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Review of Accounting Studies

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01.12.2014

The role of “other information” in analysts’ forecasts in understanding stock return volatility

verfasst von: Yaowen Shan, Stephen Taylor, Terry Walter

Erschienen in: Review of Accounting Studies | Ausgabe 4/2014

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Abstract

This study identifies “other information” in analysts’ forecasts as a legitimate proxy for future cash flows and examines its incremental role in explaining stock return volatility. We suggest that “other information” contains information about fundamentals beyond that reflected in current financial statements and reflects firms’ fundamentals on a more timely basis than dividends or earnings. Using standardized regressions, we find volatility increases when current “other information” is more uncertain and increases more in response to unfavorable news compared to favorable news. Variance decomposition analysis shows that the variance contribution of “other information” dominates that of expected-return news. The incremental role of “other information” is at least half of the effect of earnings in explaining future volatility. The results are more pronounced for firms with poor information environments. Overall, our results highlight the importance of including “other information” as an additional cash-flow proxy in future studies of stock prices and volatility.

Vorheriger Artikel Product market competition and conditional conservatism

Nächster Artikel Conservatism correction for the market-to-book ratio and Tobin’s q

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Reasons why firm-level volatility is important include the following: the relation between perceived riskiness and cost of capital (Froot et al. 1992), the fact that high volatility can make stock-price-based compensation less effective and more costly (Baiman and Verrecchia 1995), the evidence that investment strategies based on volatility can earn significant abnormal returns (e.g., Ang et al. 2006), and, finally, the fact that arbitrageurs trading to exploit the mispricing of an individual stock face risks related to volatility in the sense that larger pricing error may be associated with higher volatility (Shleifer and Vishny 1997).

See, for example, Campbell (1991); Campbell and Vuolteenaho (2004); Irvine and Pontiff (2009); Sadka (2007); Vuolteenaho (2002), and Wei and Zhang (2006).

See Lintner (1956); Chen and Zhao (2009) and Chen et al. (2013).

Campbell (1991) and Campbell and Vuolteenaho (2004) find that dividend news explains only 15–20 % of the variation in market returns. In addition, several studies argue that volatility is likely to reflect market irrationality, trading on the part of retail (noise) traders, or both (Shiller 1981; Black 1986; Brandt et al. 2010; Foucault et al. 2011).

See Ohlson (2001) and Hand (2001). Ohlson (2001) suggests that “analysts’ consensus forecasts of next-year earnings would seem to be a reasonable measure of expected earnings.” Dechow et al. (1999) use analysts’ earnings forecasts to measure the other information variables and report evidence that supports the economic modeling of residual income as an autoregressive process.

See Ramnath et al. (2008) for a review. Typically, the literature indicates that financial analysts do not fully adjust forecasts for earnings reversals, the earnings surprise in prior earnings announcements, and past abnormal accruals.

More generally, the theoretical link between other information and stock return volatility can be derived from discounted cash flow models and is not limited to the Campbell-Shiller (1988a) loglinear version. However, without loss of generality, the accounting version of the Campbell-Shiller (1988a) model enables us to derive a straightforward, closed form solution that is empirically testable. A related approach is used by Wei and Zhang (2006) and Irvine and Pontiff (2009), although with a different purpose.

Callen (2009) follows Ohlson (1995) and assumes that the error terms of Ohlson’s linear information dynamics are mean-zero independent error terms with inter-temporally homoskedastic variance and suggests a link between the unconditional variance of stock returns and the unconditional variance of other information. However, this link is difficult to test empirically, because the unconditional variance of other information is assumed to be constant over time.

Detailed discussion can be found in Sect. 2.4.

See Chan et al. (2001) and Schwert (2002) for technology bubbles, Givoly and Hayn (2000) for loss reporting, Ferreira and Laux (2007) for corporate governance effects, and Rajgopal and Venkatachalam (2011) for financial reporting quality. Our results are also robust to several different measures of stock return volatility, different sample periods, different industry categories, and different econometric estimations such as Fama–MacBeth (1973) regressions and fixed effect regressions. See Sect. 4.3 for additional discussion.

Total volatility is decomposed into systematic and idiosyncratic components by using the CAPM and the Fama–French three factor model (Fama and French 1993, 1996), respectively.

For example, an increase in the frequency and precision of disclosure might increase the number of observations drawn from the firm’s underlying earnings series and thus lower investors’ uncertainty about the parameters of the distribution of future earnings.

We follow Callen (2009) and assume that u _t and ε _t are independent of each other to simplify the exposition. Relaxing this independence assumption would result in an additional covariance term in Eqs. (6) and (7). However, the focus of our study is on the (incremental) role of other information in explaining future volatility. Furthermore, the assumption that the error terms follow a conditional heteroskedastic process ensures that the predicted link between the conditional variance of stock returns and the conditional variance of other information is empirically testable. In other words, based on this assumption, the predicted link suggests that the conditional variance of stock returns is a function of time-varying other information.

The ROE-based information dynamic can also be directly derived from Ohlson’s (1995) linear information dynamics using the assumption that the growth rate of book value is constant over time. Easton (1998) and Callen (2009) also utilize similar information dynamics.

