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Review of Accounting Studies

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01.03.2011

Do management EPS forecasts allow returns to reflect future earnings? Implications for the continuation of management’s quarterly earnings guidance

verfasst von: Jong-Hag Choi, Linda A. Myers, Yoonseok Zang, David A. Ziebart

Erschienen in: Review of Accounting Studies | Ausgabe 1/2011

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Abstract

Using 18,253 firm-year observations from 1998 through 2003, we build on literature suggesting that more informative disclosures allow returns to better reflect future earnings and test whether management earnings per share forecasts and their characteristics influence the future earnings response coefficient (FERC). We find that FERCs are greater for forecasting firms and when forecasts are more frequent or precise. We suggest that more frequent and more precise forecasts assist investors in better predicting future earnings. Importantly, we find that quarterly and short-term forecasts incrementally increase the association between returns and future earnings beyond annual and long-term forecasts; thus, even short-term, quarterly forecasts allow investors to form better expectations about future earnings. This suggests a benefit of quarterly earnings forecasts possibly overlooked in recommendations from the United States Chamber of Commerce, CFA Institute, Business Roundtable Institute for Corporate Ethics, and The Conference Board to eliminate quarterly earnings guidance.

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Fishman and Hagerty (1989) show that the information efficiency of a firm’s stock price is linked to the efficiency of its investment and production decisions, suggesting that improved stock price informativeness benefits both the firm and the economy.

Interestingly, some companies, including Berkshire Hathaway, Coca-Cola, McDonald’s, Pfizer, and The Washington Post Co., have discontinued the practice of forecasting quarterly earnings.

For example, a story in the March 19, 2007 edition of InvestmentNews states: “[i]f public companies adopted the policy of no quarterly guidance, the result might well be a dramatic improvement in the long-term performance of American corporations and the economy. Studies have shown that pressure to provide quarterly guidance …distorts investment decisions and policies of corporate management teams and imposes a short-term mind-set on them.” Also see “The market game” in The Wall Street Journal (May 8, 2002), “The last quarter of the guidance game” in CFO.com (March 17, 2006), “Ditch guidance” in CFO.com (March 30, 2006), “Dump quarterly guidance” in CFO.com (July 25, 2006), “Stop playing the guidance game” in Directorship (September 2007), and “Corporations should stop giving quarterly earnings guidance” in the Idaho Business Review (September 24, 2007).

Consistent with significant costs for forecasting firms, Cheng et al. (2005) find that frequent forecasters invest less in research and development and experience lower long-term earnings growth than firms that infrequently forecast. Moreover, Krehmeyer et al. (2006) report that among a group of over 400 financial executives, 80% state that they would decrease discretionary spending, and 50% state that they would sacrifice value creation to meet earnings forecasts.

Opponents also suggest that, if managers stop providing quarterly guidance, analysts and investors will seek information elsewhere, and managers may disclose better quality (other) information (Harbert 2003; Nolan 2006). Moreover, according to a CFA Institute survey, 76% of investment analysts would prefer more in-depth disclosures about long-term plans than continuing quarterly earnings guidance (Krehmeyer et al. 2006; Pozen 2007).

We distinguish between four forecast types. Short-term quarterly forecasts are of quarterly EPS for upcoming quarters in the current fiscal year; long-term quarterly forecasts are of quarterly EPS for quarters in a future fiscal year; short-term annual forecasts are of annual EPS in the current fiscal year; and long-term annual forecasts are of annual EPS in a future fiscal year.

Note that the ERC measures how much the market values one dollar of current earnings on average, so the ERC is unable to inform us about how well investors can predict future earnings. Our goal is to determine whether the market’s future earnings expectations, as implied in stock returns, reflect the future earnings realizations more when managers forecast (and whether this varies with the forecast characteristics). Thus, the FERC is a means to address a question that cannot be addressed using short-window ERC tests.

Similarly, Ettredge et al. (2005) find that the adoption of SFAS No. 131 on segment reporting increased FERCs, and Orpurt and Zang (2009) find that FERCs are greater when firms prepare their cash flow statements using the direct approach.

The median market value of equity for the firms in Lundholm and Myers (2002) is $1.27 billion versus $483 million in our study. Their sample includes approximately 300 firms per year versus our approximately 3,000 firms per year.

Increasing disclosure frequency can alter the timing and the content of disclosures (Botosan and Harris 2000).

Atiase et al. (2005) and Pownall et al. (1993) do not find a relation between forecast precision and the magnitude of the market reaction, but Baginski et al. (1993) find that the market reaction to a forecast surprise is increasing in forecast precision.

See, for example, Botosan (1997), Botosan and Plumlee (2002), Frankel et al. (1995), Healy et al. (1999), Healy and Palepu (2001), Lang and Lundholm (2000), Leuz and Verrecchia (2000), Marquardt and Wiedman (1998), and Welker (1995).

See, for example, Cotter et al. (2006), Kasznik and McNichols (2002), Matsumoto (2002), and Richardson et al. (2004).

