Group 1: Macro Economic Overview - The PMI has returned to expansion, with Q1 GDP expected to grow approximately 5.0% year-on-year, driven by post-holiday resumption of production and rising raw material prices [6][9] - Manufacturing PMI for March is reported at 50.4%, indicating a significant improvement of 1.4 percentage points, with demand recovering faster than production [6][9] - The industrial raw material prices have rebounded significantly, with expectations for March PPI to rise year-on-year by about 0.3% [6][9] Group 2: Food and Beverage Sector - Haidilao (603288.SH) reported revenue and net profit for 2025 at 288.7 billion and 70.4 billion yuan respectively, with year-on-year growth of 7.3% and 11.0%, exceeding expectations [17] - The company’s gross margin improved to 40.15% in 2025, up 3.15 percentage points, primarily due to lower raw material costs and operational efficiencies [20] - The product portfolio is shifting towards high-end health products, with organic and low-salt products seeing a growth rate of 48.3% [18] Group 3: Banking Sector - China Everbright Bank (601818.SH) achieved a revenue of 1263.11 billion yuan in 2025, a year-on-year decline of 6.72%, but the decline is narrowing [37] - The bank's net interest margin decreased to 1.40%, down 14 basis points year-on-year, but the decline is less severe than in 2024 [38] - The bank's asset quality remains stable, with a non-performing loan ratio of 1.27% and a capital adequacy ratio of 13.71% [39] Group 4: Real Estate and Construction Sector - China Resources Land (01209.HK) reported a revenue of 180.2 billion yuan in 2025, with a year-on-year increase of 5.7%, and a net profit of 39.7 billion yuan, up 9.4% [41][42] - The company has maintained a high dividend payout ratio, distributing 1.731 yuan per share, reflecting strong cash flow and profitability [43] - The company’s property management and commercial management segments have shown resilience, with revenue growth of 7.7% and 10.1% respectively [45] Group 5: Automotive Sector - BYD (002594.SZ) reported a revenue of 8039.65 billion yuan in 2025, with a year-on-year growth of 3.5%, while net profit decreased by 19.0% due to competitive pressures [53] - The company’s overseas sales significantly increased, accounting for 26.3% of total sales in Q4 2025, with a year-on-year growth of 95.1% [54] - The company is focusing on enhancing its electric vehicle technology and expanding its overseas market presence, with plans for new model launches [55] Group 6: Media Sector - Xindong Company (02400.HK) achieved a revenue of 57.64 billion yuan in 2025, a year-on-year increase of 15%, with net profit rising by 89% [32] - The company’s gross margin improved to 73.8%, driven by strong performance from overseas games and a higher proportion of revenue from high-margin segments [32] - The international version of "Xindong Town" is expected to drive further growth, leveraging the company's experience in domestic operations [33]

Quantitative Models and Construction Methods - **Model Name**: Barra Style Factors **Construction Idea**: The model tracks the performance of common Barra style factors, focusing on dimensions such as size, value, growth, and profitability[3][13] **Construction Process**: The model calculates the monthly returns of specific factors, including market capitalization, book-to-market ratio, growth, and earnings expectations[3][13] **Evaluation**: Provides insights into the relative performance of different style factors in the market[3][13] - **Model Name**: Open-source Trading Behavior Composite Factor **Construction Idea**: Combines multiple trading behavior factors to monitor dynamic performance[4][29] **Construction Process**: 1. Normalize individual trading behavior factors within industries 2. Use the past 12 periods' ICIR values as weights to form the composite factor 3. Apply industry market capitalization neutrality to the composite factor[29][33] **Evaluation**: Demonstrates robust performance across various stock pools, with better results in small-cap indices like CSI 1000 compared to CSI 800[29][30][33] Factor Construction Methods - **Factor Name**: Ideal Reversal Factor **Construction Idea**: Captures reversal strength by analyzing large transaction days[4][38] **Construction Process**: 1. Retrieve the past 20 days' data for selected stocks 2. Calculate daily average transaction amounts (transaction amount/number of transactions) 3. Identify the top 10 days with the highest transaction amounts and sum their returns (M_high) 4. Identify the bottom 10 days with the lowest transaction amounts and sum their returns (M_low) 5. Compute the factor as M = M_high - M_low[38][40] **Evaluation**: Highlights the micro-level reversal dynamics in A-shares[4][38] - **Factor Name**: Smart Money Factor **Construction Idea**: Identifies institutional trading activity using minute-level price-volume data[4][39] **Construction Process**: 1. Retrieve the past 10 days' minute-level data for selected stocks 2. Calculate the indicator $S_t = |R_t| / V_t^{0.25}$, where $R_t$ is the return at minute $t$, and $V_t$ is the trading volume at minute $t$[39] 3. Sort minute-level data by $S_t$ in descending order and select the top 20% cumulative