Core Insights - The article discusses the advancements in quantitative research by Galaxy Fund, focusing on the integration of linear and nonlinear analysis to enhance stock selection and uncover investment opportunities [1][2]. Group 1: Quantitative Research Approach - Galaxy Fund's quantitative team has shifted from traditional linear analysis to nonlinear methods to better adapt to complex market environments [1]. - The combination of multi-factor models and nonlinear machine learning models aims to achieve stable excess returns while reducing tracking error [1][2]. Group 2: Strategy Development - The quantitative strategies include both linear strategies, primarily using common multi-factor models, and nonlinear strategies such as XGBoost and LightGBM [2]. - XGBoost is highlighted for its ability to rank factor importance, enhancing the model's adaptability to market changes, especially in structured market conditions [2]. Group 3: Neural Network Learning - The development of complex neural network learning is emphasized, where long-term rules and short-term information are combined to improve model training [3]. - This approach helps in quickly adapting the quantitative model to market fluctuations by refining the feature extraction process [3]. Group 4: Satellite Strategies - In addition to the main strategies, satellite strategies such as dividend selection and large-cap growth selection are employed to further enhance market adaptability [4]. - The dividend selection strategy focuses on high dividend yield stocks, while the large-cap growth strategy targets large-cap, high-growth stocks [4]. Group 5: Product Offerings - Galaxy Fund has launched two index enhancement products: the Galaxy CSI 300 Index Enhanced Fund and the Galaxy CSI A500 Index Enhanced Fund, with plans for a Galaxy CSI 800 Index Enhanced Fund [4][5]. - The CSI 800 Index is noted for its balanced representation of both large-cap blue-chip and mid-cap growth styles, covering a wide range of sectors in the Chinese economy [5].