Core Viewpoint - The collective failure of traditional price and volume factors since 2026 has led to the emergence of a momentum-based industry rotation model, which provides a potential solution for enhancing portfolio stability and excess returns [1][4][54]. Group 1: Industry Rotation Model Characteristics - The industry rotation factor has shown strong characteristics, with a monthly Information Coefficient (IC) of 5.3% and an Information Coefficient Information Ratio (ICIR) of 4.0, indicating its robust performance [26][54]. - The industry rotation model has been effective in improving performance within traditional multi-factor frameworks, significantly enhancing excess returns and halting the decline in excess performance seen in recent years [2][54]. Group 2: Challenges and Conflicts - The industry rotation factor faces conflicts with the industry deviation constraints commonly used in index-enhanced frameworks, which can negatively impact its effectiveness [2][54]. - When applying the standard industry deviation constraint of 2% and individual stock deviation of 0.5%, the performance of the portfolio has declined, with excess returns turning negative in 2025 [2][54]. Group 3: Optimal Usage Strategy - The best approach for utilizing the industry rotation factor is to maintain the individual stock deviation constraint at 0.5% while relaxing the industry deviation constraint from 2% to 5%, which has shown to improve overall excess returns and reduce maximum drawdowns [3][54]. - Increasing the industry deviation to 4% or 5% has resulted in better overall performance, with maximum drawdowns decreasing, indicating a balanced approach to enhancing excess returns while controlling risk [3][54].

Report Industry Investment Rating No information provided in the content. Core Viewpoints of the Report - The collective failure of traditional quantitative and price factors in 2026 is related to their reverse logic, providing a scenario for the factorization of the industry rotation model with momentum characteristics [2]. - The industry rotation model has long lacked practical use scenarios, but its stability in excess returns meets the requirements of stock - selection factors, laying a foundation for its transformation into a stock - selection factor [2]. - The industry rotation factor has good factor characteristics, with a monthly IC of 5.3% and an ICIR of 0.40, and it can enhance the performance of the traditional multi - factor model [2][30]. - The industry rotation factor conflicts with the industry deviation constraints in the index - enhancement framework, but it still contributes to stock - selection and cannot be replaced by simple industry over - under - weighting or portfolio strategies [2][61]. - Keeping the individual stock deviation constraint at 0.5% while relaxing the industry deviation constraint is currently the best way to use the industry rotation factor [2][62]. Summary by Relevant Catalogs 1. Finding a Usage Scenario for the Industry Rotation Model: Starting from the Failure of Quantitative and Price Factors - Since 2026, index - enhancement funds tracking the CSI 500 index and active quantitative funds' quasi - index products have mostly underperformed the CSI 500 index. As of the end of January, all CSI 500 index - enhancement products underperformed the index, with an average underperformance of 3.46%, and active quantitative products underperformed by 1.96% on average [5]. - The main failed factors are quantitative and price factors such as liquidity, reversal, low - volatility, and market value, whose logic is mostly reverse - oriented. In the context of a rapid rise in the index and continuous driving of some popular sectors and themes in January, these factors not only failed but also reversed [7]. - The industry rotation model is a strongly momentum - driven model. The Shenwan Hongyuan Industry Rotation Model emphasizes momentum in its technical, fundamental, and capital aspects, and can complement traditional quantitative and price factors with reverse logic [10]. - The industry rotation model has long lacked practical use scenarios. Its long - only portfolio performance is not outstanding, and its stable excess return relative to the average of all industries has no practical significance for most investors [13][16]. 2. Factorization of the Industry Rotation Model - Transforming the industry model into a stock - selection model is relatively easy. By splicing the scores of each stock's industry in the industry model, a stock - based score can be obtained. However, due to the large number of stocks belonging to the same industry, the factor shows a segmented score characteristic, and orthogonal processing is required [22]. - The monthly IC of the original industry rotation factor has a correlation of over 0.4 with the growth factor. After orthogonalizing the original industry rotation factor against the growth factor, its performance shows good monotonicity, and its cumulative IC and long - short performance are excellent [23][25]. - From 2017 to January 2026, the monthly average IC of the industry rotation factor reached 5.3%, stronger than other traditional factors, and the ICIR was 0.40, ranking third, indicating excellent factor characteristics [30]. 3. Usage and Comparative Analysis of the Industry Rotation Factor - **Comparison of Four - Factor and Five - Factor Models**: Adding the industry rotation factor to the four - factor equal - weighted model to form a five - factor model can significantly improve the model's performance, especially in recent years, enhancing the model's offensive ability in a bull market and the stability of excess returns [35][38]. - **Factor Equal - Weighting vs. ICIR Weighting**: Changing the factor weighting method from simple equal - weighting to ICIR weighting does not show better results. The five - factor equal - weighted combination with the industry rotation factor performs best in each year and is the only combination with positive excess returns in all years [39]. - **Moving towards the Index - Enhancement Framework: Adding Industry Neutrality and Individual Stock Deviation Constraints**: Adding industry deviation and individual stock constraints to the model makes the industry rotation factor conflict with the industry deviation constraint. Although it can control the maximum drawdown in some years, it also reduces the performance of the five - factor model in terms of returns in some cases. In 2025, the annual excess return becomes negative after adding constraints [41][42]. - **Method of Constraining Industry Deviation Ranking through Industry Scoring**: Using industry scoring to control industry deviation ranking without using the industry rotation factor for stock - selection results in weaker performance compared to the five - factor model with industry and individual stock constraints. This method is not the best option [44]. - **Multi - Strategy Portfolio: Using Industry Rotation as a Satellite Portfolio "Platter"**: Using the industry rotation factor as a separate strategy to form a satellite portfolio and combining it with a four - factor portfolio does not show obvious advantages. The performance of the "platter portfolio" is difficult to outperform, and only the 3:7 ratio combination has a slight competitive edge, but it also shows negative excess returns in January 2026 [50]. - **Current Best Solution: Relaxing Industry Deviation while Maintaining Individual Stock Deviation Constraints**: Keeping the individual stock deviation constraint at 0.5% and relaxing the industry deviation constraint to 4% or 5% can improve the overall excess return of the portfolio, reduce the maximum drawdown of excess returns, and have a negligible impact on tracking error. This is currently the best way to use the industry rotation factor [53][57]. 4. Summary - The industry rotation model has long lacked practical use scenarios, but its stability characteristics provide a basis for its transformation into a stock - selection factor. - The industry rotation factor has good characteristics and can enhance the performance of the traditional multi - factor model, but it conflicts with the industry deviation constraint in the index - enhancement framework. - The industry rotation factor contributes to stock - selection and cannot be replaced by simple strategies. Relaxing the industry deviation constraint while maintaining the individual stock deviation constraint is the best solution [60][61][62].

