Abstract
This study examines the differential predictive power of past earnings volatility for analyst forecast errors and future returns. Past earnings volatility jointly captures two correlated, but distinct, earnings properties: time-series earnings variation and uncertainty in future earnings. To distinguish between these two earnings properties, we develop a forward-looking measure of earnings uncertainty that has a minimal mechanical link to variation in prior-period earnings realizations and does not rely on analyst forecasts. Our results suggest that future earnings uncertainty, and not time variation in earnings, is associated with overly optimistic future earnings expectations of equity analysts and investors. We provide the first empirical evidence on the relevance of future earnings uncertainty to analysts and investors over 1-year horizons. In addition, we provide empirical evidence showing that forecast dispersion is a poor measure of earnings uncertainty.
Notes
For example, firm size, market-to-book, forecast dispersion, and realized return volatility (along with other variables) has each been used as empirical proxies for information uncertainty.
Dechow et al. (2010) provide a more extensive discussion on the earnings smoothness literature within the broader context of earnings quality.
Alternative measures of earnings uncertainty based on forecast dispersion exist in the literature (Barron et al. 1998; Sheng and Thevenot 2011). We do not consider these alternative measures as the Barron et al. model imposes a significant look-ahead bias in its design, while the uncertainty estimate of Sheng and Thevenot requires a significant time-series of forecasts, severely limiting sample size.
For example, analyst forecasts tend to be optimistically biased early in the fiscal period and pessimistically biased by the end of the fiscal period. Analyst forecasts tend to be more optimistically biased for high accrual firms and less so for low accrual firms.
Levi and Makin (1980) use the standard deviation of inflation forecasts as a proxy for uncertainty about inflation expectations. Also, analyst forecast dispersion is measured by the standard deviation of analyst forecasts and is a proxy for earnings uncertainty (Givoly and Lakonishok 1984; Clement et al. 2003).
Blouin et al. (2010) utilize similar intuition to project the distribution of pretax income levels.
A more subtle advantage of using a matched-firm empirical design to estimate earnings uncertainty is that it sidesteps the tricky interpretive issues of using forecast dispersion, which can also be interpreted as a measure of opinion divergence associated with information asymmetry.
Consistent with prior earnings volatility studies, our earnings variable is actually earnings scaled by average total assets. We use terms earnings, profits, and profitability interchangeably.
For example, IWKS (F/Y/E 1997) had earnings of 0.076, change in earnings of 0.032, and total assets below the 10th NYSE total asset percentile. All firms with total assets below the 10th NYSE total asset decile in fiscal years 1992–1996, with earnings between 0.071 and 0.081 and 1-year change in earnings between 0.027 and 0.037 serve as IWKS’s matched-firms. (In our sample, IWKS1997 had 20 matched firms).
This screen has a minimal effect on the average number of matched firms per firm (<1 %).
For example, ALGI (F/Y/E 1996) had earnings of 0.114, change in earnings of −0.082, and total assets below the 10th NYSE total asset percentile. All firms with total assets below the 10th NYSE percentile in fiscal years 1991–1995, earnings between 0.091 and 0.137, and 1-year change in earnings between −0.066 and −0.098 serve as ALGI’s matched-firms. (In our sample, ALGI1996 had 28 matches).
If the expected earnings model is unbiased, the square of unexpected earnings, UE2, equals σ 2Earn + ε where ε ~ N(0, 1). Since (UE2)1/2 = |UE|, the absolute value of unexpected earnings is a reasonable proxy for standard deviation of expected earnings, EU.
If one assumes earnings are a normally distributed random variable, the expected value of the absolute error is less than the standard deviation from a normal distribution. In untabulated results, we assume earnings are normally distributed and correct for this friction by multiplying all absolute errors by (2/π)−1/2 ≈ 1.2533. Inferences are qualitatively identical.
Since reliable forecast data does not exist over the full sample, we report specification test results for forecast dispersion only in the latter sample. Results are qualitatively similar if we include forecast dispersion observations beginning in 1976.
For brevity, we do not report results across the subsamples of firms experiencing extreme performance as reported in Table 2. Inferences are qualitatively identical in the subsamples to that reported in the full sample.
