations ≥ 20th percentile of NYSE firms Models 7,8: All NYSE/AMEX firms with share prices ≥ $5

Full Sample Full Sample Full Sample NYSE/AMEX Sample ≥$5/share ≥$5/share ≥$5/share ≥$5/share 20th ME percentile All but Micro (1) (2) (3) (4) (5) (6) (7) (8)

Intercept 1.752 1.746 1.765 1.764 1.782 1.801 1.616 1.559 FM t-stat (6.08) (5.99) (5.62) (5.55) (4.21) (4.21) (4.74) (4.58)

CSCFVt -3.027 -1.773 -3.101 -1.792 -2.510 -1.092 -3.299 -2.014 FM t-stat (-2.42) (-2.04) (-2.46) (-2.04) (-1.63) (-1.03) (-2.89) (-2.51)

CSAccVt -2.706 -2.717 -2.505 -2.675 FM t-stat (-3.04) (-3.01) (-2.26) (-3.13)

Ind CSAccVt -3.159 -3.071 -2.370 -2.794 FM t-stat (-4.39) (-4.20) (-2.60) (-3.60)

LogSizet -0.077 -0.081 -0.078 -0.084 -0.090 -0.098 -0.064 -0.062 FM t-stat (-2.44) (-2.54) (-2.37) (-2.47) (-2.14) (-2.27) (-1.90) (-1.83)

LogB/Mt 0.245 0.246 0.240 0.243 0.185 0.194 0.186 0.193 FM t-stat (3.69) (3.46) (3.59) (3.38) (2.41) (2.38) (2.86) (2.89)

LogMomt 0.920 0.918 0.921 0.921 0.770 0.794 0.896 0.892 FM t-stat (4.62) (4.56) (4.57) (4.53) (3.27) (3.34) (4.11) (4.08)

dNWCt -2.295 -2.267 -2.347 -2.301 -2.299 -2.299 -2.631 -2.552 FM t-stat (-7.29) (-7.12) (-7.33) (-7.07) (-5.15) (-5.12) (-7.01) (-6.70)

Avg. Obs. 1,815.5 1,723.9 1,735.3 1,647.2 961.4 905.1 955.2 889.5 Avg.. R2 0.038 0.038 0.040 0.039 0.056 0.055 0.043 0.044

Results in table 6 show a strong, negative relation between the accrual components of earnings quality and future returns. In fact, the slopes noted in table 6 are much stronger than those reported in Ind table 5, suggesting that the negative slopes on CSAccVt and CSAccVt are not due (only) to smaller, low-priced stocks. Rather, results in table 6 suggest that variation in earnings quality has a stronger effect on equity price in larger firms than in the full sample. These results counter the conventional intuition that might suggest trading frictions such as lack of institutional attention or short-sale constraints are

23 responsible for the negative slopes on the earnings quality variables in the full sample. We investigate the cause of these slope differences more thoroughly in table 8.

6.4 The relation between analyst forecast errors and earnings quality

The results in tables 5 and 6 show a strong negative relation between future returns and the accrual components of earnings quality. This relation implies one of two hypotheses. The first hypothesis is that investors have overly-optimistic future earnings expectations in firms with poor earnings quality (i.e., investor expectations of future earnings are positively associated with the accrual components of earnings quality). The second hypothesis suggests that the accrual components of earnings quality are negatively correlated with firm risk. To distill these joint-hypotheses from one another, we investigate the relation between analyst forecast errors and our cross-sectional earnings quality variables. The premise of these regressions is simple. If our cross-sectional earnings quality variables are negatively associated with future returns because they are associated with risk, then there should be no relation with realized forecast errors. However, if our cross-sectional earnings quality variables are associated with overly-optimistic expectations of future earnings, which lead to future period return reversal and the negative slopes reported in tables 5 and 6, we expect negative associations Ind between CSCFVt, CSAccVt, CSAccVt and realized forecast errors.

Table 7 reports pooled slopes from regressing realized analyst forecast errors on the Ind cross-sectional earnings quality variables (CSCFVt, CSAccVt, CSAccVt ) and the set of control variables from tables 5 and 6. Since autocorrelation in the dependent variable may exist, we cluster our standard errors by firm year (gvkey and fyear).10

Consistent with the premise that earnings quality affects investor expectations of future earnings, we find a strong negative relation between analyst forecast errors and each component of earnings quality. Ind The slopes on the accrual components of earnings quality, CSAccVt and CSAccVt , are -0.050 and -0.062 respectively. Further, the slopes on the cash flow component of earnings quality are also significantly negative.

