Diagnostics for asset pricing models
研究发现六个知名因子模型和机器学习模型的定价误差存在显著反转模式，可被滞后值预测长达12个月，并能产生可观的经济利润，且无法用常见行为偏差解释，表明需开发新模型。
Abstract The validity of asset pricing models implies white‐noise pricing errors (PEs). However, we find that the PEs of six well‐known factor models all exhibit a significant reversal pattern and are predictable by their lagged values up to 12 months. Moreover, the predictability of the PEs can produce substantial economic profits. Similar conclusions hold for recently developed machine learning models too. Additional analysis reveals that the significant PE profits cannot be explained by common behavioral biases. Our results imply that much remains to be done and there is a great need to develop new asset pricing models.