量化择时和拥挤度预警周报（20260220） [Table_Authors] 郑雅斌(分析师) 中盘股或先开启上行趋势 本报告导读： 从技术面来看，高频资金流模型继续显示各大宽基指数信号依旧为负向，但偏向左 侧布局的情绪模型信号转正。结合春节后的日历效应，我们认为，以中证 500 指数 为首的中盘股或先开启上行趋势。 投资要点： | | | | | 021-23219395 | | --- | --- | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 曹君豪(分析师) | | | 021-23185657 | | | caojunhao@gtht.com | | 登记编号 | S0880525040094 | [Table_Report] 相关报告 请务必阅读正文之后的免责条款部分 金 融 工 程 周 报 高频选股因子周报（20260209-20260213） 2026.02.16 低频选股因子周报（2026.02.06-2026.02.13） 2026.02.14 绝对收益产品及策略周报（260202-260206） 2026.02.11 大 ...

Group 1 - The core viewpoint of the article indicates that the market is expected to continue its oscillation in the upcoming week, based on various technical indicators and market sentiment models [1][2]. - The liquidity shock indicator for the CSI 300 index was reported at 6.21, which is higher than the previous week's 5.07, suggesting that current market liquidity is significantly above the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF increased to 0.96 from 0.89, indicating a rising caution among investors regarding the short-term performance of the SSE 50 ETF [2]. Group 2 - The Shanghai Composite Index and Wind All A five-day average turnover rates were recorded at 1.34% and 1.97%, respectively, indicating a decrease in trading activity, positioned at the 77.24% and 82.76% percentiles since 2005 [2]. - The official manufacturing PMI for China in January was reported at 49.3, lower than the previous value of 50.1 and below the consensus expectation of 50.18, while the S&P Global China Manufacturing PMI was at 50.3, slightly above the previous value [2]. - The SAR indicator showed that the Wind All A index broke below the reversal indicator on February 2, indicating a potential downward trend [2]. Group 3 - The A-share market experienced fluctuations last week, with the SSE 50 index down by 0.93%, the CSI 300 index down by 1.33%, the CSI 500 index down by 2.68%, and the ChiNext index down by 3.28% [3]. - The current overall market PE (TTM) stands at 23.0 times, which is at the 81.0% percentile since 2005, indicating a relatively high valuation level [3]. - Observations on factor crowding indicate a decrease in high profitability factor crowding, with small-cap factor crowding at 0.06 and low valuation factor crowding at -0.31 [3].

Quantitative Models and Construction Methods 1. Model Name: Sentiment Model - **Model Construction Idea**: The sentiment model is designed to measure the strength of market sentiment using factors related to limit-up and limit-down stocks[14] - **Model Construction Process**: The model uses factors such as the proportion of net limit-up stocks, next-day returns of limit-down stocks, proportion of limit-up stocks, proportion of limit-down stocks, and high-frequency board-hitting returns. These factors are aggregated to calculate a sentiment score, with a maximum score of 5. The sentiment score for the current period is 0[14][18] - **Model Evaluation**: The sentiment model indicates that the market sentiment remains low, reflecting weak investor confidence[14][18] 2. Model Name: Moving Average Strength Index - **Model Construction Idea**: This model evaluates the strength of market trends by calculating the moving average strength index based on secondary industry indices[14] - **Model Construction Process**: The moving average strength index is calculated using the performance of secondary industry indices. The current market score is 181, which corresponds to the 62.50th percentile since 2023[14] - **Model Evaluation**: The index suggests that there is still significant room for downward movement in the market[14] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow data to generate buy and sell signals for major broad-based indices[14] - **Model Construction Process**: The model tracks the capital flow trends of indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000. The signals for all indices are currently negative, indicating a bearish outlook[14][18] - **Model Evaluation**: The model shows that all major broad-based indices have turned negative, reflecting weak market conditions[14][18] --- Model Backtesting Results 1. Sentiment Model - Sentiment score: 0 (out of 5)[14][18] 2. Moving Average Strength Index - Current score: 181 (62.50th percentile since 2023)[14] 3. High-Frequency Capital Flow Model - CSI 300: Negative signal - CSI 500: Negative signal - CSI 1000: Negative signal - CSI 2000: Negative signal[14][18] --- Quantitative Factors and Construction Methods 1. Factor Name: Factor Crowding Index - **Factor Construction Idea**: The factor crowding index measures the degree of crowding in specific factors, which can serve as a warning for factor inefficiency[19] - **Factor Construction Process**: The index is calculated using four metrics: valuation spread, pairwise correlation, long-term return reversal, and factor volatility. The composite score is derived from these metrics. For example, the crowding scores for small-cap, low-valuation, high-profitability, and high-growth factors are 0.06, -0.31, -0.01, and 0.28, respectively[19][20] - **Factor Evaluation**: The crowding index provides insights into the potential inefficiency of factors due to excessive capital allocation[19] --- Factor Backtesting Results 1. Factor Crowding Index - Small-cap factor crowding score: 0.06 - Low-valuation factor crowding score: -0.31 - High-profitability factor crowding score: -0.01 - High-growth factor crowding score: 0.28[19][20]