trading volume minutes as smart money trades 4. Compute VWAPsmart (volume-weighted average price of smart money trades) and VWAPall (volume-weighted average price of all trades) 5. Calculate the factor as $Q = \text{VWAPsmart} / \text{VWAPall}$[39][41] **Evaluation**: Effectively tracks institutional trading patterns[4][39] - **Factor Name**: APM Factor **Construction Idea**: Measures behavioral differences between morning (or overnight) and afternoon trading[4][40] **Construction Process**: 1. Retrieve the past 20 days' data for selected stocks 2. Record daily overnight stock returns ($r$) and index returns ($R$), as well as afternoon stock returns ($r$) and index returns ($R$) 3. Perform regression $r = \alpha + \beta R + \epsilon$ to obtain residuals $\epsilon$ 4. Calculate the difference between overnight and afternoon residuals $\delta_t = \epsilon_{\text{overnight}} - \epsilon_{\text{afternoon}}$ 5. Compute the statistic $\text{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t) / \sqrt{N}}$, where $\mu$ is the mean, $\sigma$ is the standard deviation, and $N$ is the sample size[42] 6. Perform cross-sectional regression to remove momentum effects, using $\text{stat} = \text{Ret20} + \epsilon$, where Ret20 represents the past 20-day momentum factor[43] 7. Use the residual $\epsilon$ as the APM factor[40][42][43] **Evaluation**: Captures intraday reversal dynamics effectively[4][40] - **Factor Name**: Ideal Amplitude Factor **Construction Idea**: Differentiates amplitude information between high and low price states[4][45] **Construction Process**: 1. Retrieve the past 20 days' data for selected stocks 2. Calculate daily amplitude as $(\text{High Price}/\text{Low Price}) - 1$ 3. Select the top 25% high-price days and compute the average amplitude (V_high) 4. Select the bottom 25% low-price days and compute the average amplitude (V_low) 5. Compute the factor as $V = V_{\text{high}} - V_{\text{low}}$[45] **Evaluation**: Reveals structural differences in amplitude information across price states[4][45] Backtesting Results - **Barra Style Factors**: - Market Capitalization Factor: Return -0.18%[3][13] - Book-to-Market Ratio Factor: Return 0.45%[3][13] - Growth Factor: Return -0.60%[3][13] - Earnings Expectations Factor: Return -0.46%[3][13] - **Open-source Trading Behavior Factors**: - Ideal Reversal Factor: - IC Mean -0.048, rankIC Mean -0.060, IR 2.37, Monthly Win Rate 77.1% (historical)[5][14] - March Return -0.47%, 12-month Win Rate 50.0%[6][14] - Smart Money Factor: - IC Mean -0.037, rankIC Mean -0.062, IR 2.68, Monthly Win Rate 80.5% (historical)[5][19] - March Return 1.35%, 12-month Win Rate 66.7%[6][19] - APM Factor: - IC Mean 0.028, rankIC Mean 0.034, IR 2.26, Monthly Win Rate 76.0% (historical)[5][23] - March Return 1.50%, 12-month Win Rate 41.7%[6][23] - Ideal Amplitude Factor: - IC Mean -0.053, rankIC Mean -0.073, IR 2.98, Monthly Win Rate 82.7% (historical)[5][26] - March Return 2.08%, 12-month Win Rate 66.7%[6][26] - **Composite Factor**: - IC Mean 0.065, rankIC Mean 0.093, IR 3.24, Monthly Win Rate 79.3% (historical)[5][29] - March Return 2.45%, 12-month Win Rate 58.3%[6][29] - Outperforms in CSI 1000 and CSI 2000 indices with IRs of 2.61 and 2.83, respectively[30]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The model tracks the performance of common style factors such as size, value, growth, and profitability[3][13] - **Specific Construction Process**: The factors are calculated based on predefined metrics. For example: - **Size Factor**: Measured by market capitalization - **Value Factor**: Measured by book-to-market ratio - **Growth Factor**: Measured by growth-related metrics - **Profitability Factor**: Measured by earnings expectations[3][13] - **Evaluation**: The model provides a comprehensive view of style factor performance across different dimensions, aiding in understanding market trends[3][13] Open-Source Trading Behavior Factors - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Identifies trading days with the strongest reversal attributes based on large transaction sizes[4][13] - **Specific Construction Process**: 1. Retrieve the past 20 days of data for a stock 2. Calculate the average transaction size per day 3. Identify the 10 days with the highest and lowest transaction sizes 4. Compute the cumulative returns for these days: \( M_{\text{high}} \) and \( M_{\text{low}} \) 5. Calculate the factor as \( M = M_{\text{high}} - M_{\text{low}} \)[40][42] - **Evaluation**: Captures micro-level reversal forces effectively[4][13] - **Factor Name**: Smart Money Factor - **Construction Idea**: Tracks institutional trading activity using minute-level price and volume data[4][13] - **Specific Construction Process**: 1. Retrieve the past 10 days of minute-level data 2. Calculate the indicator \( S_t = \frac{|R_t|}{V_t^{0.25}} \), where \( R_t \) is the return and \( V_t \) is the volume for minute \( t \) 3. Sort minutes by \( S_t \) and select the top 20% by cumulative volume 4. Compute the volume-weighted average price (VWAP) for these minutes (\( \text{VWAP}_{\text{smart}} \)) and for all minutes (\( \text{VWAP}_{\text{all}} \)) 5. Calculate the factor as \( Q = \frac{\text{VWAP}_{\text{smart}}}{\text{VWAP}_{\text{all}}} \)[41][43] - **Evaluation**: Effectively identifies institutional trading patterns[4][13] - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in trading behavior between morning and afternoon sessions[4][13] - **Specific Construction Process**: 1. Retrieve the past 20 days of data 2. Calculate daily overnight and afternoon returns for both the stock and the index 3. Perform regression to obtain residuals for overnight (\( \epsilon_{\text{overnight}} \)) and afternoon (\( \epsilon_{\text{afternoon}} \)) returns 4. Compute the daily difference \( \delta_t = \epsilon_{\text{overnight}} - \epsilon_{\text{afternoon}} \) 5. Calculate the statistic \( \text{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t) / \sqrt{N}} \), where \( \mu \) is the mean, \( \sigma \) is the standard deviation, and \( N \) is the sample size 6. Regress \( \text{stat} \) against a momentum factor and use the residual as the APM factor[42][44][45] - **Evaluation**: Captures intraday behavioral differences effectively[4][13] - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states[4][13] - **Specific Construction Process**: 1. Retrieve the past 20 days of data 2. Calculate daily amplitude as \( \text{Amplitude} = \text{High Price} / \text{Low Price} - 1 \) 3. Compute the average amplitude for the top 25% (high price) and bottom 25% (low price) trading days 4. Calculate the factor as \( V = V_{\text{high}} - V_{\text{low}} \)[47] - **Evaluation**: Highlights structural differences in price amplitude effectively[4][13] - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines the above factors using ICIR-based weights to enhance overall performance[31] - **Specific Construction Process**: 1. Standardize and winsorize individual factors within industries 2. Use the past 12 months' ICIR values as weights to compute the composite factor[31] - **Evaluation**: Provides a robust and comprehensive measure of trading behavior[31] --- Model Backtesting Results Barra Style Factors - **Size Factor**: Return of -0.44% in February 2026[3][13] - **Value Factor**: Return of 0.16% in February 2026[3][13] - **Growth Factor**: Return of -0.15% in February 2026[3][13] - **Profitability Factor**: Return of 0.00% in February 2026[3][13] Open-Source Trading Behavior Factors - **Ideal Reversal Factor**: - IC: -0.048 - RankIC: -0.060 - IR: 2.39 - Monthly win rate: 77.5% (historical), 58.3% (last 12 months) - February 2026 return: -0.40%[5][14] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.062 - IR: 2.69 - Monthly win rate: 80.4% (historical), 66.7% (last 12 months) - February 2026 return: -0.76%[5][19] - **APM Factor**: - IC: 0.028 - RankIC: 0.034 - IR: 2.25 - Monthly win rate: 75.8% (historical), 41.7% (last 12 months) - February 2026 return: -0.45%[5][23] - **Ideal Amplitude Factor**: - IC: -0.053 - RankIC: -0.073 - IR: 2.99 - Monthly win rate: 82.6% (historical), 66.7% (last 12 months) - February 2026 return: -0.67%[5][26] - **Composite Trading Behavior Factor**: - IC: 0.065 - RankIC: 0.093 - IR: 3.23 - Monthly win rate: 79.1% (historical), 58.3% (last 12 months) - February 2026 return: -0.60%[5][31]

Group 1: Report Industry Investment Rating - Not provided in the content Group 2: Report's Core View - The overall stock strategy can be treated with cautious optimism. In the short term, the market is oscillating to digest profit - taking chips, and the medium - term oscillating upward pattern remains unchanged. Recently, be vigilant about the callback of over - traded sectors and pay attention to the impact of economic data and earnings reports on the fundamentals. Currently, the sentiment repair is relatively optimistic, the returns of medium and large - cap stocks are strengthening, the profit - making ability outside the index is poor, the basis is continuously converging, and the intraday Alpha and trading - type Alpha environments have not yet recovered. The basis cost is good, the excess environment is weak, and the tail risk is moderately high [11]. - For the long - only stock strategy, currently, it is advisable to increase positions in trading - type Alpha or intraday Alpha, and strictly control the proportion of component stocks in the long - only stock strategy with a high proportion of component stocks and a low exposure to small and micro - cap stocks. For the neutral strategy, it is recommended to seize the low - cost position - building window and increase positions in strategies that replicate T and strictly control exposure without relying on the return contribution of small and micro - cap stocks (mixed neutral strategies with basis management and index T strategies), but the cost - effectiveness of increasing positions in neutral strategies whose main returns rely on the contribution of small and micro - cap stocks is relatively low at this time [11]. Group 3: Summary by Relevant Catalogs 3.1 Equity Market Review - **Factor Calendar Overview**: As of January 16, 2026, most broad - based indices rose this week. The CSI 500 index rose 2.18%, the CSI 1000 index rose 1.27%, the CSI 2000 index rose 0.94%, the CSI All - Share index rose 0.47%, the CSI A500 rose 0.13%, the Shanghai - Shenzhen 300 index fell 0.57%, and the CSI Dividend fell 1.78%. Among the Barra style factors, the top three