PCA-Chinese Stocks

We examine the prediction performance using a principal component analysis (PCA). In particular, we perform a PCA to identify significant factors (principal components) and then use these factors to form predictions of stock price movements. We apply this strategy on the Chinese stock markets. Using data from January 2, 2019 till September 16, 2021, the empirical results show substantial out-performances from the PCA-based predictions against a naïve buy-and-hold strategy and also single time-series predictions of individual stocks本研究借助主成分分析（Principal Component Analysis，PCA）评估预测性能。具体而言，我们通过该方法识别有效因子（主成分），并基于这些因子构建股价走势预测模型。我们将该策略应用于中国股票市场，使用2019年1月2日至2021年9月16日的数据集开展实证实验。结果显示，基于主成分分析的预测方案相较于朴素买入持有策略以及单只个股的单时间序列预测模型，均取得了显著的超额表现。