Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model [6][26] - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance. It selects industries with positive momentum for rotation. [26] - **Model Construction Process**: - The model calculates a diffusion index for each industry, which reflects the proportion of stocks within the industry exhibiting upward momentum. - Industries are ranked based on their diffusion index values, and the top industries are selected for portfolio allocation. [6][27] - **Model Evaluation**: The model has shown strong performance in capturing trends during momentum-driven markets but struggles during market reversals or when trends shift to mean-reversion. [26] - **Testing Results**: - 2025 YTD excess return: -0.44% [25][30] - June 2025 excess return: 1.20% [30] - Weekly average return: 0.21%, excess return over equal-weighted industry index: 0.37% [30] GRU Factor Model - **Model Name**: GRU Factor Model [7][32] - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry rotation opportunities. [37] - **Model Construction Process**: - The model uses minute-level price and volume data as input features. - A GRU neural network is trained to predict industry factor scores, which are then used to rank industries for rotation. [37] - **Model Evaluation**: The model performs well in short-term trading environments but faces challenges in long-term trend-following scenarios, especially during extreme market conditions. [37] - **Testing Results**: - 2025 YTD excess return: -4.13% [32][35] - June 2025 excess return: 0.00% [35] - Weekly average return: 0.42%, excess return over equal-weighted industry index: 0.58% [35] --- Backtesting Results of Models Diffusion Index Model - **YTD Excess Return**: -0.44% [25][30] - **June 2025 Excess Return**: 1.20% [30] - **Weekly Average Return**: 0.21% [30] - **Weekly Excess Return**: 0.37% [30] GRU Factor Model - **YTD Excess Return**: -4.13% [32][35] - **June 2025 Excess Return**: 0.00% [35] - **Weekly Average Return**: 0.42% [35] - **Weekly Excess Return**: 0.58% [35] --- Quantitative Factors and Construction GRU Industry Factor - **Factor Name**: GRU Industry Factor [7][33] - **Factor Construction Idea**: The factor is derived from GRU neural network outputs, representing the relative attractiveness of industries based on high-frequency trading data. [37] - **Factor Construction Process**: - The GRU model processes minute-level trading data to generate factor scores for each industry. - Industries are ranked based on their factor scores, and the top industries are selected for portfolio allocation. [37] - **Factor Evaluation**: The factor effectively captures short-term trading signals but may underperform in broader market trends or during periods of concentrated market themes. [37] - **Testing Results**: - Top industries by factor score (as of June 13, 2025): Steel (2.42), Construction (1.47), Transportation (0.85), Real Estate (0.59), Utilities (-0.01), Oil & Gas (-1.52) [7][33] - Bottom industries by factor score: Food & Beverage (-49.88), Comprehensive Finance (-33.65), Consumer Services (-25.42), Media (-21.94), Automotive (-20.34), Non-Banking Finance (-18.36) [33] Diffusion Index Factor - **Factor Name**: Diffusion Index Factor [6][27] - **Factor Construction Idea**: The factor measures the proportion of stocks within an industry showing upward momentum, serving as a proxy for industry strength. [6] - **Factor Construction Process**: - Calculate the diffusion index for each industry based on the percentage of stocks with positive momentum. - Rank industries by their diffusion index values to identify the strongest performers. [6][27] - **Factor Evaluation**: The factor is effective in identifying momentum-driven industries but may lag during market reversals. [26] - **Testing Results**: - Top industries by diffusion index (as of June 13, 2025): Comprehensive Finance (1.0), Non-Banking Finance (0.997), Banking (0.97), Media (0.953), Computing (0.936), Retail (0.93) [6][27] - Bottom industries by diffusion index: Coal (0.166), Oil & Gas (0.297), Food & Beverage (0.323), Utilities (0.604), Real Estate (0.629), Building Materials (0.657) [27] --- Backtesting Results of Factors GRU Industry Factor - **Top Industries by Factor Score**: Steel (2.42), Construction (1.47), Transportation (0.85), Real Estate (0.59), Utilities (-0.01), Oil & Gas (-1.52) [7][33] - **Bottom Industries by Factor Score**: Food & Beverage (-49.88), Comprehensive Finance (-33.65), Consumer Services (-25.42), Media (-21.94), Automotive (-20.34), Non-Banking Finance (-18.36) [33] Diffusion Index Factor - **Top Industries by Diffusion Index**: Comprehensive Finance (1.0), Non-Banking Finance (0.997), Banking (0.97), Media (0.953), Computing (0.936), Retail (0.93) [6][27] - **Bottom Industries by Diffusion Index**: Coal (0.166), Oil & Gas (0.297), Food & Beverage (0.323), Utilities (0.604), Real Estate (0.629), Building Materials (0.657) [27]