Qualitatively identical inferences result from Fama–MacBeth annual cross-sectional regressions with t-statistics that are Newey-West adjusted.
One exception to this claim is Minton et al. (2002), who find that fitted values from an earnings prediction model that include past earnings volatility can be used to form a profitable trading strategy.
Diether et al. report similar results in their lagged forecast analysis (Fig. 1a, p. 2131). Note hedge returns are statistically indistinguishable from zero at approximately 4 months. Zhang (2006) also finds insignificant hedge returns when forecast dispersion is used to proxy for uncertainty and only updated once a year (Table 2, p. 114).
References
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of financial markets, 5(1), 31–56.
Ang, A., Hodrick, R., Xing, Y., & Zhang, X. (2006). The cross-section of volatility and expected returns. The Journal of Finance, 61, 259–299.
Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly. Financial Analysts Journal, 67, 1–15.
Ball, R., & Watts, R. (1972). Some time series properties of accounting income. The Journal of Finance, 27(3), 663–681.
Barber, B. M., & Lyon, J. D. (1996). Detecting abnormal operating performance: The empirical power and specification of test statistics. Journal of Financial Economics, 41, 359–399.
Barron, O. E., Kim, O., Lim, S. C., & Stevens, D. E. (1998). Using analysts’ forecasts to measure properties of analysts’ information environment. The Accounting Review, 73, 421–433.
Beaver, W. H. (1970). The time series behavior of earnings. Journal of Accounting Research, 8, 62–99.
Beaver, W., Kettler, P., & Scholes, M. (1970). The association between market determined and accounting determined risk measures. The Accounting Review, 45, 654–682.
Blouin, J. L., Core, J., & Guay, W. (2010). Have the benefits of debt been overstated? Journal of Financial Economics, 98, 195–213.
Brown, L., & Laroque, S. (2013). I/B/E/S reported actual EPS and analysts’ inferred actual EPS. The Accounting Review, 88(3), 853–880.
Clement, M., Frankel, R., & Miller, J. (2003). Confirming management earnings forecasts, earnings uncertainty, and stock returns. Journal of Accounting Research, 41, 653–679.
Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under-and overreactions. Journal of Finance, 53(6), 1839–1885.
Daniel, K., & Titman, S. (2006). Market reactions to tangible and intangible information. Journal of Finance, 61, 1605–1643.
Dechow, P., Ge, W., & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50, 344–401.
Dehow, P. M., & Dichev, I. D. (2002). The quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review, 77, 35–59.
Diamond, D. W., & Verrecchia, R. E. (1987). Constraints on short-selling and asset price adjustment to private information. Journal of Financial Economics, 18, 277–311.
Dichev, I. D., & Tang, V. W. (2009). Earnings volatility and earnings predictability. Journal of Accounting and Economics, 47, 160–181.
Diether, K., Malloy, C., & Scherbina, A. (2002). Difference of opinion and the cross section of stock returns. The Journal of Finance, 57, 2113–2141.
Easley, D., & O’Hara, M. (2004). Information and the cost of capital. Journal of Finance, 59, 1553–1583.
Fama, E. F., & French, K. R. (2000). Forecasting profitability and earnings. The Journal of Business, 73(2), 161–175.
Fama, E. F., & French, K. R. (2004). New lists: Fundamentals and survival rates. Journal of Financial Economics, 73, 229–269.
Fama, E. F., & French, K. R. (2008). Average returns, B/M, and share issues. Journal of Finance, 63, 2971–2995.
Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. The Journal of Political Economy, 81(3), 607–636.
Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004). Costs of equity and earnings attributes. The Accounting Review, 79, 967–1010.
Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2005). The market pricing of accruals quality. Journal of Accounting and Economics, 39, 295–327.
Frankel, R., & Litov, L. (2009). Earnings persistence. Journal of Accounting and Economics, 47, 182–190.
Freeman, R. N., Ohlson, J. A., & Penman, S. H. (1982). Book rate-of-return and prediction of earnings changes: An empirical investigation. Journal of Accounting Research, 20(2), 639–653.
French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3–29.
Givoly, D., & Lakonishok, J. (1984). Properties of analysts’ forecasts of earnings: A review and analysis of the research. Journal of Accounting Literature, 3, 117–152.
