Core Insights - The article discusses the advancements made by Galaxy Fund's quantitative team in deep quantitative stock selection research, emphasizing the shift from traditional linear analysis to nonlinear analysis for better market insights and investment opportunities [1][2] Group 1: Quantitative Research Strategies - The quantitative research approach combines linear and nonlinear strategies, utilizing multi-factor models alongside nonlinear machine learning models to achieve stable excess returns and reduce tracking errors [1][2] - The team has developed strategies that include both linear methods, primarily multi-factor models, and nonlinear methods such as XGBoost and LightGBM, which enhance the model's adaptability to market changes [2][3] Group 2: Neural Network Development - The development of complex neural network learning is highlighted, where the approach integrates long-term rules with short-term information to improve the training of supervised learning models [3] - The focus is on extracting features from raw data while addressing the noise present in the data, which aids in the model's ability to adapt quickly to market fluctuations [3] Group 3: Satellite Strategies - To further enhance market adaptability, satellite strategies are employed, including dividend selection and large-cap growth selection, which target specific market characteristics [4] - The dividend selection strategy focuses on high dividend yield stocks, while the large-cap growth strategy emphasizes stocks with large market capitalization and high growth potential [4] Group 4: Risk Management and Product Development - A financial risk management strategy has been developed to mitigate unexpected impacts from risk events, forming a comprehensive quantitative strategy system [5] - The 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 to issue the Galaxy CSI 800 Index Enhanced Fund, which offers a balanced representation of both large-cap and mid-cap growth styles [5]

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].

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 investment opportunities [1][2] Group 1: Quantitative Research Strategies - Galaxy Fund's quantitative team has shifted from traditional linear analysis to nonlinear methods to better adapt to market changes and identify investment opportunities [1] - The combination of multi-factor models and nonlinear machine learning models aims to achieve stable excess returns while reducing tracking error in the overall portfolio [1][3] - The team has developed strategies that include both linear approaches, primarily using multi-factor models, and nonlinear methods such as XGBoost and LightGBM [1][2] Group 2: Neural Network Development - The team is advancing from simple neural network learning to more complex neural network models to improve market adaptability [2] - By integrating long-term rules with short-term information, the team enhances the feature extraction process, allowing for better training of supervised neural networks [2] Group 3: 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 [3] - The dividend selection strategy focuses on high dividend yield stocks, while the large-cap growth strategy targets stocks with high market capitalization and growth potential [3] Group 4: 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 [3] - A new product, the Galaxy CSI 800 Index Enhanced Fund, is in the process of being issued, which aims to provide a balanced representation of both large-cap blue-chip and mid-cap growth styles [4] Group 5: Index Characteristics - The CSI 800 Index is noted for its balanced coverage of A-share assets, representing the overall vitality of the Chinese economy across various sectors [4] - The index encompasses 31 primary industry categories, including traditional sectors like banking and emerging sectors such as electronics and pharmaceuticals [4]

Core Insights - The rapid development of passive investment has led to the rise of index-enhanced funds, which combine index investment features with enhanced strategies, aiming for long-term excess returns [1] - As of September 30, the average return of passive index funds was 42.94%, while index-enhanced funds achieved an average return of 44.58% [1] - The number of newly established index-enhanced funds has more than doubled compared to the entire year of 2024 [1] Group 1 - The appeal of index-enhanced funds lies in their ability to provide both beta and alpha returns by leveraging quantitative methods on representative indices [1] - The Galaxy CSI 300 Index Enhanced A fund, managed by Luo Bo, has outperformed the CSI 300 Index with returns of 75.84%, 31.87%, and 21.65% since inception, over five years, and one year respectively, with excess returns of 53.8, 30.71, and 6.15 percentage points [2] - The Galaxy CSI A500 Index Enhanced A fund has achieved a net value growth of 25.80% since inception, with an excess return of 5.71 percentage points over the CSI A500 Index [2] Group 2 - The upcoming Galaxy CSI 800 Index Enhanced Fund will track the CSI 800 Index, which represents 69% of the total market capitalization of A-shares and is expected to contribute approximately 95% of A-share net profits in 2024 [3] - The fund will utilize both linear and nonlinear models to select effective factors from the index constituents, ensuring that at least 80% of its non-cash assets are invested in the index and its alternative constituents [3] - The management strategy emphasizes maintaining close alignment with the CSI 800 Index while striving for cumulative excess returns [3]