Group 1 - The core point of the article is the launch of the first fund by Xinghua Fund Management Co., Ltd., named Xinghua Jingrui Mixed Initiation Securities Investment Fund, which focuses on equity mixed investment and is managed and sold primarily in Qingdao [1][3] - The fund employs a quantitative stock selection strategy, focusing on small and mid-cap undervalued stocks to benefit from small-cap premiums while controlling portfolio drawdown and volatility, with a risk level classified as R3, suitable for investors rated C3 and above [1][3] - Xinghua Fund has committed 10 million in initiation funds (own funds, shareholder funds, and fund manager funds) to demonstrate a shared risk and benefit approach with investors, enhancing alignment of interests and goals [1][3] Group 2 - Xinghua Fund was established on April 28, 2020, in Qingdao, Shandong Province, and is the first public fund management company in the province, contributing to the development of the financial industry in Shandong [2][4] - The company has a diverse product lineup, including interest rate bond funds, credit bond funds, FOFs, and mixed equity funds, with fixed income products performing exceptionally well, ranking second among 163 companies in terms of yield over the past two years [2][4] - As of December 31, 2025, the total assets under management by Xinghua Fund reached 8 billion [2][4]

- The "Competitive Advantage Portfolio" utilizes a competitive barrier analysis framework to categorize industries into four types: "Barrier Shield," "Highly Competitive," "Steady Progress," and "Seeking Breakthrough." The strategy focuses on identifying "sole leading" companies in "Barrier Shield" industries and "cooperative win-win" companies in industries without clear leaders. For non-"Barrier Shield" industries, it targets "efficient operation" companies that perform well even in competitive environments[11][12] - The "Margin of Safety Portfolio" emphasizes the internal factors of a company, focusing on creating entry barriers to ensure unique market positions and sustainable profitability. It calculates the intrinsic value of a company based on its profitability, selecting the top 50 stocks with the highest margin of safety from a pool of stocks with comprehensive competitive advantages. The portfolio is adjusted on May 1, September 1, and November 1 each year[17][19] - The "Dividend Low Volatility Adjusted Portfolio" aims to avoid the "high dividend trap" by considering the sustainability of company earnings and long-term value. It uses dividend yield predictions and negative screening to exclude stocks with extreme price performance or abnormal debt ratios[23] - The "AEG Valuation Potential Portfolio" uses the AEG_EP factor to select the top 100 stocks, then narrows it down to the top 50 stocks with high dividend reinvestment ratios. This strategy invests in companies whose growth potential is not yet fully recognized by the market. The AEG model calculates abnormal earnings growth as follows: $$ \begin{array}{c} A E G=Y_{t}-N_{t}=(E_{t}+r*D P S_{t-1})-(1+r)*E_{t-1} \\ \frac{V_{0}}{E_{1}}=\frac{1}{r}+\frac{1}{r}*\frac{\left(\frac{A E G_{2}}{1+r}+\frac{A E G_{3}}{(1+r)^{2}}+\frac{A E G_{4}}{(1+r)^{3}}+\cdots\right)}{E_{1}} \end{array} $$ where \(E_1\) is the first period's earnings and \(V_0\) is the current market value[28][29][31] - The "Cash Cow Portfolio" uses the CFOR analysis system to evaluate a company's profitability and cash generation efficiency. It focuses on the stability of free cash profit ratio, operating asset return rate, net profit margin, and total asset turnover rate, selecting high-quality stocks from the CSI 800 index[34][35][36] - The "Distressed Reversal Portfolio" captures short-term valuation fluctuations by utilizing inventory cycles to depict company reversals, considering accelerated recovery and undervaluation. It constructs a top 50 portfolio based on valuation improvement[41][43] Model Backtest Results - Competitive Advantage Portfolio: Annualized return since 2019 is 19.84%, Sharpe ratio 0.93, IR 0.09, maximum drawdown -19.32%, Calmar ratio 1.03[16] - Margin of Safety Portfolio: Annualized return since 2019 is 23.16%, Sharpe ratio 1.15, IR 0.16, maximum drawdown -16.89%, Calmar ratio 1.37[21] - Dividend Low Volatility Adjusted Portfolio: Annualized return since 2019 is 16.87%, Sharpe ratio 1.00, IR 0.17, maximum drawdown -21.61%, Calmar ratio 0.78[24] - AEG Valuation Potential Portfolio: Annualized return since 2019 is 25.36%, Sharpe ratio 1.16, IR 0.15, maximum drawdown -24.02%, Calmar ratio 1.06[33] - CSI 800 Cash Cow Portfolio: Annualized return since 2019 is 13.42%, Sharpe ratio 0.67, IR 0.09, maximum drawdown -19.80%, Calmar ratio 0.68[39] - Distressed Reversal Portfolio: Annualized return since 2019 is 24.53%, Sharpe ratio 0.99, IR 0.15, maximum drawdown -33.73%, Calmar ratio 0.73[43]

Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends[1][13] - **Construction Process**: The model analyzes the trading volume of various broad-based indices to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in capturing short-term market movements[1][13] 2. Model Name: Feature Dragon Tiger List Institutional Model - **Construction Idea**: This model uses institutional trading data from the Dragon Tiger List to predict market trends[1][13] - **Construction Process**: The model analyzes the trading activities of institutions listed on the Dragon Tiger List to generate buy or sell signals[1][13] - **Evaluation**: The model is useful for understanding institutional trading behavior and its impact on the market[1][13] 3. Model Name: Feature Volume Model - **Construction Idea**: This model uses specific volume characteristics to predict market trends[1][13] - **Construction Process**: The model analyzes specific volume patterns to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in identifying significant volume changes that precede market movements[1][13] 4. Model Name: Intelligent Algorithm CSI 300 Model - **Construction Idea**: This model uses intelligent algorithms to predict the CSI 300 index trends[1][13] - **Construction Process**: The model employs machine learning algorithms to analyze historical data and generate buy or sell signals for the CSI 300 index[1][13] - **Evaluation**: The model leverages advanced algorithms to improve prediction accuracy[1][13] 5. Model Name: Intelligent Algorithm CSI 500 Model - **Construction Idea**: This model uses intelligent algorithms to predict the CSI 500 index trends[1][13] - **Construction Process**: The model employs machine learning algorithms to analyze historical data and generate buy or sell signals for the CSI 500 index[1][13] - **Evaluation**: The model leverages advanced algorithms to improve prediction accuracy[1][13] 6. Model Name: Limit Up and Down Model - **Construction Idea**: This model uses the occurrence of limit up and down events to predict market trends[1][13] - **Construction Process**: The model analyzes the frequency and context of limit up and down events to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in capturing extreme market movements[1][13] 7. Model Name: Up and Down Return Difference Model - **Construction Idea**: This model uses the difference between upward and downward returns to predict market trends[1][13] - **Construction Process**: The model calculates the difference between upward and downward returns to generate buy or sell signals[1][13] - **Evaluation**: The model provides insights into market momentum and potential reversals[1][13] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model uses calendar-based patterns to predict market trends[1][13] - **Construction Process**: The model analyzes historical data to identify recurring calendar-based patterns and generate buy or sell signals[1][13] - **Evaluation**: The model is useful for identifying seasonal trends in the market[1][13] 9. Model Name: Long-term Momentum Model - **Construction Idea**: This model uses long-term momentum to predict market trends[1][14] - **Construction Process**: The model analyzes long-term price momentum to generate buy or sell signals[1][14] - **Evaluation**: The model is effective in capturing long-term market trends[1][14] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Construction Idea**: This model combines multiple factors to predict market trends[1][15] - **Construction Process**: The model integrates various indicators and models to generate a comprehensive buy or sell signal[1][15] - **Evaluation**: The model provides a holistic view of the market by combining multiple factors[1][15] 11. Model Name: A-Share Comprehensive Guozheng 2000 Model - **Construction Idea**: This model combines multiple factors to predict the Guozheng 2000 index trends[1][15] - **Construction Process**: The model integrates various indicators