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Inflation, earnings forecasts, and post-earnings announcement drift

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Abstract

We examine whether financial analysts fully incorporate expected inflation in their earnings forecasts for individual stocks. We find that expected inflation proxies, such as lagged inflation and inflation forecasts from the Michigan Survey of Consumers, predict the future earnings change of a portfolio long in high inflation exposure firms and short in low or negative inflation exposure firms, but analysts do not fully adjust for this relation. Analysts’ earnings forecast errors can be predicted using expected inflation proxies, and these systematic forecast errors are related to future stock returns. Overall, our evidence is consistent with the Chordia and Shivakumar (J Account Res 43(4):521–556, 2005) hypothesis that the post-earnings announcement drift is related to investor underestimation of the impact of expected inflation on future earnings change.

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Notes

  1. The report was written by Lisa Gil, Atif Rahim, and Michael Minchak and was dated December 6, 2004.

  2. The report was written by Jack Murphy and Teresa Ging and was dated April 27, 2004.

  3. The report was written by Mark Kalinowski, Jeffrey Carnevale, and Kwame Aryeh and was dated November 9, 2004.

  4. To simplify notation, we drop the subscript ‘t’, which refers to the forecasting month, in the equations.

  5. The power of tests is critical for our study, because our analyses are predictive in nature and our sample period is characterized by relatively low and stable inflation. Thus, unless the earnings exposure to inflation is sufficiently large, it would be difficult to identify empirically any relation between inflation and future earnings change or future forecast errors.

  6. The stationarity assumption is critical because it justifies the use of past observations on inflation and SUEs to estimate current inflation exposure.

  7. A more sophisticated time-series model would require parameter estimation and would introduce an unknown amount of noise into the expected inflation proxy. In any case, the loss of power from using lagged inflation as a proxy for expected inflation is not a concern because we find statistically significant results even with this proxy.

  8. The survey data are available monthly from 1978 and are based on a random sample of at least 500 households. The Survey Research Center at the University of Michigan conducted the telephone interviews.

  9. On average, in each quarter, there are 8,730 firms with COMPUSTAT data on earnings per share excluding extra-ordinary items. On average, in each quarter, we lose 2,742 firms lacking data needed to compute SUE i,t,q and 4,167 firms lacking data on recent analysts’ forecasts in the detailed IBES database.

  10. Standardizing seasonal earnings change (X i,q  − X i,q−4) by price at end of month t, instead of σ iq , leaves our results qualitatively unchanged.

  11. Because they do not study analysts’ earnings forecasts, Chordia and Shivakumar’s sample is not restricted to the large profitable firms that analysts tend to follow, and they can analyze the longer time period 1972–2001, which includes the high inflation 1970s.

  12. The dependent and independent variables are not measured in quarter q (that is, SUE i,q and \( {\text{INF}}^{ts}_{t} \)), as such a regression would be run on portfolios sorted on the dependent variable, and would therefore be misspecified.

  13. For regressions based on firm-level observations, we report test statistics based on Huber–White standard errors clustered at the firm level.

  14. Chordia and Shivakumar (2005) find statistical significance for eight of 10 portfolios. Their tests are likely more powerful because their sample is not restricted toward the large profitable firms that analysts tend to follow. Nance et al. (1993) show that large firms hedge more of their inflation risk, both through real decisions such as geographic diversification and through financial instruments, and so they are likely to be less exposed to inflation.

  15. Macroeconomic data are from the St. Louis Federal Reserve website at http://research.stlouisfed.org/fred2/.

  16. This slope coefficient is a weighted average, rather than the sum, of the (negative of) slope coefficient for the bottom decile and the coefficient for top decile, with the weights dependent on the number of observations in the two deciles. The reason is that we are combining the two decile samples to form a larger sample; the hypothesized effect in this larger sample is a weighted average, rather than the sum, of the effects in the constituent samples.

  17. Unlike Bernard and Thomas (1990), we analyze firms from the extreme deciles that are formed by sorting stocks on SUE i,q . This approach includes the extreme values of SUE i,q in the regressions, causing our coefficients on SUE i,q to be attenuated relative to those reported by Bernard and Thomas (1990).