Quantitative Models and Construction Methods 1. Model Name: Sentiment Model - **Model Construction Idea**: The sentiment model is designed to measure the strength of market sentiment using factors related to limit-up and limit-down stocks[14] - **Model Construction Process**: The model incorporates factors such as the proportion of net limit-up stocks, next-day returns of limit-down stocks, proportion of limit-up stocks, proportion of limit-down stocks, and high-frequency board-hitting returns. These factors are aggregated to generate a sentiment score, with a maximum score of 5. The sentiment score for the current period is 0[14][18] - **Model Evaluation**: The sentiment model indicates weak market sentiment, as reflected by the score of 0[14][18] 2. Model Name: Moving Average Strength Index - **Model Construction Idea**: This model evaluates the strength of market trends by calculating the moving average strength index based on secondary industry indices[14] - **Model Construction Process**: The moving average strength index is calculated using the performance of secondary industry indices. The current market score is 181, which corresponds to the 62.50th percentile since 2023[14] - **Model Evaluation**: The model suggests that the market still has significant downside potential[14] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow trends to generate buy and sell signals for major broad-based indices[14] - **Model Construction Process**: The model tracks high-frequency capital flows and generates signals for indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000. The signals for all indices are currently negative, indicating a bearish outlook[14][18] - **Model Evaluation**: The model shows a bearish signal across all major indices, reflecting weak market conditions[14][18] --- Model Backtesting Results 1. Sentiment Model - Sentiment score: 0 (out of 5)[14][18] 2. Moving Average Strength Index - Current score: 181 (62.50th percentile since 2023)[14] 3. High-Frequency Capital Flow Model - CSI 300: Negative signal - CSI 500: Negative signal - CSI 1000: Negative signal - CSI 2000: Negative signal[14][18] --- Quantitative Factors and Construction Methods 1. Factor Name: Factor Crowding Indicator - **Factor Construction Idea**: The factor crowding indicator measures the degree of crowding in specific factors, which can serve as a warning for factor underperformance[19] - **Factor Construction Process**: The indicator is calculated using four metrics: valuation spread, pairwise correlation, long-term return reversal, and factor volatility. These metrics are aggregated to produce a composite crowding score for each factor. For example: - Small-cap factor crowding score: 0.06 - Low-valuation factor crowding score: -0.31 - High-profitability factor crowding score: -0.01 - High-growth factor crowding score: 0.28[19][20] - **Factor Evaluation**: The crowding scores indicate varying levels of crowding across factors, with low-valuation and high-profitability factors showing negative scores, suggesting potential underperformance[19][20] --- Factor Backtesting Results 1. Factor Crowding Indicator - Small-cap factor crowding score: 0.06 - Low-valuation factor crowding score: -0.31 - High-profitability factor crowding score: -0.01 - High-growth factor crowding score: 0.28[19][20]

Core Viewpoint - The market may present certain structural opportunities in the upcoming week, despite a generally pessimistic market sentiment indicated by technical models [1][2]. Market Overview - Last week (January 26-30, 2026), the Shanghai Composite Index rose by 1.13%, while the CSI 300 Index increased by 0.08%. Conversely, the CSI 500 Index fell by 2.56%, and the ChiNext Index decreased by 0.09% [3]. - The current overall market PE (TTM) stands at 23.3 times, which is at the 82.0% percentile since 2005 [3]. Quantitative Indicators - The liquidity shock indicator for the CSI 300 Index was 5.07 on Friday, slightly lower than the previous week (5.09), indicating current market liquidity is 5.07 times the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 0.89 from 0.98, suggesting a decline in investor caution regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the Shanghai Composite Index and Wind All A were 1.75% and 2.49%, respectively, indicating increased trading activity [2]. Macro Factors - The RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.07% and 0.27%, respectively [2]. - The official manufacturing PMI for China in January was reported at 49.3, lower than the previous value (50.1) and below the consensus expectation (50.18) [2]. Seasonal Trends - Historical data since 2005 shows that major indices have a high probability of rising in the first half of February, with average gains of 2.85% for the Shanghai Composite, 3.61% for the CSI 300, 5.34% for the CSI 500, and 4.65% for the ChiNext [2]. Technical Analysis - The SAR indicator for the Wind All A Index broke downwards on January 20 but rebounded upwards on January 23 [2]. - The market score based on moving average strength is currently at 172, placing it at the 59.50% percentile for 2023 [2]. - The sentiment model score is at 0 (out of 5), indicating a negative trend signal [2]. Industry Crowding - The industry crowding levels are relatively high in telecommunications, non-ferrous metals, comprehensive sectors, electronics, and basic chemicals, with notable increases in electronics and defense industries [4].

