Quantitative Models and Construction Methods - **Model Name**: IRCF Factor **Construction Idea**: The IRCF factor is designed to capture the asymmetric behavior of institutional and retail investors under different market conditions, focusing on "institutional accumulation" and "retail panic" signals[9][48][49] **Construction Process**: 1. **Small Order Factor**: Calculate "small order sell count/small order buy count" and only take values during stock downturns to capture retail panic signals[48][49] 2. **Large Order Factor**: Calculate "large order buy count/large order sell count" and only take values during stock upturns to filter noise and identify institutional accumulation signals[48][49] 3. **Statistical Features**: Derive three statistical features (mean, standard deviation, 90th percentile) for both small and large order factors over a 40-day rolling window[48][49] 4. **Differentiated Filtering**: Exclude large order indicators for stocks with daily average turnover in the top 1/3 of the market to mitigate algorithmic trading interference[49] 5. **Normalization and Aggregation**: Standardize the six derived indicators and aggregate them to form the IRCF factor[49] **Evaluation**: The IRCF factor demonstrates strong predictive power and stability, effectively capturing micro-level trading dynamics[48][49][52] - **Model Name**: Context-Feature Factor System **Construction Idea**: This framework integrates market context and behavioral features to enhance signal precision and reduce noise[59][60] **Construction Process**: 1. **Context Definition**: Classify market states based on stock price movements, trading volume, amplitude, and intraday returns[60] 2. **Behavioral Features**: Monitor small/large order buy and sell counts and amounts to track trading footprints[60] 3. **Aggregation**: Apply statistical methods (mean, standard deviation, percentile) to refine raw sequences into actionable factors[60] **Evaluation**: The system significantly improves signal reliability by aligning behavioral features with specific market contexts[59][60] Model Backtesting Results - **IRCF Factor**: - Annualized long-short return: 25.8% - Annualized long-only excess return: 9.6% - Long-only IR: 2.13 - Monthly IC mean: 7.1% - ICIR: 3.29 - IC win rate: 85.2%[50][51][52] - **Context-Feature Factor System**: - Annualized long-short return: 18.5%-23.0% (depending on specific factors) - Annualized long-only excess return: 7.8%-9.7% - Long-only IR: 1.82-2.26 - IC mean: 6.3%-7.5% - ICIR: 2.77-3.54 - IC win rate: 75.9%-85.2%[61][62][63] Quantitative Factors and Construction Methods - **Factor Name**: Small Order Sell Count Factor **Construction Idea**: Focus on retail panic during market downturns to identify reversal signals[33][38][48] **Construction Process**: 1. Calculate "small order sell count/small order buy count" during stock downturns[33][38][48] 2. Derive statistical features (mean, standard deviation, 90th percentile) over a 40-day rolling window[44][46][48] **Evaluation**: Exhibits strong predictive power in downturn scenarios, with IC mean reaching 6.3%-7.4%[38][46][48] - **Factor Name**: Large Order Buy Count Factor **Construction Idea**: Track institutional accumulation during market upturns[37][48][49] **Construction Process**: 1. Calculate "large order buy count/large order sell count" during stock upturns[37][48][49] 2. Derive statistical features (mean, standard deviation, 90th percentile) over a 40-day rolling window[44][45][48] **Evaluation**: Demonstrates balanced performance across different market conditions, with IC mean around 5.1%-6.4%[37][45][48] Factor Backtesting Results - **Small Order Sell Count Factor**: - Annualized long-short return: 7.0%-7.8% - Annualized long-only excess return: 3.5%-7.8% - IC mean: 6.3%-7.4% - ICIR: 2.77-3.54[38][46][48] - **Large Order Buy Count Factor**: - Annualized long-short return: 5.9%-6.3% - Annualized long-only excess return: 2.3%-6.3% - IC mean: 5.1%-6.4% - ICIR: 2.10-3.13[37][45][48]