10 Our inferences are qualitatively identical if we employ Fama-Macbeth (1973) style regressions and Newey-West adjust the standard errors.

Table 7 Analyst forecast error regressions, 1983-2010 Ind This table reports the results on the relation between CSAccVt, CSAccVt , and analyst forecast errors. Regression slopes are pooled and standard errors are clustered by firm (gvkey) and year (fyear). Forecast Errort+1 (FEt+1) equals the consensus earnings forecast minus actual earnings for year t+1. The consensus forecast is based on most recent earnings forecasts for fiscal year t+1 earnings made between the 2nd and 4th month of year t+1. All other variables defined as in prior tables and winsorized annually.

Dependent Variable: FEt+1 Intercept CSAccV CSAccV Ind CSCFV LogSize LogB/M LogMom dNWC Obs. 1 - 0.062 - 0.050 - 0.061 0.007 - 0.006 0.016 - 0.026 43,859 (-11.84) (-1.74) (-1.91) (9.61) (-3.76) (3.37) (-2.22)

2 -0.062 -0.077 -0.053 0.007 -0.006 0.017 -0.027 42,033 (-12.39) (-4.33) (-2.27) (10.12) (-4.12) (3.28) (-2.32

In sum, the results reported in tables 5-7 paint a strong and consistent picture of the equity market consequences of variation in our cross-sectional measures of earnings quality. Specifically, firms with lower earnings quality, where earnings quality is viewed as how precisely future earnings can be estimated, tend realize lower future returns. Our empirical evidence strongly rejects the premise that our measures of earnings quality are negatively correlated with risk. Rather, our evidence suggests that earnings quality has a significant impact on investor earnings expectations.

7. Robustness 7.1 Extreme return realizations and earnings quality

The stronger negative slopes between the accrual components of earnings quality (CSAccVt and Ind CSAccVt ) and future returns noted in table 6 relative to the slopes noted in table 5 are unusual and counter conventional intuition and prior empirical evidence.. For example, inferences from Pastor and Veronesi (2003), Jiang et al. (2005), Zhang (2006) as well as Francis et al. (2005) and Ecker et al. (2006) would suggest that the earnings processes of mature, larger firms tend to be more certain or of higher quality. Further, slopes from regressions of future returns on current period firm characteristics tend to decay toward zero in regressions that exclude smaller firms (Fama and French 2008).

Small, low-priced firms tend to have more volatile returns. Since returns are not symmetrically distributed around zero, an extreme positive monthly return realization is likely to be larger (in absolute magnitude) than an extreme negative monthly return realization. Indeed, the cross-sectional average monthly return in our primary sample is approximately 1.39% with a cross-sectional standard deviation of 15.6%. This compares to an average monthly return 1.24% and a monthly standard deviation of 12.4% for firms trading above $5 at the beginning of the future return period.

In table 8, we examine whether firms with extreme future return realizations significantly affect inferences on current period firm characteristics. Specifically, we exclude monthly firm observations with realized returns in excess of 200%. This analysis embeds an obvious look-ahead bias and is not intended to give any perspective on a possible trading strategy. However, prior studies (e.g., Knez and Ready 1997; Kraft et al. 2006) suggest that future return inferences with respect to market capitalization and accruals are sensitive to the inclusion of very small sets of firms with extreme future return realizations. Similar to the intent of these studies, we wish to examine whether our inferences with respect to the accrual components of earnings quality are sensitive to a few influential observations that impart significant leverage on the slopes.

Both Knez and Ready (1997) and Kraft et al. (2006) employ a least-trimmed-squares empirical design which excludes observations with the most extreme regression residuals, approximately 1-2% of the total sample. Similar in spirit, we simply exclude observations with monthly returns in excess of 200%. This choice has two advantages. First, it preserves a larger sample compared to the LTS empirical design. Second, the effect of excluding these extreme observations (as opposed to employing a two-stage LTS design) is easier to interpret when comparing results against those in tables 5 and 6. Significant differences in slopes can be attributed directly to extreme positive return realizations. Thus, an investor or researcher wishing to evaluate the impact of our cross-sectional earnings quality characteristics on expected future returns can simply assess the likelihood that the stocks of interest will realize monthly returns in excess of 200%.

