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

Peter Bossaerts; Pierre Hillion

doi:10.1093/rfs/12.2.405

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

Peter Bossaerts¹; Pierre Hillion²

¹ California Institute of Technology · ² INSEAD

Review of Financial Studies 1999 open access

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.

DOI: 10.1093/rfs/12.2.405
Volume: 12 (2)
Pages: 405-428
Language: en
Export: BibTeX
Sources: openalex crossref