performing factors were BETA (1.34%), growth (0.53%), and momentum (0.26%); the bottom three were liquidity (- 0.53%), residual volatility (- 0.73%), and size (- 0.90%) [16]. - **Main Broad - based Index Review**: Most broad - based indices rose and most volatilities declined this week. The short - term, medium - term market activity is at a medium - high level. As of January 16, 2026, the average daily trading volume of the CSI All - Share index was 3.40 trillion yuan in the current 5 - day rolling average, and 2.51 trillion yuan in the current 20 - day rolling average [18][23][27]. - **Equity Industry Index Review**: This week, 41.9% of industries achieved positive returns, with the computer sector leading. The top three industries with the highest weekly returns were computer (3.82%), electronics (3.77%), and non - ferrous metals (3.03%); the bottom three were agriculture, forestry, animal husbandry and fishery (- 3.27%), real estate (- 3.52%), and national defense and military industry (- 4.92%) [28][29]. - **Equity Style Factor Review**: Among the Barra style factors, BETA, growth, and momentum factors performed well, while liquidity, residual volatility, and size factors performed poorly. Among the Giant Tide style indices, half of them rose. The top three indices with the highest returns were small - cap growth (3.61%), mid - cap growth (3.15%), and small - cap value (0.69%); the bottom three were large - cap growth (- 0.03%), mid - cap value (- 0.13%), and large - cap value (- 2.81%) [33][39]. - **Stock Index Futures Market Review**: The discount converged, and most volatilities rose. The basis of IF, IC, and IM all converged. The estimated impact of each contract's hedging on the average return of neutral products this week was - 0.10% for 300 neutral, - 0.17% for 500 neutral, and - 0.48% for 1000 neutral. Since the beginning of this year, it has been - 0.41% for 300 neutral, - 0.66% for 500 neutral, and - 0.86% for 1000 neutral [41][46]. - **Options Market Review**: The implied volatility generally increased this week, which is expected to be beneficial for option - buying and arbitrage strategies [50]. 3.2 Strategy Environment Monitoring - **Intraday Alpha Environment for Neutral and Index - Enhancement Strategies**: Overall, it is conducive to the accumulation of intraday Alpha in terms of liquidity, volatility, and the proportion of high - volatility stocks, but the net outflow of funds is not conducive to the accumulation of intraday Alpha [55][58][61]. - **Trading - Type Alpha Environment for Neutral and Index - Enhancement Strategies**: Overall, it is not conducive to the accumulation of trading - type Alpha. Although factors such as trading volume, turnover rate, and differentiation degree are beneficial, the mid - cap style and the decrease in the number of stocks that can beat the benchmark index are significantly unfavorable [64][70]. - **Holding - Type Alpha Environment for Neutral and Index - Enhancement Strategies**: The overall environment shows that it is not conducive to the accumulation of holding - type Alpha, but some factors such as the number of limit - up and limit - down stocks, liquidity, and volatility are expected to be beneficial for Alpha accumulation [76][88][91]. - **Neutral Strategy Hedging Environment Monitoring**: The basis volatility slightly decreased, and the cost control pressure increased [104]. 3.3 Future Strategy Judgement - **20 - day Rolling Returns**: As of January 16, 2026, the relative returns of the CSI 1000, CSI 2000, and CSI 500 to the Shanghai - Shenzhen 300 were in extremely high intervals, while the return of the Shanghai - Shenzhen 300 was in a relatively high interval [106]. - **Derivatives Option Sentiment Dimension**: The sentiment of the CSI 1000, Shanghai - Shenzhen 300, and CSI 500 is generally cautious but structurally differentiated, with the sentiment of the Shanghai - Shenzhen 300 being significantly bullish [110]. - **Derivatives Futures Sentiment Dimension**: The sentiment of the CSI 1000, Shanghai - Shenzhen 300, and CSI 500 is generally optimistic, and the basis of IF, IC, and IM converged, indicating that the market sentiment has recovered [113]. - **Risk Preference**: As of January 15, 2026, the margin trading balance was 2.70 trillion yuan, at an extremely high level in the past three years, indicating a high risk preference [116]. - **Style Attention Multiples**: Currently, the CSI 1000 is in a normal interval, the CSI 2000 is in a lower interval, and the CSI 500 is in an extremely high interval [122]. - **Profit Spread**: As of January 16, 2026, the profit spreads of the CSI 1000, CSI 500, CSI 2000, and Shanghai - Shenzhen 300 were in lower, extremely low, extremely low, and extremely low intervals respectively [123]. - **Dividend Spread**: As of January 16, 2026, the dividend spreads of the CSI 1000, CSI 500, CSI 2000, and Shanghai - Shenzhen 300 were all in normal intervals [127]. - **Trading Congestion of Small and Micro - Cap and TMT**: As of January 16, 2026, the trading heat of the TMT sector was in a relatively high interval, the trading heat of small and micro - cap sectors was in a normal interval, and the total market trading volume was in an extremely high interval [130].