Quantitative Models and Construction Methods 1. Model Name: Sentiment Model - **Model Construction Idea**: The sentiment model is designed to capture the market's emotional state by analyzing various market indicators[2][14] - **Model Construction Process**: - The sentiment model score is calculated based on the proportion of stocks hitting their daily limit up or down, and the subsequent returns of stocks that hit their limit down the previous day[14] - The sentiment model score is 0 out of 5, indicating a negative sentiment[14] - **Model Evaluation**: The sentiment model indicates a pessimistic market sentiment[2][14] 2. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow data to generate buy and sell signals for major indices[2][14] - **Model Construction Process**: - The model tracks the capital flow trends for major indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000[14] - Signals are generated based on the direction of capital flows: positive for buy signals and negative for sell signals[14] - For the past week, the signals were positive for CSI 300 and CSI 500, and negative for CSI 1000 and CSI 2000[2][14] - **Model Evaluation**: The model indicates that CSI 300 and CSI 500 are in a buying cycle, while CSI 1000 and CSI 2000 are in a selling cycle[2][14] Model Backtesting Results Sentiment Model - **Sentiment Model Score**: 0 out of 5[14] High-Frequency Capital Flow Model - **CSI 300**: Positive signal[14] - **CSI 500**: Positive signal[14] - **CSI 1000**: Negative signal[14] - **CSI 2000**: Negative signal[14] Quantitative Factors and Construction Methods 1. Factor Name: Small Market Cap Factor - **Factor Construction Idea**: Measures the degree of crowding in small-cap stocks[18] - **Factor Construction Process**: - The factor's crowding degree is calculated using valuation spreads, pairwise correlations, long-term return reversals, and factor volatility[18] - The composite score for the small market cap factor is 0.05[19] - **Factor Evaluation**: The crowding degree of the small market cap factor has decreased[18] 2. Factor Name: Low Valuation Factor - **Factor Construction Idea**: Measures the degree of crowding in low-valuation stocks[18] - **Factor Construction Process**: - The factor's crowding degree is calculated using the same metrics as the small market cap factor[18] - The composite score for the low valuation factor is -0.28[19] - **Factor Evaluation**: The crowding degree of the low valuation factor is relatively low[18] 3. Factor Name: High Profitability Factor - **Factor Construction Idea**: Measures the degree of crowding in high-profitability stocks[18] - **Factor Construction Process**: - The factor's crowding degree is calculated using the same metrics as the small market cap factor[18] - The composite score for the high profitability factor is 0.20[19] - **Factor Evaluation**: The crowding degree of the high profitability factor is moderate[18] 4. Factor Name: High Growth Factor - **Factor Construction Idea**: Measures the degree of crowding in high-growth stocks[18] - **Factor Construction Process**: - The factor's crowding degree is calculated using the same metrics as the small market cap factor[18] - The composite score for the high growth factor is 0.53[19] - **Factor Evaluation**: The crowding degree of the high growth factor is relatively high[18] Factor Backtesting Results Small Market Cap Factor - **Crowding Degree**: 0.05[19] Low Valuation Factor - **Crowding Degree**: -0.28[19] High Profitability Factor - **Crowding Degree**: 0.20[19] High Growth Factor - **Crowding Degree**: 0.53[19]

Quantitative Models and Construction - **Model Name**: SAR Indicator **Construction Idea**: The SAR indicator is used to identify market trends and reversals based on price movements[14][15] **Construction Process**: The SAR indicator is calculated using the following formula: $ SAR_{t+1} = SAR_t + AF \times (EP - SAR_t) $ - **SAR_t**: Current SAR value - **AF**: Acceleration factor, which increases as the trend continues - **EP**: Extreme point, the highest high or lowest low during the trend The SAR flips direction when the price crosses the current SAR value, signaling a potential trend reversal[14][15] **Evaluation**: The SAR indicator effectively captures market reversals and reflects strong market dynamics[14][15] - **Model Name**: Sentiment