- The report discusses the impact of dividends on stock index futures, specifically for the contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 indices [1][2][3][4] - The latest dividend forecast model predicts the dividend points for the June contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 indices to be 12.10, 16.30, 18.75, and 17.78, respectively [8][11] - The annualized hedging costs (excluding dividends, calculated on a 365-day basis) for the June contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 indices are 3.05%, 1.54%, 8.11%, and 14.77%, respectively [8][11] - The report provides detailed data on the closing prices, dividend points, actual spreads, and dividend-inclusive spreads for the June, July, September, and December contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 indices [2][3][4] - The remaining impact of dividends on the June contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 indices is 0.45%, 0.42%, 0.33%, and 0.29%, respectively [12][13][14][15][16] - The report outlines the process for predicting dividends, which includes estimating the net profit of constituent stocks, calculating the total pre-tax dividends for each stock, and determining the impact of dividends on the index and each contract [9][21][24][25][26][27][28][29][30] - The theoretical pricing model for stock index futures is provided, including formulas for both discrete and continuous dividend distributions [32][33] Model and Factor Construction - **Model Name**: Dividend Impact Prediction Model - **Model Construction Idea**: The model aims to predict the impact of dividends on stock index futures contracts by estimating the dividends of constituent stocks and calculating their effect on the index and futures contracts [9][21] - **Model Construction Process**: 1. Estimate the net profit of constituent stocks using available information such as annual reports, quick reports, warnings, and analyst forecasts [23][24] 2. Calculate the total pre-tax dividends for each stock based on the estimated net profit and historical dividend rates [25][28] 3. Determine the impact of dividends on the index by calculating the dividend yield and the dividend points for each stock [26] 4. Estimate the ex-dividend dates and calculate the theoretical impact of dividends on each futures contract [27][29][30] 5. Use the theoretical pricing model for stock index futures to incorporate the impact of dividends into the futures prices [32][33] - **Model Evaluation**: The model provides a systematic approach to predict the impact of dividends on stock index futures, considering various factors such as net profit estimates, dividend rates, and ex-dividend dates [9][21][24] Model Backtesting Results - **SSE 50 Futures (June Contract)**: - Closing Price: 2673.60 - Dividend Points: 12.10 - Actual Spread: -15.25 - Dividend-Inclusive Spread: -3.15 - Remaining Impact: 0.45% - Annualized Hedging Cost (365 days): 3.05% - Annualized Hedging Cost (243 days): 2.84% [2][12] - **CSI 300 Futures (June Contract)**: - Closing Price: 3855.40 - Dividend Points: 16.30 - Actual Spread: -18.58 - Dividend-Inclusive Spread: -2.28 - Remaining Impact: 0.42% - Annualized Hedging Cost (365 days): 1.54% - Annualized Hedging Cost (243 days): 1.43% [2][13] - **CSI 500 Futures (June Contract)**: - Closing Price: 5725.40 - Dividend Points: 18.75 - Actual Spread: -36.68 - Dividend-Inclusive Spread: -17.93 - Remaining Impact: 0.33% - Annualized Hedging Cost (365 days): 8.11% - Annualized Hedging Cost (243 days): 7.56% [3][14] - **CSI 1000 Futures (June Contract)**: - Closing Price: 6100.20 - Dividend Points: 17.78 - Actual Spread: -52.64 - Dividend-Inclusive Spread: -34.87 - Remaining Impact: 0.29% - Annualized Hedging Cost (365 days): 14.77% - Annualized Hedging Cost (243 days): 13.77% [4][15]

