On the Nonlinear Predictability of Stock Returns Using Financial and Economic Variables
尝试复制Qi使用贝叶斯正则化神经网络预测S&P500超额收益的研究，发现复制结果与原结论相反：基于递归神经网络预测的切换组合财富更低、风险更高。
In a recent article by Qi, neural networks trained by Bayesian regularization were used to predict excess returns on the S&P 500. The article concluded that the switching portfolio based on the recursive neural-network forecasts generates higher accumulated wealth with lower risks than that based on linear regression. Unfortunately, attempts to replicate the results were unsuccessful. Replicated results using the same software, approach and data detailed by Qi indicate that, in fact, the switching portfolio based on the recursive neural-network forecasts generates lower accumulated wealth with higher risks than that based on linear regression.