Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?
使用多种统计模型选择准则验证股票超额收益的可预测性，发现样本内存在可预测性，但最佳模型在样本外无预测能力，且这一失败并非由于统计检验力不足。
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.