自编码器驱动的隐含因子模型在中国 A 股市场的应用
毕业论文：基于 1996–2023 年 A 股数据和 91 项公司特征，构建 PCA、IPCA 及条件自编码器隐含因子模型。
Thesis research applying autoencoder-based latent factor models to the Chinese A-share market.
Problem
Traditional linear factor models may not fully capture the non-linear risk-return structure in the A-share market.
Method
Constructed PCA, Instrumented PCA (IPCA), and conditional autoencoder latent factor models using 1996–2023 A-share data with 91 firm characteristics. Evaluated portfolio return explanatory power across models.
Result
The conditional autoencoder model significantly outperformed traditional linear models in out-of-sample tests, with superior Sharpe ratio and prediction accuracy, and lower pricing errors. Profitability and market size were identified as core risk factors in the A-share market, validating the effectiveness of latent factor models in China.
核心成果
- Conditional autoencoder outperforms linear models
- 91 firm characteristics, 1996–2023
- Superior Sharpe ratio & prediction accuracy
- Profitability & size as core A-share risk factors