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Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Review of Financial Studies Oxford University Press

Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?

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References (27)

Publisher
Oxford University Press
Copyright
© 1999 The Society for Financial Studies
ISSN
0893-9454
eISSN
1465-7368
DOI
10.1093/rfs/12.2.405
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

The Review of Financial StudiesOxford University Press

Published: Apr 1, 1999

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