- Model Name: RPV (Renewed Correlation of Price and Volume); Model Construction Idea: The RPV factor integrates intraday and overnight information by dividing price and volume into four quadrants, effectively identifying the reversal and momentum effects of price-volume correlation factors through the monthly IC mean; Model Construction Process: The RPV factor is constructed by combining the best representatives of intraday and overnight price-volume correlations, incorporating "trading volume" information in the form of correlation, and completing information integration; Model Evaluation: The RPV factor is novel and effective[1][6][7] - Model Name: SRV (Smart Relative Volume); Model Construction Idea: The SRV factor splits intraday price changes into morning and afternoon changes, calculates the "smart" indicator by minute, and uses the correlation coefficient between the afternoon "smart" turnover rate and afternoon price changes; Model Construction Process: The SRV factor combines the more effective intraday price-volume correlation factor and the overnight price-volume correlation factor, where the turnover rate is replaced by the turnover rate of the last half-hour of the previous day, which has a higher proportion of informed trading; Model Evaluation: The SRV factor performs better than the RPV factor[1][6][7] Model Backtest Results - RPV Model, Annualized Return: 14.69%, Annualized Volatility: 7.75%, IR: 1.90, Monthly Win Rate: 72.79%, Maximum Drawdown: 10.63%[1][7][10] - SRV Model, Annualized Return: 17.48%, Annualized Volatility: 6.50%, IR: 2.69, Monthly Win Rate: 75.74%, Maximum Drawdown: 3.74%[1][7][10] Factor Construction and Evaluation - Factor Name: RPV; Factor Construction Idea: The RPV factor integrates intraday and overnight information by dividing price and volume into four quadrants, effectively identifying the reversal and momentum effects of price-volume correlation factors through the monthly IC mean; Factor Construction Process: The RPV factor is constructed by combining the best representatives of intraday and overnight price-volume correlations, incorporating "trading volume" information in the form of correlation, and completing information integration; Factor Evaluation: The RPV factor is novel and effective[1][6][7] - Factor Name: SRV; Factor Construction Idea: The SRV factor splits intraday price changes into morning and afternoon changes, calculates the "smart" indicator by minute, and uses the correlation coefficient between the afternoon "smart" turnover rate and afternoon price changes; Factor Construction Process: The SRV factor combines the more effective intraday price-volume correlation factor and the overnight price-volume correlation factor, where the turnover rate is replaced by the turnover rate of the last half-hour of the previous day, which has a higher proportion of informed trading; Factor Evaluation: The SRV factor performs better than the RPV factor[1][6][7] Factor Backtest Results - RPV Factor, Annualized Return: 14.69%, Annualized Volatility: 7.75%, IR: 1.90, Monthly Win Rate: 72.79%, Maximum Drawdown: 10.63%[1][7][10] - SRV Factor, Annualized Return: 17.48%, Annualized Volatility: 6.50%, IR: 2.69, Monthly Win Rate: 75.74%, Maximum Drawdown: 3.74%[1][7][10]

Group 1 - The report attempts to integrate subjective judgment and quantitative analysis to construct a style rotation framework, primarily based on five dimensions to build a core style rotation model, which will eventually be applied to actual investable portfolios [3][8] - The fundamental perspective of growth and value style rotation strategy has shown long-term excess returns compared to its balanced allocation benchmark, although the performance of the strategy is limited due to varying transmission paths and delays of different fundamental indicators under different contexts [3][10] Group 2 - The report reviews the construction of style indices and the style rotation framework, continuing to explore the growth and value style rotation from a fundamental perspective [8][17] - Common fundamental indicators are primarily micro data, but the report adopts a different perspective by observing the overall situation of the equity market or specific styles, reflecting the specific conditions of certain groups [8][30] Group 3 - The analysis of fundamental factors is conducted from five angles: growth, profitability, financial health and solvency, operational efficiency, and valuation levels, with growth, profitability, and valuation signals being relatively stable and accurate [9][31] - The overall turnover rate of the growth and value style rotation strategy is low, generally favoring long-term holdings of growth or value stocks, with an average monthly win rate of approximately 60.91% and an average annualized return of about 15.26% from January 1, 2005, to April 29, 2025 [10][31] Group 4 - The growth style index and