- The report introduces a **comprehensive allocation strategy model** that is updated monthly based on industry prosperity signals. The model allocates weights to industries with upward or stable prosperity signals, with stable industries receiving half the weight of upward industries. The strategy aims to optimize sector allocation by focusing on industries with low crowding levels and favorable prosperity trends. [4][33] - The **industry crowding monitoring indicator** is used to identify sectors with high crowding levels. As of October 9, 2025, five industries—non-ferrous metals, machinery equipment, electronics, communication, and comprehensive—triggered crowding signals, with their crowding indicators exceeding the 95% warning threshold. This suggests a cautious approach to these sectors. [3][30][31] - The report highlights the **industry divergence degree indicator**, calculated as the difference between the average growth rate and the median growth rate of the Shenwan first-level industry index. The 20-day moving average of this indicator reached the 93.7% percentile as of October 9, 2025, indicating historically high divergence. The report suggests that industry divergence tends to revert to the mean over time, implying potential for low-performing sectors to rebound. [1][11][13] - The **basic quantitative model for industry prosperity** is applied to assess the outlook for various sectors. For example, the automotive industry is expected to benefit from both domestic and international demand recovery, driven by macroeconomic improvements and global fiscal expansion. Similarly, the home appliance sector is projected to experience growth due to reduced production costs and increased export demand. The agriculture, forestry, animal husbandry, and fishery sector is highlighted for potential recovery due to the recent negative profitability in pig farming, which may accelerate capacity reduction and stimulate a turnaround. [17][18][22][24] - **Performance metrics of the comprehensive strategy model**: Over the last month (2025/9/7-2025/9/30), the strategy achieved a return of 0.1%, with excess returns of -4.6% and -4.3% relative to the industry equal-weight index and CSI 800, respectively. Over the last three months, the strategy returned 13.6%, compared to 26.3% for the equal-weight index and 19.3% for CSI 800. Over the last six months, the strategy returned 25.6%, compared to 40.1% for the equal-weight index and 32.1% for CSI 800. Year-to-date (2025/1/2-2025/9/30), the strategy returned 14.1%, compared to 29.5% for the equal-weight index and 20.9% for CSI 800. [4][33][36]

Quantitative Models and Construction 1. Model Name: Timing Model for Nonferrous Metals - **Model Construction Idea**: This model uses macroeconomic scoring to time the allocation between the CSI SW Nonferrous Metals Index and the Wind All A Index, leveraging the dominant role of copper and other industrial metals in the nonferrous metals sector[19][20] - **Model Construction Process**: - The macroeconomic score for copper is calculated based on global economic and inflationary factors - Allocation Rule: - If the macro score > 0, allocate to the CSI SW Nonferrous Metals Index - Otherwise, allocate to the Wind All A Index - Backtesting Period: March 2009 to September 2025 - Formula: Not explicitly provided, but the scoring system is based on historical macroeconomic data[19][20] - **Model Evaluation**: The model demonstrates strong timing ability, capturing the upward trends in the nonferrous metals sector, except during 2012-2013 when the sector underperformed despite a bullish signal[20] 2. Model Name: Comprehensive Allocation Strategy - **Model Construction Idea**: This strategy dynamically allocates weights to industries based on their economic cycle signals (upward, flat, or downward) and crowding levels, with flat-cycle industries receiving half the weight of upward-cycle industries[35] - **Model Construction Process**: - Identify industries with upward or flat economic cycle signals - Exclude industries with high crowding levels - Assign weights: - Upward-cycle industries: Full weight - Flat-cycle industries: Half weight - Monthly updates based on the latest signals[35] - **Model Evaluation**: The strategy underperformed its benchmarks in the most recent month, suggesting potential limitations in capturing short-term market dynamics[35] --- Model Backtesting Results 1. Timing Model for Nonferrous Metals - **Excess Return**: 245% relative to the Wind All A Index during the backtesting period (March 2009 - September 2025)[20] 2. Comprehensive Allocation Strategy - **1-Month Return**: 4.6% - **Excess Return vs. Equal-Weighted Index**: -5.7% - **Excess Return vs. CSI 800**: -3.9%[35][39] --- Quantitative Factors and Construction 1. Factor Name: Macroeconomic Score for Copper - **Factor Construction Idea**: This factor evaluates the economic and inflationary environment to assess the attractiveness of copper as a leading indicator for the nonferrous metals sector[19][21] - **Factor Construction Process**: - Historical macroeconomic data is used to calculate a score for copper - The score ranges from negative to positive, reflecting unfavorable to favorable conditions[21] - Formula: Not explicitly provided, but the scoring system is derived from macroeconomic indicators[21] 2. Factor Name: Sector Crowding Indicator - **Factor Construction Idea**: This factor measures the crowding level in various sectors to identify potential risks of over-concentration[32][34] - **Factor Construction Process**: - Calculate the crowding level for each sector based on historical trading data - Identify sectors exceeding the 95% warning threshold[32][34] --- Factor Backtesting Results 1. Macroeconomic Score for Copper - **Latest Score**: 4, indicating a historically high level of attractiveness for the nonferrous metals sector[19][21] 2. Sector Crowding Indicator - **Sectors Above 95% Threshold**: Nonferrous Metals, Electronics, Communication, Machinery, Comprehensive, Beauty & Personal Care, Defense, and Pharmaceuticals[32][34]