and models to generate a comprehensive buy or sell signal for the Guozheng 2000 index[1][15] - **Evaluation**: The model provides a holistic view of the market by combining multiple factors[1][15] 12. Model Name: Turnover Rate Inverse Volatility Model - **Construction Idea**: This model uses the inverse relationship between turnover rate and volatility to predict market trends[1][16] - **Construction Process**: The model analyzes the turnover rate and its inverse relationship with volatility to generate buy or sell signals[1][16] - **Evaluation**: The model is effective in identifying periods of high market uncertainty[1][16] Model Backtesting Results 1. Volume Model - **Indicator Value**: All broad-based indices are bullish[1][13] 2. Feature Dragon Tiger List Institutional Model - **Indicator Value**: Bullish[1][13] 3. Feature Volume Model - **Indicator Value**: Bullish[1][13] 4. Intelligent Algorithm CSI 300 Model - **Indicator Value**: Bullish[1][13] 5. Intelligent Algorithm CSI 500 Model - **Indicator Value**: Bullish[1][13] 6. Limit Up and Down Model - **Indicator Value**: Bullish[1][13] 7. Up and Down Return Difference Model - **Indicator Value**: All broad-based indices are bullish[1][13] 8. Calendar Effect Model - **Indicator Value**: Neutral[1][13] 9. Long-term Momentum Model - **Indicator Value**: Some broad-based indices are bullish[1][14] 10. A-Share Comprehensive Weapon V3 Model - **Indicator Value**: Bullish[1][15] 11. A-Share Comprehensive Guozheng 2000 Model - **Indicator Value**: Bullish[1][15] 12. Turnover Rate Inverse Volatility Model - **Indicator Value**: Bearish[1][16]

Group 1 - The article examines the application of PLS model expected factor returns in factor weighting, focusing on both single-factor multi-strategy and multi-factor single-strategy dimensions [1] - In the top 100 combinations of 20 single factors, using the PLS model for the five most volatile factor combinations resulted in an annualized return increase of approximately 4.0% compared to mean-weighted returns, and 6.6% compared to equal-weighted returns [2] - The article constructs six basic combinations including one dividend selection, one growth selection, two small-cap combinations, and two relatively balanced style combinations, achieving an annualized return increase of 3.3% over excess return mean weighting and 3.9% over equal weighting for volatile combinations [2] Group 2 - In multi-factor models, using PLS expected returns to determine factor weights can improve the expected IC and performance of top 100 combinations, although this improvement is not consistent across all cross-sections [3] - The PLS weighting method is noted to be more robust overall, but may underperform compared to mean IC weighting and ICIR weighting when factor momentum is strong, as observed in 2023 [3] - A composite quantitative fixed income + strategy using PLS expected return weighted multi-factor model for the stock side and the China Bond Short-term Index for the bond side achieved an annualized return of 8.1% with a volatility of 5.6% and a maximum drawdown of 5.4% from January 2018 to November 2025 [3]

- The report tracks the performance of single factors and major factor categories in quantitative stock selection models across different stock pools (CSI 300, CSI 500, CSI 1000, CSI 2000, and CSI All Share). It highlights the excess returns of factors over different time periods, such as weekly, monthly, and yearly[7][28][30] - Single factors with strong weekly excess returns in the CSI 300 stock pool include "120-day change in analysts' forecasted net profit FY3" (2.11%), "EPS 120-day change FY3" (2.05%), and "90-day institutional earnings forecast adjustment" (1.96%)[30] - In the CSI 500 stock pool, factors with strong weekly excess returns include "standardized unexpected single-quarter ROA with drift" (1.18%), "standardized unexpected single-quarter net profit with drift" (1.15%), and "standardized unexpected P/E ratio (parent company) with drift" (1.15%)[31] - For the CSI 1000 stock pool, factors with strong weekly excess returns include "single-quarter non-recurring ROA change" (1.26%), "1-minute path momentum" (1.15%), and "20-day intraday return" (1.02%)[32] - In the CSI 2000 stock pool, factors with strong weekly excess returns include "60-day shareholding ratio change" (1.74%), "5-day shareholding ratio change" (1.26%), and "EP 60-day change" (1.12%)[33] - Major