Quantitative Models and Construction Methods 1. Model Name: Industry Mainline Model (Relative Strength Index, RSI) - **Model Construction Idea**: The model aims to identify leading industries by calculating their relative strength index (RSI) over different time frames[1][9] - **Model Construction Process**: 1. Use primary industry indices as configuration targets, totaling 31 primary industries 2. Calculate the price changes over the past 20, 40, and 60 trading days for all industries, obtaining the cross-sectional rankings of these changes, then normalize all rankings to get RS_20, RS_40, and RS_60 3. Calculate the average of these three rankings to get the final industry relative strength index: $$ RS = \frac{RS_{20} + RS_{40} + RS_{60}}{3} $$ 4. If an industry shows an RS signal greater than 90% before the end of April, it is likely to be a leading industry for the year[9] - **Model Evaluation**: The model effectively identified leading industries in 2024, such as coal, power and utilities, home appliances, banks, oil and petrochemicals, communications, non-ferrous metals, agriculture, forestry, animal husbandry, and fishery, and automobiles[1][9] 2. Model Name: Industry Rotation Model (Prosperity-Trend-Crowding Framework) - **Model Construction Idea**: The model uses a three-dimensional framework of prosperity, trend, and crowding to recommend industry allocations[1][2][6] - **Model Construction Process**: 1. Define two industry rotation schemes: "strong trend-low crowding" and "high prosperity-strong trend" 2. Allocate industry weights based on the framework: Media 16%, Agriculture, Forestry, Animal Husbandry, and Fishery 15%, Non-bank Financials 12%, Computers 12%, Home Appliances 9%, Coal 9%, Building Materials 7%, Banks 7%, Light Industry Manufacturing 7%, Retail 6% 3. Recommend ETFs tracking indices such as CSI Steel, CSI Agriculture, Securities Companies, Communication Equipment, CSI Media, Sub-segment Chemicals, CS Artificial Intelligence, Animation Games, Sub-segment Machinery, All Information, Building Materials, etc.[2][6][15] - **Model Evaluation**: The model performed well in 2025, with an excess return of 16.4% relative to the CSI 800 index and 4.2% relative to the Wind All A index[2][6][18] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: The model aims to capture the reversal of industries in distress by analyzing sectors with low inventory pressure and long-term analyst optimism[24] - **Model Construction Process**: 1. Identify sectors currently or previously in distress with potential for inventory replenishment 2. Analyze sectors with low inventory pressure and long-term analyst optimism 3. Recommend sub-sectors such as cloud services, other light industries, oil service engineering, components, agricultural chemicals, animal husbandry, consumer electronics, special materials, and biomedicine[24][25] - **Model Evaluation**: The model achieved an absolute return of 25.4% in 2025, with an excess return of 5.4% relative to the industry equal weight index[24][27] Model Backtest Results 1. Industry Mainline Model (RSI) - **Absolute Return**: Various industries showed significant returns after the RSI signal appeared, such as banks (32.1%), communications (24.0%), home appliances (25.8%), and automobiles (12.8%)[10][12] 2. Industry Rotation Model (Prosperity-Trend-Crowding Framework) - **Annualized Return**: 21.7% - **Excess Annualized Return**: 13.8% - **Information Ratio (IR)**: 1.5 - **Maximum Drawdown**: -8.0% - **Monthly Win Rate**: 67% - **Excess Return in 2023**: 7.3% - **Excess Return in 2024**: 5.7% - **Excess Return in 2025**: 4.2%[13][14] 3. Left-Side Inventory Reversal Model - **Absolute Return in 2023**: 13.4% - **Excess Return in 2023**: 17.0% - **Absolute Return in 2024**: 26.5% - **Excess Return in 2024**: 15.4% - **Absolute Return in 2025**: 25.4% - **Excess Return in 2025**: 5.4%[24][27]