value style index are constructed based on similar logic, with the main difference being the sorting of constituent stocks using growth and value factors respectively [18][21] - The report outlines the style rotation framework, which is expected to be based on five major dimensions to construct the core style rotation model, focusing on the fundamental dimension of growth and value style rotation [27][30] Group 5 - The report categorizes fundamental indicators into two main types: market overall indicators and style difference indicators, further divided into growth indicators, profitability indicators, financial health and solvency indicators, operational efficiency indicators, and valuation indicators [30][31] - The financial health and solvency indicators focus on the reasonableness of capital structure and short-term liquidity, with asset-liability ratio and current ratio being particularly effective in the context of growth and value style rotation [57][65]

- The report systematically reviews 80 new quantitative finance-related research papers in May 2025, covering areas such as equity research, fixed income, fund studies, asset allocation, machine learning applications, and ESG-related studies [1][2][3] - Equity research includes studies on fundamental factors, price-volume and alternative factors, factor research, active quantitative strategies, and other categories, exploring investor behavior biases, asset pricing models, market structure distortions, prediction model innovations, and corporate resilience mechanisms [2][10] - Fixed income research focuses on high-frequency inflation forecasting, sovereign risk premium decomposition, and stochastic interest rate model innovations, with findings such as weekly online inflation rates predicting yield curve slope factors and semi-Markov-modulated Hull-White/CIR models achieving semi-analytical pricing for zero-coupon bonds [22][23] - Fund studies investigate fund selection factors, fund style evaluation, and behavioral biases, revealing strategies like liquidity picking driving excess returns and public pension funds underperforming benchmarks due to alternative investment errors post-2008 [28][30] - Asset allocation research explores multi-asset portfolio management paradigm shifts, systematic currency management, and volatility connectedness constraints, demonstrating dynamic adaptation mechanisms and enhanced performance during crises [32][33][35] - Machine learning applications in finance include innovations in volatility forecasting, credit risk prediction using GraphSAGE models, and long-memory stochastic interval models, significantly improving prediction accuracy and economic value [36][38][40] - ESG-related studies analyze green innovation drivers, ESG evaluation distortions, and corporate environmental response strategies, highlighting mechanisms like family business constraints on green innovation and AI-driven manufacturing green transformation [42][43][45]

Group 1: Quantitative Finance Research Overview - A total of 80 new quantitative finance-related research papers were added this month, with the following distribution: 31 on equity research, 4 on fund research, 8 on bond research, 9 on asset allocation, 3 on machine learning applications in finance, and 22 on ESG-related research[1] - Equity research covers various topics including investor behavior biases, asset pricing models, and market structure distortions, impacting capital markets[2] - Bond research focuses on interest rate bonds, credit bonds, and other bond markets, analyzing high-frequency inflation forecasting and pricing distortion mechanisms[2] Group 2: Specific Findings in Research - High-frequency online inflation rates predict yield curve slope factors with a contribution rate of 61%[22] - The sovereign risk premium in the Eurozone is primarily driven by credit risk premiums, with Italy accounting for 78% of this effect[22] - Climate disasters lead to a temporary premium for green bonds over brown bonds, which diminishes within five months due to behavioral overreaction[24] Group 3: Machine Learning and Risk Management - Machine learning models significantly improve the prediction of implied volatility, showing economic value superior to traditional models[38] - The GraphSAGE model enhances credit risk prediction accuracy by 19% through integrating stock returns, risk spillovers, and trading networks[38] - Long Memory Stochastic Interval Models (LMSR) capture persistent characteristics in volatility, reducing out-of-sample prediction loss by 38%[38]

Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of future diffusion index changes, predicting potential turning points in the market[6][43] - **Model Construction Process**: - The horizontal axis represents the relative price change of stocks in the micro-cap index components over a future period, ranging from +10% to -10% - The vertical axis represents the length of the review or forecast window, ranging from 20 days to 10 days - For example, a value of 0.16 at the intersection of a -5% price change (horizontal axis) and a 15-day window (vertical axis) indicates the diffusion index value under these conditions - The model uses historical data to calculate the diffusion index for different scenarios and predicts the likelihood of market turning points based on these values[43][45] - **Model Evaluation**: The model provides a systematic way to identify potential market turning points, but its accuracy depends on the stability of the index components and market conditions[6][43] - **Model Testing Results**: - Current diffusion index value: 0.91 - Historical signals: - Left-side threshold method triggered a sell signal on May 8, 2025, with a value of 0.9850[47] - Right-side threshold method triggered a sell signal on May 15, 2025, with a value of 0.8975[51] - Dual moving average method triggered a buy signal on April 30, 2025[52] --- Quantitative Factors and Construction Leverage Factor - **Factor Name**: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies, indicating their risk and potential return[5][38] - **Factor Construction Process**: Calculated as the ratio of total debt to equity or assets, normalized for comparison across companies[5][38] - **Factor Evaluation**: Demonstrated strong performance in the current week, with a rank IC of 0.143, significantly above its historical average of -0.006[5][38] Turnover Factor - **Factor Name**: Turnover Factor - **Factor Construction Idea**: Reflects the liquidity of stocks, with higher turnover indicating more active trading[5][38] - **Factor Construction Process**: Calculated as the ratio of trading volume to free float market capitalization over a specific period[5][38] - **Factor Evaluation**: Rank IC of 0.051 this week, outperforming its historical average of -0.08[5][38] PB Inverse Factor - **Factor Name**: PB Inverse Factor - **Factor Construction Idea**: Represents the inverse of the price-to-book ratio, identifying undervalued stocks[5][38] - **Factor Construction Process**: Calculated as 1 divided by the price-to-book ratio, normalized for comparison[5][38] - **Factor Evaluation**: Rank IC of 0.042 this week, slightly above its historical average of 0.034[5][38] Free Float Ratio Factor - **Factor Name**: Free Float Ratio Factor - **Factor Construction Idea**: Measures the proportion of shares available for public trading, indicating potential liquidity[5][38] - **Factor Construction Process**: Calculated as the ratio of free float shares to total shares outstanding[5][38] - **Factor Evaluation**: Rank IC of 0.04 this week, outperforming its historical average of -0.012[5][38] 10-Day Return Factor - **Factor Name**: 10-Day Return Factor - **Factor Construction Idea**: Captures short-term momentum by analyzing recent stock performance[5][38] - **Factor Construction Process**: Calculated as the percentage change in stock price over the past 10 trading days[5][38] - **Factor Evaluation**: Rank IC of 0.025 this week, significantly above its historical average of -0.061[5][38] Non-Adjusted Stock Price Factor - **Factor Name**: Non-Adjusted Stock Price Factor - **Factor Construction Idea**: Reflects the raw stock price without adjustments for splits or dividends[5][38] - **Factor Construction Process**: Directly uses the stock's current market price[5][38] - **Factor Evaluation**: Rank IC of -0.19 this week, underperforming its historical average of -0.017[5][38] PE_TTM Inverse Factor - **Factor Name**: PE_TTM Inverse Factor - **Factor Construction Idea**: Represents the inverse of the price-to-earnings ratio based on trailing twelve months, identifying undervalued stocks[5][38] - **Factor Construction Process**: Calculated as 1 divided