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]

- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the observation of industry diffusion indices; Detailed Construction Process: The model tracks the weekly changes in diffusion indices for various industries, ranking them based on their performance. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Advancing Stocks}}{\text{Total Number of Stocks}} $; Model Evaluation: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[6][14][27] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data; Detailed Construction Process: The model ranks industries based on GRU factor scores, which are derived from the GRU network's analysis of trading data. The formula used is $ \text{GRU Factor Score} = \text{GRU Network Output} $; Model Evaluation: The model has achieved substantial excess returns by capturing trading information, though it has faced challenges in certain market conditions[7][14][34] Model Backtest Results - Diffusion Index Model, Average Weekly Return: 0.72%, Excess Return: 0.11%, Year-to-Date Excess Return: -2.26%[32] - GRU Factor Model, Average Weekly Return: 1.07%, Excess Return: 0.44%, Year-to-Date Excess Return: -3.71%[37] Factor Construction and Evaluation - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is constructed using GRU deep learning networks to analyze minute-level trading data; Detailed Construction Process: The factor scores are calculated based on the GRU network's output, which evaluates the trading data to rank industries. The formula used is $ \text{GRU Factor Score} = \text{GRU Network Output} $; Factor Evaluation: The factor has shown significant improvements in certain industries, indicating its effectiveness in capturing trading information[7][14][35] Factor Backtest Results - GRU Industry Factor, Top Industries: Automotive (2.84), Steel (1.85), Media (1.48), Power Equipment and New Energy (1.35), Communication (0.88), Coal (0.66)[7][14][35]

Quantitative Models and Construction 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance[28][38] - **Model Construction Process**: The model calculates the diffusion index for each industry, ranking them based on their relative performance. Industries with higher diffusion indices are recommended for allocation. The model tracks weekly and monthly changes in the diffusion index to adjust allocations dynamically[5][14][29] - **Model Evaluation**: The model has shown strong performance in capturing momentum trends during upward markets but may underperform during market reversals[28][38] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency volume and price data, aiming to identify industry rotation opportunities[39] - **Model Construction Process**: The GRU network is trained on historical minute-level data to predict industry factor rankings. The model dynamically adjusts allocations based on the predicted rankings, focusing on industries with higher GRU factor scores[6][34][39] - **Model Evaluation**: The model performs well in short-term scenarios due to its adaptability but may face challenges in long-term or extreme market conditions[39] --- Backtesting Results of Models 1. Diffusion Index Model - **2025 YTD Excess Return**: -2.75%[27][32] - **April 2025 Excess Return**: -0.68%[32] - **Weekly Portfolio Return**: -0.18%[32] 2. GRU Factor Model - **2025 YTD Excess Return**: -3.54%[34][37] - **April 2025 Excess Return**: 0.68%[37] - **Weekly Portfolio Return**: -0.78%[37] --- Quantitative Factors and Construction 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of industry performance to identify upward trends[5][14] - **Factor Construction Process**: The diffusion index is calculated as the proportion of stocks in an industry with