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]

Core Viewpoint - The article emphasizes the importance of monitoring market expectations and institutional movements in response to unpredictable major events like the US-China tariff disputes, suggesting that strategies should be adjusted flexibly to balance risk and return [1]. Summary by Sections Phase 1: Accumulation of Bullish Forces Before April 3 Tariff Implementation - The market from late March to early April 2025 can be divided into "consolidation" and "breakout" phases, where indicators such as KUP1, cors, HIGH0, and KSFT1 effectively reflected the accumulation of bullish forces, signaling an impending market turning point [2]. - HIGH0 was the first indicator to issue a bullish signal [2]. - The weak fluctuation in treasury futures alongside rising volume-price correlation indicated a weakening of bearish energy, with cors subsequently issuing a bullish signal [2]. - KUP1 and KSFT1 indicators confirmed the bullish turning point simultaneously [2]. Phase 2: Marginal Exit of Speculative Funds Before May 12 Negotiation Results - In early May 2025, the treasury futures market exhibited characteristics of "price increase with volume decrease," reflecting the marginal withdrawal of speculative funds [2]. - On May 7, KUP1, HIGH0, and KSFT1 simultaneously indicated a contraction in bullish momentum [2]. - The contraction of bullish forces was further confirmed by cors on May 9, with a release of profit-taking demand leading to a subsequent reduction in trading volume [2]. Current Situation and Recommendations - As of late May, the US tariff policy remains volatile, and the intensification of the Russia-Ukraine conflict alongside Middle Eastern risks creates a turbulent overseas environment; however, the domestic bond market remains in a narrow fluctuation pattern [3]. - The KSFT1 indicator has issued a mildly bullish signal, suggesting a release of bearish sentiment, while other indicators have yet to confirm further changes in bullish and bearish forces [3]. - Historical backtesting shows that KSFT1 broke below the threshold on May 28 and May 30, indicating that bearish sentiment may have been sufficiently released, issuing a mildly bullish signal [3]. - Continuous monitoring of the aforementioned volume-price indicators is recommended to capture the movements of major institutional investors [3].

Report Summary 1. Report Industry Investment Rating No information about the industry investment rating is provided in the report. 2. Core View of the Report The report presents the market conditions of treasury bond futures on May 27, 2025, including price changes, trading volume, and related factors, and also mentions the situation of the equity market, money market, and macro - industry news, indicating that the expectations for treasury bond futures are limited and the market is in a state of waiting for a direction [1]. 3. Summary by Related Catalogs 3.1 Treasury Bond Futures Market Conditions - On May 27, treasury bond futures closed down across the board, with the 30 - year, 10 - year, 5 - year, and 2 - year main contracts down 0.26%, 0.11%, 0.03%, and 0.02% respectively [1]. - The treasury bond futures index was - 0.12. The volume - price factor was bullish, and the fundamental factor was bearish. Without leverage, the cumulative returns of the strategy in the past 20, 60, 120, and 240 days were 0.04%, - 0.53%, 0.14%, and 1.27% respectively [1]. - The trading volume of the 2 - year, 5 - year, 10 - year, and 30 - year main contracts was 32,028, 43,924, 58,575, and 62,401 respectively, and the open interest was 104,798, 128,934, 165,848, and 92,091 respectively [3]. - The IRR of the 2 - year, 5 - year, 10 - year, and 30 - year active CTD bonds was 1.95%, 2.07%, 1.88%, and 3.58% respectively, and the current R007 was about 1.6794% [3]. 3.2 Equity Market Conditions - On May 27, the equity market oscillated and adjusted throughout the day, with the ChiNext Index leading the decline. The Shanghai Composite Index fell 0.18%, the Shenzhen Component Index fell 0.61%, and the ChiNext Index fell 0.68%. The market hotspots were scattered, and the number of rising and falling stocks was basically the same [1]. 