Quantitative Models and Construction Methods 1. **Model Name**: CSI 300 Enhanced Portfolio - **Model Construction Idea**: The model aims to achieve excess returns over the CSI 300 Index by leveraging quantitative strategies and factor-based stock selection - **Model Construction Process**: The model is constructed by selecting stocks from the CSI 300 Index based on specific quantitative factors and optimizing the portfolio to maximize excess returns while managing risk. The exact factors and optimization techniques are not detailed in the report - **Model Evaluation**: The model has shown consistent performance in generating excess returns over the CSI 300 Index in the year-to-date period[5][9][15] 2. **Model Name**: CSI 500 Enhanced Portfolio - **Model Construction Idea**: The model seeks to outperform the CSI 500 Index by utilizing quantitative strategies and factor-based stock selection - **Model Construction Process**: Stocks are selected from the CSI 500 Index based on quantitative factors, and the portfolio is optimized to achieve excess returns while controlling risk. Specific details of the factors and optimization are not provided in the report - **Model Evaluation**: The model's performance has been mixed, with negative excess returns in the year-to-date period[5][9][15] 3. **Model Name**: CSI 1000 Enhanced Portfolio - **Model Construction Idea**: The model aims to generate excess returns over the CSI 1000 Index through quantitative strategies and factor-based stock selection - **Model Construction Process**: Stocks are selected from the CSI 1000 Index using quantitative factors, and the portfolio is optimized to maximize excess returns while managing risk. Specific details of the factors and optimization are not provided in the report - **Model Evaluation**: The model has demonstrated positive excess returns in the year-to-date period[5][9][15] 4. **Model Name**: PB-Profit Combination Portfolio - **Model Construction Idea**: The portfolio combines price-to-book (PB) ratio and profitability factors to identify undervalued stocks with strong earnings potential - **Model Construction Process**: The portfolio is constructed by selecting stocks with low PB ratios and high profitability metrics. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has shown strong performance, with significant positive excess returns over the CSI 300 Index in the year-to-date period[5][31][33] 5. **Model Name**: GARP Portfolio - **Model Construction Idea**: The portfolio follows the Growth at a Reasonable Price (GARP) strategy, focusing on stocks with a balance of growth and valuation metrics - **Model Construction Process**: Stocks are selected based on a combination of growth and valuation factors. The specific factors and their weights are not detailed in the report - **Model Evaluation**: The portfolio has achieved significant positive excess returns over the CSI 300 Index in the year-to-date period[5][35] 6. **Model Name**: Small-Cap Value Portfolio 1 - **Model Construction Idea**: The portfolio targets small-cap stocks with value characteristics, aiming to outperform the micro-cap index - **Model Construction Process**: Stocks are selected based on small-cap and value factors. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has underperformed the micro-cap index in the year-to-date period[5][37] 7. **Model Name**: Small-Cap Value Portfolio 2 - **Model Construction Idea**: Similar to Small-Cap Value Portfolio 1, this portfolio focuses on small-cap stocks with value characteristics - **Model Construction Process**: Stocks are selected based on small-cap and value factors. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has outperformed the micro-cap index in the year-to-date period[5][39] 8. **Model Name**: Small-Cap Growth Portfolio - **Model Construction Idea**: The portfolio targets small-cap stocks with growth characteristics, aiming to outperform the micro-cap index - **Model Construction Process**: Stocks are selected based on small-cap and growth factors. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has underperformed the micro-cap index in the year-to-date period[5][41] --- Model Backtesting Results 1. **CSI 300 Enhanced Portfolio** - Weekly return: -0.39% - Weekly excess return: -0.47% - Year-to-date return: 6.85% - Year-to-date excess return: 5.20%[9][15] 2. **CSI 500 Enhanced Portfolio** - Weekly return: -1.74% - Weekly excess return: 0.82% - Year-to-date return: 11.11% - Year-to-date excess return: -1.01%[9][15] 3. **CSI 1000 Enhanced Portfolio** - Weekly return: -0.97% - Weekly excess return: 1.58% - Year-to-date return: 11.99% - Year-to-date excess return: 3.31%[9][15] 4. **PB-Profit Combination Portfolio** - Weekly return: 0.92% - Weekly excess return: 0.84% - Year-to-date return: 6.17% - Year-to-date excess return: 4.52%[31][33] 5. **GARP Portfolio** - Weekly return: 0.95% - Weekly excess return: 0.87% - Year-to-date return: 11.43% - Year-to-date excess return: 9.78%[35] 6. **Small-Cap Value Portfolio 1** - Weekly return: -2.44% - Weekly excess return: -1.29% - Year-to-date return: 