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Implementing statistical criteria to select return forecasting models: What do we learn?
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DOI：10.1093/rfs/12.2.405.png)
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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.
Keyword:
ASSET PRICING-MODELS
STOCK RETURNS
PREDICTABILITY
INFERENCE
MARKETS
期刊
IF:
5.4
论文数:
2.8K
被引数:
3.0W
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SIMPLE TECHNICAL TRADING RULES AND THE STOCHASTIC PROPERTIES OF STOCK RETURNS.png)
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JOURNAL OF FINANCEIF9.5
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