3.3 Money Market Conditions - On May 27, the overnight shibor was 1.4520%, down 5.4bp from the previous trading day; the 7 - day shibor was 1.5980%, up 1.9bp; the 14 - day shibor was 1.6670%, down 2.1bp; the 1 - month shibor was 1.6140%, up 0.2bp [2]. - The bank - to - bank pledged repurchase market traded 2.4 billion yuan, an increase of 1.62%. The overnight rate closed at 1.45%, up 1bp from the previous trading day; the 7 - day rate closed at 1.70%, up 19bp; the 14 - day rate closed at 1.65%, down 4bp; the 1 - month rate closed at 1.60%, down 6bp [4]. 3.4 Bond Yield Curve Conditions - The treasury bond yield curve rose by 0.29 - 1.10BP (the 2 - year yield rose 0.29BP to 1.47%; the 5 - year yield rose 0.78BP to 1.57%; the 10 - year yield rose 0.38BP to 1.72%; the 30 - year yield rose 1.10BP to 1.90%). The credit bond yield curve showed mixed changes [4]. 3.5 Net Long Position Changes by Institution Type - The daily net long position of private funds decreased by 3.27%, foreign capital decreased by 2.46%, and wealth management subsidiaries decreased by 2.4%. The weekly net long position of private funds decreased by 5.28%, foreign capital decreased by 4.11%, and wealth management subsidiaries decreased by 3.69% [6]. 3.6 Macro and Industry News - On May 27, the central bank conducted 448 billion yuan of 7 - day reverse repurchase operations at an operating rate of 1.40%, unchanged from before. There were 357 billion yuan of reverse repurchases due on the same day [8]. 3.7 Trend Intensity - The trend intensity of treasury bond futures was 0, indicating a neutral state [9].

Quantitative Models and Construction Methods 1. Model Name: Volume-Price Industry Rotation Strategy - **Model Construction Idea**: The strategy is based on six dimensions of volume-price factors, including momentum, trading volatility, turnover rate, long-short comparison, volume-price divergence, and volume-amplitude alignment. These factors are tested on a single-factor basis at the monthly frequency for the CSI Level-1 industries, resulting in 11 effective and logically strong industry factors[6] - **Model Construction Process**: 1. Construct 11 volume-price factors based on the six dimensions mentioned above 2. At the end of each month, select the top five industries with the highest composite factor scores from the CSI Level-1 industries (excluding "Comprehensive" and "Comprehensive Finance") 3. Apply equal weighting within factors and equal weighting across industries to form the final strategy[7] - **Model Evaluation**: The model demonstrates strong logical consistency and effectiveness in identifying outperforming industries[6] --- Quantitative Factors and Construction Methods 1. Factor Name: Second-Order Momentum - **Factor Construction Idea**: Measures the exponential weighted moving average (EWMA) of the closing price relative to its historical mean[7] - **Factor Construction Process**: $ \text{Second-Order Momentum} = \text{Close}_t \cdot \text{EWMA}(\text{Close}_{t-\text{window1}:t}) - \text{mean}(\text{Close}_{t-\text{window1}:t}) $ - Parameters: "Close" represents the closing price, "window1" defines the lookback period[7] 2. Factor Name: Momentum Term Spread - **Factor Construction Idea**: Captures the difference in momentum over two different time windows[7] - **Factor Construction Process**: $ \text{Momentum Term Spread} = \frac{\text{Close}_t - \text{Close}_{t-\text{window1}}}{\text{Close}_{t-\text{window1}}} - \frac{\text{Close}_t - \text{Close}_{t-\text{window2}}}{\text{Close}_{t-\text{window2}}} $ - Parameters: "window1" and "window2" represent two different lookback periods[7] 3. Factor Name: Trading Amount Volatility - **Factor Construction Idea**: Measures the standard deviation of trading amounts over a specific window[7] - **Factor Construction Process**: $ \text{Trading Amount Volatility} = -\text{STD}(\text{Amount}) $ - Parameters: "Amount" refers to the trading amount, and "STD" is the standard deviation operator[7] 4. Factor Name: Volume-Price Divergence Covariance - **Factor Construction Idea**: Measures the covariance between ranked closing prices and ranked volumes over a specific window[7] - **Factor Construction Process**: $ \text{Volume-Price Divergence Covariance} = \text{rank}(\text{covariance}[\text{rank}(\text{Close}), \text{rank}(\text{Volume}), \text{window}]) $ - Parameters: "Close" represents the closing price, "Volume" represents the trading volume, and "window" defines the lookback