Core Insights - The report suggests increasing allocation to large-cap value stocks while focusing on TMT (Technology, Media, and Telecommunications) and cyclical sectors [3][30] - The industry rotation model has been optimized to adapt to market conditions, incorporating high-frequency factors and enhancing the strategy's effectiveness [4][26] - The latest industry rotation model identifies non-bank financials, steel, media, non-ferrous metals, environmental protection, and telecommunications as preferred sectors [30][33] Market Review and Fund Flow Tracking - As of October 31, 2025, the total monthly trading volume of A-shares reached 36.78 trillion yuan, with a slight decrease in daily average trading volume by 10.49% compared to the previous month [12][18] - The average stock return dispersion for the past month was 2.41%, indicating a slight decline but remaining above the median level for the past six months [12][18] - The industry rotation speed has continued to expand, significantly exceeding the average level since 2015 [12][18] Industry Rotation Model and ETF Fund Configuration - The report emphasizes the importance of focusing on large-cap value and cyclical sectors, particularly in the context of the current unclear market leadership [3][30] - The recommended ETF portfolio includes six funds: E Fund CSI 300 Non-Bank ETF, Guotai Junan CSI Steel ETF, GF CSI Media ETF, Southern CSI Non-Ferrous Metals ETF, Southern Yangtze River Protection Theme ETF, and Guotai Junan CSI All-Share Communication Equipment ETF [3][34] - The model's historical performance has shown consistent positive excess returns, outperforming major benchmark indices [5][42] Historical Performance and Model Effectiveness - The industry rotation model has maintained a strong performance over the years, achieving excess returns compared to industry averages, with a notable performance in 2025 [5][42] - The model's win rates over the past 1, 3, and 5 years are 83.33%, 69.44%, and 71.67% respectively, indicating its robustness [43][44] - The report highlights the significance of emotional and price-volume factors in capturing market dynamics, especially in weak market conditions [42][43]

Core Viewpoint - The Q4 style rotation model indicates signals for small-cap and growth stocks, with recommended sectors including communication, electric equipment, and renewable energy for November [1][2]. Group 1: Style Rotation Model - The Q4 style rotation model has issued signals favoring small-cap stocks, with a comprehensive score of -1 as of September 30, 2025 [3]. - The value-growth style rotation model also shows a preference for growth stocks, with a comprehensive score of -3 for Q4 2025 [4]. Group 2: Industry Rotation Insights - For October, the composite factor strategy yielded an excess return of -0.69%, while the single-factor multi-strategy had an excess return of -0.93% [4]. - In November, the single-factor multi-strategy recommends bullish sectors including media, communication, electronics, non-bank financials, electric equipment, and renewable energy [4]. - The composite factor strategy suggests bullish sectors such as communication, computer, electric and utility services, media, electric equipment, and renewable energy [4].

Quantitative Models and Construction Methods 1. Model Name: Style Rotation Model - **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum, gradually constructing a style timing and scoring system[4][9] - **Model Construction Process**: 1. Construct 640 micro features based on 80 basic micro indicators[9] 2. Use common indices as style stock pools to replace the absolute proportion division of style factors, constructing new style returns as labels[4][9] 3. Use a random forest model for style timing and obtain the current score for each style[4][9] 4. Integrate the timing results and scoring results to construct a monthly frequency style rotation model[4][9] - **Model Evaluation**: The model effectively avoids overfitting risks through rolling training of the random forest model and constructs a comprehensive framework from style timing to style scoring and from style scoring to actual investment[9] Model Backtesting Results 1. **Style Rotation Model**: - Annualized Return: 16.18%[10][11] - Volatility: 20.28%[10][11] - Information Ratio (IR): 0.80[10][11] - Win Rate: 59.43%[10][11] - Maximum Drawdown: 25.20%[11] 2. **Market Benchmark (Hedged)**: - Annualized Return: 10.36%[10][11] - Volatility: 10.85%[10][11] - Information Ratio (IR): 0.95[10][11] - Win Rate: 54.72%[10][11] - Maximum Drawdown: 8.53%[11]

