Quantitative Models and Construction 1. Model Name: Industry Crowdedness Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowdedness levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowdedness levels to provide insights into market dynamics[4] - **Model Construction Process**: The model calculates the crowdedness levels of various industries based on daily data. It identifies industries with significant changes in crowdedness levels and tracks the inflow and outflow of major funds across industries over different time periods[4] - **Model Evaluation**: The model effectively highlights industries with extreme crowdedness levels and significant changes, providing actionable insights for market participants[4] 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to screen ETF products for potential arbitrage opportunities by calculating the Z-score of premium rates over a rolling window[5] - **Model Construction Process**: The Z-score is calculated as follows: $ Z = \frac{(P - \mu)}{\sigma} $ - Where $P$ represents the premium rate of the ETF, $\mu$ is the mean premium rate over the rolling window, and $\sigma$ is the standard deviation of the premium rate over the same period. The model identifies ETFs with extreme Z-scores as potential arbitrage opportunities[5] - **Model Evaluation**: The model provides a systematic approach to identify ETFs with potential mispricing, though it also highlights the need to be cautious of potential price corrections[5] --- Model Backtesting Results 1. Industry Crowdedness Monitoring Model - **Top Crowded Industries (Previous Trading Day)**: Defense & Military, Textile & Apparel, Machinery Equipment[4] - **Low Crowdedness Industry**: Coal[4] - **Significant Changes in Crowdedness**: Communication and Non-Banking Financials experienced large single-day changes in crowdedness levels[4] - **Major Fund Flows (Last 3 Days)**: - **Inflow**: Defense & Military, Communication, Electric Equipment - **Outflow**: Computers, Basic Chemicals, Electronics[4] 2. Premium Rate Z-Score Model - **Identified ETFs with Arbitrage Signals**: Specific ETFs were flagged based on their Z-scores, though detailed numerical results were not provided in the report[5] --- Quantitative Factors and Construction 1. Factor Name: Crowdedness Factor - **Factor Construction Idea**: Measures the level of crowdedness in industries to identify overbought or oversold conditions[4] - **Factor Construction Process**: The crowdedness factor is derived from daily industry-level data, incorporating metrics such as fund flows and relative changes in crowdedness levels over time[4] - **Factor Evaluation**: The factor is effective in identifying industries with extreme market positioning, aiding in tactical allocation decisions[4] --- Factor Backtesting Results 1. Crowdedness Factor - **Top Industries by Crowdedness (Previous Trading Day)**: Defense & Military, Textile & Apparel, Machinery Equipment[4] - **Industries with Low Crowdedness**: Coal[4] - **Industries with Significant Crowdedness Changes**: Communication, Non-Banking Financials[4]

Investment Rating - The industry investment rating is not explicitly stated in the report, but it implies a positive outlook for certain sectors based on fund flows and market dynamics [15]. Core Insights - The report highlights significant fund inflows into sectors such as computer, electronics, and machinery, while sectors like defense, light manufacturing, and agriculture have seen outflows [3][13]. - The report suggests monitoring the crowdedness of various industries, indicating that textiles, light manufacturing, and beauty care are currently crowded, while coal has a lower level of crowdedness, suggesting potential investment opportunities [3]. - The ETF products show a mixed performance, with notable inflows in industry-themed ETFs like robotics and semiconductor ETFs, while some broad-based ETFs experienced outflows [7][14]. Summary by Sections Fund Flows - The report indicates a net outflow of 4.45 billion CNY from broad-based ETFs, with the top inflows seen in the Shanghai 50 ETF (+5.55 billion CNY) and the top outflows in the CSI 300 ETF (-2.09 billion CNY) [7]. - Industry-themed ETFs saw a net inflow of 9.35 billion CNY, with the robotics ETF leading at +3.33 billion CNY [7]. Industry Crowdedness Monitoring - The crowdedness model shows that textiles, light manufacturing, and beauty care are currently at high levels of crowdedness, while coal is at a lower level, suggesting a potential opportunity for investment [3]. ETF Product Signals - The report provides signals for potential ETF products to watch, including the Hong Kong National Enterprise ETF and the High-End Manufacturing ETF, indicating they may present investment opportunities [14].