period[7] 5. Factor Name: Volume-Amplitude Alignment - **Factor Construction Idea**: Measures the correlation between ranked volumes and ranked price ranges over a specific window[7] - **Factor Construction Process**: $ \text{Volume-Amplitude Alignment} = \text{correlation}[\text{rank}(\text{Volume}_{i-1}), \text{rank}(\text{High}_i - \text{Low}_i), \text{window}] $ - Parameters: "High" and "Low" represent the highest and lowest prices, respectively, and "window" defines the lookback period[7] --- Backtesting Results of the Model 1. Volume-Price Industry Rotation Strategy - **Cumulative Return (2010-2025)**: 694.50%[9] - **Cumulative Excess Return over Equal-Weighted Industry Portfolio**: 605.20%[9] - **April 2025 Monthly Return**: -1.59%[9] - **April 2025 Excess Return over Equal-Weighted Industry Portfolio**: 0.81%[9]

Report Industry Investment Rating - No relevant content provided Core Viewpoints of the Report - The styrene industry chain starts from crude oil, goes through the production of benzene and ethylene, then to the production of styrene and its derivatives, and is finally applied in multiple fields such as packaging, automotive, electronics, and construction. It is an important part of the petrochemical industry, with characteristics of high dependence on crude oil, a long chain, and wide - ranging demand. The factors affecting styrene futures prices are complex. Fundamental quantitative factors cover 9 aspects, and volume - price factors include 7 aspects. By back - testing and screening, setting parameters such as back - testing time, handling fees, and leverage, and combining factors in a simple equal - weight addition way, a trend strength signal can be output [3]. - The fundamental multi - factor portfolio has an annualized return of 50.7% and a Sharpe ratio of 2.85 since 2019. The volume - price multi - factor portfolio has an annualized return of 35.3% and a Sharpe ratio of 2.14 since 2019. In the comprehensive model, all single factors are equally weighted, with an annualized return of 32.2% and a Sharpe ratio of 1.86 since 2019. Fundamental factors and volume - price factors have a low correlation. Investors can adjust the proportion of fundamental and volume - price factors in the comprehensive model according to their target returns and risk requirements [4]. Summary According to the Directory 1. Styrene Single - Commodity Timing Factor Framework - Styrene is an important organic chemical raw material with a clear upstream - downstream industrial chain. The model divides factors into two categories: fundamental quantitative factors and volume - price factors. Fundamental factors are constructed from dimensions such as inventory, basis, upstream inventory, profit, and overseas prices. Volume - price factors are constructed from dimensions such as momentum, moving averages, and technical indicators based on daily - frequency market data. As of the writing of the report, the model includes 9 fundamental quantitative factors and 7 volume - price factors [8][10]. - When back - testing and screening factors, the back - testing time for most fundamental factors and volume - price factors starts from October 2019, with the out - of - sample back - testing starting from January 2023 and ending in December 2024. The handling fee is set at a bilateral rate of 0.03%, and the leverage is 1x. Other settings such as cumulative return calculation, factor value mapping, and signal update rules are also specified [11][12][13] 2. Introduction and Back - Testing Results of Styrene Fundamental Quantitative Factors 2.1 Styrene Weekly Shipment Volume - A significant increase in styrene weekly shipment volume may lead to an oversupply situation if downstream demand does not increase synchronously, causing price decline. The data used is from the East China region, Jiangsu Province, China, and is published every Monday. Since 2019, its back - testing performance shows an annualized return of 30.3%, a Sharpe ratio of 1.68, a Calmar ratio of 1.23, a win rate of 51.0%, an average holding period of 13.7 days, and a maximum drawdown of 24.6% [19]. 