  18. IBES-provided consensus forecasts often include stale forecasts. O’Brien (1988) shows that a consensus forecast, constructed from recent individual forecasts, is more accurate than the IBES consensus forecast. Brown (1991) shows that timely composite earnings forecasts are more accurate than either the mean of all outstanding forecasts or the most recent forecast. Our sample size increases by a quarter if we use IBES-provided consensus forecasts, but our results remain qualitatively unchanged. Also, we have replicated our results using split-unadjusted data from IBES.

  19. In other words, if at time t there is no consensus forecast for a specific firm-quarter, then that observation is dropped, even in the analyses of SUEi,q+j .

  20. Our results are qualitatively unaffected when stocks with prices less than $1 are deleted from the analysis.

  21. This result is robust to including lagged forecast errors \( \left( {{\text{that is, FE}}^{*}_{i,q} {\text{ to FE}}^{*}_{i,q - 3} } \right) \) as additional explanatory variables.

  22. Our results are qualitatively similar if we redefine PMN using all SUE deciles as in Sect. 4.4.1 (that is, long in the top five SUE deciles, P 6 through P 10, and short in the bottom five SUE deciles, P 1 through P 5).

  23. We have alternatively constructed the portfolio-level variables at a quarterly rather than monthly frequency so as to eliminate overlapping observations and obtained qualitatively similar results.

  24. Keane and Runkle (1998) also report that predictability of forecast errors based on lagged earnings is not robust to controls for cross-correlation. Also, we emphasize that our results are not directly comparable to those of Abarbanell and Bernard (1992), who study average effects, whereas our tests examine cross-sectional variation in the predictive ability of earnings change for forecast errors.

  25. The lagged earnings changes \( {\text{SUE}}^{*}_{i,q - k} \) (k = 0–3) and proxies for expected inflation are mean-adjusted to aid interpretation of interactive terms.

  26. In untabulated parallel regressions with future earnings changes as the dependent variables, the interaction terms are statistically significant for the time-series proxy but not for the survey proxy.

  27. The results in Table 1 panel B indicate that the PMN portfolio is not sensitive to industrial production growth, suggesting that correlated omitted variables are less of an issue in our analyses.

  28. As Ball and Bartov (1996) point out, the negative coefficients for the first three lags of SUE arise because these variables are positively correlated with expected earnings of the current quarter, and stock returns—because of their positive relation with earnings surprises—are negatively related to the current quarter’s expected earnings. A similar argument applies to the positive coefficient for the fourth lagged of SUE.

  29. Following the suggestions of Basu and Markov (2004), we re-examine the evidence in Table 3 using LAD regressions. The LAD estimations provide weaker evidence of forecast error predictability, implying that differences in the reward functions faced by analysts could partly explain the economically inefficient use of expected inflation in their forecasts.

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Acknowledgments

We thank Stephen Ryan (editor), Devin Shantikumar, Ramgopal Venkataraman, two anonymous referees, and workshop participants at London Business School, Emory University, Pennsylvania State University, Columbia University, Tilburg University, RSM Erasmus University, the 2006 Financial Management Association European Conference, 2006 American Accounting Association annual meetings, and 2007 Indian School of Business Conference for their helpful suggestions. We also thank IBES for making available data on analysts’ forecasts.

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Authors and Affiliations

  1. Fox School of Business, Temple University, 455 Alter Hall, 1801 Liacouras Walk, Philadelphia, PA, 19122, USA

    Sudipta Basu

  2. School of Management, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75083-0688, USA

    Stanimir Markov

  3. London Business School, Regent’s Park, London, NW1 4SA, UK

    Lakshmanan Shivakumar

Authors
  1. Sudipta Basu
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  2. Stanimir Markov
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  3. Lakshmanan Shivakumar
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Corresponding author

Correspondence to Lakshmanan Shivakumar.

Appendix: Variable construction and measurement

Appendix: Variable construction and measurement

A timeline is presented first to show when different underlying raw variables are observed. We then define each variable as it is constructed from the underlying raw variables (Table 9).

Table 9  
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Basu, S., Markov, S. & Shivakumar, L. Inflation, earnings forecasts, and post-earnings announcement drift. Rev Account Stud 15, 403–440 (2010). https://doi.org/10.1007/s11142-009-9112-9

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