For completeness, we report results in table 8 across five samples: the full sample examined in table 5 and the four subsamples of larger firms examined in table 6. Two primary results emerge from table 8. First, a very small subset of low priced stocks realizing extreme positive returns has a significant influence on the CSAccVt slope in model 3 of table 5. Specifically, the slope on CSAccVt goes from -1.88 (t-stat of -2.02) to -2.615 (t-stat of -2.98). With respect to the full sample differences across table 5 and table 8, the average cross-sectional regression in table 8 drops 0.6 firms per month (~0.025%), or 260 total firm-months from more than 1.4 million monthly firm observations examined in the full sample. Second, slopes and t-stats across the other models in table 8 (models 3-10) are largely consistent with those in table 6. This finding is not surprising given that realized monthly returns in excess of 200% are almost non-existent for firms trading above $5 per share and those with market capitalizations above a minimal threshold.

Table 8 Cross-sectional regressions of future returns - trimmed, 1974:04 – 2010:03 This table reports the average slopes and t-statistics from monthly cross-sectional regressions. The future monthly return series is 12 months and begins in the fourth month after fiscal year end (4/t+1 – 3/t+2). We exclude all monthly observations with returns in excess of 200%. All explanatory variables are defined in previous tables, winsorized monthly, and updated at the beginning of month 4 of year t+1. Slopes are multiplied by 100 and t-statistics are computed from the time-series variation in the monthly slopes (Fama and MacBeth 1973).

Full Sample Full Sample Full Sample Full Sample NYSE/AMEX ≥$5/share ≥$5/share ≥$5/share Sample ≥$5/share 20th ME percentile All but Tiny (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Intercept 2.100 2.148 1.744 1.730 1.770 1.761 1.795 1.813 1.614 1.556 FM t-stat (7.17) (7.14) (6.04) (5.93) (5.63) (5.53) (4.24) (4.23) (4.73) (4.46)

CSCFVt -3.339 -2.137 -3.283 -1.943 -3.357 -1.954 -2.702 -1.276 -3.473 -2.148 FM t-stat (-2.68) (-2.34) (-2.70) (-2.30) (-2.73) (-2.28) (-1.80) (-1.24) (-3.02) (-2.62)

CSAccVt -2.615 -2.880 -2.896 -2.526 -2.764 FM t-stat (-2.98) (-3.30) (-3.28) (-2.29) (-3.27)

Ind CSAccVt -3.469 -3.303 -3.203 -2.370 -2.905 FM t-stat (-4.88) (-4.70) (-4.48) (-2.60) (-3.80)

LogSizet -0.118 -0.130 -0.073 -0.077 -0.077 -0.081 -0.090 -0.098 -0.062 -0.060 FM t-stat (-3.24) (-3.45) (-2.32) (-2.40) (-2.32) (-2.40) (-2.14) (-2.27) (-1.86) (-1.79)

LogB/Mt 0.307 0.301 0.242 0.245 0.238 0.242 0.182 0.191 0.187 0.194 FM t-stat (5.27) (4.85) (3.65) (3.45) (3.55) (3.37) (2.37) (2.34) (2.88) (2.92)

LogMomt 0.763 0.751 0.968 0.966 0.970 0.971 0.796 0.820 0.916 0.913 FM t-stat (4.25) (4.16) (5.01) (4.97) (4.97) (4.94) (3.45) (3.53) (4.24) (4.22)

dNWCt -1.959 -2.044 -2.302 -2.270 -2.321 -2.267 -2.264 -2.262 -2.671 -2.592 FM t-stat (-7.04) (-7.14) (-7.38) (-7.18) (-7.29) (-7.01) (-5.05) (-5.01) (-7.11) (-6.80)

Avg. Obs. 2,401.6 2,284.3 1,815.4 1,723.8 1,735.1 1,647.1 961.3 905.0 955.2 889.5 Avg.. R2 0.034 0.033 0.039 0.038 0.040 0.039 0.056 0.055 0.044 0.044

7.2 Time-series variation in the components of earnings quality

Inferences with respect to our cross-sectional measures of earnings quality and future returns represent the average monthly slope over the entire 432 month sample period. These average monthly slopes provide no perspective on whether the relation between future returns and earnings quality is becoming more or less important over time. Prior studies have noted that the size effect (Dichev 1998) and accrual anomaly (Green et al. 2011; Richardson et al. 2010) are less significant in recent years compared to earlier years. Accordingly, a significant relation between future returns and our measures of earnings quality that is attenuating over time is perhaps less interesting and relevant to investors and regulators than a relation that is stable (or increasing) over time.