A large part of the accounting literature suggests that the market is naive in recognizing the time-series properties of earnings, resulting in significant post-earnings-announcement abnormal returns (Kothari 2001). However, recent studies refine our understanding of the drift. For example, Brown and Han (2000) suggest the market is not entirely naive but rather underestimates the parameters of the true process.

Damodaran (1985) suggests that investors react to news in different ways depending on how they think the information affects the future payoff of their assets and how big a surprise the information was for them. Given that the level of other information is an aggregate indicator of all other information news, the relation between other information news and volatility is ambiguous.

For example, to reconcile the empirical findings of overreaction and under reaction, Daniel et al. (1998) use psychological concepts of overconfidence and self-attribution to construct a model of investor sentiment in the sense that “stock prices overreact to private information signals and underreact to public signals” (p. 1,841). Barberis et al. (1998) model investors as typically (but not always) believing that earnings are more stable than they really are. In such a situation, bad news following a series of good news events generates a large negative response because it is a surprise, whereas good news generates little response because it is anticipated.

Veronesi (1999) suggests that, in good times, bad news decreases future expected cash flow and increases investors’ uncertainty about a regime shift in the underlying cash flow process. Risk-averse investors thus require a higher discount rate for bearing the increasing risk of a regime shift, and this reinforces the effect of the bad news in good times. However, as there is no similar reinforcement in the case of good news, volatility increases more in response to bad news.

See, for example, Pástor and Veronesi (2003), Wei and Zhang (2006), Ferreira and Laux (2007), Irvine and Pontiff (2009), Brandt et al. (2010) and Rajgopal and Venkatchalam (2011).

The results remain similar if we do not impose any requirement for market equity.

Our results remain quantitatively similar if we trim the top and bottom percentiles of key variables or keep them in the analysis.

The Compustat data reveal that more than 95 % firms release their annual reports within 3 months after the financial year. Our results are similar if we use earnings forecasts from I/B/E/S in the fourth month after the fiscal year.

The results are quantitatively similar if we calculate returns from the fourth or fifth month after the fiscal year-end.

Inferences are unchanged when the definition of volatility adopted by French et al. (1987) is used.

The deflator is commonly used to reduce heteroskedasticity. We also standardize by the firm’s stock price at the end of year. The main results are qualitatively similar.

Our results remain similar if we follow Manry et al. (2003) and exclude BVPS as a regressor in Eq. (9).

The results are similar if we use the consensus median forecasts rather than the mean forecast of earnings per share.

We also estimate a separate regression for each fiscal year, with each regression using all available observations in the sample from previous years, going back as far as 1981. The main results hold.

Ohlson (1995) defines his other information variable, v _t, as the difference between the conditional expectation of abnormal earnings for period t + 1 based on all available information and the expectation of abnormal earnings based only on current period abnormal earnings.

Following Dechow et al. (1999), we also estimate a separate regression for each fiscal year, with each regression using all available observations from previous years, going back as far as 1950. The results are similar to those reported in the text.

A sample restricted to observations without missing values for each persistence determinant yields qualitatively similar results.

We thus control for forecast bias in our regression analysis presented in Sect. 4.

The t-statistics reported in parentheses are adjusted for autocorrelation and conditional heteroskedasticity (Newey and West 1987). The results of Fama–MacBeth regressions for other tables (untabulated) are similar to the results based on two-way clustering estimation.

We also employ a fixed effect regression to address possible concerns about unobserved individual heterogeneity, where every firm and every year in the sample is assigned a dummy variable. The results (untabulated) are qualitatively similar to those reported above.

Results for systematic and idiosyncratic volatility estimated from the CAPM are qualitatively similar and are available upon request.

Following Francis and Schipper (1999), firms in 14 three-digit SIC codes (283, 357, 360-368, 481, 737, and 873) are identified as technology-intensive industries.

For profit firms, the effect of V⁻ becomes stronger than VV, ROE, and VROE (e.g., using V1, the comparison is −0.142 compared to 0.111, −0.135, and 0.129, respectively). However, the impact of V⁻ becomes much weaker in the case of losses (e.g., −0.061 for losses compared to −0.142 for profit firms).

It is also well documented that different markets provide different degrees of liquidity (Christie and Schultz 1994) and cost of executing trades (Huang and Stoll 1996), both of which may differentially influence our volatility analyses.

For instance, firms that operate in finance-related industries (e.g., banks, insurance, life assurance, and investment companies) and utility industries (e.g., water, electricity and gas distribution companies) are highly regulated and have to comply with stringent legal requirements pertaining to their financing.

The five-industry classification approach of Fama and French is available on Ken French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html.

We thank Andrew Metrick for providing the IRRC governance index.

Earnings quality is measured based on the McNichols (2002)’s modification of the Dechow-Dichev (2002) model. We also use two additional measures of earnings quality based on the modified Jones approach as in Rajgopal and Venkatachalam (2011) and the original Dechow and Dichev (2002) model. All the results are qualitatively similar.

We acknowledge the suggestion of an anonymous reviewer in conducting this analysis.

Our results are robust to an alternative ranking based on the entire sample distribution rather than year by year.

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Titel: The role of “other information” in analysts’ forecasts in understanding stock return volatility
verfasst von: Yaowen Shan
Stephen Taylor
Terry Walter
Publikationsdatum: 01.12.2014
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 4/2014
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-013-9272-5

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