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On the Expected Earnings Hypothesis Explanation of the Aggregate Returns–Earnings Association Puzzle

Published online by Cambridge University Press:  18 October 2019

Warren Bailey
Affiliation:
Bailey, wbb1@cornell.edu, Cornell University Johnson Graduate School of Management and Fudan University Fanhai International School of Finance and China Institute of Economics and Finance
Huiwen Lai*
Affiliation:
Lai, afhlai@polyu.edu.hk, The Hong Kong Polytechnic University School of Accounting and Finance
*
Lai (corresponding author), afhlai@polyu.edu.hk

Abstract

We provide strong support for the underappreciated expected earnings hypothesis of a negative correlation between aggregate stock returns and earnings. For 1970–2000, our powerful modeling strategy incorporating macroeconomic information reveals that aggregate returns are significantly and negatively correlated with expected aggregate earnings changes but uncorrelated with unexpected aggregate earnings changes. However, this negative correlation changes after 2000, perhaps from heightened volatility or accounting changes. We also show that underlying macroeconomic information explains the power of aggregate earnings to predict future gross domestic product growth.

Type
Research Article
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2019

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Footnotes

We thank Dichu Bao, Xiangpei Chen, Kim Sau Chung, Azhar Iqbal, Alon Kalay, Eric Ng, Gil Sadka, Jing Wang, Shuye Wang, and participants at the 2018 Hong Kong Economic Association and the 2018 Frontiers of Business Research in China International Symposium for their helpful comments and assistance. Lai gratefully acknowledges funding from the Research Grants Council of Hong Kong (PolyU 155062/15B).

