Implementing statistical criteria to select return forecasting models: What do we learn?

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.


Published in:
Review of Financial Studies, 12, 2, 405-428
Year:
1999
Note:
HSS 228-77, California Institute of Technology, Pasadena, CA 91125, United States
TY - JOUR
Cited By (since 1996): 67
Export Date: 10 March 2008
Source: Scopus
Other identifiers:
Scopus: 2-s2.0-0033409775
Laboratories:




 Record created 2008-03-12, last modified 2018-03-17


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