The paper examines the performance of regression models (OLS linear
regression, Ridge regression, Random Forest, and Fully-connected Neural
Network) on the prediction of CMA (Conservative Minus Aggressive) factor
premium and the performance of factor timing investment with them.
Out-of-sample R-squared shows that more flexible models have better performance
in explaining the variance in factor premium of the unseen period, and the back
testing affirms that the factor timing based on more flexible models tends to
over perform the ones with linear models. However, for flexible models like
neural networks, the optimal weights based on their prediction tend to be
unstable, which can lead to high transaction costs and market impacts. We
verify that tilting down the rebalance frequency according to the historical
optimal rebalancing scheme can help reduce the transaction costs.

该论文研究了回归模型（OLS 线性回归、Ridge 回归、随机森林和全连接神经网络）在 CMA（保守与激进）因子溢价预测和因子择时投资方面的表现。外样本 R 方显示更灵活的模型在解释未知时期因子溢价的方差方面具有更好的性能，回测证实基于更灵活模型的因子择时倾向于超过线性模型。然而，对于神经网络等灵活模型，其预测基于的最优权重往往不稳定，这可能导致高交易成本和市场影响。我们验证了根据历史最优再平衡方案将再平衡频率降低可以有助于减少交易成本。