Core Insights - The report emphasizes a dynamic balance strategy that has achieved an annualized absolute return of 18.85% and a relative return of 12.26% from January 2015 to January 30, 2026, with a maximum drawdown of 10.18% [3] - Recommended industries for February 2026 include non-ferrous metals, basic chemicals, electric equipment and new energy, communication, light manufacturing, and steel [3][25] - The macro-driven strategy has generated an annualized excess return of 4.77% since January 2016, with a maximum drawdown of 9.51% [4][45] - The multi-strategy approach has yielded an annualized relative return of 6.32% since May 2011, with a maximum drawdown of 13.24% [5][66] - The extreme style high beta strategy has achieved an annualized relative return of 9.93% since July 2013, but has underperformed in 2026 with a relative excess return of -4.02% [5][80] Industry Performance Summary - In January 2026, the A-share market saw the CSI 300 index rise by 1.65%, while the CSI 500 index increased by 12.12% [16] - The top-performing sectors in January were non-ferrous metals, media, oil and petrochemicals, building materials, and electronics [16] - The dynamic balance strategy outperformed its benchmark in January with an absolute return of 9.18% and an excess return of 4.05% [22][55] - The macro-driven strategy achieved an absolute return of 6.76% in January, with an excess return of 1.20% [4][48] - The multi-strategy approach recorded an absolute return of 4.65% in January, but underperformed its benchmark with an excess return of -0.42% [5][69] Recommended Industries - The dynamic balance strategy recommends non-ferrous metals, basic chemicals, electric equipment and new energy, communication, light manufacturing, and steel for February 2026 [3][25] - The macro-driven strategy suggests food and beverage, defense and military, pharmaceuticals, non-ferrous metals, communication, and basic chemicals for February 2026 [4][24] - The multi-strategy approach recommends real estate, construction, banking, communication, textiles and apparel, pharmaceuticals, basic chemicals, and non-ferrous metals for February 2026 [5][56] - The extreme style high beta strategy recommends transportation, electric utilities, basic chemicals, machinery, banking, and oil and petrochemicals for February 2026 [5][74]

Core Viewpoint - The overall market sentiment indicators are stable, with rapid fund switching observed, indicating a bullish sentiment in the market [4][5]. Group 1: Market Sentiment Indicators - The market sentiment structure indicators include various metrics such as industry trading volatility, trading congestion, price-volume consistency, and others, which collectively inform the sentiment direction [2][3]. - As of January 30, the market sentiment indicator value is 2.6, a slight increase from 2.35 the previous week, suggesting a stable sentiment with a bullish bias [4]. - The sentiment structure indicator has fluctuated around the zero axis within the range of [-6, 6] over the past five years, with significant volatility observed in 2023 [3]. Group 2: Sub-indicator Analysis - The industry trading volatility has shown a slight recovery, indicating increased frequency of fund switching between different sectors, while the industry trend indicator has rapidly declined, suggesting growing divergence in short-term industry outlooks [5][18]. - The price-volume consistency indicator remains high, reflecting a strong correlation between market attention and stock price movements, indicating active market sentiment [7]. - The financing balance ratio has slightly increased, indicating that leveraged funds are maintaining a high level of sentiment, with overall investor risk appetite remaining positive [19]. Group 3: Sector Performance and Trends - The short-term score for the food and beverage sector has risen significantly, while growth and small-cap styles are currently favored [26]. - The highest short-term scores are observed in the oil and petrochemical, construction materials, and non-ferrous metals sectors, indicating strong performance in these areas [26][27]. - The average congestion levels are highest in sectors like non-ferrous metals and oil and petrochemicals, while the lowest are in transportation and real estate, suggesting varying levels of market focus and potential risks [32][34].

Group 1 - The market sentiment indicator as of January 30 is at 2.6, a slight increase from 2.35 the previous week, indicating overall stability in sentiment with a bullish model perspective [2][9]. - The price-volume consistency indicator remains high, suggesting a strong correlation between market attention and stock price movements, reflecting an active market sentiment [13][16]. - The trading volume of the entire A-share market increased by 9.44% week-on-week, with an average daily trading volume of 30,632.46 billion yuan, indicating a slight recovery in market activity [19]. Group 2 - The short-term score rankings show that the oil and petrochemical, construction materials, non-ferrous metals, light industry manufacturing, and communication sectors are leading, with both oil and petrochemical and construction materials scoring 98.31, the highest among sectors [43][44]. - The industry crowding indicator shows a positive correlation with weekly price changes, with high crowding sectors like oil and petrochemical leading in gains, while low crowding sectors like commercial retail and environmental protection lag behind [46][50]. - The model indicates a preference for small-cap and growth styles, with the 5-day RSI showing a rapid decline relative to the 20-day RSI, suggesting potential weakening of signals in the near term [43][53].