by the PE_TTM ratio, normalized for comparison[5][38] - **Factor Evaluation**: Rank IC of -0.143 this week, underperforming its historical average of 0.018[5][38] ROE (Single Quarter) Factor - **Factor Name**: ROE (Single Quarter) Factor - **Factor Construction Idea**: Measures the profitability of companies based on their return on equity for a single quarter[5][38] - **Factor Construction Process**: Calculated as net income divided by shareholders' equity for the most recent quarter[5][38] - **Factor Evaluation**: Rank IC of -0.124 this week, underperforming its historical average of 0.023[5][38] Nonlinear Market Cap Factor - **Factor Name**: Nonlinear Market Cap Factor - **Factor Construction Idea**: Captures the nonlinear relationship between market capitalization and stock performance[5][38] - **Factor Construction Process**: Applies a nonlinear transformation to market capitalization data, such as logarithmic or polynomial adjustments[5][38] - **Factor Evaluation**: Rank IC of -0.116 this week, underperforming its historical average of -0.033[5][38] Log Market Cap Factor - **Factor Name**: Log Market Cap Factor - **Factor Construction Idea**: Measures the logarithmic transformation of market capitalization to reduce skewness[5][38] - **Factor Construction Process**: Calculated as the natural logarithm of market capitalization[5][38] - **Factor Evaluation**: Rank IC of -0.116 this week, underperforming its historical average of -0.033[5][38] --- Factor Backtesting Results - **Leverage Factor**: Rank IC 0.143[5][38] - **Turnover Factor**: Rank IC 0.051[5][38] - **PB Inverse Factor**: Rank IC 0.042[5][38] - **Free Float Ratio Factor**: Rank IC 0.04[5][38] - **10-Day Return Factor**: Rank IC 0.025[5][38] - **Non-Adjusted Stock Price Factor**: Rank IC -0.19[5][38] - **PE_TTM Inverse Factor**: Rank IC -0.143[5][38] - **ROE (Single Quarter) Factor**: Rank IC -0.124[5][38] - **Nonlinear Market Cap Factor**: Rank IC -0.116[5][38] - **Log Market Cap Factor**: Rank IC -0.116[5][38]

Group 1 - The A-share market experienced a contraction with major indices declining, indicating a cautious market sentiment amid reduced trading volume [3] - The REITs market showed an upward trend in secondary market prices, with the weighted REITs index closing at 139.74 and a weekly return of 1.36%, outperforming other major asset classes [4] - The copper industry is facing pressure from trade conflicts and rising domestic inventory, but prices may gradually increase with potential domestic stimulus policies and U.S. interest rate cuts [5] Group 2 - In the livestock sector, the average weight of slaughtered pigs has decreased, and the price of pigs has seen a larger decline, indicating a potential turning point in inventory levels and a long-term upward profit cycle post-deinventory [6] - Nobon Co., a leading player in the spunlace non-woven fabric industry, has shown strong performance in 2024 and Q1 2025, with advanced production lines and a focus on high-margin clients [7] - The small-cap style is currently favored in the market, with private equity research strategies showing significant excess returns [8]

Group 1 - The article discusses various industry reports and investment outlooks scheduled for late May 2023, focusing on sectors such as sugar-free tea, electronics, transportation, media, engineering machinery, and raw pharmaceutical materials [1][2][3][4][5][6][7][8][9][10][11][12][26][27][28][29] - Each report is led by a chief analyst specializing in the respective field, indicating a structured approach to industry analysis and investment recommendations [1][2][3][4][5][6][7][8][9][10][11][12][26][27][28][29] - The meetings are set to provide insights into current market trends, potential investment opportunities, and sector-specific developments, which are crucial for investors looking to make informed decisions [1][2][3][4][5][6][7][8][9][10][11][12][26][27][28][29] Group 2 - The schedule includes a deep dive into the sugar-free tea market, highlighting its growth potential and consumer trends [1] - The electronics industry report aims to provide an investment perspective amidst technological advancements and market shifts [2] - The transportation sector analysis will focus on the performance of companies like 安徽皖通高速, assessing their financial health and market position [3] - The media industry report will explore investment opportunities in the evolving landscape of digital content and advertising [4] - The engineering machinery update will cover recent developments and market dynamics affecting the sector [5] - The raw pharmaceutical materials report will summarize the market outlook for 2024 and the first quarter of 2025, focusing on supply chain and regulatory factors [6][7]

