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Review of Accounting Studies

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Open Access 10.10.2020

Earnings beta

verfasst von: Atif Ellahie

Erschienen in: Review of Accounting Studies | Ausgabe 1/2021

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Abstract

The literature on cash flow or earnings beta is theoretically well-motivated in its use of fundamentals, instead of returns, to measure systematic risk. However, empirical measures of earnings beta based on either log-linearizing the return equation or log-linearizing the clean-surplus accounting identity are often difficult to construct. I construct simple earnings betas based on various measures of realized and expected earnings and find that an earnings beta based on price-scaled expectations shocks performs consistently well in explaining the cross-section of returns over 1981–2017. I also examine the relation between different measures of beta and several firm characteristics that are either theoretically connected to systematic risk or are empirically associated with returns and find evidence in support of the construct validity of an earnings beta based on price-scaled expectations shocks. Overall, the findings suggest that this easy-to-construct earnings beta can be suitable for future researchers requiring a measure of systematic risk.

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Most prior literature uses the term cash flow beta, even though the estimation of beta is based either on dividends in the first approach or on accounting earnings in the second approach, neither of which are actually cash flows. Instead, I prefer to use the term “earnings beta,” since the beta estimation procedure involves accounting earnings.

According to Penman (2016), these potentially problematic assumptions include (1) requiring firms to pay dividends, (2) assuming log book-to-price converges to zero (or to a constant) in the long run, (3) assuming return on equity converges to expected returns in the long run, and (4) potentially violating the assumptions of Miller and Modigliani (1961) by using logs. Penman (2016) also notes that the assumption of a zero premium of price over book is tantamount to assuming “unbiased accounting” of Ohlson (1995). Hence the model does not consider how the accounting system deals with potential future earnings growth tied to the unconditionally conservative accounting for uncertain investments, such as research and development and advertising expenditures (Feltham and Ohlson 1995).

In this study, “earnings” refers to core earnings, which is net income before extraordinary items and tax-adjusted special items minus preferred dividends. It is a measure of continuing earnings generated by the core business of the firms and is adjusted for transitory and nonrecurring items. Expectations of earnings refers to forecasts developed by equity research analysts, which are also typically forecasts of normalized or core earnings.

I also estimate earnings betas using revisions in analysts’ expectations of long-term growth, which are available for fewer firms and cover a slightly shorter period. While this expected long-term growth beta is positively associated with returns, the magnitude and statistical significance is smaller. This could either be due to imprecise beta estimates resulting from insufficient variation in long-term growth forecasts, short-horizon return variation being driven primarily by short-term earnings, or the risk premium for short-term earnings being higher. Along similar lines, Binsbergen et al. (2012) find that short-term dividends carry a higher risk premium than long-term dividends.

Selected examples include Fama and French (1992, 1993, 1996, 2006), Jegadeesh and Titman (1993), Ou and Penman (1989), Sloan (1996), Pastor and Stambaugh (2003), and Dechow et al. (2004). Also see Harvey et al. (2016) for a comprehensive list.

Relatedly, Easton et al. (1992) note “the market buys earnings” (p. 126), and Brown and Ball (1967) and Ball et al. (2009) conclude that firm-level earnings contain a strong systematic component. It is also worth noting that measuring systematic risk using the comovement of earnings with aggregate earnings also indirectly tests whether aggregate earnings is a candidate state variable that is correlated with aggregate consumption and investment opportunities (Merton 1973). Since corporate earnings directly and indirectly contribute to economy-wide output, a link to consumption is plausible. Indeed, Kothari et al. (2006) find a positive correlation between aggregate earnings and several macroeconomic variables, including consumption. In untabulated results, I also find similar positive associations between several of the aggregate earnings series considered here and changes in consumption. Finally, aggregate earnings news could indicate macroeconomic news that drives aggregate stock returns (see Anilowski et al. 2007; Shivakumar 2007).

The distortion is due to the expensing of these uncertain investments as they are incurred, which depresses the current level of earnings. Further, firms recognize earnings from these investments only when they are generated in the future, which inflates earnings in future periods.

Negative earnings also preclude the use of log earnings growth. Another alternative is to scale change in earnings by absolute values of prior period earnings. However, this measure is not well behaved when prior period earnings are close to zero. A benefit of using portfolios in this study is that the incidence of negative portfolio-level earnings is much less frequent, ranging from less than 1.0% to 5.6% depending on the portfolio sorting variable.

Similarly, Da and Warachka (2009) track revisions in analyst earnings forecasts to develop a cash flow news measure and compute cash flow beta. Taking a slightly different approach, Nallareddy (2012) measures earnings shocks as returns driven by revisions to expectations of future earnings and finds that returns to the post-earnings-announcement drift (PEAD) strategy relate to the differential responses of individual stock returns to aggregate earnings shocks.

Present value models also support the role of changes in expected earnings (cash flow) for changes in prices (i.e., expected returns). The first effect is in the numerator by altering the path of future cash flows. The second effect is in the denominator by altering the systematic risk embedded in expected returns. And if the changes in expected earnings indicate aggregate-level shocks, the third effect is through time-varying market risk premiums.

Similarly, Da and Warachka (2011) note that equity research analysts have weak incentives to promptly incorporate information into their long-term growth forecasts, because the average analyst’s career lasts four years. In my sample LTG forecasts are revised 22% of the time, while two-year-ahead EPS forecasts are revised 74% of the time.

Using one-year- and two-year-ahead forecasts provides the broadest possible coverage of firms in the cross-section; requiring earnings forecasts for three and four years ahead would reduce the sample by about one-half. Further, long-term forecasts are only available for about two-thirds of the firm-months and are revised infrequently, likely due to stronger analyst incentives to focus on the short-term.

Interestingly, even though expected ROE beta and expected earnings yield beta have negative and significant estimated risk premiums in Table 4, I find that several of the fitted excess returns for the 50 portfolios generated from these two approaches to estimate earnings beta are close to the average excess returns, as shown by the cluster of data points along the diagonal in Figure 1 (see Panels g and k). This visualization of the fit corroborates the R² of 20% and 30% for expected ROE beta and expected earnings yield beta, respectively. One potential explanation for the negative risk premiums could be that the pricing tests are weak and are sensitive to the selection of characteristics used to sort firms into portfolios. Therefore, I conduct additional portfolio-level and firm-level pricing tests for robustness and find that these two betas have consistently positive risk premiums.

In a similar vein, the literature documents that momentum portfolio returns are not due to compensation for systematic risk and possibly are due to delayed stock price reaction (Jegadeesh and Titman 1993).

In untabulated results, I also estimate Eq. (3) using panel regressions with month fixed effects and find generally consistent results. I find that the four earnings betas that have positive and significant risk premiums in Table 8 are also positive and significant in panel regressions. The largest coefficient is for the price-scaled expectations shock beta of 0.085 with a t-statistic of 3.55 where the standard errors are clustered by firm and month. I also find that, while the betas based on levels of realized earnings (i.e., realized ROE beta and realized earnings yield beta) are insignificant, the earnings betas based on changes in realized earnings are weakly significant.

Bowman (1979) shows that theoretically earnings variability does not have a (direct) role in determining systematic risk since it is covariability of earnings that matters. However, variability can have an indirect effect since beta can be viewed as correlated relative volatility of earnings. The volatility of earnings in relation to volatility in aggregate earnings will scale the correlation term and in turn affect the magnitude of beta.

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Titel: Earnings beta
verfasst von: Atif Ellahie
Publikationsdatum: 10.10.2020
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 1/2021
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-020-09561-w

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