2.2 Styrene Overseas Price - An increase in overseas styrene prices may push up domestic prices, while a decrease may suppress domestic prices. This factor mainly considers prices in the US Gulf, Rotterdam, and South Korea. The data is published with a one - day lag. Since 2016, its back - testing performance shows an annualized return of 19.6%, a Sharpe ratio of 0.99, a Calmar ratio of 0.73, a win rate of 52.5%, an average holding period of 19.1 days, and a maximum drawdown of 27% [21]. 2.3 Styrene Basis - When the market supply is tight, the basis widens; when the supply is excessive, the basis narrows. The data is from the Guojun Futures database and is published daily. Since 2019, its back - testing performance shows an annualized return of 27.7%, a Sharpe ratio of 1.12, a Calmar ratio of 0.71, a win rate of 51.9%, an average holding period of 2.6 days, and a maximum drawdown of 39.1% [23]. 2.4 Pure Benzene: Port Inventory - A low level of pure benzene port inventory may increase the production cost of styrene. The data is from the East China region and is published every Friday. Since 2019, its back - testing performance shows an annualized return of 15.8%, a Sharpe ratio of 0.67, a Calmar ratio of 0.42, a win rate of 50.8%, an average holding period of 38.2 days, and a maximum drawdown of 37.3% [25]. 2.5 Styrene: Non - Integrated Plant: Production Gross Margin - A high production gross margin of non - integrated styrene plants may encourage enterprises to increase production, leading to an increase in market supply. The data is from the Steel Union and is published after the market closes. Since 2019, its back - testing performance shows an annualized return of 12.5%, a Sharpe ratio of 0.46, a Calmar ratio of 0.3, a win rate of 50.4%, an average holding period of 11.3 days, and a maximum drawdown of 34.4% [27]. 2.6 Styrene Capacity Utilization Rate - An increase in styrene capacity utilization rate may lead to an oversupply situation and price decline. The data is from the Steel Union and is published weekly. Since 2019, its back - testing performance shows an annualized return of 16.5%, a Sharpe ratio of 0.91, a Calmar ratio of 0.85, a win rate of 50%, an average holding period of 28.6 days, and a maximum drawdown of 19.4% [27]. 2.7 Styrene Warehouse Receipts - An increase in warehouse receipts indicates sufficient market supply, while a decrease indicates tight supply. The data is from Flush and is published after the market closes. Since 2020, its back - testing performance shows an annualized return of 22.6%, a Sharpe ratio of 1.34, a Calmar ratio of 1.04, a win rate of 50.2%, an average holding period of 9.6 days, and a maximum drawdown of 21.7% [30]. 2.8 Styrene Arbitrage Spread - The internal - external spread has a mean - reversion characteristic. This factor considers styrene prices in Europe, Asia, and the Americas. The data is from the Steel Union and is updated with a one - day lag. Since 2019, its back - testing performance shows an annualized return of 33.8%, a Sharpe ratio of 1.68, a Calmar ratio of 0.93, a win rate of 53.2%, an average holding period of 12.5 days, and a maximum drawdown of 36.4% [32]. 2.9 Styrene: Spot Inventory - High inventory usually means sufficient or excessive market supply, while low inventory may indicate tight supply. The data is from the Steel Union and is updated every Monday. Since 2019, its back - testing performance shows an annualized return of 25.9%, a Sharpe ratio of 1.45, a Calmar ratio of 1.52, a win rate of 52.3%, an average holding period of 114.6 days, and a maximum drawdown of 17.1% [35]. 2.10 Fundamental Multi - Factor - By equally weighting the above 9 fundamental single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 50.7%, a Sharpe ratio of 2.85, a Calmar ratio of 2.08, a win rate of 52.6%, an average holding period of 6 days, and a maximum drawdown of 24.4% [37]. 3. Introduction and Back - Testing Results of Styrene Volume - Price Factors 3.1 Intraday Momentum - Intraday momentum is defined as the average of the daily high and low prices divided by the opening price. A larger value indicates a faster price increase. Since 2020, its back - testing performance shows an annualized return of 27.6%, a Sharpe ratio of 1.51, a Calmar ratio of 1.7, a win rate of 47.2%, an average holding period of 3.7 days, and a maximum drawdown of 16.2% [40]. 3.2 Median Double Moving Averages - Similar to double moving averages, but the price for calculating the moving average is the median of the daily high and low prices. Since 2019, its back - testing performance shows an annualized return of 18%, a Sharpe ratio of 0.81, a Calmar ratio of 0.56, a win rate of 51.6%, an average holding period of 8.5 days, and a maximum drawdown of 32.4% [42]. 