Bossaerts, Peter and Hillion, Pierre (1999) Implementing statistical criteria to select return forecasting models: what do we learn? Review of Financial Studies, 12 (2). pp. 405-428. ISSN 0893-9454. http://resolver.caltech.edu/CaltechAUTHORS:BOSrfs99
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Statistical model selection criteria provide an informed choice of the model with best external (i.e., out-of-sample) validity. Therefore they guard against overfitting ('data snooping'). We implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. We confirm the presence of in-sample predictability in an international stock market dataset, but discover that even the best prediction models have no out-of-sample forecasting power. The failure to detect out-of-sample predictability is not due to lack of power.
|Additional Information:||© 1999 the Society for Financial Studies P. Bossaerts thanks First Quadrant for financial support through a grant to the California Institute of Technology. First Quadrant also provided the data that were used in this study. The article was revised in part when the first author was at the Center for Economic Research, Tilburg University. P. Hillion thanks the Hong Kong University of Science and Technology for their hospitality while doing part of the research. Comments from Michael Dacorogna, Rob Engle. Joel Hasbrouck, Andy Lo, P. C. B. Phillips, Richasrd Roll, Mark Taylor, and Ken West, from two anonymous referees, and the editor (Ravi Jagannathan), as well as seminar participants at the Hong Kong Univeristy of Science and Technology, University of California San Diego, University of California Santa Barbara, the 1994 NBER Spring Conference on Asset Pricing, the 1994 Western Finance Association Meetings, and the 1995 CEPR/LIFE Conference on International Finance are gratefully acknowledged.|
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|Deposited On:||18 Jan 2006|
|Last Modified:||04 Mar 2016 00:39|
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