Option Return Predictability via Machine Learning: New Evidence From China
用多种机器学习方法构建中国期权市场的收益预测因子，发现基于期货对冲的Delta中性组合能显著提升年化收益和夏普比率，且模型对新合约有强泛化能力。
ABSTRACT We extend the literature on empirical asset pricing to the Chinese options market by building and analyzing a comprehensive set of return prediction factors using various machine learning methods. In contrast to previous studies for the US market, we emphasize the uniqueness of this emerging market, investigate daily hedging strategies to construct delta‐neutral portfolios, and identify the most important characteristics for return prediction. Short‐selling restrictions in China's financial market diminish the effectiveness of spot hedging, whereas delta‐neutral portfolios based on futures hedging deliver substantial improvements in both annual returns and Sharpe ratios. Machine learning models not only outperform the IPCA benchmark, but also demonstrate strong generalization ability when applied to newly issued option contracts. The out‐of‐sample performance remains economically significant after accounting for transaction costs.