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The model tracks the performance of common style factors such as size, value, growth, and profitability in the market - **Construction Process**: The factors are calculated based on specific financial metrics. For example: - Size factor is based on market capitalization - Book-to-market ratio is used for the value factor - Growth factor is derived from growth-related metrics - Profitability factor is based on earnings expectations[3][13] - **Evaluation**: The model provides insights into the performance of different market styles, helping investors understand factor contributions to returns[3][13] Kaiyuan Behavioral Factors - **Model Name**: Kaiyuan Behavioral Factors - **Construction Idea**: These factors are based on trading behaviors, aiming to capture alpha signals from microstructure patterns in the market - **Construction Process**: - **Ideal Reversal Factor**: Measures the reversal strength of trading days by analyzing the average transaction size. It identifies days with the strongest reversal attributes[4][13] - **Smart Money Factor**: Tracks institutional trading activity using minute-level price and volume data. It identifies "smart money" trades by sorting minute data based on a constructed indicator and calculating the volume-weighted average price (VWAP) of these trades[4][40][42] - **APM Factor**: Measures the difference in trading behavior between morning and afternoon sessions. It uses regression analysis on overnight and afternoon returns to calculate residuals, which are then used to construct the factor[4][41][43][44] - **Ideal Amplitude Factor**: Captures the structural differences in stock price amplitude under high and low price states. It calculates the difference between the average amplitude of high-price and low-price days[4][46] - **Evaluation**: These factors are recognized for their ability to capture unique trading behavior patterns and provide alpha signals[4][13] Kaiyuan Behavioral Composite Factor - **Model Name**: Kaiyuan Behavioral Composite Factor - **Construction Idea**: Combines the above behavioral factors into a single composite factor to enhance performance - **Construction Process**: - Standardizes and winsorizes individual factors within industries - Uses the past 12 periods' ICIR values as weights to calculate the composite factor - Applies industry and market capitalization neutrality adjustments[30][34] - **Evaluation**: The composite factor demonstrates superior performance in small and mid-cap stock pools compared to large-cap pools[30][35] --- Backtesting Results of Models Barra Style Factors - **Size Factor**: Return of 1.06% in December 2025[3][13] - **Book-to-Market Ratio Factor**: Return of -0.18% in December 2025[3][13] - **Growth Factor**: Return of 0.20% in December 2025[3][13] - **Profitability Factor**: Return of 0.94% in December 2025[3][13] Kaiyuan Behavioral Factors - **Ideal Reversal Factor**: - IC: -0.049 - RankIC: -0.060 - IR: 2.42 - Long-Short Monthly Win Rate: 77.8% - December 2025 Long-Short Return: 0.14% - 12-Month Long-Short Monthly Win Rate: 58.3%[5][14][17] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.061 - IR: 2.69 - Long-Short Monthly Win Rate: 80.1% - December 2025 Long-Short Return: -0.24% - 12-Month Long-Short Monthly Win Rate: 75.0%[5][19][22] - **APM Factor**: - IC: 0.028 - RankIC: 0.034 - IR: 2.25 - Long-Short Monthly Win Rate: 76.2% - December 2025 Long-Short Return: 1.08% - 12-Month Long-Short Monthly Win Rate: 41.7%[5][23][26] - **Ideal Amplitude Factor**: - IC: -0.053 - RankIC: -0.073 - IR: 2.99 - Long-Short Monthly Win Rate: 83.0% - December 2025 Long-Short Return: -0.63% - 12-Month Long-Short Monthly Win Rate: 58.3%[5][26][29] Kaiyuan Behavioral Composite Factor - **Composite Factor**: - IC: 0.066 - RankIC: 0.093 - IR: 3.25 - Long-Short Monthly Win Rate: 78.8% - December 2025 Long-Short Return: -0.04% - 12-Month Long-Short Monthly Win Rate: 58.3% - Annualized Return of Long-Short Portfolio: 8.09% - Sharpe Ratio: 2.56 - Monthly Win Rate: 77.4% - IR in Small and Mid-Cap Pools: 2.83 (China Securities 2000), 2.62 (China Securities 1000), 1.00 (China Securities 800)[5][30][34][35]

Report Structure - The report includes sections on the macro clock four - factor dual - channel model and the equity market and Barra style factors [3] Performance Comparison - The risk - parity portfolio has an annualized return of 4.21%, an annualized volatility of 1.00%, an annualized downside volatility of 0.59%, a maximum drawdown of - 0.93%, a Sharpe ratio of 4.23, a Calmar ratio of 4.55, and a Sortino ratio of 7.09. The CSI FOF fund has an annualized return of 1.55%, an annualized volatility of 6.07%, an annualized downside volatility of 4.26%, a maximum drawdown of - 18.41%, a Sharpe ratio of 0.26, a Calmar ratio of 0.08, and a Sortino ratio of 0.41 [16] Sub - sections in the Report Macro Clock Four - Factor Dual - Channel Model - It contains a review of macro factors and the risk - parity portfolio [3] Equity Market and Barra Style Factors - It includes a review of equity market styles and an equity style timing strategy [3]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The model tracks the performance of common style factors such as size, value, growth, and profitability in the market[3][13] - **Specific Construction Process**: The factors are constructed based on predefined dimensions: - Size factor: Measures the impact of market capitalization - Book-to-market ratio factor: Captures the value dimension - Growth factor: Reflects growth characteristics - Profitability factor: Tracks expected earnings performance[3][13] - **Evaluation**: The factors provide insights into the performance of different market styles, helping to understand market trends and dynamics[3][13] Open-Source Trading Behavior Factors - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Identifies the strongest reversal days by analyzing the average transaction size of large trades[4][14] - **Specific Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the