7.89% - Year-to-date excess return: -2.83%[37] 7. **Small-Cap Value Portfolio 2** - Weekly return: -1.64% - Weekly excess return: -0.48% - Year-to-date return: 12.37% - Year-to-date excess return: 1.66%[39] 8. **Small-Cap Growth Portfolio** - Weekly return: -2.07% - Weekly excess return: -0.92% - Year-to-date return: 9.13% - Year-to-date excess return: -1.59%[41] --- Quantitative Factors and Construction Methods 1. **Factor Name**: Market Capitalization (Size) Factor - **Construction Idea**: Small-cap stocks tend to outperform large-cap stocks over time - **Construction Process**: Stocks are ranked by market capitalization, and the top 10% (smallest) and bottom 10% (largest) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown mixed performance across different indices and time periods[43][44][45] 2. **Factor Name**: Price-to-Book (PB) Factor - **Construction Idea**: Low PB stocks are expected to outperform high PB stocks - **Construction Process**: Stocks are ranked by PB ratio, and the top 10% (lowest PB) and bottom 10% (highest PB) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown strong performance in the short term but mixed results in the year-to-date period[43][44][45] 3. **Factor Name**: Price-to-Earnings (PE_TTM) Factor - **Construction Idea**: Low PE stocks are expected to outperform high PE stocks - **Construction Process**: Stocks are ranked by PE ratio, and the top 10% (lowest PE) and bottom 10% (highest PE) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown positive short-term performance but mixed year-to-date results[43][44][45] 4. **Factor Name**: Reversal Factor - **Construction Idea**: Stocks with recent underperformance are expected to outperform in the short term - **Construction Process**: Stocks are ranked by recent performance, and the top 10% (worst performers) and bottom 10% (best performers) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown positive short-term performance but negative year-to-date results[49][50] 5. **Factor Name**: Turnover Factor - **Construction Idea**: Stocks with lower turnover rates are expected to outperform those with higher turnover rates - **Construction Process**: Stocks are ranked by turnover rate, and the top 10% (lowest turnover) and bottom 10% (highest turnover) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown strong short-term performance but negative year-to-date results[49][50] 6. **Factor Name**: Volatility Factor - **Construction Idea**

Group 1 - The core viewpoint of the article highlights the outstanding performance of the CSI 500 index since the beginning of 2026, with a rise of 15.06% as of January 23, 2026, outperforming other major indices like the CSI 300, CSI 1000, and CSI 2000 [1][2] - The article notes that the CSI 500 index's strong performance is attributed to its concentration in sectors that have performed well since 2026, including electronics, non-ferrous metals (7.148%), and defense industry (6.364%) [5] - A small number of stocks have significantly contributed to the index's gains, with the top 5 stocks contributing 1.47% and the top 10 stocks contributing 2.41%, indicating a high concentration of performance among a few stocks [6][8] Group 2 - The article discusses the challenges faced by enhanced index funds, which have collectively underperformed the CSI 500 index since 2026, with an average underperformance of 2.5% [10][11] - Active quantitative strategies have also struggled, with average underperformance reaching 3.91%, highlighting the difficulties in achieving excess returns in a strong market [12] - The article analyzes the changes in factors within the CSI 500 index, noting that many traditional factors have shown negative performance, contributing to the overall decline in excess returns [15][20] Group 3 - Historical comparisons indicate that the current market conditions represent an extreme situation for factor performance, with the article suggesting that the current environment is not solely due to a single factor's poor performance [28][29] - The article reviews past instances of similar market conditions, suggesting that extreme market behavior is unlikely to persist indefinitely, and a return to rational pricing based on factors is expected [31][45] - Future outlooks suggest that while factor reversals may not last long, adjustments to models should be cautious, as historical data indicates that significant factor failures typically do not exceed two months [46][47]