Ind In figure 3 we plot the 10-year (120 month) rolling average of the monthly slopes on CSAccVt

27 from the regressions examined in table 5 (model 4) and table 6 (model 6).11 The purpose of these figures is to examine time-series trends in the relation between future returns and our cross-sectional measures of earnings quality across the various subsamples of firms. Given our future return sample begins on 1974:04, the plots begin on 1984:03 and continue to sample end 2010:03. For comparative purposes, we also include the rolling slope averages of accruals (dNWC) and return momentum (LogMom).

Full Sample – 10 year average rolling slopes All-but-micro – 10 year average rolling slopes

Fig. 3: 10 year rolling slope analysis, 1984:03 -2010:03. This figure plots the rolling 120 month (10 year) regression slopes from monthly regressions of future returns regressed on the two cross-sectional components of earnings quality (CSCFVt and Ind CSAccVt ), LogSizet, LogB/Mt, LogMomt, and dNWCt. The full sample includes all firms (model 4, table 5). The ‘all-but-micro’ includes all firms with market capitalizations above the 20th NYSE market capitalization percentile (model 8, table 6).

Ind Figure 3 shows that the average slopes on CSAccVt are comfortably below zero across the sample. In contrast, the average slopes on return momentum and accruals both attenuate to zero over the latter part of the sample period. These results are consistent to the findings of Lewellen (2011) with respect to return momentum and Green et al. (2011) with respect to working capital accruals. Second, we note a global maximum in the average slopes around the peak of the dotcom bull market of the late 1990’s in the full sample and a global minimum around that same period in the ‘all-but-micro’ sample. This shift in slopes can be interpreted with a dynamic systematic risk hypothesis or a behavioral pricing bias hypothesis. We are agnostic as to the cause, but note that the results fit the perception that investor preferences changed in the late 1990s with respect to accounting-based measures of value.12

8 Conclusion

The equity market consequences of earnings quality remains an intensely debated area in the accounting literature. Debate spawns from the fact that despite theory positing that earnings quality is

11 For brevity, we only report two figures. Plots of the other three models in table 6 (models 2, 4, and 8) are qualitatively very similar to that reported in the ‘all-but-micro’ figure. 12 We also note the shift in slopes correspond to that documented by Diether et al. (2002) who note that firms with high forecast dispersion loaded very heavily on the HML factor whereas low dispersion loaded more heavily on growth factor between 1983-1999. In 2000, Diether et al. note this pattern sharply reversed.

28 relevant to the equity market, empirical evidence to date has failed to find a significant relation between current period measures of earnings quality and future period returns.

We suggest that the time-series nature of the earnings quality measures examined in prior studies poorly capture current period earnings quality dynamics, jointly capturing dynamics associated with economic uncertainty and uncertainty associated with financial reporting discretion. We propose and empirically validate a new cross-sectional measure of earnings quality based on a matched-firm approach. Our empirical approach (i) requires a minimal time series (two-years), (ii) is not mechanically linked to prior period earnings quality estimates, (iii) directly maps into the precision that future earnings can be estimated, and (iv) captures the economic uncertainty element of earnings quality.

Our empirical results produce strong, consistent inferences across multiple subsets of firms with respect to the equity market consequences of earnings quality. Specifically, firms with low earnings quality tend to realize lower returns in future periods. The average cross-sectional monthly slope (in percent) from regressing future returns on our cross-sectional component of earnings quality ranges between -0.024 in the ‘all-but-micro sample’ to -0.038 in the full sample, all more than 3.8 standard errors less than zero.

Our study makes two primary contributions to the earnings quality literature. First, our forward-looking measures of earning quality capture the component of earnings quality associated with economic uncertainty as measured by GAAP. The empirical earnings quality constructs examined in prior studies jointly capture uncertainty associated with economic fundamentals and uncertainty associated with managerial reporting discretion. Our empirical design produces an estimate of uncertainty that is minimally affected by the idiosyncratic reporting choices of managers.

Second, we propose and empirically validate a distinct, and economically significant, effect of earnings quality on equity value. Prior debate has focused on the relation between earnings quality and cost of equity capital. Our empirical evidence suggests that variation in earnings quality has a significant effect on investor expectations of future earnings. Our results do not refute or rebuff the contention that variation in earnings quality is associated with a firm’s cost of equity capital. In fact, the tenor of the analytic studies on the relation between earnings quality and cost of capital relates to how managerial reporting discretion affects earnings quality, and hence, cost of capital (for example, see Lambert et al. 2007). Our empirical measure does not capture this component of earnings quality. However, our results do suggest empirical caution as, to the extent that earnings quality is interpreted in terms of precision, the joint correlation between earnings quality and both elements of stock price (investor earnings expectations and cost of equity) could lead to incorrect empirical inferences.

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