factor categories with strong weekly excess returns in the CSI 300 stock pool include "analysts' surprise" (1.96%), "growth" (1.5%), and "analysts" (1.24%). For the full year, the best-performing categories are "profitability" (33.2%), "analysts' surprise" (30.25%), and "growth" (29.73%)[37][38][40] - In the CSI 500 stock pool, the best-performing major factor categories for the year are "growth" (16.68%), "analysts" (10.84%), and "analysts' surprise" (8.32%)[42][43] - For the CSI 1000 stock pool, the top-performing major factor categories for the year are "growth" (18.41%), "analysts" (10.99%), and "analysts' surprise" (10.86%)[47][48] - In the CSI 2000 stock pool, the best-performing major factor categories for the year are "market capitalization" (22.73%), "analysts' surprise" (20.54%), and "growth" (20.25%)[52][55] - The CSI All Share stock pool shows the best-performing major factor categories for the year as "market capitalization" (43.82%), "growth" (26.45%), and "analysts' surprise" (23.55%)[57][58] - The report also tracks the performance of index enhancement strategies based on multi-factor stock selection models. For the CSI 300 stock pool, the strategy achieved a weekly return of 2.38% and an annual return of 27.19%, with an excess return of 8.84% and a maximum drawdown of -3.15%[59][60] - For the CSI 500 stock pool, the strategy achieved a weekly return of 3.59% and an annual return of 31.54%, with an excess return of 1.28% and a maximum drawdown of -4.76%[60] - The CSI 1000 stock pool's strategy achieved a weekly return of 3% and an annual return of 42.2%, with an excess return of 14.54% and a maximum drawdown of -5.59%[66] - The CSI 2000 stock pool's strategy achieved a weekly return of 1.76% and an annual return of 63.87%, with an excess return of 27.31% and a maximum drawdown of -5.23%[66]

Quantitative Models and Construction Model Name: DTLC_RL (Decoupled Temporal Contrastive Learning with Reinforcement Learning) - **Model Construction Idea**: The model aims to combine the nonlinear predictive power of deep learning with interpretability by decoupling feature spaces, enhancing representation through contrastive learning, ensuring independence via orthogonal constraints, and dynamically fusing spaces using reinforcement learning[2][11][12] - **Model Construction Process**: - **Feature Space Decoupling**: Three orthogonal latent spaces are constructed to capture market systemic risk (β space), stock-specific signals (α space), and fundamental information (θ space). Each space is equipped with a specialized encoder: TCN for β space, Transformer for α space, and gated residual MLP for θ space[11][12][92] - **Contrastive Learning**: Introduced within each space to enhance robustness by constructing positive and negative sample pairs based on return similarity. The InfoNCE loss function is used to maximize the similarity of positive pairs while minimizing that of negative pairs: $$L_{\mathrm{InfotNCE}}=-E\left[l o g~\frac{e x p\left(f(x)^{\top}f(x^{+})/\tau\right)}{e x p\left(f(x)^{\top}f(x^{+})/\tau\right)+\sum_{i=1}^{N-1}~e x p\left(f(x)^{\top}f(x_{i}^{-})/\tau\right)}\right]$$ where \(f(x)\) is the feature representation, \(x^+\) is the positive sample, \(x^-\) is the negative sample, and \(\tau\) is the temperature parameter[55][56] - **Orthogonal Constraints**: A loss function is added to ensure the outputs of the three spaces are statistically independent, reducing multicollinearity and enhancing interpretability[12][104] - **Reinforcement Learning Fusion**: A PPO-based reinforcement learning mechanism dynamically adjusts the weights of the three spaces based on market conditions. The reward function includes components for return correlation, weight stability, and weight diversification: $$r_{t}=R_{t}^{I C}\big(\widehat{y_{t}},y_{y}\big)+\lambda_{s}R_{t}^{s t a b l e}+\lambda_{d}R_{t}^{d i v}$$ The PPO optimization process includes GAE advantage estimation and a clipped policy loss: $$L^{C L P}=E\left[\operatorname*{min}(r\dot{A},c l i p(r,1-\varepsilon,1+\varepsilon)\dot{A})\right]$$[58][120][121] - **Model Evaluation**: The DTLC_RL model demonstrates strong predictive power and interpretability, with dynamic adaptability to market conditions[2][12][122] Model Name: DTLC_Linear - **Model Construction Idea**: A baseline model for comparison, using a linear layer to fuse the three feature spaces[98][100] - **Model Construction Process**: - The encoded information from the three spaces is concatenated and passed through a linear layer with a Softmax activation to generate fusion weights. The model is trained with a multi-task loss function, including IC maximization, contrastive learning loss, and orthogonal constraints[98][104] - **Model Evaluation**: Provides a benchmark for evaluating the contribution of reinforcement learning in DTLC_RL[98][103] Model Name: DTLC_Equal - **Model Construction Idea**: A simpler baseline model that equally weights the three feature spaces without dynamic adjustments[98] - **Model Construction Process**: The outputs of the three spaces are directly averaged to generate predictions[98] - **Model Evaluation**: Serves as a control group to assess the benefits of dynamic weighting in DTLC_RL[98][103] --- Model Backtesting Results DTLC_RL - **IC**: 0.1250[123] - **ICIR**: 4.38[123] - **Top 10% Portfolio Annualized Return**: 34.77%[123] - **Annualized Volatility**: 25.41%[123] - **IR**: 1.37[123] - **Maximum Drawdown**: 40.65%[123] - **Monthly Turnover**: 0.71X[123] DTLC_Linear - **IC**: 0.1239[105] - **ICIR**: 4.25[105] - **Top 10% Portfolio Annualized Return**: 32.95%[105] - **Annualized Volatility**: 24.39%[105] - **IR**: 1.35[105] - **Maximum Drawdown**: 35.94%[105] - **Monthly Turnover**: 0.76X[105] DTLC_Equal - **IC**: 0.1202[105] - **ICIR**: 4.06[105] - **Top 10% Portfolio Annualized Return**: 32.46%[105] - **Annualized Volatility**: 25.29%[105] - **IR**: 1.28[105] - **Maximum Drawdown**: 40.65%[105] - **Monthly Turnover**: 0.71X[105] --- Quantitative Factors and Construction Factor Name: Beta_TCN - **Factor Construction Idea**: Captures market systemic risk by quantifying stock sensitivity to common risk factors like macroeconomic fluctuations and market sentiment[67] - **Factor Construction Process**: - Five market-related features are selected, including beta to market returns, volatility sensitivity, liquidity beta, size exposure, and market sentiment sensitivity[72] - A TCN encoder processes 60-day time-series data, using dilated causal convolutions to capture short- and medium-term trends. The output is a 32-dimensional vector representing systemic risk features[68] - **Factor Evaluation**: Demonstrates moderate stock selection ability and effectively captures market-related information[73] Factor Name: Alpha_Transformer - **Factor Construction Idea**: Extracts stock-specific alpha signals from price-volume time-series data[76] - **Factor Construction Process**: - Thirteen price-volume features are encoded using a multi-scale Transformer model, with separate layers for short-, medium-, and long-term information. Outputs are fused using a gated mechanism and passed through a fully connected layer for return prediction[77][78] - **Factor Evaluation**: Exhibits strong predictive power and stock selection ability, with relatively low correlation to market benchmarks[81][82] Factor Name: Theta-ResMLP - **Factor Construction Idea**: Focuses on fundamental information to assess financial safety margins and risk resistance[88] - **Factor Construction Process**: - Eight core financial indicators, including PE, PB, ROE, and dividend yield, are encoded using a gated residual MLP. The architecture includes input projection, gated residual blocks, and a final output layer[92] - **Factor Evaluation**: Provides stable stock selection performance with lower turnover and drawdown compared to other spaces[95][96] --- Factor Backtesting Results Beta_TCN - **IC**: 0.0969[73] - **ICIR**: 3.73[73] - **Top 10% Portfolio Annualized Return**: 27.73%[73] - **Annualized Volatility**: 27.19%[73] - **IR**: 1.02[73] - **Maximum Drawdown**: 45.80%[73] - **Monthly Turnover**: 0.79X[73] Alpha_Transformer - **IC**: 0.1137[81] - **ICIR**: 4.19[81] - **Top 10% Portfolio Annualized Return**: 32.66%[81] - **Annualized Volatility**: 23.04%[81] - **IR**: 1.42[81] - **Maximum Drawdown**: 27.59%[81] - **Monthly Turnover**: 0.83X[81] Theta-ResMLP - **IC**: 0.0485[95] - **ICIR**: 1.87[95] - **Top 10% Portfolio Annualized Return**: 23.88%[95] - **Annualized Volatility**: 23.96%[95] - **IR**: 0.99[95] - **Maximum Drawdown**: 37.41%[95] - **Monthly Turnover**: 0.41X[95]

