Does model complexity add value to asset allocation? Evidence from machine learning forecasting models
研究评估了将多种机器学习和预测组合方法的回报预测纳入样本外资产配置框架的经济价值，发现模型复杂性在多数情况下能提升效果，收缩方法和浅层神经网络表现最佳。
Summary This study evaluates the benefits of integrating return forecasts from a variety of machine learning and forecast combination methods into an out‐of‐sample asset allocation framework. The economic evaluation of the forecasts shows that model complexity translates to improved results in the majority of cases considered, with shrinkage methods and shallow neural networks generating the highest individual performance. Overall, an investor would consistently realize superior out‐of‐sample gains by incorporating forecast combinations of machine learning models in the portfolio formation process.