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Review of Accounting Studies

Erschienen in:

01.03.2015

The predictive qualities of earnings volatility and earnings uncertainty

verfasst von: Dain C. Donelson, Robert J. Resutek

Erschienen in: Review of Accounting Studies | Ausgabe 1/2015

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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.

Vorheriger Artikel The impact of increased disclosure requirements and the standardization of accounting practices on earnings management through the reserve for income taxes

Nächster Artikel The effect of CEO inside debt holdings on financial reporting quality

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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 _Earn ² + 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.

Minton and Schrand (1999), Minton et al. (2002), and Diether et al. (2002) each measure volatility using this form of the coefficient of variation.

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).

Fama and French (2008, p. 2985, Eq. 7) propose a simpler empirical design to measure intangible returns than Daniel and Titman (2006), specifically, intangible return = log(BM_t−1)–log(BM_t).

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Titel: The predictive qualities of earnings volatility and earnings uncertainty
verfasst von: Dain C. Donelson
Robert J. Resutek
Publikationsdatum: 01.03.2015
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 1/2015
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-014-9308-5

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