- The style rotation model is constructed based on the Dongwu quantitative multi-factor system, starting from micro-level stock factors. It selects 80 underlying factors as original features, including valuation, market capitalization, volatility, and momentum, and further constructs 640 micro features. The model replaces the absolute proportion division of style factors with common indices as style stock pools, creating new style returns as labels. A random forest model is trained in a rolling manner to avoid overfitting risks, optimizing features and obtaining style recommendations. The framework integrates style timing, scoring, and actual investment[9][4] - The performance of the style rotation model during the backtesting period (2017/01/01-2025/09/30) shows an annualized return of 16.41%, annualized volatility of 20.43%, IR of 0.80, monthly win rate of 58.49%, and a maximum drawdown of 25.54%. When hedging against the market benchmark, the annualized return is 10.54%, annualized volatility is 10.85%, IR is 0.97, monthly win rate is 55.66%, and the maximum drawdown is 8.79%[10][11] - The style rotation model's latest timing directions for October 2025 are value, large market capitalization, momentum, and low volatility[2][19] - The latest holdings of the style rotation model for October 2025 include indices such as CSI Central Enterprise Dividend (ETF code: 561580.SH), CSI Bank (ETF code: 512700.SH), CSI Film and Television (ETF code: 159855.SZ), CS Battery (ETF code: 159796.SZ), and CSI All Real Estate (ETF code: 512200.SH)[3][19]

Group 1: Report Information - Report Date: October 10, 2025 [1] - Report Title: Shenwan Hongyuan Gold ETF Portfolio 202510 [1] - Analysts: Shen Siyi, Deng Hu [3] - Research Support: Bai Haotian [3] - Contact: Shen Enyi [3] Group 2: Investment Ratings - No industry investment ratings are provided in the report. Group 3: Core Views - The report constructs four ETF portfolios, including the macro industry portfolio, macro + momentum industry portfolio, core - satellite portfolio, and trinity style rotation ETF portfolio, based on macro - sensitivity and momentum analysis, aiming to capture investment opportunities in different market environments [5][8]. - The current economic leading indicators are rising, liquidity indicators are slightly tight, and credit indicators remain positive. The portfolios are shifting towards a more balanced allocation, with an increased proportion of consumer sectors [5]. - The trinity style rotation model combines macro - liquidity, fundamental, and market sentiment factors to construct a medium - to long - term style rotation model, providing insights into market style preferences [5][9]. Group 4: ETF Portfolio Construction Methods 4.1 Based on Macro - Method - Calculate macro - sensitivity for broad - based, industry - theme, and Smart Beta ETFs based on economic, liquidity, and credit variables. Traditional cyclical industries are sensitive to the economy, TMT to liquidity, and consumption to credit [8]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) using macro - sensitivity and momentum, and rebalance monthly [8]. 4.2 Trinity Style Rotation ETF Portfolio - Build a medium - to long - term style rotation model centered on macro - liquidity, comparing with the CSI 300 index. Screen macro, fundamental, and market sentiment factors to construct three types of models (growth/value, market - cap, and quality) [9]. Group 5: Portfolio Details 5.1 Macro Industry Portfolio - Select the top 6 industry - theme indices based on macro - sensitivity scores, and equally weight the largest - scale corresponding ETFs. Currently, the portfolio is more balanced with an increased consumer proportion [5][10]. - October 2025 holdings include ETFs related to tourism, home appliances, chemicals, etc. [14]. - In 2025, the portfolio had varying monthly excess returns, with positive excess returns in September [15]. 5.2 Macro + Momentum Industry Portfolio - Combine macro and momentum methods. The pharmaceutical sector's weight is further reduced, and rare earth and battery sectors are selected on the momentum side [5][16]. - October 2025 holdings include multiple industry - themed ETFs [18]. - The portfolio performed well in 2025, with positive excess returns in September after a drawdown in August [19]. 5.3 Core - Satellite Portfolio - Use the CSI 300 as the core and combine broad - based, industry, and Smart Beta portfolios. Weight them at 50%, 30%, and 20% respectively [20][21]. - October 2025 holdings include a mix of broad - based and industry - themed ETFs [24][25]. - The portfolio performed steadily in 2025, outperforming the index almost every month [25]. 5.4 Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth and high - quality styles. The portfolio's factor exposure and historical performance are presented [26][27]. - October 2025 holdings include ETFs related to small - cap indices and high - growth sectors [31]. - The portfolio has shown certain performance since 2021, with positive excess returns in September 2025 [30].

Core Insights - The style rotation model accurately predicted trends in Q3 2025, with signals favoring small-cap and growth stocks for Q4 2025 [1] - The industry rotation model showed positive excess returns in September, with a monthly return of 3.33% and an excess return of 2.43% relative to the benchmark [1] Style Rotation Model - For Q4 2025, the dual-driven rotation strategy indicates a comprehensive score of -1, predicting a preference for small-cap stocks [2] - The growth style is favored in Q4 2025, with a comprehensive score of -3 from the dual-driven rotation strategy [3] Industry Rotation Insights - In September, the composite factor strategy achieved an excess return of 2.43%, while the single-factor multi-strategy had an excess return of -1.02% [3] - For October, the recommended long positions in single-factor multi-strategy include the computer, communication, electronic, non-bank financial, and banking sectors [3] - The composite factor strategy recommends long positions in home appliances, non-ferrous metals, electronics, communication, and computers [3]

