Market Sentiment - The market sentiment indicator as of March 27 is 1.2, down from 1.7 the previous week, indicating a bearish outlook as sentiment continues to decline throughout the week [1][6]. - The price-volume consistency indicator and RSI have both turned negative this week, reflecting a shift from previous oscillation to a sustained bearish view, indicating a weakening market [1][8]. Trading Volume - The total trading volume for the A-share market decreased by 0.65% week-on-week, with an average daily trading volume of 1,394.17 billion yuan, suggesting a slight decline in market activity compared to the previous week [1][10]. Industry Performance - As of March 27, the short-term score rankings for industries show that utilities, coal, power equipment, telecommunications, and oil and petrochemicals are leading, with utilities scoring 91.53, the highest among industries, and coal scoring 84.75, second [1][34]. - The industry crowding indicator shows a low correlation of 0.17 with the weekly price changes, indicating that the crowding level is not significantly impacting price movements [1][37]. Risk Appetite - The relative trading volume of the Sci-Tech 50 index remains low, indicating that market risk appetite is also low, with a slight fluctuation observed [1][13]. - The financing balance ratio has slightly increased this week, suggesting a minor rise in market sentiment and trading activity in the financing market [1][21]. Technical Indicators - The RSI indicator has penetrated the lower boundary and continues to decline rapidly, indicating a weakening short-term momentum [1][25]. - The main buying power indicator has shown a downward trend, reflecting reduced willingness from institutional investors to actively allocate capital in the market [1][28].

Market Overview - The market is currently in a state of stock game with significant funding divergence, as indicated by the liquidity shock index for the CSI 300, which was -0.49 last Friday, lower than the previous week at 0.49, suggesting current market liquidity is above the average level of the past year by -0.49 standard deviations [1] - The PUT-CALL ratio for the SSE 50 ETF options has been rising, reaching 0.83 last Friday, up from 0.67 the previous week, indicating increased caution among investors regarding the short-term performance of the SSE 50 ETF [1] - The average turnover rates for the SSE Composite Index and Wind All A-shares were 1.38% and 1.94%, respectively, indicating a decrease in trading activity, positioned at the 78.04% and 81.46% percentile since 2005 [1] Macro Factors - The RMB exchange rate fluctuated last week, with onshore and offshore rates showing weekly declines of -0.42% and -0.2%, respectively [1] - The U.S. stock market experienced a downward trend, with the Dow Jones Industrial Average, S&P 500, and Nasdaq indices reporting weekly returns of -0.9%, -2.12%, and -3.23% [1] - The National Bureau of Statistics reported that profits of large-scale industrial enterprises in China reached 1.02 trillion yuan in January-February 2026, a year-on-year increase of 15.2%, accelerating by 14.6 percentage points compared to the previous year [1] Market Sentiment - The A-share market experienced some fluctuations and divergence last week, with heightened risk aversion due to ongoing geopolitical tensions, which have suppressed short-term risk appetite [1] - Technical analysis indicates multiple intraday reversals in the A-share market, suggesting significant funding divergence and a stock game state, leading to a low probability of upward trends in the short term [1] Factor Analysis - The overall market PE (TTM) stands at 22.5 times, positioned at the 77.5% percentile since 2005 [2] - The small-cap factor's congestion level has decreased to -0.11, while the low valuation factor's congestion level is at -0.54, indicating a shift in market dynamics [2] - Industry congestion levels are relatively high in sectors such as comprehensive, communication, non-ferrous metals, basic chemicals, and oil & petrochemicals, with the latter two sectors showing a significant increase in congestion [2]