- The report discusses multiple quantitative timing models for A-shares, including short-term, medium-term, and long-term models. The short-term models include the Volume Model (neutral for all broad-based indices), Feature Institutional Model (bullish), Feature Volume Model (bearish), and Smart Algorithm Models (bullish for CSI 300, bearish for CSI 500)[1][12][77]. Medium-term models include the Limit-Up-Limit-Down Model (neutral) and the Up-Down Return Difference Model (bullish for all broad-based indices)[13][78]. The long-term model, the Long-Term Momentum Model, is bullish[14][79]. Comprehensive models like the A-Share Comprehensive Weapon V3 Model and the A-Share Comprehensive CSI 2000 Model are bearish[15][80] - For Hong Kong stocks, the medium-term models include the Turnover-to-Volatility Model (bullish) and the Hang Seng Index Up-Down Return Difference Model (neutral)[16][81] - The report emphasizes that timing strategies are not achieved through a single model but require a multi-cycle, multi-strategy system. It highlights the use of price-volume, acceleration, trend, momentum, and limit-up-limit-down perspectives to construct eight major models for market timing[9] - Backtesting results for the Double-Bottom Pattern show a weekly return of 0.29%, outperforming the Shanghai Composite Index by 0.27% during the same period. Since December 31, 2020, the cumulative return of the Double-Bottom Pattern portfolio is 11.79%, slightly underperforming the Shanghai Composite Index's cumulative return of 12.02%[44] - Backtesting results for the Cup-and-Handle Pattern show a weekly return of 0.3%, outperforming the Shanghai Composite Index by 0.27% during the same period. Since December 31, 2020, the cumulative return of the Cup-and-Handle Pattern portfolio is 9.27%, underperforming the Shanghai Composite Index's cumulative return of 12.02%[44]

Report Industry Investment Rating No information about the report industry investment rating is provided in the content. Core Viewpoints of the Report - The issuance speed of US ETFs at the end of the year has increased again, with multiple quantitative enhancement products being issued [2][7]. - The capital inflow of US ETFs has remained above $40 billion, and the risk appetite of capital has remained at a high level [2][13]. - Stock long - short and other alternative strategies of US ETFs have performed well [2][19]. - The redemption pressure of US non - money mutual funds in October 2025 was still high, and domestic stock funds and hybrid products have continued to experience outflows recently, while bond funds have seen a slight inflow [2][20]. Summary by Relevant Catalogs 1. US ETF Innovation Products: Multiple Quantitative Enhancement Products Issued - Last week, 43 new products were issued in the US, including 6 individual stock leverage products and 3 digital currency - related products [2][7]. - Motley Fool issued 3 single - factor ETFs, each holding about 150 stocks [9]. - BlackRock's quantitative team, NEOS, Hedgeye, Global X, Franklin Templeton, Sterling Capital, and Columbia all issued different types of ETFs last week, with many using quantitative strategies [10][11]. 2. US ETF Dynamics 2.1 US ETF Capital: All Types of Assets Maintain Inflows - In the past week, the inflow of US ETFs has remained above $40 billion, and the inflow of domestic stock products has exceeded $30 billion [2][13]. - The S&P 500 ETF of BlackRock continued to have the largest outflow, while the products of Vanguard had a large - scale inflow of over $40 billion, with a capital flow difference of over $80 billion between the two. The Russell 2000 and high - yield bond ETFs had inflows [2][15]. 2.2 US ETF Performance: Stock Long - Short and Other Alternative Strategies Perform Well - Many stock long - short products were issued last week, and products combining futures replication and multiple hedge fund strategies have been increasing in the past two years. Among the top ten alternative strategy products in the US, the multi - strategy product of State Street and the stock long - short product of Convergence performed the best [2][19]. 3. Recent Capital Flows of US Ordinary Mutual Funds - In October 2025, the total amount of US non - money mutual funds was $23.7 trillion, an increase of $0.22 trillion compared to September. The scale of domestic stock products increased by 0.9%, but the redemption pressure was still high [2][20]. - From November 25th to December 3rd, the outflow of US domestic stock funds remained above $15 billion. Hybrid products have continued to experience outflows recently, while bond funds have seen a slight inflow [2][20].