References

Bai, J., and Ng, S.. “Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions.” Econometrica, 74 (2006), 11331150.CrossRefGoogle Scholar
Ball, R., and Brown, P.. “An Empirical Evaluation of Accounting Income Numbers.” Journal of Accounting Research, 6 (1968), 159178.CrossRefGoogle Scholar
Ball, R.; Ghysels, E.; and Zhou, H.. 2014. “Can We Automate Earnings Forecasts and Beat Analysts?” Discussion Paper No. DP10186, Centre for Economic Policy Research. Available at papers.ssrn.com/sol3/papers.cfm?abstract_id=2506022.Google Scholar
Ball, R., and Sadka, G.. “Aggregate Earnings and Why They Matter.” Journal of Accounting Literature, 34 (2015), 3957.CrossRefGoogle Scholar
Ball, R.; Sadka, G.; and Sadka, R.. “Aggregate Earnings and Asset Prices.” Journal of Accounting Research, 47 (2009), 10971133.CrossRefGoogle Scholar
Bernanke, B. S., and Boivin, J.. “Monetary Policy in a Data-Rich Environment.” Journal of Monetary Economics, 50 (2003), 525546.CrossRefGoogle Scholar
Bernard, V. L., and Thomas, J. K.. “Evidence that Stock Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings.” Journal of Accounting and Economics, 13 (1990), 305340.CrossRefGoogle Scholar
Brown, L. D., and Rozeff, M. S.. “Univariate Time-Series Models of Quarterly Accounting Earnings Per Share: A Proposed Model.” Journal of Accounting Research, 17 (1979), 179189.CrossRefGoogle Scholar
Campbell, J. Y.A Variance Decomposition for Stock Returns.” Economic Journal, 101 (1991), 157179.CrossRefGoogle Scholar
Chauvet, M., and Potter, S.. “Forecasting Recessions Using the Yield Curve.” Journal of Forecasting, 24 (2005), 77103.CrossRefGoogle Scholar
Chen, N.Financial Investment Opportunities and the Macroeconomy.” Journal of Finance, 46 (1991), 529554.CrossRefGoogle Scholar
Chen, Z.; Iqbal, A.; and Lai, H.. “Forecasting the Probability of US Recessions: A Probit and Dynamic Factor Modelling Approach.” Canadian Journal of Economics, 44 (2011), 651672.CrossRefGoogle Scholar
Choi, J. H.; Kalay, A.; and Sadka, G.. “Earnings News, Expected Earnings, and Aggregate Stock Returns.” Journal of Financial Markets, 29 (2016), 110143.CrossRefGoogle Scholar
Clements, M. P., and Galvão, A. B.. “Macroeconomic Forecasting with Mixed-Frequency Data: Forecasting Output Growth in the United States.” Journal of Business and Economic Statistics, 26 (2008), 546554.CrossRefGoogle Scholar
Cready, W. M., and Gurun, U. G.. “Aggregate Market Reaction to Earnings Announcements.” Journal of Accounting Research, 48 (2010), 289334.CrossRefGoogle Scholar
Dichev, I. D., and Zhao, J.. “Comparing GAAP to NIPA Earnings.” Working Paper, Hong Kong Polytechnic University (2018).CrossRefGoogle Scholar
Estrella, A., and Mishkin, F. S.. “Predicting U.S. Recessions: Financial Variables as Leading Indicators.” Review of Economics and Statistics, 80 (1998), 4561.CrossRefGoogle Scholar
Foroni, C.; Marcellino, M.; and Schumacher, C.. “Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials.” Journal of the Royal Statistical Society, 178 (2015), 5782.CrossRefGoogle Scholar
Foster, G.Quarterly Accounting Data: Time-Series Properties and Predictive-Ability Results.” Accounting Review, 52 (1977), 121.Google Scholar
Foster, G.; Olsen, C.; and Shevlin, T.. “Earnings Releases, Anomalies, and the Behavior of Security Returns.” Accounting Review, 59 (1984), 574603.Google Scholar
Gallo, L. A.; Hann, R. N.; and Li, C.. “Aggregate Earnings Surprises, Monetary Policy, and Stock Returns.” Journal of Accounting and Economics, 62 (2016), 103120.CrossRefGoogle Scholar
Ghysels, E.; Santa-Clara, P.; and Valkanov, R.. “The MIDAS Touch: Mixed Data Sampling Regression Models.” Working Paper, CIRANO (2004).Google Scholar
Ghysels, E.; Santa-Clara, P.; and Valkanov, R.. “There is a Risk-Return Trade-Off After All.” Journal of Financial Economics, 76 (2005), 509548.CrossRefGoogle Scholar
Ghysels, E.; Santa-Clara, P.; and Valkanov, R.. “Predicting Volatility: Getting the Most Out of Return Data Sampled at Different Frequencies.” Journal of Econometrics, 131 (2006), 5995.CrossRefGoogle Scholar
Greene, W. H. Econometric Analysis. New York, NY: Pearson (2012).Google Scholar
He, W., and Hu, M.. “Aggregate Earnings and Market Returns: International Evidence.” Journal of Financial and Quantitative Analysis, 49 (2014), 879901.CrossRefGoogle Scholar
Hecht, P., and Vuolteenaho, T.. “Explaining Returns with Cash-Flow Proxies.” Review of Financial Studies, 19 (2006), 159194.CrossRefGoogle Scholar
Kalay, A.; Nallareddy, S.; and Sadka, G.. “Uncertainty and Sectoral Shifts: The Interaction Between Firm-Level and Aggregate-Level Shocks, and Macroeconomic Activity.” Management Science, 64 (2016), 198214.CrossRefGoogle Scholar
Kauppi, H., and Saikkonen, P.. “Predicting U.S. Recessions with Dynamic Binary Response Models.” Review of Economics and Statistics, 90 (2008), 777791.CrossRefGoogle Scholar
Konchitchki, Y., and Patatoukas, P. N.. “Accounting Earnings and Gross Domestic Product.” Journal of Accounting and Economics, 57 (2014a), 7688.CrossRefGoogle Scholar
Konchitchki, Y., and Patatoukas, P. N.. “Taking the Pulse of the Real Economy Using Financial Statement Analysis: Implications for Macro Forecasting and Stock Valuation.” Accounting Review, 89 (2014b), 669694.CrossRefGoogle Scholar
Kothari, S. P.; Lewellen, J.; and Warner, J. B.. “Stock Returns, Aggregate Earnings Surprises, and Behavioral Finance.” Journal of Financial Economics, 79 (2006), 537568.CrossRefGoogle Scholar
Kothari, S. P.; Shivakumar, L.; and Urcan, O.. “Aggregate Earnings Surprises and Inflation Forecasts.” Working Paper, London Business School (2013).CrossRefGoogle Scholar
Lai, H., and Ng, E. C. Y.. “Predicting Recessions Using a Flexible and Inclusive Modelling Approach.” Working Paper, Hong Kong Polytechnic University and Hong Kong Baptist University (2019).Google Scholar
Nallareddy, S., and Ogneva, M.. “Predicting Restatements in Macroeconomic Indicators Using Accounting Information.” Accounting Review, 92 (2017), 151182.CrossRefGoogle Scholar
Patatoukas, P. N.Detecting News in Aggregate Accounting Earnings: Implications for Stock Market Valuation.” Review of Accounting Studies, 19 (2014), 134160.CrossRefGoogle Scholar
Sadka, G.Understanding Stock Price Volatility: The Role of Earnings.” Journal of Accounting Research, 45 (2007), 199228.CrossRefGoogle Scholar
Sadka, G., and Sadka, R.. “Predictability and the Earnings–Returns Relation.” Journal of Financial Economics, 94 (2009), 87106.CrossRefGoogle Scholar
Shivakumar, L., and Urcan, O.. “Why Does Aggregate Earnings Growth Reflect Information about Future Inflation?Accounting Review, 92 (2017), 247276.CrossRefGoogle Scholar
Silvia, J.; Bullard, S.; and Lai, H.. “Forecasting U.S. Recessions with Probit Stepwise Regression Models.” Business Economics, 43 (2008), 718.CrossRefGoogle Scholar
Stock, J. H., and Watson, M. W.. “Forecasting Inflation.” Journal of Monetary Economics, 44 (1999), 293335.CrossRefGoogle Scholar
Stock, J. H., and Watson, M. W.. “Macroeconomic Forecasting Using Diffusion Indexes.” Journal of Business and Economic Statistics, 20 (2002a), 147162.CrossRefGoogle Scholar
Stock, J. H., and Watson, M. W.. “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association, 97 (2002b), 11671179.CrossRefGoogle Scholar
Zolotoy, L.; Frederickson, J. R.; and Lyon, J. D.. “Aggregate Earnings and Stock Market Returns: The Good, the Bad, and the State-Dependent.” Journal of Banking and Finance, 77 (2017), 157175.CrossRefGoogle Scholar