Investment Rating - The report indicates a net outflow of 26.229 billion yuan from industry-themed ETFs this week, with significant fluctuations observed in the steel and basic chemical sectors [2][31][36]. Core Insights - As of March 20, 2026, there are a total of 1,456 listed ETFs in mainland China, with a total scale of 5.10 trillion yuan. Among these, stock ETFs account for the largest share, both in number (1,140) and scale (2.95 trillion yuan) [7][8]. - The A-share market saw a decline this week, with the Shanghai Composite Index closing at 3,957.05, reflecting a drop of 3.38%. Notably, the communication and banking sectors experienced gains of 2.10% and 0.36%, respectively, while the non-ferrous and basic chemical sectors faced declines of 11.82% and 10.53% [13][14][21]. - In terms of fund flows, broad-based ETFs saw a net inflow of 9.078 billion yuan, with the top three inflows coming from the CSI 500 ETF Southern (+4.450 billion yuan), the CSI 300 ETF Huatai-PB (+4.333 billion yuan), and the SSE 50 ETF (+3.056 billion yuan). Conversely, industry-themed ETFs experienced a net outflow of 26.229 billion yuan, with the top outflows from the chemical ETF (-4.373 billion yuan) and the non-ferrous metal ETFs [31][32][36]. Summary by Sections ETF Market Overview - As of March 20, 2026, the total number of ETFs is 1,456, with stock ETFs being the most prevalent, comprising 78.30% of the total number and 57.76% of the total scale [7][8][10]. Domestic and International Equity Market Index Performance - The A-share market indices showed a downward trend, with the Shanghai Composite Index down 3.38%. The communication and banking sectors were the only ones to show positive performance this week [13][14][21]. Stock ETF Fund Flows - Broad-based ETFs had a net inflow of 9.078 billion yuan, while industry-themed ETFs saw a significant net outflow of 26.229 billion yuan, indicating a shift in investor sentiment [31][32][36]. Industry Congestion Monitoring - The report highlights that the utility and communication sectors are currently experiencing higher congestion levels, while the automotive and textile sectors are less congested, suggesting potential investment opportunities [34][36].

Market Sentiment Overview - As of March 20, the market sentiment indicator is at 1.7, up from 1.55 the previous week, indicating a neutral sentiment despite fluctuations throughout the week [1][4] - Multiple sub-indicators have shown a decline compared to the previous week, influenced by ongoing external political risks, suggesting a potential further drop in market sentiment [1][4] Sub-indicator Analysis - The price-volume consistency indicator has rapidly declined, reflecting a weaker correlation between price increases and market attention, indicating an overall bearish sentiment [7][9] - Total trading volume for the A-share market decreased by 12.49% week-on-week, with an average daily trading volume of 14,098.98 billion, further indicating reduced market activity [11] - The proportion of the STAR 50 index relative to the total A-share trading volume has consistently decreased, suggesting a decline in risk appetite [15] - The inter-industry trading volatility has been on the rise, reaching historical highs for 2023, indicating increased activity in switching funds between different sectors [16] - The industry trend indicator initially rose but later showed a downward trend, indicating a reduction in divergence among industry views and a slight increase in consensus on short-term value judgments [18] - The financing balance ratio has slightly decreased, indicating a reduction in market leverage and a decline in investor risk appetite [19] - The RSI indicator has penetrated the lower boundary, suggesting increased downward momentum and reduced buying power, reflecting an overall decline in market sentiment [20] - The net inflow of main funds has shown a downward trend, indicating weakened buying power and reduced enthusiasm from institutional investors [24] Industry Crowding and Trading Heat - The highest average crowding levels as of March 20 are in the utilities, basic chemicals, electrical equipment, construction decoration, and environmental protection sectors, while the lowest are in automotive, defense, social services, retail, and textiles [30][31] - The correlation between crowding and weekly price changes is near zero, indicating that high crowding does not necessarily lead to price increases, with sectors like construction decoration and environmental protection showing low price changes despite high crowding [32] Trend Scoring Model Insights - The short-term scoring model indicates that sectors such as coal, utilities, electrical equipment, communication, and construction decoration are leading in trend scores, with coal having the highest score of 93.22 [25][28] - The model suggests a preference for growth and large-cap styles, with the current signals indicating a strong preference for large-cap stocks [35]

Core Viewpoint - The A-share market is expected to remain in a state of fluctuation in the short term, as indicated by various technical and quantitative indicators [1][2]. Market Overview - During the week of March 16-20, 2026, the Shanghai Composite Index fell by 2.47%, the CSI 300 Index decreased by 2.19%, the CSI 500 Index dropped by 5.82%, while the ChiNext Index rose by 1.26% [3]. - The current overall market PE (TTM) stands at 22.6 times, which is at the 78.3% percentile since 2005 [3]. - Historical data shows that the CSI 500 Index has performed well in the latter half of March since 2005 [3]. Factor Crowding Observation - The small-cap factor crowding has increased, with a current value of 0.09. The low valuation factor crowding is at -0.31, while the high profitability factor crowding is at 0.24, and the high profitability growth factor crowding is at 0.25 [3]. Industry Crowding - Industries such as comprehensive, communication, non-ferrous metals, steel, and electronics exhibit relatively high crowding levels. The oil and petrochemical, as well as agriculture, forestry, animal husbandry, and fishery industries have seen a significant increase in crowding [4].