average transaction size (transaction amount/number of transactions) for each day 3. Identify the 10 days with the highest transaction sizes and sum their returns (M_high) 4. Identify the 10 days with the lowest transaction sizes and sum their returns (M_low) 5. Compute the factor as \( M = M_{high} - M_{low} \)[43] - **Evaluation**: Captures micro-level reversal forces in the market, providing a unique perspective on trading behavior[4][14] - **Factor Name**: Smart Money Factor - **Construction Idea**: Tracks institutional trading activity by analyzing minute-level price and volume data[4][14] - **Specific Construction Process**: 1. Retrieve the past 10 days' minute-level data for a stock 2. Construct the indicator \( S_t = \frac{|R_t|}{V_t^{0.25}} \), where \( R_t \) is the return at minute \( t \) and \( V_t \) is the volume at minute \( t \) 3. Sort minute-level data by \( S_t \) in descending order and select the top 20% of minutes by cumulative volume as "smart money" trades 4. Calculate the volume-weighted average price (VWAP) for smart money trades (\( VWAP_{smart} \)) and all trades (\( VWAP_{all} \)) 5. Compute the factor as \( Q = \frac{VWAP_{smart}}{VWAP_{all}} \)[42][44] - **Evaluation**: Effectively identifies institutional trading patterns, offering a valuable alpha source[4][14] - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in stock behavior between morning (or overnight) and afternoon trading sessions[4][14] - **Specific Construction Process**: 1. Retrieve the past 20 days' data for a stock 2. Calculate daily overnight and afternoon returns for both the stock and the market index 3. Perform a regression of stock returns on market index returns to obtain residuals 4. Compute the difference between overnight and afternoon residuals for each day 5. Calculate the statistic \( \text{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}} \), where \( \mu \) is the mean, \( \sigma \) is the standard deviation, and \( N \) is the number of observations 6. Regress the statistic on momentum factors and use the residual as the APM factor[43][45][46] - **Evaluation**: Highlights intraday trading behavior differences, providing insights into market dynamics[4][14] - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states[4][14] - **Specific Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the daily amplitude as \( \text{Amplitude} = \text{(High Price/Low Price)} - 1 \) 3. Compute the average amplitude for the top 25% of days by closing price (V_high) 4. Compute the average amplitude for the bottom 25% of days by closing price (V_low) 5. Compute the factor as \( V = V_{high} - V_{low} \)[48] - **Evaluation**: Captures structural differences in price amplitude, offering a unique perspective on market behavior[4][14] - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines the above trading behavior factors using ICIR-based weights to enhance performance[32] - **Specific Construction Process**: 1. Perform industry-level outlier removal and standardization for each factor 2. Use the past 12 months' ICIR values as weights to combine the factors 3. Construct the composite factor as a weighted sum of the individual factors[32] - **Evaluation**: Demonstrates superior performance in small and mid-cap stock pools, providing robust alpha generation[32] --- Backtesting Results of Models and Factors Barra Style Factors - **Size Factor**: Return of -0.18% in November 2025[3][13] - **Book-to-Market Ratio Factor**: Return of 0.20% in November 2025[3][13] - **Growth Factor**: Return of -0.23% in November 2025[3][13] - **Profitability Factor**: Return of -0.35% in November 2025[3][13] Open-Source Trading Behavior Factors - **Ideal Reversal Factor**: - IC: -0.049 - RankIC: -0.060 - IR: 2.44 - Monthly win rate: 77.7% - November 2025 return: -1.52% - 12-month win rate: 58.3%[5][15] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.062 - IR: 2.72 - Monthly win rate: 81.3% - November 2025 return: 0.22% - 12-month win rate: 83.3%[5][19] - **APM Factor**: - IC: 0.028 - RankIC: 0.033 - IR: 2.23 - Monthly win rate: 76.0% - November 2025 return: -0.43% - 12-month win rate: 41.7%[5][23] - **Ideal Amplitude Factor**: - IC: -0.054 - RankIC: -0.074 - IR: 3.03 - Monthly win rate: 83.4% - November 2025 return: 0.49% - 12-month win rate: 66.7%[5][27] - **Composite Trading Behavior Factor**: - IC: 0.066 - RankIC: 0.093 - IR: 3.30 - Monthly win rate: 79.4% - November 2025 return: -0.21% - 12-month win rate: 66.7%[5][32]

- The report tracks the performance of Barra style factors for October 2025, showing that the market capitalization factor recorded a return of -1.49%, the book-to-market ratio factor recorded a return of 0.39%, the growth factor recorded a return of -0.34%, and the earnings expectations factor recorded a return of 0.12%[4][14] - The report introduces a series of stock selection factors based on trading behavior, including the Ideal Reversal Factor, Smart Money Factor, APM Factor, and Ideal Amplitude Factor[5][15] - The Ideal Reversal Factor is constructed by segmenting the traditional reversal factor using W-shaped cuts, focusing on the average transaction amount per trade to identify the trading days with the strongest reversal attributes[41] - The Smart Money Factor is constructed by analyzing minute-level price and volume data to identify the involvement of institutional investors, using a specific formula to calculate the factor value[42][44] - The APM Factor measures the difference in stock price behavior between morning (or overnight) and afternoon trading sessions, using a regression model to calculate the residuals and then constructing a statistical measure to quantify the difference[43][45][46] - The Ideal Amplitude Factor measures the difference in amplitude information between high and low price states, calculating the average amplitude for the highest and lowest 25% of trading days and then taking the difference[48] - The historical performance of the Ideal Reversal Factor shows an IC mean of -0.050, rankIC mean of -0.061, IR of 2.48, and a long-short monthly win rate of 78.1%[6][16] - The historical performance of the Smart Money Factor shows an IC mean of -0.038, rankIC mean of -0.062, IR of 2.74, and a long-short monthly win rate of 81.2%[6][21] - The historical performance of the APM Factor shows an IC mean of 0.028, rankIC mean of 0.034, IR of 2.25, and a long-short monthly win rate of 76.5%[6][25] - The historical performance of the Ideal Amplitude Factor shows an IC mean of -0.054, rankIC mean of -0.074, IR of 3.03, and a long-short monthly win rate of 83.3%[6][28] - The historical performance of the composite trading behavior factor shows an IC mean of 0.067, rankIC mean of 0.093, IR of 3.33, and a long-short monthly win rate of 80.0%[6][32] - In October 2025, the Ideal Reversal Factor recorded a long-short return of 1.63% with a 12-month long-short monthly win rate of 66.7%[7][16] - In October 2025, the Smart Money Factor recorded a long-short return of 2.90% with a 12-month long-short monthly win rate of 83.3%[7][21] - In October 2025, the APM Factor recorded a long-short return of -1.13% with a 12-month long-short monthly win rate of 41.7%[7][25] - In October 2025, the Ideal Amplitude Factor recorded a long-short return of 3.33% with a 12-month long-short monthly win rate of 66.7%[7][28] - In October 2025, the composite trading behavior factor recorded a long-short return of 3.73% with a 12-month long-short monthly win rate of 75.0%[7][32]

- Model Name: Barra Style Factors; Model Construction Idea: The model tracks the performance of common Barra style factors; Model Construction Process: The model calculates the returns of various style factors such as market capitalization, book-to-market ratio, growth, and earnings expectations; Model Evaluation: The model provides insights into the performance of different style factors over a specific period[4][14] - Factor Name: Ideal Reversal Factor; Factor Construction Idea: The factor identifies the strongest reversal days based on the average transaction amount per trade; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the average transaction amount per trade for each day 3. Sum the returns of the top 10 days with the highest average transaction amount (M_high) 4. Sum the returns of the bottom 10 days with the lowest average transaction amount (M_low) 5. Calculate the Ideal Reversal Factor as M = M_high - M_low 6. Repeat the above steps for all stocks to calculate their respective Ideal Reversal Factors[5][39][41] - Factor Name: Smart Money Factor; Factor Construction Idea: The factor identifies the participation of institutional investors based on minute-level price and volume data; Factor Construction Process: 1. Retrieve the past 10 days of minute-level data for the selected stock 2. Construct the indicator $St = \frac{|Rt|}{Vt^{0.25}}$, where $Rt$ is the return at minute t and $Vt$ is the volume at minute t 3. Sort the minute-level data by $St$ in descending order and select the top 20% of minutes by cumulative volume as smart money trades 4. Calculate the volume-weighted average price (VWAP) for smart money trades (VWAPsmart) 5. Calculate the VWAP for all trades (VWAPall) 6. Calculate the Smart Money Factor as $Q = \frac{VWAPsmart}{VWAPall}$[5][40][42] - Factor Name: APM Factor; Factor Construction Idea: The factor measures the difference in stock behavior between morning (or overnight) and afternoon sessions; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the overnight and afternoon returns for the stock and the index 3. Perform a regression of the stock returns on the index returns to obtain residuals 4. Calculate the difference between overnight and afternoon residuals 5. Construct the statistic $stat = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}}$ 6. Perform a cross-sectional regression of the statistic on the momentum factor to obtain residuals, which are used as the APM Factor[5][41][43][44] - Factor Name: Ideal Amplitude Factor; Factor Construction Idea: The factor measures the difference in amplitude information between high and low price states; Factor Construction Process: 1. Retrieve the past 20 days of data for the selected stock 2. Calculate the daily amplitude (high price/low price - 1) 3. Calculate the average amplitude for the top 25% of days with the highest closing prices (V_high) 4. Calculate the average amplitude for the bottom 25% of days with the lowest closing prices (V_low) 5. Calculate the Ideal Amplitude Factor as V = V_high - V_low[5][46] - Composite Factor: Kaisheng Trading Behavior Composite Factor; Construction Idea: The composite factor combines multiple trading behavior factors using their ICIR values as weights; Construction Process: 1. Perform outlier removal and standardization for each trading behavior factor within the industry 2. Use the past 12 periods' ICIR values as weights to form the composite factor 3. Calculate the composite factor's returns and performance metrics[5][31] Model Backtest Results - Barra Style Factors: Market Capitalization Factor return: 1.73%, Book-to-Market Ratio Factor return: -0.31%, Growth Factor return: 0.13%, Earnings Expectations Factor return: -0.09%[4][14] Factor Backtest Results - Ideal Reversal Factor: IC: -0.050, rankIC: -0.060, IR: 2.46, Long-Short Monthly Win Rate: 77.4%, September Long-Short Return: -0.42%, Last 12 Months Long-Short Monthly Win Rate: 58.3%[6][15] - Smart Money Factor: IC: -0.037, rankIC: -0.061, IR: 2.70, Long-Short Monthly Win Rate: 81.8%, September Long-Short Return: 0.30%, Last 12 Months Long-Short Monthly Win Rate: 83.3%[6][18] - APM Factor: IC: 0.029, rankIC: 0.034, IR: 2.29, Long-Short Monthly Win Rate: 76.4%, September Long-Short Return: 1.68%, Last 12 Months Long-Short Monthly Win Rate: 50.0%[6][22] - Ideal Amplitude Factor: IC: -0.053, rankIC: -0.073, IR: 2.98, Long-Short Monthly Win Rate: 83.2%, September Long-Short Return: 0.40%, Last 12 Months Long-Short Monthly Win Rate: 66.7%[6][26] - Composite Factor: IC: 0.066, rankIC: 0.091, IR: 3.23, Long-Short Monthly Win Rate: 82.1%, September Long-Short Return: 0.57%, Last 12 Months Long-Short Monthly Win Rate: 75.0%[6][31]