Model **Construction Idea**: The sentiment model evaluates market sentiment using factors such as limit-up and limit-down board data[14][17] **Construction Process**: - Factors include net limit-up ratio, next-day return after limit-down, limit-up ratio, limit-down ratio, and high-frequency board trading returns - Each factor is scored, and the sentiment model aggregates these scores to produce a final sentiment score ranging from 0 to 5[14][17] **Evaluation**: The sentiment model provides a stable measure of market sentiment, indicating a positive trend[14][17] - **Model Name**: High-Frequency Capital Flow Model **Construction Idea**: This model uses high-frequency capital flow data to generate buy/sell signals for major indices[14][17] **Construction Process**: - Signals are generated for indices such as CSI 300, CSI 500, and CSI 1000 based on capital flow trends - The model evaluates aggressive and conservative long/short positions for each index[14][17] **Evaluation**: The model demonstrates strong predictive capabilities for index movements, supporting buy signals across major indices[14][17] Model Backtesting Results - **SAR Indicator**: No specific numerical backtesting results provided[14][15] - **Sentiment Model**: Sentiment score = 2 (out of 5), indicating stable market sentiment[14][17] - **High-Frequency Capital Flow Model**: - CSI 300: Aggressive long = 1, Aggressive short = 1, Conservative long = 1, Conservative short = 1 - CSI 500: Aggressive long = 1, Aggressive short = 1, Conservative long = 1, Conservative short = 1 - CSI 1000: Aggressive long = 1, Aggressive short = 1, Conservative long = 1, Conservative short = 1[14][17] Quantitative Factors and Construction - **Factor Name**: Small Market Cap Factor **Construction Idea**: Measures the performance of small-cap stocks and their market dynamics[18][19] **Construction Process**: - Metrics include valuation spread, pairwise correlation, market volatility, and return reversal - Composite score = 0.28, calculated using these metrics[18][19] **Evaluation**: The factor shows moderate crowding, indicating stable performance[18][19] - **Factor Name**: Low Valuation Factor **Construction Idea**: Tracks stocks with low valuation metrics to identify undervalued opportunities[18][19] **Construction Process**: - Metrics include valuation spread (-1.39), pairwise correlation (0.24), market volatility (1.39), and return reversal (-1.90) - Composite score = -0.42, reflecting moderate crowding[18][19] **Evaluation**: The factor exhibits negative crowding, suggesting potential risks in its effectiveness[18][19] - **Factor Name**: High Profitability Factor **Construction Idea**: Focuses on stocks with strong profitability metrics[18][19] **Construction Process**: - Metrics include valuation spread (-0.61), pairwise correlation (0.15), market volatility (0.15), and return reversal (1.57) - Composite score = 0.31, indicating moderate crowding[18][19] **Evaluation**: The factor demonstrates stable performance with moderate crowding[18][19] - **Factor Name**: High Growth Factor **Construction Idea**: Identifies stocks with high growth potential based on financial metrics[18][19] **Construction Process**: - Metrics include valuation spread (1.12), pairwise correlation (-0.49), market volatility (-0.21), and return reversal (0.97) - Composite score = 0.35, reflecting moderate crowding[18][19] **Evaluation**: The factor shows positive crowding, indicating strong market interest[18][19] Factor Backtesting Results - **Small Market Cap Factor**: Composite score = 0.28[18][19] - **Low Valuation Factor**: Composite score = -0.42[18][19] - **High Profitability Factor**: Composite score = 0.31[18][19] - **High Growth Factor**: Composite score = 0.35[18][19]