1. Report Industry Investment Rating Not provided in the content 2. Core Viewpoints of the Report - Since 2026, the CSI 500 Index has performed prominently among major broad - based indices, breaking the historical monotonicity of performance based on market - value factors. Whether this phenomenon will continue requires further observation. The concentration of hot industries and a small number of stocks contributing a large portion of the index's gains have made it difficult to outperform the index. Also, factor inefficiencies, especially the reversal of price - volume factors, have led to the underperformance of index - enhancement products and quantitative strategies [1][4]. - The current market situation is an extreme case in factor performance. Although no single factor has reached its historical worst, the combined performance of multiple factors is the worst in history. However, based on historical experience, factor logic will return as market volatility decreases, usually within two months [1][40]. - Looking ahead, the situation of factor inefficiency or reversal is not expected to last long, so major model adjustments are not advisable at present. In the long run, a detailed risk - control framework for CSI 500 index enhancement should be established, and the construction of price - volume factors should be optimized [1][70]. 3. Summary According to the Table of Contents 3.1 2026 Year - to - Date CSI 500 Index Performance Highlights - As of January 23, 2026, the CSI 500 Index has risen 15.06%, outperforming the SSE 300, CSI 1000, and CSI 2000 indices during the same period, breaking the historical monotonicity of broad - based index performance related to market - value factors [4]. - The index's strong performance is due to its concentration in sectors that have performed well in 2026, such as electronics, non - ferrous metals, and national defense and military industries. A small number of stocks have contributed significantly to the index's gains; the top 5 stocks contributed 1.47% of the increase, and the top 40 stocks contributed nearly half of the increase [7][11]. 3.2 Factor Perspective: Why Is It Difficult to Outperform the CSI 500 Index? 3.2.1 Index - Enhancement Funds Collectively Underperform the Index - All CSI 500 index - enhancement funds have underperformed the CSI 500 Index in 2026, with an average underperformance of 2.5%. The best - performing product underperformed by 0.12%, and the worst by 7.93%. Active quantitative quasi - index - enhancement products were more affected, with an average underperformance of 3.91%, the best - performing product underperforming by 2.07%, and the worst by 7.61% [13][15]. 3.2.2 Factor Changes within the CSI 500 Index - Since 2017, the market - value factors in the SSE 300, CSI 500, and CSI 1000 indices have shown continuous reversal and decline. The market - value factor in the CSI 1000 index rebounded strongly in 2021, while those in the CSI 500 and SSE 300 indices only had a weak rebound [16]. - In 2026, many factors in the CSI 500 Index showed significant anomalies. Fundamental factors such as profitability, dividend yield, and valuation were negative, and price - volume factors such as liquidity, reversal, market value, and volatility not only reversed but also had larger IC values. The short - term rapid market rise and overheated market led to the continued rise of theme stocks with fast short - term gains, high turnover, and high volatility, causing the reversal and ineffectiveness of price - volume factors [19][20]. - The long - term winning rates of factors such as valuation, momentum, reversal, market value, and liquidity are poor, around 50% or lower. In 2026, there was a concentrated reversal of price - volume factors, and the low - volatility factor, which had a high long - term winning rate, also reversed in January 2026 [26]. - The changes in the four price - volume factors (market value, reversal, low liquidity, and low volatility) generally started in the third quarter of 2025, gradually flattening or reversing. The top - performing stocks in 2026 generally ranked low in these price - volume factors, making it difficult for traditional multi - factor frameworks to select them [35]. 3.3 Historical Similar Situations Review and Future Outlook 3.3.1 The Current Market Is an Extreme Case in Factor Performance - In January 2026, no single factor reached its historical worst IC value. However, the combined performance of the four price - volume factors was the worst in history, and when considering all nine factors, it was the second - worst, only after June 2022 [39][40]. 3.3.2 Historical Similar Situations of the CSI 500 and Subsequent Developments - Similar extreme situations in factor performance have occurred in June 2018, August 2025, etc. Market fluctuations are an important factor affecting factor effectiveness. When the market fluctuates significantly, factors are likely to become ineffective, and when the market stabilizes, factor logic tends to return. Historical experience shows that factor inefficiency usually does not last more than two months [42][69]. 3.3.3 Future Outlook - The situation of factor inefficiency or reversal is not expected to last long, so major model adjustments are not recommended at present. - In the long run, a detailed risk - control framework for CSI 500 index enhancement should be established, including differential constraints on individual stocks with different excess - volatility characteristics and industry - constraint frameworks based on industry - scoring models. - The construction of price - volume factors should be optimized to improve their winning rates and reduce non - linear characteristics [70][71].