Quantitative Models and Construction Methods 1. Model Name: Industry Mainline Model (Relative Strength Index, RSI) - **Model Construction Idea**: This model identifies leading industries by calculating their relative strength (RS) based on historical price performance. Industries with RS > 90% are considered potential leaders for the year [10] - **Model Construction Process**: 1. Use 29 first-level industry indices as the investment universe [10] 2. Calculate the price change over the past 20, 40, and 60 trading days for each industry index [10] 3. Rank the price changes for each period and normalize the rankings to obtain RS_20, RS_40, and RS_60 [10] 4. Compute the average of the three rankings to derive the final relative strength index: $ RS = (RS_{20} + RS_{40} + RS_{60}) / 3 $ where RS_20, RS_40, and RS_60 represent the normalized rankings of price changes over 20, 40, and 60 trading days, respectively [10] - **Model Evaluation**: The model successfully identified leading industries in 2024, such as coal, banking, and AI-related sectors, which showed strong performance during the year [10][12] 2. Model Name: Industry Rotation Model (Prosperity-Trend-Crowding Framework) - **Model Construction Idea**: This model combines three dimensions—prosperity, trend, and crowding—to recommend industry allocations. It includes two sub-strategies: "Strong Trend-Low Crowding" and "High Prosperity-Strong Trend" [7][15] - **Model Construction Process**: 1. Define prosperity as the core metric, supplemented by trend and crowding dimensions [15] 2. For the "High Prosperity-Strong Trend" strategy, focus on industries with high prosperity and strong trends while avoiding highly crowded industries [15] 3. For the "Strong Trend-Low Crowding" strategy, prioritize industries with strong trends and low crowding while avoiding low-prosperity industries [15] 4. Allocate weights to industries based on the framework, e.g., November 2025 allocation: Basic Chemicals (18%), Media (16%), Agriculture (12%), Light Manufacturing (12%), Computers (12%), Home Appliances (9%), Real Estate (9%), Retail (6%), New Energy (4%), Coal (3%) [7][15] - **Model Evaluation**: The model demonstrated strong performance, with an annualized excess return of 13.7% and an IR of 1.5. It also showed a high monthly win rate of 67% [15][22] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: This model identifies industries in a recovery phase from distress by analyzing inventory levels and analyst expectations. It aims to capture reversal opportunities in industries with low inventory pressure and potential for restocking [29] - **Model Construction Process**: 1. Focus on industries experiencing current or past distress with signs of recovery [29] 2. Identify industries with low inventory pressure and restocking potential [29] 3. Incorporate analyst long-term positive outlooks for these industries [29] - **Model Evaluation**: The model achieved an absolute return of 27.9% and an excess return of 7.5% relative to equal-weighted industry benchmarks in 2025 (up to October) [29] --- Model Backtesting Results 1. Industry Mainline Model (RSI) - Annualized excess return: Not explicitly stated - IR: Not explicitly stated - Maximum drawdown: Not explicitly stated - Monthly win rate: Not explicitly stated - 2024 performance: Identified leading industries such as coal, banking, and AI, which showed strong performance during the year [10][12] 2. Industry Rotation Model (Prosperity-Trend-Crowding Framework) - Annualized excess return: 13.7% [15] - IR: 1.5 [15] - Maximum drawdown: -8.0% [15] - Monthly win rate: 67% [15] - 2023 excess return: 7.3% [15] - 2024 excess return: 5.7% [15] - 2025 excess return (up to October): 2.0% [15] 3. Left-Side Inventory Reversal Model - Annualized excess return: Not explicitly stated - IR: Not explicitly stated - Maximum drawdown: Not explicitly stated - Monthly win rate: Not explicitly stated - 2023 performance: Absolute return of 13.4%, excess return of 17.0% [29] - 2024 performance: Absolute return of 26.5%, excess return of 15.4% [29] - 2025 performance (up to October): Absolute return of 27.9%, excess return of 7.5% [29]

Quantitative Models and Construction Methods 1. Model Name: Industry Relative Strength (RSI) Model - **Model Construction Idea**: This model identifies leading industries by calculating their relative strength (RS) based on historical price performance over different time windows [10] - **Model Construction Process**: 1. Use 29 first-level industry indices as the configuration targets [10] 2. Calculate the price change rates for the past 20, 40, and 60 trading days for each industry index [10] 3. Rank the industries based on their price change rates for each time window and normalize the rankings to obtain RS_20, RS_40, and RS_60 [10] 4. Calculate the average of the three rankings to derive the final RS value: $ RS = \frac{RS_{20} + RS_{40} + RS_{60}}{3} $ [10] 5. Industries with RS > 90% by the end of April are identified as potential leading industries for the year [10] - **Model Evaluation**: The model successfully identified key annual industry trends, such as high dividend, resource products, exports, and AI, which were validated by market performance throughout