- The report introduces an industry crowding monitoring model to track daily crowding levels of Shenwan primary industry indices. The model identifies high crowding levels in industries such as beauty care, basic chemicals, and utilities, while industries like media and electrical equipment exhibit lower crowding levels[3] - A Z-score model is constructed to monitor ETF product signals. This model calculates rolling measurements to identify potential arbitrage opportunities in ETFs while also warning of potential risks of price corrections[4] - The Z-score model is applied to ETF products, providing signals for potential arbitrage opportunities and highlighting risks associated with price adjustments[4] - The report provides detailed data on ETF fund flows, categorizing them into broad-based ETFs, industry-themed ETFs, style-strategy ETFs, and cross-border ETFs. It highlights the top three funds with the highest inflows and outflows for each category[6] - The industry crowding monitoring model tracks the movement of main funds across industries over the past three trading days, showing significant inflows into industries like automobiles and utilities, while outflows are observed in industries such as electronics and retail[3][11]

- The report introduces an "Industry Crowdedness Monitoring Model" to track the crowdedness levels of Shenwan primary industry indices on a daily basis. The model identifies industries with high and low crowdedness levels, such as agriculture, banking, and environmental protection being highly crowded, while automotive and media are less crowded. The model also monitors significant daily changes in crowdedness levels, highlighting industries like automotive and home appliances with notable variations[6][11] - A "Premium Rate Z-score Model" is constructed to screen ETF products for potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations in premium rates, which may indicate arbitrage potential or risks of price corrections[6][14]

- The report uses the Mahalanobis distance to measure industry crowding by analyzing the price and trading volume changes of industry indices over the past week[3][15] - The first quadrant represents industries with rising prices and volumes, while the third quadrant represents industries with falling prices and volumes. Points outside the ellipse indicate industries with a price and trading volume deviation confidence level exceeding 99%, signifying short-term significant crowding[15] - Last week, the beauty care and food & beverage industries showed significant trading crowding[3][16]

Quantitative Factors and Their Construction 1. Factor Name: Small-Cap Factor - **Construction Idea**: The small-cap factor measures the performance of stocks with smaller market capitalization, which historically tend to outperform larger-cap stocks under certain market conditions [17][18] - **Construction Process**: The factor's crowding level is calculated using four indicators: valuation spread, pairwise correlation, long-term return reversal, and factor volatility. These indicators are combined into a composite score to assess the degree of crowding [18] - **Evaluation**: The small-cap factor showed a positive crowding level, indicating relatively strong performance and lower risk of factor failure [19] 2. Factor Name: Low-Valuation Factor - **Construction Idea**: This factor identifies stocks with lower valuation metrics, such as price-to-earnings or price-to-book ratios, which are expected to generate higher returns over time [17][18] - **Construction Process**: Similar to the small-cap factor, the low-valuation factor's crowding level is assessed using the same four indicators (valuation spread, pairwise correlation, long-term return reversal, and factor volatility) and combined into a composite score [18] - **Evaluation**: The low-valuation factor exhibited a slightly negative crowding level, suggesting moderate underperformance or potential risks of factor inefficiency [19] 3. Factor Name: High-Profitability Factor - **Construction Idea**: This factor targets stocks with strong profitability metrics, such as high return on equity (ROE) or net profit margins, which are often associated with stable and superior returns [17][18] - **Construction Process**: The factor's crowding level is calculated using the same methodology as the small-cap and low-valuation factors, combining the four indicators into a composite score [18] - **Evaluation**: The high-profitability factor showed a negative crowding level, indicating potential underperformance or risks of factor inefficiency [19] 4. Factor Name: High-Growth Factor - **Construction Idea**: This factor focuses on stocks with high growth rates in earnings or revenues, which are expected to deliver higher returns in growth-oriented market environments [17][18] - **Construction Process**: The high-growth factor's crowding level is also derived from the four indicators (valuation spread, pairwise correlation, long-term return reversal, and factor volatility) and combined into a composite score [18] - **Evaluation**: The high-growth factor exhibited the most negative crowding level among the factors analyzed, indicating significant underperformance and a higher risk of factor failure [19] --- Backtesting Results of Factors 1. Small-Cap Factor - **Valuation Spread**: 1.74 [19] - **Pairwise Correlation**: -0.32 [19] - **Market Volatility**: -0.38 [19] - **Return Reversal**: 1.43 [19] - **Composite Score**: 0.62 [19] 2. Low-Valuation Factor - **Valuation Spread**: -0.33 [19] - **Pairwise Correlation**: 0.05 [19] - **Market Volatility**: 0.15 [19] - **Return Reversal**: -0.29 [19] - **Composite Score**: -0.10 [19] 3. High-Profitability Factor - **Valuation Spread**: -1.23 [19] - **Pairwise Correlation**: -0.05 [19] - **Market Volatility**: 0.28 [19] - **Return Reversal**: -0.44 [19] - **Composite Score**: -0.36 [19] 4. High-Growth Factor - **Valuation Spread**: -2.04 [19] - **Pairwise Correlation**: 0.08 [19] - **Market Volatility**: -0.65 [19] - **Return Reversal**: -1.02 [19] - **Composite Score**: -0.91 [19]