- The report focuses on the performance of stock selection factors in 2025, highlighting the effectiveness of factors such as transaction count, liquidity, crowding, price stability, and reversal in stock selection across the market [1][5][15] - Factors are categorized into two main groups: volume-price factors and growth factors. Volume-price factors are further divided into two representative categories: price stability and reversal, while liquidity, crowding, and transaction count serve as average representatives of volume-price factors [6][24] - The construction of major factors involves market capitalization and industry neutrality, outlier removal, and standardization, followed by equal-weight synthesis into major factors [13] - Sub-factors are detailed with their calculation methods, such as residual volatility derived from the Fama-French three-factor model regression residual volatility, turnover rate variation coefficient calculated as turnover rate divided by the standard deviation over the mean, and entropy of transaction volume proportion using the entropy formula [13] - The report provides statistical data on the performance of major factors, including IC, ICIR, excess returns, maximum drawdowns, IR, long-short returns, long-short maximum drawdowns, and long-short Sharpe ratios. For example, liquidity factor achieved an IC of 9.72%, ICIR of 1.08, excess return of 23.67%, and IR of 3.43 [15][16] - Sub-factors with notable performance include short-term reversal (IC 6.27%, ICIR 1.21, excess return 4.86%), residual volatility (IC 9.42%, ICIR 1.22, excess return 1.53%), and turnover rate (IC 10.75%, ICIR 1.29, excess return 17.46%) [17] - The report highlights the time-series performance of factors, noting that the main profit periods for all factors were concentrated between January and November 2025, with significant drawdowns occurring between September and December 2025. Growth, SUE, and price stability factors had lower profit levels and higher drawdowns, while liquidity factors had higher profit levels and higher drawdowns [19][20][23] - The correlation analysis of excess returns among factors shows that price stability has a high correlation with other factors, while reversal has a low correlation with other factors. Liquidity, crowding, and transaction count factors exhibit low mutual correlation [21][24]

Quantitative Models and Construction Methods 1. Model Name: Layered Progressive Stock Selection for Profitability Factor - **Model Construction Idea**: The model aims to enhance the profitability factor by progressively filtering stocks based on historical ROE and financial stability, ensuring higher future ROE probabilities [38][35][36] - **Model Construction Process**: - Step 1: Select the top 100 stocks based on historical ROE (ROE_ttm) [38] - Step 2: From the top 100, further filter the top 50 stocks with the highest financial stability scores, which include metrics like ROE stability, revenue growth stability, and leverage stability [27][38] - Step 3: Construct an equal-weighted portfolio with the final 50 stocks [38] - **Model Evaluation**: The layered approach effectively reduces the probability of ROE decline by one interval (5%) and increases the likelihood of maintaining high ROE levels in the future [38][36] 2. Model Name: Dividend Growth Factorization - **Model Construction Idea**: This model predicts future dividend growth by constructing a stock pool based on historical dividend stability and earnings growth expectations [49][51] - **Model Construction Process**: - Step 1: Select stocks with stable dividend payout ratios over the past three years and positive earnings growth expectations [49] - Step 2: Select stocks with dividend amounts growing over the past two years and positive earnings growth expectations [49] - Step 3: Combine the two pools to form a comprehensive stock pool [49] - Step 4: Construct sub-factors such as dividend payout deviation, sell-side forecast count, and recent financial report growth, standardize and sum them, and take the maximum value across perspectives [51] - **Model Evaluation**: The model improves the prediction accuracy of dividend growth, achieving over a 10% improvement in win rates for both the CSI 300 and CSI 500 indices [51][52] 3. Model Name: Growth Factor Improvement via Reverse Exclusion - **Model Construction Idea**: Instead of further refining high-growth stocks, this model excludes stocks unlikely to achieve future net profit growth, enhancing the growth factor's predictive power [70][69] - **Model Construction Process**: - Step 1: Start with 100 high-growth stocks based on historical growth factors [70] - Step 2: Exclude stocks meeting any of the following conditions: - FY1 consensus forecast ≤ 0 - FY1 consensus forecast is null - Consensus forecast downgraded in the past 4, 13, or 26 weeks [70] - Step 3: Construct a portfolio with the remaining stocks [70] - **Model Evaluation**: The exclusion method significantly improves the prediction rate of actual net profit growth and reduces the probability of selecting companies with declining net profits [70][69] 4. Model Name: Composite Three-Factor Portfolio - **Model Construction Idea**: This model integrates the improved profitability, dividend, and growth factors into a unified portfolio to enhance index performance [81][83] - **Model Construction Process**: - Step 1: Combine the stock pools from the three improved factors (profitability, dividend, growth) [81] - Step 2: Select approximately 120 stocks from the combined pool, ensuring industry neutrality and periodic rebalancing [83] - **Model Evaluation**: The composite portfolio demonstrates consistent performance improvement over the equal-weighted three-factor portfolio, with notable gains in the CSI 300 and CSI 500 indices [83][86] 5. Model Name: Three-Factor Portfolio + Volume-Price Factors - **Model Construction Idea**: This model incorporates volume-price factors (low volatility, low liquidity, momentum) into the three-factor portfolio to capture