Quantitative Models and Construction Methods Quantitative Factors and Their Construction 1. **Factor Name**: Intraday Skewness Factor **Construction Idea**: Captures the skewness of intraday stock returns to identify potential return asymmetry[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (19) - High-Frequency Factors on Stock Return Distribution Characteristics"[11] **Evaluation**: Demonstrates mixed performance with positive returns in some periods but underperformance in others[3][6] 2. **Factor Name**: Downside Volatility Proportion Factor **Construction Idea**: Measures the proportion of downside volatility in intraday price movements to assess risk[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (25) - High-Frequency Factors on Realized Volatility Decomposition"[16] **Evaluation**: Shows consistent positive returns in certain periods but limited robustness in others[3][6] 3. **Factor Name**: Post-Open Buy Intention Proportion Factor **Construction Idea**: Quantifies the proportion of buy orders after market open to gauge investor sentiment[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Using Intuitive Logic and Machine Learning"[20] **Evaluation**: Exhibits moderate performance with occasional strong returns[3][6] 4. **Factor Name**: Post-Open Buy Intention Intensity Factor **Construction Idea**: Measures the intensity of buy orders after market open to reflect market momentum[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Using Intuitive Logic and Machine Learning"[24] **Evaluation**: Performance is inconsistent, with periods of underperformance[3][6] 5. **Factor Name**: Post-Open Large Order Net Buy Proportion Factor **Construction Idea**: Tracks the proportion of large net buy orders after market open to identify institutional activity[3][6] **Construction Process**: Derived from high-frequency trading data[30] **Evaluation**: Generally positive performance with strong returns in specific periods[3][6] 6. **Factor Name**: Post-Open Large Order Net Buy Intensity Factor **Construction Idea**: Measures the intensity of large net buy orders after market open to capture market trends[3][6] **Construction Process**: Derived from high-frequency trading data[35] **Evaluation**: Mixed results with moderate returns in some periods[3][6] 7. **Factor Name**: Improved Reversal Factor **Construction Idea**: Enhances traditional reversal factors by incorporating high-frequency data[3][6] **Construction Process**: Derived from intraday price reversals[40] **Evaluation**: Limited performance improvement over traditional reversal factors[3][6] 8. **Factor Name**: Tail-End Trading Proportion Factor **Construction Idea**: Measures the proportion of trading activity near market close to capture end-of-day effects[3][6] **Construction Process**: Derived from high-frequency trading data[45] **Evaluation**: Underperformance in most periods[3][6] 9. **Factor Name**: Average Single Transaction Outflow Proportion Factor **Construction Idea**: Tracks the proportion of outflows in single transactions to assess liquidity[3][6] **Construction Process**: Derived from high-frequency trading data[50] **Evaluation**: Limited effectiveness in predicting returns[3][6] 10. **Factor Name**: Large Order Push-Up Factor **Construction Idea**: Measures the impact of large orders on price increases to identify market movers[3][6] **Construction Process**: Derived from high-frequency trading data[55] **Evaluation**: Moderate performance with occasional strong returns[3][6] 11. **Factor Name**: Deep Learning High-Frequency Factor (Improved GRU(50,2)+NN(10)) **Construction Idea**: Combines GRU and neural networks to capture complex patterns in high-frequency data[3][6] **Construction Process**: Utilizes GRU(50,2) and NN(10) architectures for feature extraction and prediction[59] **Evaluation**: Strong performance in certain periods but underperformance in others[3][6] 12. **Factor Name**: Deep Learning High-Frequency