This assumes that long-term forecasts are informative about long-term earnings realizations (i.e., that realizations are closer to forecasts than to pre-forecast market expectations).

Our results are robust to measuring returns with a three-month lag as in Lundholm and Myers (2002).

If b ₁ = −b ₂, earnings follow a random walk.

See footnote 5 in Lundholm and Myers (2002) for a discussion of the alternative ERC definition and note that the inclusion of future earnings may confound the traditional interpretation of the ERC (Lundholm and Myers 2002).

Thus, if a firm makes a point forecast and a minimum forecast in a given year, the value of PREC is 3 (i.e., [4 + 2]/2). In untabulated analyses, we alternatively measure forecast precision as the proportion of forecasts made in the year that are quantitative (i.e., point and range forecasts). Our results and inferences are qualitatively unchanged.

As a robustness test, we convert these raw continuous control variables to fractional rankings in their (two-digit SIC) industries and years. The results are qualitatively similar. We tabulate results using raw values because the first-stage of our two-stage least-squares model (explained later) uses raw values.

Untabulated results indicate that in the restricted sample, most forecasts are only for the current fiscal year or for a quarter in the current year (mean DCF_ONLY = 0.7796), and approximately 20 percent are for both current and future years (mean DCL_JOINT = 0.2025). Long-term forecasts only for future years or for quarters in future years are rare (mean DLF_ONLY = 0.0178).

The average of LNF is calculated after the values are logged. The raw average number of forecasts is reported in Table 1, panel A.

The correlation between LNF and DF is very high (p = 0.878), but these variables do not enter the same regression.

We add these variables and their interactions with X _t3 to model (3), and find that the coefficient on INST _t*X _t3 is marginally significant (p = 0.0457), but the coefficients on BDIND _t*X _t3 and D_CAP _t*X _t3 are not significant. When the other control variables (SIZE, LOSS, GROWTH, EARNSTD, and NANAL) and their interactions with X _t3 are included (as in model (4)), INST _t*X _t3, BDIND _t*X _t3, and D_CAP _t*X _t3 are all insignificant.

Results from the Heckman two-stage approach for all other analyses using the full sample are robust. For parsimony, we report only OLS results in subsequent tables.

We thank the reviewer for suggesting this approach.

This test limits the sample to firms that forecast at least once so it provides some control for self-selection.

In contrast, the coefficient on LNF _t *X _t is not significant for the full sample, suggesting that, on average, how investors respond to current earnings (the ERC) is not influenced by management forecast frequency. However, when we limit our analyses to the restricted sample (columns 2, 3, and 4), the coefficient on LNF _t *X _t is negative. The negative coefficients on LNF _t *X _t and positive coefficients on LNF _t *X _t3 suggest that investors focus more on forecasted future earnings than on current earnings in setting stock prices as forecast frequency increases.

The results are qualitatively similar when we do not control for the number of forecasts issued (LNF).

Using two fitted variables in the same second-stage regression does not cause econometric problems even if the two fitted variables are estimated using similar variables in the first-stage regression (Gul et al. 2009). In addition, the Pearson correlation between the fitted values of LNF and PREC is only 0.1684.

This lack of significance may be due to low power resulting from a small number of observations with only long-term forecasts. Among our 7,353 restricted sample observations, 131 issue long-term-only forecasts while 5,733 (1,489) observations issued short-term-only forecasts (both short- and long-term forecasts). Another possibility is that long-term forecasts are perceived as less credible when short-term forecasts are not provided.

We obtain board independence (BDIND _t) data from the Board Analytics and Investor Responsibility Research Center (IRRC) databases. This data item is missing for 4,973 of 13,420 sample observations. Due to a large number of missing data observations, we use the ‘modified zero-order regression’ method suggested by Maddala (1977) and Greene (2003), which substitutes a zero for missing values and adds an indicator variable coded 1 if the corresponding variable is missing. That is, we set BDIND _t to zero if it is missing and set MISSING _t to 1 if BDIND _t is set to zero because it is missing, and we set MISSING _t to zero if BDIND _t is not missing. Our main results are qualitatively unchanged if we exclude observations missing data, but we tabulate the results with modified zero-order regressions because this method requires fewer assumptions about the missing values.

When we perform the Hausman (1978) test for endogeneity for each model in our restricted samples, we find that endogeneity is significant only when both forecast frequency and precision are jointly included in the model (as in column 3 in Table 4 (p = 0.0251)). Although we find endogeneity for only this specification of the models, to enhance comparability, we employ 2SLS procedures for all restricted sample models.

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Titel: Do management EPS forecasts allow returns to reflect future earnings? Implications for the continuation of management’s quarterly earnings guidance
verfasst von: Jong-Hag Choi
Linda A. Myers
Yoonseok Zang
David A. Ziebart
Publikationsdatum: 01.03.2011
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 1/2011
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-010-9131-6

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