- The liquidity shock indicator for the CSI 300 Index was 5.09 on Friday, indicating that the current market liquidity is 5.09 standard deviations higher than the average level over the past year [4][8] - The PUT-CALL ratio of the SSE 50ETF options trading volume increased to 0.98 on Friday, suggesting a rise in investor caution regarding the short-term trend of the SSE 50ETF [4][8] - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.50% and 2.21%, respectively, indicating a decrease in trading activity [4][8] - The SAR technical indicator showed a reversal within the week, indicating strong market contention between bulls and bears [4][7][14] - The sentiment model score was 2 out of 5, with both the trend model and weighted model signals being positive [4][14] - The high-frequency capital flow model indicated a buy signal for major broad-based indices, including the CSI 300, CSI 500, and CSI 1000 [4][14] - The congestion levels for small-cap, low-valuation, high-profitability, and high-growth factors were 0.28, -0.42, 0.31, and 0.35, respectively [4][18][19][21] - The congestion levels for the non-ferrous metals, comprehensive, communication, electronics, and defense industries were relatively high, with the defense and electronics industries showing significant increases [4][25][27][28]

Group 1 - The core viewpoint of the article indicates that the growth-oriented selected portfolio is expected to achieve a cumulative return of 84.1% by 2025, outperforming the 885001 index by 50.9% [1] - The article suggests that the ICIR weighted return method is superior to the IC mean weighted method from the perspective of index enhancement [1] Group 2 - The market outlook for the upcoming week (January 19-23, 2026) anticipates a potential upward trend, supported by a liquidity shock indicator of 3.32, which is significantly higher than the previous week's 0.60, indicating current market liquidity is 3.32 times the average level over the past year [2] - The PUT-CALL ratio for the Shanghai Stock Exchange 50 ETF has increased to 0.80 from 0.64, reflecting a growing caution among investors regarding the short-term performance of the index [2] - The five-day average turnover rates for the Shanghai Composite Index and Wind All A Index are 1.71% and 2.71%, respectively, indicating increased trading activity, positioned at the 84.10% and 92.01% percentiles since 2005 [2] - The article notes that the RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.19% and 0.12%, respectively [2] - New RMB loans in December reached 910 billion, exceeding the Wind consensus estimate of 679.4 billion and the previous value of 390 billion [2] - The broad money supply (M2) grew by 8.5% year-on-year, surpassing the Wind consensus estimate of 7.93% and the previous value of 8% [2] - Technical analysis indicates that the Wind All A Index broke above the reversal indicator on December 1, and the market score based on the moving average strength index is currently at 213, positioned at the 76.93% percentile for 2023 [2] - The sentiment model score is 2 out of 5, with a positive trend model signal and a negative weighted model signal [2] - The quantitative team has issued buy signals for the CSI 300, CSI 500, and CSI 1000 indices based on high-frequency capital flow analysis [2] Group 3 - The market review for the previous week (January 12-16, 2026) shows that the Shanghai 50 Index fell by 1.74%, while the CSI 300 Index decreased by 0.57%. In contrast, the CSI 500 Index rose by 2.18%, and the ChiNext Index increased by 1% [3] - The current overall market PE (TTM) stands at 23.3 times, which is at the 82.0% percentile since 2005 [3] Group 4 - The factor crowding degree remains stable, with small-cap factor crowding at 0.20, low valuation factor crowding at -0.75, high profitability factor crowding at 0.35, and high profitability growth factor crowding at 0.55 [4] Group 5 - The industry crowding degree is relatively high in telecommunications, non-ferrous metals, comprehensive sectors, electronics, and national defense industries, with significant increases noted in the crowding degree of national defense and electronics [5]

Quantitative Models and Construction 1. Model Name: Liquidity Shock Indicator - **Model Construction Idea**: The model measures market liquidity by assessing deviations from the average liquidity level over the past year[4][8] - **Model Construction Process**: The liquidity shock indicator is calculated based on the standard deviation of the current market liquidity relative to the average liquidity over the past year. For the CSI 300 Index, the indicator value on Friday was 3.32, which is 3.32 standard deviations above the average liquidity level of the past year[4][8] - **Model Evaluation**: Indicates that the current market liquidity is significantly higher than the historical average, suggesting a favorable environment for trading[4][8] 2. Model Name: Sentiment Model - **Model