Factor (Residual Attention LSTM(48,2)+NN(10)) **Construction Idea**: Incorporates residual attention mechanisms with LSTM and neural networks for enhanced prediction[3][6] **Construction Process**: Utilizes LSTM(48,2) and NN(10) architectures with residual attention layers[61] **Evaluation**: Consistently strong performance across multiple periods[3][6] 13. **Factor Name**: Deep Learning Factor (Multi-Granularity Model - 5-Day Label) **Construction Idea**: Uses multi-granularity modeling with 5-day labels for short-term predictions[3][6] **Construction Process**: Trained using bidirectional AGRU[64] **Evaluation**: Strong performance with high returns in most periods[3][6] 14. **Factor Name**: Deep Learning Factor (Multi-Granularity Model - 10-Day Label) **Construction Idea**: Uses multi-granularity modeling with 10-day labels for medium-term predictions[3][6] **Construction Process**: Trained using bidirectional AGRU[65] **Evaluation**: Consistently strong performance across multiple periods[3][6] AI-Enhanced Portfolio Construction 1. **Portfolio Name**: CSI 500 AI Enhanced Wide Constraint Portfolio **Construction Idea**: Maximizes expected returns under wide constraints using deep learning factors[69][70] **Construction Process**: - Weekly rebalancing - Constraints on individual stocks, industries, market cap, and other factors - Objective function: $$ max\sum\mu_{i}w_{i} $$ where \( w_i \) is the weight of stock \( i \) and \( \mu_i \) is its expected excess return[71] **Evaluation**: Strong cumulative excess returns since 2017[72] 2. **Portfolio Name**: CSI 500 AI Enhanced Strict Constraint Portfolio **Construction Idea**: Similar to the wide constraint portfolio but with stricter constraints[69][70] **Construction Process**: Same as above with stricter constraints on market cap, ROE, SUE, and volatility[71] **Evaluation**: Moderate cumulative excess returns since 2017[73] 3. **Portfolio Name**: CSI 1000 AI Enhanced Wide Constraint Portfolio **Construction Idea**: Maximizes expected returns under wide constraints using deep learning factors for smaller-cap stocks[69][70] **Construction Process**: Same as CSI 500 portfolios but applied to CSI 1000 index[71] **Evaluation**: Strong cumulative excess returns since 2017[76] 4. **Portfolio Name**: CSI 1000 AI Enhanced Strict Constraint Portfolio **Construction Idea**: Similar to the wide constraint portfolio but with stricter constraints for smaller-cap stocks[69][70] **Construction Process**: Same as above with stricter constraints on market cap, ROE, SUE, and volatility[71] **Evaluation**: Strong cumulative excess returns since 2017[79] Backtest Results for Factors 1. **Intraday Skewness Factor**: IC (2025): 0.057, Multi-Period Returns: 14.35% (2025)[3][6] 2. **Downside Volatility Proportion Factor**: IC (2025): 0.055, Multi-Period Returns: 11.77% (2025)[3][6] 3. **Post-Open Buy Intention Proportion Factor**: IC (2025): 0.033, Multi-Period Returns: 10.32% (2025)[3][6] 4. **Post-Open Buy Intention Intensity Factor**: IC (2025): 0.026, Multi-Period Returns: 11.19% (2025)[3][6] 5. **Post-Open Large Order Net Buy Proportion Factor**: IC (2025): 0.039, Multi-Period Returns: 12.32% (2025)[3][6] 6. **Post-Open Large Order Net Buy Intensity Factor**: IC (2025): 0.028, Multi-Period Returns: 6.78% (2025)[3][6] 7. **Improved Reversal Factor**: IC (2025): 0.003, Multi-Period Returns: 9.34% (2025)[3][6] 8. **Tail-End Trading Proportion Factor**: IC (2025): 0.022, Multi-Period Returns: 5.43% (2025)[3][6] 9. **Average Single Transaction Outflow Proportion Factor**: IC (2025): 0.012, Multi-Period Returns: 0.82% (2025)[3][6] 10. **Large Order Push-Up Factor

Quantitative Models and Construction Methods 1. Model Name: Interest Rate Price-Volume Multi-Cycle Timing Strategy - **Model Construction Idea**: This model uses kernel regression algorithms to identify support and resistance levels in interest rate trends. It integrates signals from long, medium, and short investment cycles to form a composite timing strategy[10][23]. - **Model Construction Process**: 1. **Signal Identification**: - Use kernel regression to capture the shape of interest rate trends and identify support and resistance levels[10]. - Classify signals into long-cycle (monthly frequency), medium-cycle (bi-weekly frequency), and short-cycle (weekly frequency)[10]. 