additional returns during strong volume-price factor periods [100][97] - **Model Construction Process**: - Step 1: Start with the three-factor composite portfolio [100] - Step 2: Select the top 75 stocks based on volume-price factor scores (low volatility, low liquidity, momentum) [100] - Step 3: Construct an equal-weighted portfolio with the selected stocks [100] - **Model Evaluation**: The addition of volume-price factors further enhances long-term returns and maintains stable excess returns compared to the equal-weighted six-factor portfolio [100][103] 6. Model Name: 75+25 Composite Portfolio - **Model Construction Idea**: This model combines the three-factor portfolio with a 25-stock pool selected based on volume-price factors across the entire market, aiming to maximize expected returns [109][112] - **Model Construction Process**: - Step 1: Select 75 stocks from the three-factor portfolio [109] - Step 2: Select 25 stocks from the entire market based on volume-price factors (growth, profitability, low volatility, small market cap) [109] - Step 3: Combine the two pools into a 100-stock portfolio [109] - **Model Evaluation**: The 75+25 portfolio achieves significant improvements in annualized returns and Sharpe ratios, benefiting from the strong performance of volume-price factors in recent years [112][125] --- Model Backtest Results 1. Layered Progressive Stock Selection for Profitability Factor - CSI 300: Win rate improved from 78.03% to 86.28% [36] - CSI 500: Win rate improved from 78.72% to 86.55% [36] 2. Dividend Growth Factorization - CSI 300: Win rate improved from 54.90% to 73.24% [51] - CSI 500: Win rate improved from 40.14% to 54.28% [51] 3. Growth Factor Improvement via Reverse Exclusion - CSI 300: Win rate improved from 83.38% to 92.88% [69] - CSI 500: Win rate improved from 80.21% to 90.13% [69] 4. Composite Three-Factor Portfolio - CSI 300: Annualized return improved from 6.36% to 9.34%, Sharpe ratio improved from 0.34 to 0.49 [86] - CSI 500: Annualized return improved from 5.46% to 7.36%, Sharpe ratio improved from 0.26 to 0.34 [86] 5. Three-Factor Portfolio + Volume-Price Factors - CSI 300: Annualized return improved from 7.81% to 11.55%, Sharpe ratio improved from 0.40 to 0.62 [103] - CSI 500: Annualized return improved from 6.75% to 9.15%, Sharpe ratio improved from 0.32 to 0.45 [103] 6. 75+25 Composite Portfolio - CSI 300: Annualized return improved from 7.84% to 14.56%, Sharpe ratio improved from 0.41 to 0.75 [112] - CSI 500: Annualized return improved from 7.35% to 13.18%, Sharpe ratio improved from 0.36 to 0.62 [112]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - Building an effective timing factor framework for Shanghai copper futures can identify price trend turning points through quantitative means, providing a scientific basis for trading decisions and helping investors capture excess returns. The framework includes 14 fundamental and macro - quantitative factors and 7 volume - price factors. After back - testing and screening, factors are combined equally weighted to output trend strength signals. The fundamental and volume - price factors have low correlation, and investors can adjust the proportion of the two types of factors according to their target returns and risk requirements [3][4]. 3. Summary According to the Table of Contents 3.1 Shanghai Copper Single Commodity Timing Factor Framework - The model divides factors into fundamental and macro - quantitative factors and volume - price factors. Fundamental factors are constructed from dimensions such as inventory, basis, upstream inventory, profit, spread, and macro - indicators, while volume - price factors are constructed from dimensions such as momentum, moving averages, trading volume, price - volume correlation, and technical indicators [6]. - The model currently contains 14 fundamental and macro - quantitative factors and 7 volume - price factors, with specific factor names provided [8]. - Back - testing and screening settings: Fundamental factors are back - tested from January 2016, volume - price factors from January 2010, and out - of - sample back - testing from January 2022 to December 2024. Other settings include unified bilateral commission of 0.03%, 1 - fold leverage, cumulative return calculation, factor value mapping to 0, 1, - 1, and more [9][10][11]. 3.2 Introduction and Back - testing Results of Shanghai Copper Fundamental Quantitative Factors - **Processing Profit**: Low processing profit may reduce supply and pressure prices, while high profit may increase supply and support prices. From 2020, its back - tested annualized return is 23.9%, with a Sharpe ratio of 1.94 [17]. - **Downstream Processing Fee**: Rising fees may increase demand and push up prices, while falling fees may reduce demand and prices. From 2021, its back - tested annualized return is 10.5%, with a Sharpe ratio of 0.66 [19]. - **Cathode Copper Inventory**: Rising inventory indicates supply surplus and may pressure prices. From 2016, its back - tested annualized return is 17.4%, with a Sharpe ratio of 1.62 [21]. - **Basis**: Expanding basis may indicate supply shortage, while narrowing basis may indicate supply surplus. From 2016, its back - tested annualized return is 17.4%, with a Sharpe ratio of 1.71 [23]. - **Social Inventory**: Similar to cathode copper inventory, rising social inventory may pressure prices. From 2016, its back - tested annualized return is 15.4%, with a Sharpe ratio of 1.6 [25]. - **LME Electrolytic Copper Inventory**: An important external market inventory factor. From 2016, its back - tested annualized return is 21.8%, with a Sharpe ratio of 1.99 [27]. - **Futures Inventory**: Similar to the logic of warehouse receipts. From 2016, its back - tested annualized return is 19.1%, with a Sharpe ratio of 1.73 [30]. - **Comex Copper Inventory**: Different from other inventory factors, more inventory indicates stronger buying sentiment. From 2016, its back - tested annualized return is 15.3%, with a Sharpe ratio of 1.26 [32]. - **Scrap Copper