Group 1 - Market sentiment indicators show a weakening trend, with the sentiment index at 1.7 as of March 20, up from 1.55 the previous week, indicating a neutral model perspective [3][9] - The price-volume consistency indicator has rapidly declined, reflecting a decrease in the correlation between price increases and market attention, suggesting an overall bearish sentiment [13][16] - The total trading volume for the A-share market decreased by 12.49% week-on-week, with an average daily trading volume of 1,409.90 billion yuan, indicating reduced market activity [19][22] Group 2 - The short-term scoring model ranks coal, public utilities, electric equipment, communication, and construction decoration as the top industries, with coal scoring 93.22, the highest among all [45][46] - The model indicates that the market is currently favoring large-cap stocks, with a strong signal suggesting that large-cap style is dominant, while growth and value styles show divergence [56] - The industry crowding indicator shows a correlation coefficient of -0.02 with weekly returns, indicating that most industries performed poorly, with only banking and communication yielding positive returns [48][50]

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 events[4][13] - **Model Construction Process**: The model uses factors such as net limit-up ratio, next-day return after limit-down, limit-up ratio, limit-down ratio, and high-frequency board-hitting returns to quantify sentiment. Each factor is scored, and the overall sentiment score is calculated on a scale of 0 to 5[13][17] - **Model Evaluation**: The sentiment model score is currently 0, indicating weak market sentiment[13][17] 2. Model Name: Trend Model - **Model Construction Idea**: The trend model identifies market trends by analyzing technical indicators and price movements[4][13] - **Model Construction Process**: The model uses the SAR (Stop and Reverse) indicator to detect trend reversals. For example, the Wind All-A Index broke below the SAR indicator on March 3, signaling a negative trend[13][14] - **Model Evaluation**: The trend model currently emits a negative signal, indicating a bearish market trend[13] 3. Model Name: Weighted Model - **Model Construction Idea**: The weighted model combines multiple signals, including sentiment and trend, to provide a comprehensive market outlook[4][13] - **Model Construction Process**: The weighted model aggregates signals from the sentiment and trend models, assigning weights to each component. The current weighted model signal is negative, reflecting overall bearishness in the market[13] - **Model Evaluation**: The model effectively integrates multiple signals but currently indicates a negative market outlook[13] 4. 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][13] - **Model Construction Process**: The model tracks capital flow trends for indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000. Signals are categorized into aggressive long, aggressive short, conservative long, and conservative short. For the week, all indices (CSI 300, CSI 500, CSI 1000, CSI 2000) showed negative signals (-1 for aggressive short and conservative short)[13][17] - **Model Evaluation**: The model provides timely signals for broad-based indices but currently reflects a bearish sentiment across all indices[13][17] --- Model Backtesting Results 1. Sentiment Model - Sentiment score: 0 (out of 5)[13][17] 2. Trend Model - SAR indicator: Negative signal for Wind All-A Index[13][14] 3. Weighted Model - Weighted signal: Negative[13] 4. High-Frequency Capital Flow Model - CSI 300: Aggressive short (-1), Conservative short (-1)[13][17] - CSI 500: Aggressive short (-1), Conservative short (-1)[13][17] - CSI 1000: Aggressive short (-1), Conservative short (-1)[13][17] - CSI 2000: Aggressive short (-1), Conservative short (-1)[13][17] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Measures the performance of small-cap stocks and their crowding level[18][19] - **Factor Construction Process**: The factor's crowding level is calculated using metrics such as valuation spread, pairwise correlation, market volatility, and return reversal. The composite crowding score is 0.09[18][19] - **Factor Evaluation**: The small-cap factor shows a slight increase in crowding, indicating moderate interest from investors[18][19] 2. Factor Name: Low-Valuation Factor - **Factor Construction Idea**: Tracks the performance of low-valuation stocks and their crowding level[18][19] - **Factor Construction Process**: The factor's crowding level is calculated using the same metrics as the small-cap factor. The composite crowding score is -0.31[18][19] - **Factor Evaluation**: The low-valuation factor shows a decrease in crowding, suggesting reduced investor interest[18][19] 3. Factor Name: High-Profitability Factor - **Factor Construction Idea**: Measures the performance of high-profitability stocks and their crowding level[18][19] - **Factor Construction Process**: The factor's crowding level is calculated using the same metrics as the small-cap factor. The composite crowding score is 0.24[18][19] - **Factor Evaluation**: The high-profitability factor shows moderate crowding, indicating stable investor interest[18][19] 4. Factor Name: High-Growth Factor - **Factor Construction Idea**: Tracks the performance of high-growth stocks and their crowding level[18][19] - **Factor Construction Process**: The factor's crowding level is calculated using the same metrics as the small-cap factor. The composite crowding score is 0.25[18][19] - **Factor Evaluation**: The high-growth factor shows moderate crowding, reflecting steady investor interest[18][19] --- Factor Backtesting Results 1. Small-Cap Factor - Composite crowding score: 0.09[18][19] 2. Low-Valuation Factor - Composite crowding score: -0.31[18][19] 3. High-Profitability Factor - Composite crowding score: 0.24[18][19] 4. High-Growth Factor - Composite crowding score: 0.25[18][19]