Report Investment Rating - The report gives a "★☆☆" rating to CITIC's five-style stability, indicating a slightly bullish view with limited operability in the market [5]. Core Viewpoints - In the week ending October 17, 2025, Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index had weekly returns of -3.39%, 0.21%, and -1.14% respectively. In the public fund market, equity long strategies retreated, pure bonds outperformed, neutral strategy products showed mixed performance, and among commodities, precious metal ETFs rose while non-ferrous metal ETFs declined, and energy chemical and soybean meal ETFs continued to weaken [5]. - Among CITIC's five styles, the financial style rose last week while others fell. The style rotation chart shows that the growth and consumption styles weakened marginally in terms of relative strength, and the financial style increased significantly in terms of indicator momentum. In the public fund pool, cyclical style funds had better excess performance in the past week, and other style funds underperformed the index on average. The product's deviation from cyclical and consumption styles increased marginally, and the overall market congestion indicator increased marginally this week, with the cyclical style currently in a historically high congestion range [5]. - In the neutral strategy, the stock index basis showed a marginal recovery trend last week. The IM contract rebounded from below the -2 standard deviation of the three - month average to within one standard deviation, and the premium rates of the corresponding spot index ETFs of IH and IF were in the top 20% quantile range of the past three months [5]. - Among Barra factors, the residual momentum factor had better performance in the past week with a weekly excess return of 2.49%, while the momentum and capital flow factors had excess drawdowns. The win - rates of the profitability and leverage factors improved. The cross - section rotation speed of factors increased significantly this week and is currently in a relatively high quantile range in the past year [5]. - According to the latest scoring results of the style timing model, the consumption and financial styles recovered marginally this week, the cyclical style declined, and the current signal favors the stable style. The return of the style timing strategy last week was 0.52%, with an excess return of 1.45% compared to the benchmark equal - weighted allocation [5]. Summary by Directory Fund Market Review - In the public fund market, equity long strategies had a drawdown in the past week, pure bonds had better returns, neutral strategy products showed mixed performance, precious metal ETFs in commodities had large increases, non - ferrous metal ETFs had a return correction, and energy chemical and soybean meal ETFs' net values continued to weaken [5]. - Among CITIC's five styles, the financial style rose last week while others fell. Cyclical style funds had better excess performance in the public fund pool, and other style funds underperformed the index on average. The product's deviation from cyclical and consumption styles increased marginally, and the overall market congestion indicator increased marginally this week, with the cyclical style in a historically high congestion range [5]. - In the neutral strategy, the stock index basis recovered marginally last week, and the premium rates of the corresponding spot index ETFs of IH and IF were in the top 20% quantile range of the past three months [5]. - Among Barra factors, the residual momentum factor had a weekly excess return of 2.49%, the momentum and capital flow factors had excess drawdowns, and the win - rates of the profitability and leverage factors improved. The factor cross - section rotation speed increased significantly and is in a relatively high quantile range in the past year [5]. - According to the style timing model, the consumption and financial styles recovered marginally this week, the cyclical style declined, and the style timing strategy had a return of 0.52% last week, with an excess return of 1.45% compared to the benchmark [5]. 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, along with data on the establishment scale of public funds in the past year, the maximum drawdown of major public fund strategy indices in the past three months, and the weekly returns of major public fund strategy indices [7]. CITIC Style Index - The net value trends of CITIC's financial, cyclical, consumption, growth, and stable style indices are shown, as well as the relative rotation chart of these style indices, which reflects the relative strength and momentum of different styles in different time periods [8][9]. - The excess return performance of CITIC style - based fund style indices in different time periods (weekly, monthly, quarterly, semi - annual, annual) is presented, along with the congestion levels of different styles (excluding the stable style due to data limitations) [10][11]. Barra Factors - The preference levels of Barra single - factors (ranging from 0 - 1) are shown, indicating the degree of preference for different factors. The excess return performance of Barra single - factor style strategies in different time periods (weekly, monthly) is also presented, as well as the excess net value trends of Barra single - factor styles since this year [13][14][17].