Group 1 - The core viewpoint of the article indicates that the market is favoring small-cap and growth styles, with the style rotation model for Q3 2025 confirming this trend [1][2] - In August, the small-cap stocks outperformed large-cap stocks with a monthly excess return of 1.34%, while growth stocks outperformed value stocks with a monthly excess return of 12.76% [1][3] - The industry rotation model showed that in August, two industry combinations achieved absolute returns exceeding 12%, with excess returns above 4% [1][3] Group 2 - The dual-driven rotation strategy for Q3 2025 indicated a signal for small-cap stocks based on the latest data as of June 30, 2025, with a composite score of -3 [2] - The dual-driven rotation strategy for Q3 2025 also indicated a signal for growth stocks, with a composite score of -5 [3] - In August, the composite factor strategy achieved an excess return of 4.38%, while the single-factor multi-strategy achieved an excess return of 4.59% [3]

- The style rotation model is constructed based on micro-level stock factors, including valuation, market capitalization, volatility, and momentum. It utilizes 80 base factors to generate 640 micro features, replacing absolute proportion division of style factors with common indices as style stock pools. Random forest is employed for rolling training to avoid overfitting, enabling feature selection and style recommendation. The framework integrates style timing, scoring, and investment implementation[3][8][9] - The performance of the style rotation model during the backtesting period (2017/01/01-2025/08/31) shows an annualized return of 17.08%, annualized volatility of 20.07%, IR of 0.85, monthly win rate of 55.77%, and maximum drawdown of -29.89%. When hedging against the market benchmark, the annualized return is 10.42%, annualized volatility is 13.03%, IR is 0.80, monthly win rate is 56.73%, and maximum drawdown is -9.57%[9][10] - The style rotation model's September 2025 timing direction focuses on growth, large-cap, momentum, and high-volatility factors[17] - The latest holdings of the style rotation model for September 2025 include ETFs such as Semiconductor Leaders ETF (159665.SZ), Big Data ETF (159739.SZ), Artificial Intelligence ETF (159819.SZ), Fintech ETF (159851.SZ), and 5G ETF (159994.SZ)[2][20]

Group 1: Report Industry Investment Rating - No industry investment rating is provided in the report. Group 2: Core Viewpoints of the Report - The report constructs multiple ETF portfolios using macro - based methods and a trinity style rotation model, aiming to capture investment opportunities in different market conditions [2][5]. - Different portfolios have different characteristics. For example, the macro - industry portfolio is adjusted monthly based on economic, liquidity, and credit conditions, and currently leans towards growth with more pharmaceutical holdings [8][11]. - The macro + momentum industry portfolio combines macro and momentum methods, and has performed well this year, almost outperforming the index every month [14][19]. - The core - satellite portfolio uses the CSI 300 as the base - position and combines different sub - portfolios to achieve relatively stable performance [20][24]. - The trinity style rotation ETF portfolio uses macro - liquidity as the core to construct a style rotation model and provides 8 style preference results [6][25]. Group 3: Summary According to Relevant Catalogs 1. ETF Portfolio Construction Methods 1.1 Based on Macro Method of ETF Portfolio Construction - Calculate macro - sensitivity of indexes tracked by broad - based, industry - themed, and Smart Beta ETFs according to economic, liquidity, and credit variables. Consider adding momentum indicators for complementarity [5]. - Traditional cyclical industries are sensitive to the economy, TMT is sensitive to liquidity, and consumption is sensitive to credit. State - owned enterprises and ESG - related themes have low sensitivity to liquidity and credit [5]. - Construct three ETF portfolios: macro - industry portfolio, macro + momentum industry portfolio, and core - satellite industry portfolio, and rebalance monthly [5]. 2.2 Trinity Style Rotation ETF Portfolio Construction - Build a medium - to long - term style rotation model centered on macro - liquidity, and compare it with the CSI 300 index. - Construct three types of models: growth/value rotation model, market - capitalization model, and quality model. Combine the results of the three models to get the final style preference, with a total of 8 style preference results [6]. 2. Macro Industry Portfolio - Select industry - themed indexes tracked by ETFs with a listing period of over 1 year and a current scale of over 200 million. Calculate sensitivity scores of economy, liquidity, and credit monthly, adjust the score directions according to the latest indicators, and sum them up. Select the top 6 industry - themed indexes and allocate equally among the corresponding largest - scale ETFs [8]. - Currently, due to the economic downturn, slightly tight liquidity, and good credit, it selects ETFs insensitive to the economy and sensitive to credit, leaning towards growth with more pharmaceutical holdings [11]. 3. Macro + Momentum Industry Portfolio - Combine macro and momentum methods. Use clustering to divide industry - themed indexes into 6 groups, and select the product with the highest 6 - month increase in each group for equal - weight allocation [14]. - Both the macro and momentum parts select many pharmaceutical - sector products, and the gaming and Internet sectors also account for a large proportion. The portfolio has performed well this year, outperforming the index almost every month [16][19]. 4. Core - Satellite Portfolio - Design a "core - satellite" portfolio with the CSI 300 as the base - position to address the high volatility and rapid industry rotation of industry - themed ETFs [20]. - Build three sub - portfolios: a broad - based portfolio, an industry portfolio (using the macro + momentum industry portfolio), and a Smart Beta portfolio. Weight the three sub - portfolios at 50%, 30%, and 20% respectively to get the final portfolio. The portfolio has performed stably this year, also outperforming the index almost every month [21][24]. 5. Trinity Style Rotation ETF Portfolio - The model currently leans towards small - cap growth + high - quality. The factor exposure and historical performance are presented, and the portfolio's monthly returns and August holdings are also provided [25][30].