2. **Signal Aggregation**: - Count the number of upward and downward breakthroughs across the three cycles. - If at least two cycles show the same directional breakthrough, the composite signal is determined based on the majority[10]. 3. **Portfolio Construction**: - Allocate assets based on the composite signal: - Full allocation to long-duration bonds if at least two cycles show downward breakthroughs and the trend is not upward. - Equal allocation to medium- and long-duration bonds if at least two cycles show downward breakthroughs but the trend is upward. - Full allocation to short-duration bonds if at least two cycles show upward breakthroughs and the trend is not downward. - Equal allocation to medium- and short-duration bonds if at least two cycles show upward breakthroughs but the trend is downward. - Equal allocation across short-, medium-, and long-duration bonds in other cases[23]. 4. **Stop-Loss Mechanism**: - Adjust holdings to equal allocation if the daily excess return of the portfolio falls below -0.5%[23]. 5. **Benchmark**: - The benchmark is an equal-duration strategy with one-third allocation to short-, medium-, and long-duration bonds[23]. - **Model Evaluation**: The model effectively captures multi-cycle resonance in interest rate trends and provides a systematic approach to timing strategies[23]. --- Model Backtesting Results 1. Interest Rate Price-Volume Multi-Cycle Timing Strategy - **Long-Term Performance (2007.12.31 to Latest Report Date)**: - Annualized Return: 6.19% - Maximum Drawdown: 1.53% - Return-to-Drawdown Ratio: 2.26 - Excess Annualized Return: 1.67% - Excess Return-to-Drawdown Ratio: 1.18[23][24] - **Short-Term Performance (Since 2023 Year-End)**: - Annualized Return: 7.5% - Maximum Drawdown: 1.61% - Return-to-Drawdown Ratio: 6.43 - Excess Annualized Return: 2.35% - Excess Return-to-Drawdown Ratio: 2.47[23][24] - **Historical Success Rates (18 Years)**: - Absolute Return > 0: 100% - Excess Return > 0: 100%[24] - **Year-by-Year Performance**: - 2008: Absolute Return 17.08%, Excess Return 4.41% - 2009: Absolute Return 1.03%, Excess Return 1.20% - 2010: Absolute Return 4.59%, Excess Return 2.49% - 2011: Absolute Return 7.25%, Excess Return 2.10% - 2012: Absolute Return 4.33%, Excess Return 0.68% - 2013: Absolute Return 0.91%, Excess Return 1.67% - 2014: Absolute Return 13.47%, Excess Return 2.67% - 2015: Absolute Return 11.14%, Excess Return 2.31% - 2016: Absolute Return 3.20%, Excess Return 1.76% - 2017: Absolute Return 1.11%, Excess Return 1.38% - 2018: Absolute Return 11.16%, Excess Return 2.36% - 2019: Absolute Return 6.24%, Excess Return 1.44% - 2020: Absolute Return 3.46%, Excess Return 0.47% - 2021: Absolute Return 5.40%, Excess Return 0.33% - 2022: Absolute Return 3.62%, Excess Return 0.47% - 2023: Absolute Return 4.81%, Excess Return 0.46% - 2024: Absolute Return 9.35%, Excess Return 2.52% - 2025: Absolute Return 1.14%, Excess Return 0.75%[24][27] --- Quantitative Factors and Construction Methods 1. Factor Name: Interest Rate Structural Indicators (Level, Term, Convexity) - **Factor Construction Idea**: These factors decompose the yield-to-maturity (YTM) data of government bonds into three structural dimensions: level, term, and convexity. The factors are analyzed from a mean-reversion perspective[7][9]. - **Factor Construction Process**: 1. **Data Transformation**: - Convert the YTM data of 1- to 10-year government bonds into three structural indicators: - **Level**: Average YTM across all maturities - **Term**: Difference between long-term and short-term YTM - **Convexity**: Curvature of the yield curve[7]. 2. **Historical Percentile Analysis**: - Calculate the rolling 3-, 5-, and 10-year percentiles for each structural indicator to assess their relative positions[8][9]. - **Factor Evaluation**: These factors provide insights into the current state of the interest rate market and its deviation from historical norms[7][9]. --- Factor Backtesting Results 1. Interest Rate Structural Indicators - **Level**: - Current Value: 1.58% - Weekly Change: -0.24BP - Historical Percentiles: 10% (3-Year), 6% (5-Year), 3% (10-Year)[9] - **Term**: - Current Value: 0.27% - Weekly Change: +4.42BP - Historical Percentiles: 7% (3-Year), 4% (5-Year), 8% (10-Year)[9] - **Convexity**: - Current Value: -0.04% - Weekly Change: -6.28BP - Historical Percentiles: 8% (3-Year), 5% (5-Year), 5% (10-Year)[9]