3.3 Kaufman Adaptive Moving Average (KAMA) - Calculated through steps such as efficiency coefficient (ER) and smoothing constant (SC). Since 2019, its back - testing performance shows an annualized return of 21.1%, a Sharpe ratio of 1.23, a Calmar ratio of 1.19, a win rate of 48.8%, an average holding period of 30.6 days, and a maximum drawdown of 17.8% [45]. 3.4 On - Balance Volume (OBV) - Calculated based on the relationship between daily closing prices and trading volumes, and a long - short double moving average strategy is constructed. Since 2020, its back - testing performance shows an annualized return of 21.2%, a Sharpe ratio of 1.17, a Calmar ratio of 1.28, a win rate of 50.4%, an average holding period of 72.4 days, and a maximum drawdown of 16.6% [49]. 3.5 Commodity Channel Index (CCI) - When CCI breaks through + 100, it is a potential selling signal; when it breaks through - 100, it is a potential buying signal. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 12.0% [53]. 3.6 TRIX - Defined through exponential moving averages and a long - short double moving average strategy is constructed based on its daily change rate. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 14.6% [55]. 3.7 MESA Adaptive Moving Average - Hilbert transform is used to process price data. MAMA and FAMA lines are calculated, and a double moving average strategy is constructed for timing. Since 2019, its back - testing performance shows an annualized return of 20.5%, a Sharpe ratio of 1.11, a Calmar ratio of 1.11, a win rate of 49.8%, an average holding period of 29.3 days, and a maximum drawdown of 18.5% [55]. 3.8 Volume - Price Multi - Factor - By equally weighting the above 7 volume - price single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 35.3%, a Sharpe ratio of 2.14, a Calmar ratio of 2.41, a win rate of 51.5%, an average holding period of 10.3 days, and a maximum drawdown of 14.7% [59]. 4. Fundamental Quantitative and Volume - Price Multi - Factor Comprehensive Model 4.1 All - Factor Portfolio Long - Short Model - By equally weighting all 16 single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 32.2%, a Sharpe ratio of 1.86, a Calmar ratio of 2.07, a win rate of 46.6%, an average holding period of 5.1 days, and a maximum drawdown of 15.6% [61]. 4.2 Only - Long Model - Fundamental only - long model: By equally weighting the first 9 single factors, when a short - selling signal is generated, it is regarded as closing the existing long position or staying in cash; when a long - buying signal is triggered, open a long position or hold the existing long contract. Since 2019, the back - testing shows an annualized return of 29.6%, a Sharpe ratio of 1.89, a Calmar ratio of 1.31, an average holding period of 6.7 days, and a maximum drawdown of 22.6%. - Volume - price only - long model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 22.1%, a Sharpe ratio of 1.57, a Calmar ratio of 1.68, an average holding period of 10.6 days, and a maximum drawdown of 13.1%. - All - factor comprehensive only - long model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.32, a Calmar ratio of 1.27, an average holding period of 7.6 days, and a maximum drawdown of 15.8% [64][67][69]. 4.3 Only - Short Model - Fundamental only - short model: By equally weighting the first 9 single factors, when a long - buying signal is generated, it is regarded as closing the existing short position or staying in cash; when a short - selling signal is triggered, open a short position or hold the existing short contract. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.48, a Calmar ratio of 1.28, an average holding period of 6.3 days, and a maximum drawdown of 15.7%. - Volume - price only - short model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 12.5%, a Sharpe ratio of 0.87, a Calmar ratio of 0.9, an average holding period of 16.7 days, and a maximum drawdown of 13.9%. - All - factor comprehensive only - short model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 11.8%, a Sharpe ratio of 0.87, a Calmar ratio of 0.82, an average holding period of 9 days, and a maximum drawdown of 14.4% [72][75][76].