Graham, J., Harvey, C., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40, 3–73.
Green, J., Hand, J. R. M., & Soliman, M. T. (2011). Going, going, gone? The apparent demise of the accruals anomaly. Management Science, 57, 797–816.
Jayaraman, S. (2008). Earnings volatility, cash flow volatility, and informed trading. Journal of Accounting Research, 46, 809–851.
Jiang, G., Lee, C., & Zhang, G. (2005). Information uncertainty and expected returns. Review of Accounting Studies, 10, 185–221.
Johnson, T. C. (2004). Forecast dispersion and the cross section of expected returns. Journal of Finance, 59, 1957–1978.
Kothari, S. P., Laguerre, T. E., & Leone, A. J. (2002). Capitalization versus expensing: Evidence on the uncertainty of future earnings from capital expenditures versus R&D outlays. Review of Accounting Studies, 7, 355–382.
Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163–197.
Lambert, R., Leuz, C., & Verrecchia, R. E. (2007). Accounting information, disclosure, and the cost of capital. Journal of Accounting Research, 45, 385–420.
Lang, M., Lins, K., & Miller, D. (2003). ADRs, analysts, and accuracy: Does cross listing in the United States improve a firm’s information environment and increase market value? Journal of Accounting Research, 41, 317–345.
Leuz, C., Nanda, D., & Wysocki, P. D. (2003). Earnings management and investor protection: An international comparison. Journal of Financial Economics, 69, 505–527.
Levi, M. D., & Makin, J. H. (1980). Inflation uncertainty and the Phillips curve: Some empirical evidence. American Economic Review, 70, 1022–1027.
Lewellen, J. (2014). The cross-section of expected stock returns. Critical Finance Review (forthcoming).
McInnis, J. (2010). Earnings smoothness, average returns, and implied cost of equity capital. The Accounting Review, 85, 315–341.
McNichols, M. F., & O’Brien, P. (1997). Self-selection and analyst coverage. Journal of Accounting Research, 353, 167–199.
Minton, B. A., & Schrand, C. (1999). The impact of cash flow volatility on discretionary investment and the costs of debt and equity financing. Journal of Financial Economics, 54, 423–460.
Minton, B. A., Schrand, C. M., & Walther, B. R. (2002). The role of volatility in forecasting. Review of Accounting Studies, 7, 195–215.
Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedacity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.
Rosenberg, B., & McKibben, W. (1973). The prediction of systematic and specific risk in common stocks. Journal of Financial and Quantitative Analysis, 8(2), 317–333.
Rountree, B., Weston, J. P., & Allayannis, G. (2008). Do investors value smooth performance? Journal of Financial Economics, 90, 237–251.
Schwert, G. W. (1989). Why does stock return volatility change over time? Journal of Finance, 44, 1115–1153.
Schwert, G. W. (1990). Stock volatility and the crash of’87. Review of Financial Studies, 3(1), 77–102.
Sheng, X., & Thevenot, M. (2011). A new measure of earnings forecast uncertainty. Journal of Accounting and Economics, 53, 21–33.
Sloan, R. G. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review, 71, 289–315.
Tucker, J. W., & Zarowin, P. A. (2006). Does income smoothing improve earnings informativeness? The Accounting Review, 81, 251–270.
Watts, R. L., & Leftwich, R. W. (1977). The time series of annual accounting earnings. Journal of Accounting Research, 15(2), 253–271.
Zhang, X. F. (2006). Information uncertainty and stock returns. The Journal of Finance, 61, 105–137.
Acknowledgments
We thank Andy Bernard, Steve Kachelmeier, Chad Larson, Shai Levi, Jonathan Lewellen, Matt Lyle, John McInnis, Stephen Penman (editor), Phil Stocken, an anonymous reviewer, and workshop participants at Dartmouth (Tuck School of Business), 2011 American Accounting Association annual meeting, the University of Mississippi and the University of Texas at Austin for helpful comments.
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Donelson, D.C., Resutek, R.J. The predictive qualities of earnings volatility and earnings uncertainty. Rev Account Stud 20, 470–500 (2015). https://doi.org/10.1007/s11142-014-9308-5
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DOI: https://doi.org/10.1007/s11142-014-9308-5