- The report discusses multiple quantitative models for market timing, including short-term, medium-term, and long-term models. These models are constructed based on principles such as price-volume relationships, momentum, and calendar effects. The short-term models include the "Volume Model," "Feature Institutional Model," and "Feature Volume Model," while medium-term models include the "Limit-Up/Down Model" and "Up/Down Return Difference Model." The long-term model is the "Long-Term Momentum Model"[8][11][12][13] - The construction process of these models involves combining signals from different time horizons and strategies. For example, the "Volume Model" evaluates market activity through trading volume, while the "Momentum Model" focuses on price trends. The "Limit-Up/Down Model" identifies market sentiment by analyzing the frequency of limit-up and limit-down events. The "Up/Down Return Difference Model" measures the difference between upward and downward returns to gauge market direction[8][11][12] - The evaluation of these models suggests that combining signals from different models enhances robustness. For instance, some models are defensive, while others are aggressive, allowing for a balanced approach. The report emphasizes that simplicity in model design often leads to better generalization and performance[8][11][12] - Backtesting results for these models indicate varying levels of effectiveness. For example, the "Long-Term Momentum Model" is currently bullish, while the "Up/Down Return Difference Model" shows a positive outlook across all broad-based indices. The "Feature Institutional Model" is bullish, whereas the "Feature Volume Model" is bearish. The "Volume Model" remains neutral across all indices[11][12][13]

Group 1 - The report highlights that the current valuation levels of the small-cap indices, represented by the CSI 500 and CSI 1000, are reasonable with strong expected earnings, showing a high margin of safety compared to other broad indices [1][8][9] - The CSI 500 and CSI 1000 indices have a balanced industry distribution, making active stock selection challenging, while providing significant opportunities for quantitative stock selection due to their high dispersion [1][16][19] - Quantitative strategies in small-cap indices have a higher ability to generate excess returns, indicating that small-cap stocks possess greater alpha value compared to large-cap stocks [1][26][24] Group 2 - Historical performance shows that the Yingyin CSI 500 Index Enhanced A fund has achieved a steady excess return of 6.6% over the past year, while the Yingyin CSI 1000 Index Enhanced A fund has delivered a significant excess return of 9.7% [2][30] - Both funds maintain a high excess return rate, with a monthly excess return rate of 75%, indicating strong stability in outperforming their benchmarks [2][38] - The alpha generated by these funds primarily comes from stock selection rather than asset allocation or sector allocation, confirming a clear and stable source of excess returns [3][48][51] Group 3 - The Yingyin CSI 500 Index Enhanced A fund is managed by a team with extensive quantitative investment experience, ensuring a robust governance structure and stable operations [4][3] - The fund management strategy focuses on strict control of tracking error while aiming to create excess returns, with a low average tracking deviation [35][36] - The industry allocation of the funds is closely aligned with their respective benchmarks, with minimal deviations, emphasizing a focus on stock selection to achieve excess returns [44][47]