Group 1 - Market sentiment has improved slightly, with the sentiment index rising to 1.55 as of March 13, up from 1.40 the previous week, indicating a neutral model perspective despite ongoing external political risks [7][11]. - The industry trend indicators have rebounded sharply, suggesting a reduction in divergence among funds' industry views, with increased consistency in market sector performance [27][43]. - The average daily trading volume for the entire A-share market decreased by 18.33% week-on-week, with an average daily turnover of 16,158.69 billion yuan, indicating a decline in market activity [17][20]. Group 2 - The short-term scores for banks have increased significantly, with coal and public utilities leading the short-term score rankings at 98.31, indicating strong upward trends in these sectors [43][44]. - The highest average crowding levels are observed in public utilities, basic chemicals, construction decoration, environmental protection, and electric equipment, while the lowest are in commercial retail, textiles, social services, food and beverage, and real estate [46][48]. - The correlation between industry crowding and weekly price changes is relatively low at 0.40, suggesting that sectors with high crowding, such as public utilities and coal, may experience significant price movements, while sectors with low crowding, like defense and retail, may offer better long-term value [49].

Investment Rating - The report maintains a neutral investment rating for the A-share market, indicating that it is currently difficult to break the oscillating pattern in the short term, but there may be potential for a rebound due to some macroeconomic indicators showing signs of recovery [1][7]. Core Insights - The A-share market continues to exhibit a volatile pattern, with the Shanghai Composite Index down by 1.2% and the CSI 300 Index up by 0.19% over the past week. The current market PE (TTM) stands at 23.3 times, which is at the 81.9% percentile since 2005 [7][9]. - Liquidity indicators based on the CSI 300 Index show a liquidity shock index of 0.41, significantly higher than the previous week's 0.05, indicating that current market liquidity is above the average level of the past year [8]. - The report highlights a cautious sentiment among investors, as evidenced by the rising PUT-CALL ratio for the SSE 50 ETF, which reached 0.94, up from 0.73 the previous week [8]. - Macro factors indicate a mixed picture, with February CPI rising to 1.3% year-on-year, exceeding both the previous value of 0.2% and the consensus forecast of 0.88%. Meanwhile, PPI showed a year-on-year decline of -0.9%, better than the previous -1.4% [9][10]. Summary by Sections Market Overview - The A-share market remains in a state of oscillation, with the potential for a rebound as some macroeconomic indicators show recovery. The market's trading activity has increased, with the average turnover rates for the Shanghai Composite Index and Wind All A Index at 1.56% and 2.13%, respectively, both at high percentiles since 2005 [7][8]. Factor Crowding Observation - The report notes a continued decline in the crowding of small-cap factors, with a crowding score of -0.14. Low valuation factors also show a crowding score of -0.32, while high profitability and high growth factors have scores of 0.20 and 0.29, respectively [16][18]. Industry Crowding - The report identifies high crowding levels in the industries of comprehensive, non-ferrous metals, communication, basic chemicals, and steel. The crowding in the oil and petrochemical and agriculture, forestry, animal husbandry, and fishery sectors has increased significantly [20][23].

Group 1 - Market sentiment has declined, with the sentiment indicator dropping to 1.40 from 1.85, indicating a neutral to bearish outlook [2][8] - The inter-industry trading volatility has risen to high levels, suggesting increased sector rotation and a decline in market risk appetite [12][24] - The average daily trading volume for the entire A-share market decreased by 26.52% week-on-week, with an average of 17,932.48 billion yuan, indicating reduced market activity [18][23] Group 2 - The short-term score for industries shows that utilities, oil and petrochemicals, coal, environmental protection, and transportation are leading, with utilities scoring 100, the highest [41][44] - The correlation between industry congestion and weekly price changes is low at 0.39, indicating that high congestion sectors like oil and petrochemicals are experiencing significant price increases, but caution is advised for potential pullbacks [45][49] - The current model indicates a preference for large-cap and value styles, with signals suggesting a potential strengthening in the future [52][53]