低估值反攻 ——2025 年 3 月量化行业配置月报 核心观点 主线切换，低估值反攻有望开启，看好小市值与顺周期的交集方向，建议关注基础化 工、汽车、新能源等方向的配置机会。 风格维度，我们基于龙虎榜数据构建的大资金活跃度指标目前仍处上行趋势，这 意味着小盘风格或仍将相对占优。在市场成交活跃的背景下，若前期主线陷入调 整，市场的自发选择或是寻找其他低位补涨方向，而非选择红利进行防御。目前 来看，两会公布的政策力度基本符合市场预期，未来进一步的上行催化或来自基 本面的企稳修复。从高频数据来看，地产成交热度回升是当前较为显著的边际变 化，北上深的二手房成交量节后已基本修复至去年"924"政策后的高水平，"金 三"逐步成型，顺周期方向或逐步迎来基本面的顺风期。综合来看，或可关注顺 周期板块中整体市值偏小的细分行业，建议关注基础化工、汽车、新能源等方向 的配置机会。 ❑ 机械设备、通信、计算机、电子四行业触发拥挤信号。 截至 3 月 5 日，机械设备、通信、计算机、电子四行业的拥挤度较高，位于滚动 3 年的 95%分位预警阈值以上，或需警惕股价潜在的波动风险。 ❑ 综合策略最近一个月收益 4.6%，相对行业等权指数及 ...

Market Liquidity Overview - R007 increased from 1.79% to 2.03%, a rise of 24.65 basis points; DR007 rose from 1.76% to 1.94%, an increase of 17.83 basis points. The spread between R007 and DR007 widened by 6.82 basis points. Additionally, the China-US interest rate spread decreased by 6.83 basis points [2][15]. - The net inflow of funds this week was -48.886 billion yuan, a decrease of 74.815 billion yuan from the previous week. Fund supply was -2.811 billion yuan, while fund demand was 46.075 billion yuan. Specifically, fund supply decreased by 44.876 billion yuan, with ETF net subscriptions down by 30.171 billion yuan and financing net purchases down by 13.269 billion yuan; fund demand increased by 29.939 billion yuan [2][19]. Industry Sector Liquidity Tracking - Most industries showed an upward trend in returns this week, with the media and computer sectors experiencing significant gains of 9.87% and 7.81%, respectively. In contrast, the coal and defense industries saw declines of 1.36% and 0.76% [3][27]. - The banking and food & beverage sectors had negative net purchases of leveraged funds, while other industries experienced net inflows. The main stocks in the food & beverage, machinery, and communication sectors saw net inflows, while the electronic sector experienced significant outflows [3][32]. Style Sector Liquidity Tracking - The daily trading volume share of consumer and stable styles increased, while other styles saw a decrease. Specifically, the daily trading volume share of consumer and stable styles rose by 1.37% and 0.58%, respectively, while cyclical, growth, and financial styles decreased by 0.88%, 0.93%, and 0.14% [4]. - All style indices experienced gains, with consumer and growth styles showing the largest increases of 2.19% and 2.09%, respectively. The daily trading volume share of the growth style has increased compared to the past month, three months, and six months [4].