Spread**: Widening spread may suppress refined copper prices, while narrowing spread may support prices. From 2016, its back - tested annualized return is 7.9%, with a Sharpe ratio of 0.86 [34]. - **Imported Copper Concentrate Index (TC)**: Higher TC may increase supply and pressure prices, while lower TC may reduce supply and support prices. From 2020, its back - tested annualized return is 18.8%, with a Sharpe ratio of 1.43 [36]. - **CFTC Non - Commercial Position**: Net long position has a positive predictive effect on prices. From 2016, its back - tested annualized return is 11.0%, with a Sharpe ratio of 0.79 [38]. - **US Dollar Index**: Rising dollar index may suppress copper prices. From 2016, its back - tested annualized return is 8.0%, with a Sharpe ratio of 0.71 [40]. - **VIX Index**: Copper prices are mostly negatively correlated with the VIX index. From 2016, its back - tested annualized return is 11.4%, with a Sharpe ratio of 1.02 [42]. - **US Manufacturing PMI**: As a leading economic indicator, it affects copper prices. From 2016, its back - tested annualized return is 15.3%, with a Sharpe ratio of 1.32 [44]. - **Fundamental Multi - Factor**: Combining the first 4 fundamental single factors equally weighted, from 2016, the back - tested annualized return is 33.5%, with a Sharpe ratio of 4.0 [46]. 3.3 Introduction and Back - testing Results of Shanghai Copper Volume - Price Factors - **Intraday Momentum**: A larger value indicates a stronger upward momentum. From 2010, its back - tested annualized return is 8.9%, with a Sharpe ratio of 1.3 [48]. - **Median Double Moving Averages**: Short - term moving average crossing above the long - term moving average is a buy signal, and vice versa. From 2010, its back - tested annualized return is 10.1%, with a Sharpe ratio of 0.92 [50]. - **Kaufman Adaptive Moving Average (KAMA)**: Calculated through efficiency coefficient and smoothing constant. From 2010, its back - tested annualized return is 7.2%, with a Sharpe ratio of 0.56 [52][53]. - **On - Balance Volume (OBV)**: Calculated based on price and volume, and a long - short double moving average strategy is constructed. From 2010, its back - tested annualized return is 8.9%, with a Sharpe ratio of 0.77 [55][56]. - **Price - Volume Correlation**: Stronger correlation is more likely to form a trending market. From 2010, its back - tested annualized return is 7.3%, with a Sharpe ratio of 0.63 [61]. - **Rebound Momentum**: Calculated based on the difference between closing price and low price, and high price and low price. From 2010, its back - tested annualized return is 11.1%, with a Sharpe ratio of 0.93 [61]. - **TRIX**: A long - short double moving average strategy is constructed based on the daily change rate of EX3. From 2010, its back - tested annualized return is 12.4%, with a Sharpe ratio of 1.16 [63][66]. - **Volume - Price Multi - Factor**: Combining the first 7 volume - price single factors equally weighted, from 2010, the back - tested annualized return is 13.5%, with a Sharpe ratio of 1.32 [68]. 3.4 Comprehensive Model of Fundamental Quantification and Volume - Price Multi - Factors - **All - Factor Combined Long - Short Model**: Combining all single factors equally weighted, from 2010, the back - tested annualized return is 18.1%, with a Sharpe ratio of 1.53 [70]. - **Long - Only Model**: - Fundamental long - only model: Combining the first 14 single factors equally weighted, from 2010, the back - tested annualized return is 8.0%, with a Sharpe ratio of 0.51 [72]. - Volume - price long - only model: Combining the last 7 single factors equally weighted, from 2010, the back - tested annualized return is 7.4%, with a Sharpe ratio of 0.82 [73]. - All - factor comprehensive long - only model: Combining all single factors equally weighted, from 2010, the back - tested annualized return is 10.1%, with a Sharpe ratio of 0.92 [76]. - **Short - Only Model**: - Fundamental short - only model: Combining the first 14 single factors equally weighted, from 2010, the back - tested annualized return is 7.1%, with a Sharpe ratio of 0.77 [77]. - Volume - price short - only model: Combining the last 7 single factors equally weighted, from 2010, the back - tested annualized return is 5.1%, with a Sharpe ratio of 0.55 [79]. - All - factor comprehensive short - only model: Combining all single factors equally weighted, from 2010, the back - tested annualized return is 7.7%, with a Sharpe ratio of 0.85 [81]. - The long - only and short - only models can help enterprises with timing hedging. The comprehensive model of factors is relatively stable in different years, and investors can adjust the proportion of fundamental and volume - price factors according to their target returns and risks [85].

Quantitative Models and Factor Analysis Quantitative Models and Construction - **Model Name**: Macro Timing Strategy **Model Construction Idea**: This model evaluates macroeconomic signals to determine optimal equity allocation levels. It incorporates economic growth and monetary liquidity signals to generate recommended equity positions[27][28] **Model Construction Process**: 1. The model assigns weights to two dimensions: economic growth and monetary liquidity. 2. Signal strength for each dimension is calculated as a percentage. 3. The final equity allocation recommendation is derived based on the combined signal strength. **Evaluation**: The model is designed for stable and moderately bullish configurations, with a focus on balancing growth and liquidity signals[27][28] - **Model Name**: Micro-Cap Timing Model **Model Construction Idea**: This model uses risk warning indicators to assess the timing for micro-cap stock investments. It incorporates volatility congestion and interest rate changes as key metrics[30] **Model Construction Process**: 1. **Volatility Congestion**: Measured as the year-over-year change in volatility. A threshold of 0.55 is used to trigger risk warnings. 