positive momentum. Weekly and monthly changes in the index are tracked to adjust rankings dynamically[5][14][29] - **Factor Evaluation**: Effective in capturing momentum trends but sensitive to market reversals[28][38] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and predict industry rankings[39] - **Factor Construction Process**: The GRU network processes minute-level volume and price data to generate factor scores for industries. Industries with higher scores are prioritized for allocation[6][34][39] - **Factor Evaluation**: Strong adaptability in short-term scenarios but limited in long-term or extreme market conditions[39] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Top 6 Industries (as of April 30, 2025)**: Banking (0.988), Non-Banking Financials (0.94), Comprehensive Financials (0.928), Computers (0.884), Retail (0.88), Automobiles (0.872)[5][14][29] - **Weekly Change Leaders**: Steel (0.17), Comprehensive (0.095), Automobiles (0.065)[5][31] 2. GRU Industry Factor - **Top 6 Industries (as of April 30, 2025)**: Real Estate (4.62), Textiles & Apparel (4.14), Comprehensive Financials (2.89), Transportation (1.71), Light Manufacturing (1.7), Construction (1.41)[6][35] - **Weekly Change Leaders**: Pharmaceuticals, Real Estate, Comprehensive Financials[6][35]

- The report discusses two main quantitative models: the Diffusion Index Model and the GRU Factor Model[6][7][14][33] Diffusion Index Model 1. **Model Name**: Diffusion Index Model 2. **Model Construction Idea**: The model is based on the principle of price momentum, capturing industry trends by observing the diffusion index of various sectors[6][27] 3. **Model Construction Process**: - Calculate the diffusion index for each industry - Rank industries based on their diffusion index values - Select top industries for investment based on their diffusion index rankings - Formula: $ \text{Diffusion Index} = \frac{\text{Number of advancing stocks}}{\text{Total number of stocks}} $ 4. **Model Evaluation**: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[26][30] 5. **Model Test Results**: - 2025 YTD excess return: -3.16%[25] - April 2025 excess return: -1.08%[30] - Weekly excess return: 0.43%[30] GRU Factor Model 1. **Model Name**: GRU Factor Model 2. **Model Construction Idea**: The model leverages GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data, aiming to capture trading information and trends[7][33] 3. **Model Construction Process**: - Collect minute-level price and volume data - Train a GRU network on historical data to identify patterns - Rank industries based on GRU factor scores - Select top industries for investment based on their GRU factor rankings - Formula: $ \text{GRU Factor} = \text{GRU Network Output} $ 4. **Model Evaluation**: The model has shown strong performance in short cycles but may struggle in long cycles or extreme market conditions[33][36] 5. **Model Test Results**: - 2025 YTD excess return: -3.33%[33] - April 2025 excess return: 0.92%[36] - Weekly excess return: -0.31%[36] Factor Rankings and Performance 1. **Diffusion Index Rankings (as of April 25, 2025)**: - Top industries: Banking (0.986), Non-Banking Financials (0.948), Comprehensive Financials (0.926), Computers (0.873), Retail (0.847), Communication (0.841)[14][27] - Bottom industries: Coal (0.105), Oil & Petrochemicals (0.175), Food & Beverage (0.257), Agriculture (0.396), Steel (0.423), Utilities (0.491)[27][28] 2. **GRU Factor Rankings (as of April 25, 2025)**: - Top industries: Banking (3.81), Transportation (2.77), Non-Banking Financials (2.37), Textiles & Apparel (2.34), Media (1.98), Light Manufacturing (1.81)[7][34] - Bottom industries: Automobiles (-5.31), Agriculture (-4.05), Pharmaceuticals (-4.03), Home Appliances (-3), Coal (-2.67), Defense (-2.64)[34] Weekly and Monthly Performance 1. **Diffusion Index Weekly Performance**: - Top weekly gainers: Construction (0.189), Real Estate (0.187), Building Materials (0.136), Light Manufacturing (0.089), Textiles & Apparel (0.081), Communication (0.069)[29] - Top weekly losers: Steel (-0.111), Utilities (-0.038), Non-Ferrous Metals (-0.018), Coal (0.003), Transportation (0.007), Computers (0.009)[29] 2. **GRU Factor Weekly Performance**: - Top weekly gainers: Banking, Textiles & Apparel, Consumer Services[34] - Top weekly losers: Coal, Automobiles, Construction[34]