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Modeling the Cross Section of Stock Returns: A Model Pooling Approach

Published online by Cambridge University Press:  04 October 2012

Michael O’Doherty
Affiliation:
odohertym@missouri.edu, Trulaske College of Business, University of Missouri,513 Cornell Hall, Columbia, MO 65211
N. E. Savin
Affiliation:
gene-savin@uiowa.edu, ashish-tiwari@uiowa.edu, Tippie College of Business, University of Iowa, 108 PBB, Iowa City, IA 52242
Ashish Tiwari
Affiliation:
gene-savin@uiowa.edu, ashish-tiwari@uiowa.edu, Tippie College of Business, University of Iowa, 108 PBB, Iowa City, IA 52242

Abstract

Model selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.

Type
Research Articles
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2012

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References

Amisano, G., and Giacomini, R.. “Comparing Density Forecasts via Weighted Likelihood Ratio Tests.” Journal of Business and Economic Statistics, 25 (2007), 177190.CrossRefGoogle Scholar
Bates, J. M., and Granger, C. W. J.. “The Combination of Forecasts.” Operational Research Quarterly, 20 (1969), 451468.CrossRefGoogle Scholar
Box, G. E. P. “Sampling and Bayes Inference in Scientific Modeling and Robustness.” Journal of the Royal Statistical Society Series A, 143 (1980), 383430.CrossRefGoogle Scholar
Breeden, D. T.; Gibbons, M. R.; and Litzenberger, R. H.. “Empirical Test of the Consumption-Oriented CAPM.” Journal of Finance, 44 (1989), 231262.Google Scholar
Carhart, M. M. “On Persistence in Mutual Fund Performance.” Journal of Finance, 52 (1997), 5782.CrossRefGoogle Scholar
Chen, N.-F.; Roll, R.; and Ross, S. A.. “Economic Forces and the Stock Market.” Journal of Business, 59 (1986), 383403.CrossRefGoogle Scholar
Clemen, R. T. “Combining Forecasts: A Review and Annotated Bibliography.” International Journal of Forecasting, 5 (1989), 559583.CrossRefGoogle Scholar
Cochrane, J.“Financial Markets and the Real Economy.” Working Paper, University of Chicago (2006).CrossRefGoogle Scholar
Diebold, F. X., and Lopez, J. A.. “Forecast Evaluation and Combination.” In Statistical Methods in Finance, Vol. 14, Maddala, G. S. and Rao, C. R., eds. Amsterdam: Elsevier Science BV (1996).CrossRefGoogle Scholar
Fama, E. F., and French, K. R.. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics, 33 (1993), 356.CrossRefGoogle Scholar
Fama, E. F., and French, K. R.. “Multifactor Explanations of Asset Pricing Anomalies.” Journal of Finance, 51 (1996), 5584.CrossRefGoogle Scholar
Fama, E. F., and MacBeth, J. D.. “Risk, Return, and Equilibrium: Empirical Tests.” Journal of Political Economy, 81 (1973), 607636.CrossRefGoogle Scholar
Geweke, J., and Amisano, G.. “Optimal Prediction Pools.” Journal of Econometrics, 164 (2011),130141.CrossRefGoogle Scholar
Giacomini, R., and White, H.. “Tests of Conditional Predictive Ability.” Econometrica, 74 (2006), 15451578.CrossRefGoogle Scholar
Good, I. J. “Rational Decisions.” Journal of the Royal Statistical Society Series B, 14 (1952), 107114.Google Scholar
Guidolin, M., and Timmermann, A.. “Forecasts of U.S. Short-Term Interest Rates: A Flexible Forecast Combination Approach.” Journal of Econometrics, 150 (2009), 297311.CrossRefGoogle Scholar
Hall, S. G., and Mitchell, J.. “Combining Density Forecasts.” International Journal of Forecasting,23 (2007), 113.CrossRefGoogle Scholar
Hendry, D. F., and Clements, M. P.. “Pooling of Forecasts.” Econometrics Journal, 7 (2004), 131.CrossRefGoogle Scholar
Hoeting, J. A.; Madigan, D.; Raftery, A. E.; and Volinsky, C. T.. “Bayesian Model Averaging: A Tutorial.” Statistical Science, 14 (1999), 382417.Google Scholar
Lewellen, J.; Nagel, S.; and Shanken, J.. “A Skeptical Appraisal of Asset-Pricing Tests.” Journal of Financial Economics, 96 (2010), 175194.CrossRefGoogle Scholar
Mamaysky, H.; Spiegel, M.; and Zhang, H.. “Improved Forecasting of Mutual Fund Alphas and Betas.” Review of Finance, 11 (2007), 359400.CrossRefGoogle Scholar
Newbold, P., and Harvey, D. I.. “Forecast Combination and Encompassing.” In A Companion to Economic Forecasting, Clements, M. P. and Hendry, D. F., eds. Oxford: Blackwell (2002).Google Scholar
Rapach, D. E.; Strauss, J. K.; and Zhou, G.. “Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy.” Review of Financial Studies, 23 (2010), 821862.CrossRefGoogle Scholar
Stock, J. H., and Watson, M. W.. “Forecasting Output and Inflation: The Role of Asset Prices.” Journal of Economic Literature, 41 (2003), 788829.CrossRefGoogle Scholar
Stock, J. H., and Watson, M. W.. “Combination Forecasts of Output Growth in a Seven-Country Data Set.” Journal of Forecasting, 23 (2004), 405430.CrossRefGoogle Scholar
Timmermann, A.Forecast Combinations.” In Handbook of Economic Forecasting, Elliott, G., Granger, C. W. J., and Timmermann, A., eds. Amsterdam: North Holland (2006).Google Scholar
Wallis, K. F. “Combining Density and Interval Forecasts: A Modest Proposal.” Oxford Bulletin of Economics and Statistics, 67 (2005), 983994.CrossRefGoogle Scholar
Zellner, A.An Introduction to Bayesian Inference in Econometrics. Hoboken, NJ: Wiley (1971).Google Scholar