2. **Interest Rate Change**: Measured as the year-over-year change in the 10-year government bond yield. A threshold of 0.30 is used to trigger risk warnings. 3. If neither indicator exceeds its threshold, the model suggests continuing to hold micro-cap stocks[30][31] **Evaluation**: The model is effective in identifying risk levels and provides clear signals for long-term investors[30] Model Backtesting Results - **Macro Timing Strategy**: - Equity allocation recommendation: 45% for June[27][28] - Signal strength: Economic growth at 50%, monetary liquidity at 40%[27][28] - Year-to-date return: 1.06%, compared to Wind All-A return of 1.90%[27] - **Micro-Cap Timing Model**: - Volatility congestion: -50.09%, below the 0.55 threshold[31] - Interest rate change: -28.69%, below the 0.30 threshold[31] --- Quantitative Factors and Construction - **Factor Name**: Value Factor **Factor Construction Idea**: Measures the relative valuation of stocks based on financial metrics such as book-to-market ratio and earnings yield[43] **Factor Construction Process**: 1. **Book-to-Market Ratio (BP_LR)**: Calculated as the latest book value divided by market capitalization. 2. **Earnings Yield (EP_FTTM)**: Calculated as the forward 12-month consensus earnings divided by market capitalization. 3. **Sales-to-Enterprise Value (Sales2EV)**: Calculated as the past 12-month revenue divided by enterprise value[43] **Evaluation**: The value factor consistently delivers strong excess returns, particularly in large-cap stocks[34][35] - **Factor Name**: Quality Factor **Factor Construction Idea**: Evaluates the financial health and operational efficiency of companies[43] **Factor Construction Process**: 1. **Operating Cash Flow to Current Debt (OCF2CurrentDebt)**: Measures the ratio of operating cash flow to average current liabilities over the past 12 months. 2. **Gross Margin (GrossMargin_TTM)**: Measures the gross profit margin over the past 12 months. 3. **Revenue-to-Asset Ratio (Revenues2Asset_TTM)**: Measures the revenue generated per unit of average total assets over the past 12 months[43] **Evaluation**: The quality factor is a key driver of excess returns, particularly in mid-cap and small-cap stocks[34][35] - **Factor Name**: Growth Factor **Factor Construction Idea**: Focuses on companies with strong earnings and revenue growth potential[43] **Factor Construction Process**: 1. **Quarterly Revenue Growth (Revenues_SQ_Chg1Y)**: Measures the year-over-year growth in quarterly revenue. 2. **Quarterly Operating Income Growth (OperatingIncome_SQ_Chg1Y)**: Measures the year-over-year growth in quarterly operating income. 3. **Return on Equity (ROE_FTTM)**: Measures the forward 12-month consensus net income divided by average shareholder equity[43] **Evaluation**: The growth factor performs well in mid-cap stocks, particularly in the China A-share market[34][35] Factor Backtesting Results - **Value Factor**: - IC mean: 0.23 in the CSI 300 pool[34] - Multi-long-short return: 1.75% in the CSI 300 pool[34] - **Quality Factor**: - IC mean: 0.0702 in the CSI 500 pool, 0.064 in the CSI 1000 pool[34] - Multi-long-short return: 1.45% in the All A-share pool[34] - **Growth Factor**: - IC mean: 0.11 in the CSI 500 pool[34] - Multi-long-short return: 2.83% in the CSI 500 pool[34] - **Other Factors**: - Momentum and low-volatility factors showed weaker performance, with negative returns in some pools[34][35] --- Convertible Bond Factors and Construction - **Factor Name**: Convertible Bond Valuation Factor **Factor Construction Idea**: Evaluates convertible bonds based on their valuation relative to underlying stocks and market conditions[39] **Factor Construction Process**: 1. **Parity-Premium Ratio**: Measures the premium of the convertible bond price over its parity value. 2. **Underlying Stock Factors**: Incorporates stock-specific factors such as growth, quality, and valuation metrics[39] **Evaluation**: The valuation factor is effective in identifying mispriced convertible bonds[39] Convertible Bond Factor Backtesting Results - **Convertible Bond Valuation Factor**: - Multi-long-short return: 0.97% last week[39] - Other stock-related factors (e.g., growth, quality) showed mixed performance, with growth factor declining by 0.35%[39]

Core Insights - The report emphasizes the importance of monitoring volume and price indicators to respond to unpredictable major events like the US-China tariff negotiations, focusing on market expectations and risk-reward balance [2][5] - Key volume-price factors that signaled market movements before critical dates include KUP1, cors, HIGH0, and KSFT1 [5][6] Phase 1: Accumulation of Bullish Forces Before April 3 - The market from late March to early April 2025 can be divided into "consolidation" and "breakout" phases, with bullish sentiment gradually building up [6][7] - On March 28 to April 2, the market showed signs of bullish testing, with T2506 fluctuating between 107.3 and 108.0, indicating tentative bullish entry without significant volume increase [6][7] - On April 3, the announcement of a dual tariff system by the US led to a bullish breakout, with T2506 opening significantly higher and surpassing previous highs [6][8] Phase 2: Marginal Exit of Speculative Funds Before May 12 - In early May 2025, the bond futures market exhibited a "price increase with volume decrease" characteristic, indicating cautious market sentiment ahead of tariff negotiation results [11][12] - On May 7, indicators KUP1, HIGH0, and KSFT1 signaled a reduction in bullish momentum, reflecting a shift in market dynamics [11][12] - By May 9, the cors indicator confirmed the marginal exit of bullish forces, suggesting a potential increase in downside risk [12][13] Current Indicator Performance - As of late May, the US tariff policy remains uncertain, and the domestic bond market is in a narrow fluctuation pattern, highlighting the need for timely market observation [15] - The KSFT1 indicator has issued a bullish signal, indicating a release of bearish sentiment, while other indicators have yet to confirm further changes in market dynamics [15]