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]

Core Viewpoint - The market sentiment is stable with high price-volume consistency, indicating a sideways trend in the market [1] Group 1: Market Sentiment Indicators - The market sentiment indicator value as of January 23 is 2.35, a slight increase from 2.25 the previous week, indicating a neutral sentiment [3] - Key indicators such as the proportion of transactions in the Sci-Tech 50 and inter-industry trading volatility have shown signs of recovery, suggesting a marginal improvement in market risk appetite [6][15][17] - The price-volume consistency indicator remains high, reflecting a strong correlation between market attention and stock price movements, indicating active market sentiment [9] - The financing balance ratio has shown a slight upward trend, indicating that leveraged funds are maintaining a high level of sentiment, suggesting overall market risk appetite remains positive [22] Group 2: Industry Trends and Performance - The scoring model indicates that non-ferrous metals, communication, and defense industries are leading in trend scores, with non-ferrous metals achieving a short-term score of 100.00, the highest among industries [30][31] - The average industry congestion level is highest in utilities, computers, media, banks, and oil and petrochemicals, while the lowest is in environmental protection, textiles, and light manufacturing [33] - The correlation between industry congestion and weekly price changes is negligible, indicating that high congestion sectors like oil and petrochemicals are experiencing significant price increases, while sectors with low congestion are lagging [35] Group 3: Technical Indicators - The RSI indicator has shown a decline, suggesting a decrease in short-term upward momentum and an increase in selling pressure, indicating a potential weakening of market sentiment [25][37] - The model indicates that small-cap and growth styles are currently favored, although there are signs of weakening in the short-term signals for these styles [38]

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]

Core Viewpoint - The article emphasizes a positive market sentiment with increasing trading volume and consistency in price and volume, indicating a potential upward trend in the market [1][4]. Group 1: Market Sentiment Indicators - The market sentiment structure indicators include various metrics such as industry trading volatility, trading congestion, price-volume consistency, and others, which collectively suggest a positive sentiment direction [2]. - As of January 16, the market sentiment index reached 2.25, a significant increase from 1.6 the previous week, indicating a recovery in sentiment [4]. - The price-volume consistency indicator has shown a rapid increase, reflecting a strong correlation between market attention and price movements, suggesting an active market sentiment [6][10]. Group 2: Trading Activity and Volume - The total trading volume for the A-share market increased by 21.25% week-on-week, with an average daily trading volume of 34,650.61 billion yuan, highlighting heightened market activity [10]. - On January 14, a historical trading volume peak was recorded at 39,868.62 billion yuan, indicating strong market engagement [10]. Group 3: Sector Performance and Risk Appetite - The trading volatility between industries is on a downward trend, indicating a slowdown in capital switching between sectors, which may reflect a cautious market environment [13]. - The industry trend indicators remain stable, suggesting a high level of consensus on short-term value judgments across sectors, with a dominant beta effect in the market [16]. - The financing balance ratio remains high, indicating that leveraged capital sentiment is still elevated, reflecting a relatively positive risk appetite among investors [19]. Group 4: Short-term and Long-term Trends - The short-term scoring model indicates that sectors such as computers, pharmaceuticals, and media are showing upward trends, with the non-ferrous metals sector having the highest short-term score of 98.31 [25]. - The article notes that the correlation between industry congestion and weekly price changes is positive, suggesting that sectors with high congestion, like computers and media, are likely to experience significant price movements [28].

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]

- The report discusses the "Liquidity Shock Indicator" for the CSI 300 Index, which measures market liquidity. The indicator was 0.60 on Friday, higher than the previous week's 0.34, indicating that current market liquidity is 0.60 standard deviations above the average of the past year [2][8] - The "PUT-CALL Ratio" for SSE 50ETF options is analyzed, showing a decline to 0.64 on Friday from 0.88 the previous week, reflecting increased short-term optimism among investors regarding the SSE 50ETF [2][8] - The "Turnover Rate" for the SSE Composite Index and Wind All A Index is highlighted, with 5-day average turnover rates of 1.41% and 2.24%, respectively, corresponding to the 79.01% and 87.08% percentiles since 2005, indicating increased trading activity [2][8] - The "Moving Average Strength Index" is introduced as a technical indicator, with the current market score at 261, placing it in the 95.22% percentile since 2023, suggesting strong market momentum [14][19] - The "Sentiment Timing Model" is discussed, which incorporates factors such as net limit-up ratio, next-day return after limit-down, and high-frequency board trading returns. The sentiment model score is 4 out of 5, with both the trend and weighted models showing positive signals [14][17] - The "Factor Crowding Index" is analyzed for various factors, including small-cap, low-valuation, high-profitability, and high-growth factors. The composite crowding scores are 0.37, -0.57, 0.63, and 1.09, respectively, with high-growth factors showing the highest crowding level [18][20][21] - The report evaluates "Industry Crowding Levels," identifying sectors such as communication, comprehensive, non-ferrous metals, defense, and electronics as having relatively high crowding levels. Defense and comprehensive sectors show the largest increases in crowding compared to the previous month [23][25][26]