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]

- The report discusses a multi-factor model suitable for the constituents of the CSI 300 Index, combined with a small-cap high-growth portfolio as an external satellite strategy to improve the performance of the CSI 300 enhanced strategy[1][3][5] - The internal part of the enhanced strategy uses a multi-factor model based on fundamental and momentum indicators, including factors such as ROE, ROE YoY, SUE, expected net profit adjustment, accelerated growth, cash flow ratio, value (dividend yield and BP equal weight composite), momentum, buy-in strength after opening, and large order-driven rise[16][17] - The external part of the enhanced strategy uses a small-cap high-growth portfolio, constructed using factors such as SUE, EAV, expected net profit adjustment, cumulative R&D investment, PB_INT, small-cap, late trading volume ratio, and large order net buy-in ratio after opening[35][36] - The internal multi-factor model shows more stable stock selection performance within the CSI 300 Index constituents compared to the all-A multi-factor model, with higher IC and RankIC information ratios[16][17] - The small-cap high-growth portfolio has an annualized return of 25.0% since 2016, with an annualized excess return of 24.4% relative to the CSI 300 Index, but also higher tracking error[35][36] - The GARP strategy, which balances growth potential and reasonable pricing, is also considered as an external satellite strategy, showing an annualized return of 20.9% for the GARP 20 portfolio and 17.4% for the GARP 50 portfolio since 2016[39][40][42] - Combining the internal multi-factor model and external satellite strategies (small-cap high-growth or GARP) can significantly improve the performance of the CSI 300 enhanced strategy, with annualized excess returns not less than 10% and information ratios above 2.0 since 2016[29][45][55] Model and Factor Construction Process - **Internal Multi-Factor Model**: Constructed using fundamental and momentum indicators, including ROE, ROE YoY, SUE, expected net profit adjustment, accelerated growth, cash flow ratio, value (dividend yield and BP equal weight composite), momentum, buy-in strength after opening, and large order-driven rise[16][17] - **Small-Cap High-Growth Portfolio**: Constructed using factors such as SUE, EAV, expected net profit adjustment, cumulative R&D investment, PB_INT, small-cap, late trading volume ratio, and large order net buy-in ratio after opening[35][36] - **GARP Strategy**: Constructed by excluding high-risk stocks, using PB and dividend yield as value factors, and ROE, SUE, EAV, expected net profit adjustment, and two-year compound growth rate as growth factors, selecting the top 20 or 50 stocks based on composite scores[41][42] Model and Factor Performance Metrics - **Internal Multi-Factor Model**: IC monthly average 6.36%, IC monthly win rate 67.0%, annualized ICIR 1.67; RankIC monthly average 7.53%, RankIC monthly win rate 72.2%, annualized ICIR 2.00[17] - **Small-Cap High-Growth Portfolio**: Annualized return 25.0%, annualized excess return 24.4%, tracking error 20.3%, information ratio 1.21, relative drawdown 39.6%, monthly win rate 61.4%[36] - **GARP 20 Portfolio**: Annualized return 20.9%, annualized excess return 20.3%, tracking error 15.8%, information ratio 1.26, relative drawdown 36.0%[42] - **GARP 50 Portfolio**: Annualized return 17.4%, annualized excess return 16.8%, tracking error 14.6%, information ratio 1.14, relative drawdown 37.2%[42] Combined Strategy Performance - **Internal 20% + External 10% (Small-Cap High-Growth)**: Annualized excess return 11.7%, information ratio 2.35, tracking error 5.2%, relative drawdown 21.9%[45][48] - **Internal 20% + External 10% (GARP)**: Annualized excess return 11.3%, information ratio 2.41, tracking error 4.3%, relative drawdown 5.8%[50][53]