Construction Idea**: The model evaluates market sentiment using factors such as limit-up and limit-down board data to assess the strength of market sentiment[4][14] - **Model Construction Process**: The sentiment model score is derived from various sub-factors, including: - Net limit-up ratio - Next-day return after limit-down events - Proportion of limit-up boards - Proportion of limit-down boards - High-frequency board-hitting returns The overall sentiment score is 2 out of 5, indicating a moderate sentiment level[4][14][19] - **Model Evaluation**: The model reflects a weakening in market sentiment but still indicates a positive trend[4][14] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow data to generate buy/sell signals for major broad-based indices[4][14] - **Model Construction Process**: The model tracks the capital flow trends for indices such as CSI 300, CSI 500, and CSI 1000. Based on the data, the model generates signals for aggressive long, aggressive short, conservative long, and conservative short positions. For all three indices, the signals are consistently positive, indicating a "buy" recommendation[4][14][19] - **Model Evaluation**: The model suggests that the major indices are in a "buy" cycle, supporting a positive market outlook[4][14] --- Model Backtesting Results 1. Liquidity Shock Indicator - CSI 300 Index: Indicator value = 3.32 (3.32 standard deviations above the historical average)[4][8] 2. Sentiment Model - Overall sentiment score: 2/5 - Sub-factor signals: - Net limit-up ratio: 1 - Next-day return after limit-down events: 0 - Proportion of limit-up boards: 1 - Proportion of limit-down boards: 0 - High-frequency board-hitting returns: 0[4][14][19] 3. High-Frequency Capital Flow Model - CSI 300 Index: All signals (aggressive long, aggressive short, conservative long, conservative short) = 1 - CSI 500 Index: All signals = 1 - CSI 1000 Index: All signals = 1[4][14][19] --- Quantitative Factors and Construction 1. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Measures the performance of small-cap stocks relative to the market[20][21] - **Factor Construction Process**: The factor's crowding level is calculated using four metrics: - Valuation spread - Pairwise correlation - Market volatility - Return reversal The composite score for the small-cap factor is 0.20[20][21] - **Factor Evaluation**: The factor's crowding level is stable, indicating no significant risk of factor failure[20][21] 2. Factor Name: Low-Valuation Factor - **Factor Construction Idea**: Tracks the performance of low-valuation stocks[20][21] - **Factor Construction Process**: Similar to the small-cap factor, the crowding level is calculated using the same four metrics. The composite score for the low-valuation factor is -0.75[20][21] - **Factor Evaluation**: The negative score suggests a potential risk of underperformance due to crowding[20][21] 3. Factor Name: High-Profitability Factor - **Factor Construction Idea**: Focuses on stocks with high profitability metrics[20][21] - **Factor Construction Process**: The factor's crowding level is calculated using the same four metrics. The composite score for the high-profitability factor is 0.35[20][21] - **Factor Evaluation**: Indicates moderate crowding but still within acceptable levels[20][21] 4. Factor Name: High-Growth Factor - **Factor Construction Idea**: Targets stocks with high growth potential[20][21] - **Factor Construction Process**: The factor's crowding level is calculated using the same four metrics. The composite score for the high-growth factor is 0.55[20][21] - **Factor Evaluation**: Suggests a favorable environment for high-growth stocks[20][21] --- Factor Backtesting Results 1. Small-Cap Factor - Valuation spread: 0.43 - Pairwise correlation: 0.22 - Market volatility: -0.28 - Return reversal: 0.41 - Composite score: 0.20[20][21] 2. Low-Valuation Factor - Valuation spread: -1.22 - Pairwise correlation: -0.05 - Market volatility: 0.26 - Return reversal: -2.01 - Composite score: -0.75[20][21] 3. High-Profitability Factor - Valuation spread: -0.55 - Pairwise correlation: 0.31 - Market volatility: -0.01 - Return reversal: 1.65 - Composite score: 0.35[20][21] 4. High-Growth Factor - Valuation spread: 1.09 - Pairwise correlation: 0.46 - Market volatility: -0.29 - Return reversal: 0.95 - Composite score: 0.55[20][21]