【广发金工】机器学习选股训练手册

Core Viewpoint - The article discusses the increasing application of machine learning in quantitative stock selection, particularly focusing on GBDT and neural network models, as traditional factors have become less effective [1][4]. Group 1: Model Selection - Machine learning has been widely adopted in quantitative stock selection, with GBDT models (including LGBM, XGBoost, and CatBoost) and neural networks (including GRU, TCN, and Transformer) being the primary focus [1]. - GBDT models are effective for handling manually constructed features, while neural networks excel in capturing temporal changes in features [2]. Group 2: Feature Data Preparation - Different model types require different feature types; tree models handle price and fundamental features well, while neural networks perform better with high-frequency data [22][27]. - Feature selection methods, particularly SHAP, can effectively reduce the number of features while maintaining model performance [2][31]. - Standardization of features before feeding them into models is crucial for improving model performance [2][35]. Group 3: Loss Function Adjustment and Prediction Target Processing - Besides the common MSE loss function, investors often use IC as a loss function, with various ranking loss functions showing improved performance [2][37]. - Using cross-sectional normalization helps the model focus on differences in cross-sectional returns, enhancing factor performance [3][50]. Group 4: Machine Learning Models - GBDT is highlighted as a superior algorithm due to its iterative approach of updating target values based on residuals from previous trees [10][11]. - Neural networks, including RNN, LSTM, GRU, CNN, TCN, and Transformer, are discussed for their effectiveness in various domains, particularly in time series prediction [12][19]. Group 5: Index Enhancement Strategies - The article presents the performance of various index enhancement strategies, with the CSI 300 index showing an annualized excess return of 10.03% and a maximum drawdown of -5.42% [3]. - The CSI 500 index strategy has a slightly lower annualized excess return of 8.41% with a maximum drawdown of -10.78%, while the CSI 1000 index strategy shows a more stable performance with an annualized excess return of 11.44% and a maximum drawdown of -7.95% [3].