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The core idea is that "the decline in a shrinking volume market is significantly greater than the rise in a shrinking volume market, so avoiding shrinking volume signals can achieve higher trading odds"[3][22][24] - **Model Construction Process**: 1. Calculate the rolling 60-day average and standard deviation of the turnover and turnover rate of the index or market[23] 2. Standardize the daily turnover data: - If the turnover is within ±2 standard deviations, map the score to -1~+1 - If the turnover exceeds ±2 standard deviations, assign a score of +1/-1 3. Combine the scores of turnover and turnover rate equally[23] 4. Generate signals based on the combined score: - Method 1: Go long if the score > 0, stay out if the score < 0 - Method 2: Use the rolling 5-year or 3-year percentile of the score; go long if above the 50th percentile, stay out if below[23] 5. The report adopts the simpler method of directly judging whether the score is greater than 0[23] - **Model Evaluation**: The model is not a high-win-rate strategy but achieves relatively high odds by avoiding significant market adjustments during shrinking volume periods[24] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of growth and value styles based on macroeconomic cycles, valuation differences, and market sentiment[52][54] - **Model Construction Process**: 1. **Fundamentals**: - Growth is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Value is favored under the opposite conditions[52] 2. **Valuation**: - Growth is favored when the PE and PB valuation differences between growth and value are in the lower percentiles and mean-reverting upward[52] 3. **Sentiment**: - Growth is favored when turnover and volatility differences between growth and value are low[52] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between growth and value[52] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[53][55] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of small-cap and large-cap styles based on macroeconomic cycles, valuation differences, and market sentiment[56][58] - **Model Construction Process**: 1. **Fundamentals**: - Small-cap is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Large-cap is favored under the opposite conditions[56] 2. **Valuation**: - Large-cap is favored when the PE and PB valuation differences between small-cap and large-cap are in the higher percentiles and mean-reverting downward[56] 3. **Sentiment**: - Small-cap is favored when turnover differences are high - Large-cap is favored when volatility differences are mean-reverting downward[56] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between small-cap and large-cap[56] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[57][60] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines the conclusions of the growth-value and small-cap-large-cap rotation models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[61][63] - **Model Construction Process**: 1. Use the growth-value model to determine the allocation between growth and value 2. Use the small-cap-large-cap model to determine the allocation between small-cap and large-cap 3. Combine the two models to allocate across the four styles[61] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance, with consistent outperformance in most years[61][63] --- Model Backtest Results 1. Volume Timing Signal - **Win Rate**: 47.34%[24] - **Odds**: 1.75[24] - **Annualized Excess Return**: 6.87% (based on next-day open price)[34] - **Maximum Drawdown**: 31.40%[34] - **Return-to-Drawdown Ratio**: 0.4634[34] 2. Growth-Value Style Rotation Model - **Annualized Return**: 11.76%[55] - **Annualized Volatility**: 20.77%[55] - **Maximum Drawdown**: 43.07%[55] - **Sharpe Ratio**: 0.5438[55] - **Return-to-Drawdown Ratio**: 0.2731[55] 3. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.45%[60] - **Annualized Volatility**: 22.65%[60] - **Maximum Drawdown**: 50.65%[60] - **Sharpe Ratio**: 0.5441[60] - **Return-to-Drawdown Ratio**: 0.2459[60] 4. Four-Style Rotation Model - **Annualized Return**: 13.37%[63] - **Annualized Volatility**: 21.51%[63] - **Maximum Drawdown**: 47.91%[63] - **Sharpe Ratio**: 0.5988[63] - **Return-to-Drawdown Ratio**: 0.2790[63]