Quantitative Models and Construction Methods 1. Model Name: Convertible Bond Random Forest Strategy - **Model Construction Idea**: Utilizes the Random Forest machine learning method to identify convertible bonds with potential for excess returns by leveraging decision trees[16][17] - **Model Construction Process**: 1. Data preprocessing and feature engineering to prepare convertible bond datasets 2. Training a Random Forest model with historical data to identify patterns of excess return potential 3. Selecting bonds with the highest predicted scores for portfolio construction 4. Weekly rebalancing of the portfolio based on updated predictions[17] - **Model Evaluation**: Demonstrated strong performance in generating excess returns, indicating high predictive accuracy[16] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: Combines macro, meso, micro, and derivative signals to create a four-dimensional non-linear timing model for market positioning[18][19] - **Model Construction Process**: 1. Macro signals: Derived from liquidity, interest rates, credit, economic growth, and exchange rates 2. Meso signals: Based on industry-level business cycle indicators 3. Micro signals: Captures structural risks using valuation, risk premium, volatility, and liquidity factors 4. Derivative signals: Generated from the basis of stock index futures 5. Aggregation: Signals are synthesized into a composite timing signal[18][19][24] - **Model Evaluation**: Effective in identifying market trends and providing actionable signals, with the latest signal indicating a bullish stance[19][24] 3. Model Name: Industry Rotation Strategy 2.0 - **Model Construction Idea**: Constructs an industry rotation strategy based on economic quadrants and multi-dimensional industry style factors[69] - **Model Construction Process**: 1. Define economic quadrants using corporate earnings and credit conditions 2. Develop industry style factors such as expected business climate, earnings surprises, momentum, valuation bubbles, and inflation beta 3. Test factor effectiveness within each quadrant 4. Allocate to high-expected-return industries based on factor signals[69][71] - **Model Evaluation**: Demonstrates strong adaptability to the A-share market, with annualized excess returns of 9.44% (non-exclusion version) and 10.14% (double-exclusion version)[71] 4. Model Name: Genetic Programming Index Enhancement Models - **Model Construction Idea**: Uses genetic programming to discover and optimize stock selection factors for index enhancement strategies[88][93][97] - **Model Construction Process**: 1. Stock pools: Defined for CSI 300, CSI 500, CSI 1000, and CSI All Share indices 2. Training: Genetic programming generates initial factor populations and iteratively evolves them through multiple generations 3. Factor selection: Top-performing factors are combined into a composite score 4. Portfolio construction: Selects top 10% of stocks within each industry based on scores, with weekly rebalancing[88][93][97][102] - **Model Evaluation**: - CSI 300: Annualized excess return of 17.91%, Sharpe ratio of 1.05[91] - CSI 500: Annualized excess return of 11.78%, Sharpe ratio of 0.85[95] - CSI 1000: Annualized excess return of 17.97%, Sharpe ratio of 0.93[98] - CSI All Share: Annualized excess return of 24.84%, Sharpe ratio of 1.33[103] --- Model Backtest Results 1. Convertible Bond Random Forest Strategy - Weekly excess return: 0.64%[16] 2. Multi-Dimensional Timing Model - Latest composite signal: Bullish (1)[19][24] 3. Industry Rotation Strategy 2.0 - Annualized excess return (non-exclusion version): 9.44% - Annualized excess return (double-exclusion version): 10.14%[71] 4. Genetic Programming Index Enhancement Models - CSI 300: - Annualized excess return: 17.91% - Sharpe ratio: 1.05[91] - CSI 500: - Annualized excess return: 11.78% - Sharpe ratio: 0.85[95] - CSI 1000: - Annualized excess return: 17.97% - Sharpe ratio: 0.93[98] - CSI All Share: - Annualized excess return: 24.84% - Sharpe ratio: 1.33[103] --- Quantitative Factors and Construction Methods 1. Factor Name: Industry Business Climate Index 2.0 - **Factor Construction Idea**: Tracks industry fundamentals by analyzing revenue, pricing, and cost dynamics[27] - **Factor Construction Process**: 1. Analyze industry revenue and cost structures 2. Calculate daily market-cap-weighted industry indices 3. Aggregate indices into a composite business climate index[27][30] - **Factor Evaluation**: Demonstrates predictive power for A-share earnings expansion cycles[28] 2. Factor Name: Barra CNE6 Style Factors - **Factor Construction Idea**: Evaluates market performance using 9 primary and 20 secondary style factors, including size, volatility, momentum, quality, value, and growth[45] - **Factor Construction Process**: 1. Calculate factor returns for each style factor 2. Aggregate factor performance to assess market trends[45][46] - **Factor Evaluation**: Size factor performed well during the week, while volatility factor underperformed[46] 3. Factor Name: Industry Rotation Factors - **Factor Construction Idea**: Captures industry rotation dynamics using factors like expected business climate, earnings surprises, momentum, and valuation bubbles[69] - **Factor Construction Process**: 1. Define and calculate individual factors 2. Test factor effectiveness within economic quadrants 3. Combine factors for industry allocation[69] - **Factor Evaluation**: Demonstrates strong historical performance, with factors like expected business climate and momentum showing significant returns[57][59] --- Factor Backtest Results 1. Industry Business Climate Index 2.0 - Current value: 0.913 - Excluding financials: 1.288[28] 2. Barra CNE6 Style Factors - Size factor: Strong performance during the week[46] 3. Industry Rotation Factors - Historical annualized returns: - Expected business climate: 0.40% - Momentum: -0.95% - Valuation beta: 2.37%[57]