the year [10][12] 2. Model Name: Industry Sentiment-Trend-Crowding Framework - **Model Construction Idea**: This framework provides two industry rotation strategies based on market conditions: 1. High sentiment + strong trend, avoiding high crowding (aggressive strategy) 2. Strong trend + low crowding, avoiding low sentiment (conservative strategy) [6][14] - **Model Construction Process**: 1. Evaluate industries based on three dimensions: sentiment, trend, and crowding [6][14] 2. Use sentiment as the core metric for the aggressive strategy, with crowding as a risk control factor [14] 3. Use trend as the core metric for the conservative strategy, avoiding low-sentiment industries [14] 4. Allocate weights to industries based on their scores in the three dimensions [6][14] - **Model Evaluation**: The framework is effective in adapting to different market conditions and has shown strong performance in historical backtests [6][14] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: This model identifies industries with potential for recovery by analyzing sectors in distress or those with low inventory pressure and high analyst optimism [24] - **Model Construction Process**: 1. Identify industries currently in distress or recovering from past distress [24] 2. Focus on sectors with low inventory pressure and potential for restocking [24] 3. Incorporate analyst long-term positive outlooks for these industries [24] - **Model Evaluation**: The model effectively captures recovery opportunities in industries undergoing inventory restocking cycles, providing significant absolute and relative returns [24] --- Model Backtesting Results 1. Industry Relative Strength (RSI) Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Industries with RS > 90% by April 2024 included coal, utilities, home appliances, banking, petrochemicals, communication, non-ferrous metals, agriculture, and automotive [10] - These industries showed strong performance, with key themes being high dividends, resource products, exports, and AI [10][12] 2. Industry Sentiment-Trend-Crowding Framework - **Annualized Return**: 22.1% (long-only portfolio) [14] - **Excess Return**: 13.8% (annualized) [14] - **Information Ratio (IR)**: 1.51 [14] - **Maximum Drawdown**: -8.0% [14] - **Monthly Win Rate**: 68% [14] - **Performance Highlights**: - 2023 excess return: 7.3% [14] - 2024 excess return: 5.7% [14] - 2025 YTD excess return: 2.8% [14] 3. Left-Side Inventory Reversal Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: - 2023: 17.0% (relative to equal-weighted industry benchmark) [24] - 2024: 15.4% (relative to equal-weighted industry benchmark) [24] - 2025 YTD: 7.8% (relative to equal-weighted industry benchmark) [24] - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Absolute return: - 2023: 13.4% [24] - 2024: 26.5% [24] - 2025 YTD: 26.4% [24] --- Quantitative Factors and Construction Methods 1. Factor Name: Sentiment Factor - **Factor Construction Idea**: Measures the overall sentiment of an industry to identify high-growth opportunities [14] - **Factor Construction Process**: 1. Evaluate the sentiment of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by sentiment scores [14] - **Factor Evaluation**: Sentiment is a core metric in the aggressive strategy of the Industry Sentiment-Trend-Crowding Framework, providing strong signals for high-growth opportunities [14] 2. Factor Name: Trend Factor - **Factor Construction Idea**: Measures the strength of market trends to identify industries with strong momentum [14] - **Factor Construction Process**: 1. Evaluate the trend of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by trend scores [14] - **Factor Evaluation**: Trend is a core metric in the conservative strategy of the Industry Sentiment-Trend-Crowding Framework, offering a simple and replicable approach to industry allocation [14] 3. Factor Name: Crowding Factor - **Factor Construction Idea**: Measures the level of crowding in an industry to identify overbought or underbought sectors [14] - **Factor Construction Process**: 1. Evaluate the crowding level of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by crowding scores [14] - **Factor Evaluation**: Crowding is used as a risk control factor in both aggressive and conservative strategies of the Industry Sentiment-Trend-Crowding Framework [14] --- Factor Backtesting Results 1. Sentiment Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 2. Trend Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 3. Crowding Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned