Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

Group 1: Report Industry Investment Rating - No relevant content provided Group 2: Core Viewpoints of the Report - As of the week ending January 16, 2026, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 0.45%, 0.15%, and 1.13% respectively. In the public - fund market, the common stock strategy continued to lead the gains with a weekly return of 1.26%. The convertible bond strategy outperformed the pure - bond strategy. Among commodities, the returns of energy - chemical and soybean meal futures ETFs declined, while precious metals and non - ferrous metals ETFs rose, with the silver ETF having a weekly increase of 23.15% [3]. - In terms of the CITIC five - style, the growth and cyclical styles rose in the past week, while the others fell. The style rotation chart showed that the relative strength of the stable and consumer styles strengthened marginally, and the relative strength momentum of the stable style rebounded. All fund style indices outperformed the benchmark in the past week, with the financial style fund index having an excess return of 2.33%. The market's deviation from the consumer style decreased. The crowding indicator rose slightly this week, and the consumer style was in a historically high - crowding range [3]. - Among Barra factors, the short - cycle momentum factor had a better performance with a weekly excess return of 2.19%. The profitability and leverage factors continued to decline. In terms of winning rates, the residual volatility factor strengthened marginally, and the ALPHA factor weakened slightly. The cross - section rotation speed of factors decreased compared to the previous week and was in the lower - quantile range of the past year. According to the latest score of the style timing model, the stable style rebounded marginally this week, and the current signal favored the growth style. The return of the style timing strategy last week was 1.78%, with an excess return of 2.19% compared to the benchmark balanced allocation [3]. Group 3: Summary by Related Catalogs Fund Market Review - The common stock strategy led the gains in the public - fund market with a weekly return of 1.26%. The neutral - strategy products had more gains than losses. The convertible bond strategy outperformed the pure - bond strategy. Energy - chemical and soybean meal futures ETFs had return corrections, while precious metals and non - ferrous metals ETFs rose, with the silver ETF up 23.15% [3]. CITIC Five - Style Analysis - The growth and cyclical styles rose in the past week, while the others fell. The relative strength of the stable and consumer styles strengthened marginally, and the relative strength momentum of the stable style rebounded. All fund style indices outperformed the benchmark, with the financial style fund index having an excess return of 2.33%. The market's deviation from the consumer style decreased, and the consumer style was in a historically high - crowding range [3]. Barra Factor Analysis - The short - cycle momentum factor had a weekly excess return of 2.19%. The profitability and leverage factors continued to decline. The residual volatility factor strengthened marginally, and the ALPHA factor weakened slightly. The cross - section rotation speed of factors decreased compared to the previous week and was in the lower - quantile range of the past year [3]. Style Timing Model - The stable style rebounded marginally this week, and the current signal favored the growth style. The return of the style timing strategy last week was 1.78%, with an excess return of 2.19% compared to the benchmark balanced allocation [3].

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the vague concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of timing signals from 10 selected indicators[9][14][15] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions (e.g., 20-day Bollinger Bands, 20-day price deviation rate, 60-day turnover rate volatility, etc.)[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score[9][14] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Dividend Style Timing Model - **Model Construction Idea**: The model times the dividend style by analyzing the relative performance of the CSI Dividend Index against the CSI All Share Index, using three indicators: relative momentum, 10Y-1Y term spread, and interbank pledged repo trading volume[16][19] - **Model Construction Process**: 1. Generate daily signals (0, +1, -1) for each indicator, representing neutral, bullish, and bearish views, respectively 2. Aggregate the scores to determine the overall long/short view on the dividend style 3. When bullish, fully allocate to the CSI Dividend Index; when bearish, fully allocate to the CSI All Share Index[16][19] - **Model Evaluation**: The model has consistently maintained a bearish view on the dividend style this year, favoring growth style instead[16] 3. Model Name: Large-Cap vs. Small-Cap Style Timing Model - **Model Construction Idea**: The model evaluates the crowding level of large-cap and small-cap styles based on momentum and trading volume differences, adjusting the strategy based on whether the market is in a high or low crowding state[20][22][24] - **Model Construction Process**: 1. Calculate momentum differences and trading volume ratios between the Wind Micro-Cap Index and the CSI 300 Index over multiple time windows 2. Derive crowding scores for both large-cap and small-cap styles based on percentile rankings of the calculated metrics 3. Use a dual moving average model with smaller parameters in high crowding states and larger parameters in low crowding states to determine trends[20][22][24] - **Model Evaluation**: The model effectively captures the medium- to long-term trends in low crowding states and reacts to potential reversals in high crowding states[22] 4. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model employs genetic programming to directly extract factors from industry index data (e.g., price, volume, valuation) without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[27][30][31] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| and NDCG@5 2. Combine multiple factors with weak collinearity into industry scores using greedy strategies and variance inflation factors 3. Select the top five industries with the highest composite scores for equal-weighted allocation[30][33][37] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks[30][33] 5. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro factor risk parity framework, emphasizing diversification across underlying macro risk sources (growth and inflation surprises) rather than asset classes[38][41] - **Model Construction Process**: 1. Divide macroeconomic scenarios into four quadrants based on growth and inflation surprises 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, actively overweighting favorable quadrants[41][42] - **Model Evaluation**: The strategy achieves enhanced performance by actively allocating based on macroeconomic expectations[38][41] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.67% - Annualized Volatility: 17.33% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.87[15] 2. Dividend Style Timing Model - Annualized Return: 16.65% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.91 - Calmar Ratio: 0.65 - YTD Return: 5.78%[17] 3. Large-Cap vs. Small-Cap Style Timing Model - Annualized Return: 27.79% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.16 - Calmar Ratio: 0.87 - YTD Return: 6.27%[25] 4. Industry Rotation Model (Genetic Programming) - Annualized Return: 31.95% - Annualized Volatility: 17.44% - Maximum Drawdown: -19.62% - Sharpe Ratio: 1.83 - Calmar Ratio: 1.63 - YTD Return: 3.31%[30] 5. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.82% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.91 - Calmar Ratio: 1.88 - YTD Return: 2.02%[42]

1. Report Industry Investment Rating - The operation rating for CITIC Five-Style - Cycle is ☆☆★ [2] 2. Core Views of the Report - As of the week ending December 31, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were -0.31%, -0.20%, and -0.71% respectively [3] - In the public fund market, the performance of stock and bond strategies diverged in the past week. The short-term pure bond strategy performed strongly, the ordinary stock strategy index declined slightly, and most neutral strategy products rose. In the commodity market, the net value of precious metal ETFs corrected, with the adjustment of gold ETFs greater than that of silver. The non-ferrous and energy chemical ETFs continued to rise [3] - Among the CITIC five styles, the cyclical style rose last week, while the other styles declined. The style rotation chart shows that the relative strength of the stable and consumer styles has declined marginally recently, and the relative strength momentum of the five styles has decreased month-on-month [3] - In the public fund pool, the average performance of consumer and financial style funds outperformed the benchmark in the past week. From the trend of the fund style coefficient, the market's deviation from the consumer style has increased. This week, the congestion indicator has increased compared with last week, and the congestion of growth style funds has risen to the middle and high percentile range of history [3] - Among the Barra factors, the medium- and long-term momentum factor had a better performance in the past week, with a weekly excess return of 0.89%. The excess return of the profitability factor weakened, the winning rate of the liquidity and capital flow factors strengthened marginally, and the volatility factor weakened slightly during the week. This week, the cross-sectional rotation speed of the factors continued to decline, falling to the middle and low percentile range in the past year [3] - According to the latest scoring results of the style timing model, the growth style has recovered month-on-month this week, and the current signal favors the cyclical style. The return of the style timing strategy last week was -1.41%, and the excess return compared with the benchmark balanced allocation was -0.76% [3] 3. Summary by Relevant Catalogs Recent Market Returns - The weekly, monthly, quarterly, and semi-annual returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond (net), and Nanhua Commodity are presented in the report [5] - The weekly returns of major public fund strategy indices are also provided [5] - The establishment scale of public fund products in recent years is shown in the report [5] - The maximum drawdown of major public fund strategy indices in the past three months is presented [5] CITIC Style Index - The net value trend of the CITIC style index from December 1 to December 30, 2025, is shown, including the financial, cyclical, consumer, growth, and stable styles [7] - The relative rotation chart of the CITIC style index shows the relative strength and momentum of different styles in the past week, last week, past month, past three months, past six months, and past year [9] - The excess return performance of the fund style index in different time periods is presented [10] - The congestion of different fund styles is shown, with the data as the percentile in the past year [11] Barra Factor - The preference of Barra single-factor styles this week is presented, with the preference range from 0 to 1, where a value closer to 1 indicates a higher degree of preference [12] - The excess return performance of Barra single-factor style strategies in different time periods is shown [14] - The excess net value trend of Barra single-factor styles in the past year is presented [17]

Core Viewpoint - The key to maintaining long-term competitiveness in the increasingly competitive quantitative industry is to return to the essence of finance and leverage sustainable AI quantitative strategies based on professional knowledge [1] Industry Competition Landscape - The quantitative industry is experiencing a decline in entry barriers due to lower computing costs, widespread programming tools, and easier data access, leading to increased homogeneity among strategies [2] - Current quantitative strategies can be categorized into two types: popular multi-factor models that dominate the market and niche strategies based on professional financial understanding, which are more unique and capable of enduring market cycles [2] - The future competition in the quantitative industry will be driven by professional understanding rather than just tools, with AI technology amplifying the differentiation between these two models [2] Strategy Differentiation - The company focuses on a unique strategy that shifts attention from alpha (excess returns) to beta (systematic returns), emphasizing style timing as a core strategy [3] - The strategy utilizes a three-dimensional framework of style valuation, momentum, and effective capital flow to capture factor beta, with style valuation being the most critical indicator [3] - The model can predict style changes across different time frames, from daily to monthly, allowing for timely adjustments [3] Risk Management - The company's risk management capabilities are highlighted as a key indicator of model maturity, with the ability to adjust factor exposure to a balanced state during market downturns, resulting in lower drawdowns compared to similar models [4] Market Outlook - The current market is viewed as being in a phase of ample liquidity, with significant upward potential remaining, indicating that the market trend has not yet reached its end [5] - Investors are advised to focus on relative valuations of styles rather than chasing hot sectors, with recommendations to seek opportunities in sectors with long-term value [6] - Within the technology sector, there are opportunities for rotation and switching between high and low valuations, as many sub-sectors have substantial growth potential [6]

Quantitative Models and Construction Methods - **Model Name**: Style Rotation Model **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum. It incorporates a style timing and scoring system, leveraging micro-level features and machine learning techniques to optimize style selection and rotation[4][9]. **Model Construction Process**: 1. Start with 80 fundamental micro factors as raw features, categorized based on the proprietary multi-factor system of Dongwu Securities[9]. 2. Construct 640 micro-level features from these factors[4][9]. 3. Replace the absolute proportion division of style factors with commonly used indices as style stock pools, creating new style returns as labels[4][9]. 4. Use a rolling training process with a Random Forest model to avoid overfitting, select optimal features, and generate style recommendations[4][9]. 5. Combine style timing results and scoring outcomes to build a monthly frequency style rotation framework[4][9]. **Model Evaluation**: The model effectively integrates micro-level features and machine learning to enhance style rotation performance, mitigating overfitting risks[4][9]. Model Backtesting Results - **Style Rotation Model**: - Annualized Return: 16.52% - Annualized Volatility: 20.46% - IR: 0.81 - Monthly Win Rate: 57.01% - Maximum Drawdown: 25.68% - Excess Annualized Return (Hedged against Benchmark): 11.04% - Excess Annualized Volatility (Hedged against Benchmark): 11.08% - Excess IR (Hedged against Benchmark): 1.00 - Excess Monthly Win Rate (Hedged against Benchmark): 55.14% - Maximum Drawdown (Hedged against Benchmark): 9.00%[4][10][11]

Quantitative Models and Construction A-Share Market Timing Model - **Model Name**: A-Share Multi-Dimensional Timing Model [10] - **Construction Idea**: The model integrates valuation, sentiment, capital, and technical dimensions to assess the directional outlook of the A-share market [10][12][16] - **Construction Process**: - Signals are generated daily for each dimension, with values of 0, ±1 representing neutral, bullish, and bearish views respectively [10] - **Valuation Dimension**: Uses equity risk premium (ERP) to capture mean-reversion characteristics [12][16] - **Sentiment Dimension**: Includes option put-call ratio, implied volatility, and futures member position ratio to reflect market sentiment [12][16] - **Capital Dimension**: Tracks financing purchase amounts to identify market trends [12][16] - **Technical Dimension**: Employs Bollinger Bands and individual stock turnover ratio differences to capture trend continuation [12][16] - The final market view is determined by the sum of scores across all dimensions [10] - **Evaluation**: The model effectively combines mean-reversion and trend-following strategies, balancing risk avoidance and opportunity capture [10] Style Timing Model - **Model Name**: Dividend Style Timing Model [18] - **Construction Idea**: Targets the relative performance of the CSI Dividend Index against the CSI All Index using trend-based indicators [18][22] - **Construction Process**: - Three indicators are used to generate daily signals (0, ±1 for neutral, bullish, bearish views) [18] - **Relative Momentum**: Positive indicator for dividend style [22] - **10Y-1Y Term Spread**: Negative indicator for dividend style, as wider spreads favor growth assets [22] - **Interbank Repo Volume**: Positive indicator for dividend style, reflecting asset scarcity [22] - Signals are aggregated to determine the overall view on dividend style [18] - **Evaluation**: The model captures dividend style trends effectively, leveraging macroeconomic and liquidity factors [18] - **Model Name**: Large-Cap vs Small-Cap Style Timing Model [23] - **Construction Idea**: Differentiates between macro-driven trends in low congestion and fund-driven reversals in high congestion [23][25] - **Construction Process**: - **Momentum Difference**: Calculates the difference in momentum between the Wind Micro-Cap Index and CSI 300 Index across multiple windows, averaging the top/bottom results for small/large-cap scores [27] - **Turnover Ratio**: Similar calculation for turnover ratio differences across windows, averaged for small/large-cap scores [27] - **Congestion Score**: Combines momentum and turnover scores to determine congestion levels (high congestion >90% for small-cap, <10% for large-cap) [27] - **Trend Model**: Uses small/large parameter double moving average models based on congestion levels [25] - **Evaluation**: The model adapts to market conditions, balancing long-term trends and short-term reversals [23][25] Sector Rotation Model - **Model Name**: Genetic Programming Sector Rotation Model [30] - **Construction Idea**: Directly mines factors from sector index data using genetic programming without relying on predefined scoring rules [30][33] - **Construction Process**: - **Factor Mining**: Utilizes NSGA-II algorithm to optimize for monotonicity and top-group performance simultaneously [33][34] - **Factor Combination**: Combines factors with weak collinearity using greedy strategy and variance inflation coefficient [34] - **Weekly Rebalancing**: Selects top five sectors based on multi-factor scores for equal-weight allocation [30] - **Example Factor**: Calculates covariance between standardized weekly low prices and monthly open prices over 25 days, adjusted by standardized weekly high prices over 15 days [38] - **Evaluation**: The model enhances factor diversity and reduces overfitting risks, achieving robust sector rotation performance [33][34] All-Weather Enhanced Portfolio - **Model Name**: China All-Weather Enhanced Portfolio [39] - **Construction Idea**: Implements macro factor risk parity to diversify risks across underlying macro drivers rather than assets [39][42] - **Construction Process**: - **Macro Quadrant Division**: Divides growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations [42] - **Quadrant Portfolio Construction**: Constructs sub-portfolios within each quadrant, focusing on downside risk [42] - **Risk Budgeting**: Adjusts quadrant weights monthly based on macro momentum indicators combining buy-side and sell-side expectations [42] - **Evaluation**: The strategy demonstrates strong defensive attributes during market downturns while maintaining consistent returns [40][43] --- Backtesting Results A-Share Market Timing Model - **Annualized Return**: 24.94% [15] - **Maximum Drawdown**: -28.46% [15] - **Sharpe Ratio**: 1.16 [15] - **Calmar Ratio**: 0.88 [15] - **YTD Return**: 43.84% [15] - **Weekly Return**: 5.28% [15] Dividend Style Timing Model - **Annualized Return**: 15.67% [21] - **Maximum Drawdown**: -25.52% [21] - **Sharpe Ratio**: -0.26 [21] - **Calmar Ratio**: 0.85 [21] - **YTD Return**: 20.86% [21] - **Weekly Return**: -3.63% [21] Large-Cap vs Small-Cap Style Timing Model - **Annualized Return**: 27.04% [28] - **Maximum Drawdown**: -32.05% [28] - **Sharpe Ratio**: 1.13 [28] - **Calmar Ratio**: 0.84 [28] - **YTD Return**: 71.14% [28] - **Weekly Return**: -7.80% [28] Sector Rotation Model - **Annualized Return**: 30.83% [33] - **Annualized Volatility**: 17.74% [33] - **Sharpe Ratio**: 1.74 [33] - **Maximum Drawdown**: -19.63% [33] - **Calmar Ratio**: 1.57 [33] - **YTD Return**: 35.44% [33] - **Weekly Return**: -4.39% [33] All-Weather Enhanced Portfolio - **Annualized Return**: 11.51% [43] - **Annualized Volatility**: 6.18% [43] - **Sharpe Ratio**: 1.86 [43] - **Maximum Drawdown**: -6.30% [43] - **Calmar Ratio**: 1.83 [43] - **YTD Return**: 10.75% [43] - **Weekly Return**: -1.53% [43]

Group 1 - Market sentiment score has continued to rise, reaching 3 as of November 7, up from 2.7 the previous week, indicating further recovery in market sentiment and a bullish outlook [7][11][19] - The trading volatility between industries has increased rapidly, breaking through the upper Bollinger Band, suggesting accelerated sector switching and a short-term improvement in sentiment [19][22] - The average daily trading volume for the entire A-share market decreased slightly to 20,123.50 billion yuan, with the highest trading day on November 3 at 21,329.04 billion yuan [14][18] Group 2 - The short-term trend scores for industries such as banking, petrochemicals, light manufacturing, electric equipment, and steel have shown significant upward movement, with utilities currently having the highest short-term score of 100 [38][39] - The crowdedness of capital in sectors like electric equipment, steel, and coal has increased, indicating potential volatility risks due to high valuations and sentiment corrections [40][44] - The model indicates a preference for large-cap and value styles, with signals suggesting that these styles may strengthen in the future [49][56]

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]

Report Industry Investment Rating - The operation rating for CITIC Five Styles - Finance is ★☆☆, indicating a bullish bias but with limited operability in the market [3][4]. Core Viewpoints - As of the week ending October 24, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 3.42%, -0.03%, and 0.94% respectively. In the public - fund market, enhanced index strategies led the gains with a weekly return of 3.89%. Neutral strategies had more gains than losses. Among commodities, precious - metal ETFs pulled back, while soybean - meal and non - ferrous - metal ETFs had a slight rebound, and energy - chemical ETFs stabilized [4]. - All CITIC five styles closed up last Friday, with the growth style leading in returns. The style rotation chart showed that the cyclical and consumer styles weakened compared to the previous period, and the growth style had a significant increase in the indicator momentum. In the public - fund pool, financial and cyclical style funds had better excess performance in the past week. The deviation of products from the consumer style increased marginally, and the overall market congestion indicator continued to rise this week, with the growth and financial styles in a historically high - congestion range [4]. - In the neutral strategy, the stock - index basis showed a marginal recovery trend during the week. The IC contract recovered to around 0.5 times the standard deviation above the three - month average. The average premium rates of the spot - index ETFs corresponding to IC and IM were relatively high, in the top 80% quantile range of the past three months [4]. - Among Barra factors, the medium - and long - term momentum factor had a better return performance this week, with a weekly excess return of 1.70%. The residual volatility and ALPHA factors retreated, and the winning rates of the dividend and leverage factors improved. The cross - section rotation speed of factors continued to increase this week, currently in the top 80% quantile range of the past year [4]. - According to the latest scoring results of the style timing model, the growth and financial styles recovered marginally this week, while the cyclical and stable styles declined. The current signal favors the financial style. The return of the style timing strategy last week was 1.45%, and the excess return compared to the benchmark balanced allocation was - 0.98% [4]. Summary by Related Catalogs Fund Market Review Recent Market Returns - The weekly, monthly, quarterly, and semi - annual returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond (net), and Nanhua Commodity are presented in a chart [6]. - The maximum drawdowns of the main public - fund strategy indices in the past three months and their weekly returns are also shown in charts [6]. CITIC Style Index - The net - value trends of CITIC style indices (finance, cycle, consumption, growth, stability) from September 24 to October 23, 2025, are presented in a chart [8][9]. - The relative rotation chart of CITIC style indices shows the relative strength and relative - strength momentum of different styles in different time periods (recent week, last week, recent month, recent three months, recent six months, recent year) [10][11]. - The excess - return performance of fund style indices in different time periods is provided in a table [12]. - The fund - style congestion chart shows the congestion levels of cycle, growth, consumption, and finance styles from September 28 to October 26, 2025 [13]. Barra Factors - The style preference of Barra single factors is within the range of 0 - 1, with a higher value indicating a stronger preference. The excess - return performance of Barra single - factor style strategies and the net - value trends of Barra single - factor style excess since this year are presented in charts [14][16][18].