Core Viewpoint - The market is expected to remain in a volatile trend next week, influenced by global events and technical indicators [1][2]. Market Overview - The liquidity shock indicator for the CSI 300 index was 0.74, indicating higher liquidity compared to the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options increased to 0.99, reflecting growing caution among investors regarding short-term trends [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 0.94% and 1.57%, respectively, showing increased trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced slight fluctuations, with weekly changes of 0.05% and -0.02% respectively [2]. - China's May CPI was -0.1%, consistent with the previous value and above the Wind consensus expectation of -0.17% [2]. - The PPI for May was -3.3%, lower than the previous value of -2.7% and the Wind consensus expectation of -3.17% [2]. - New RMB loans in May amounted to 620 billion, below the Wind consensus expectation of 802.65 billion but higher than the previous value of 280 billion [2]. - M2 growth was 7.9%, below both the Wind consensus expectation of 8.08% and the previous value of 8% [2]. Technical Analysis - The Wind All A Index broke above the SAR reversal point on June 4 [2]. - The market score based on the moving average strength index is currently at 155, which is at the 61.5% percentile since 2021 [2]. - The A-share market showed a pattern of rising and then declining, with global markets reacting negatively to the outbreak of conflict in the Middle East [2]. Performance Summary - For the week of June 9-13, the SSE 50 Index fell by 0.46%, the CSI 300 Index decreased by 0.25%, and the CSI 500 Index dropped by 0.38%, while the ChiNext Index rose by 0.22% [3]. - The overall market PE (TTM) stands at 19.3 times, at the 53.5% percentile since 2005 [3]. Factor and Industry Analysis - The small-cap factor's congestion level continues to rise, currently at 1.13, while low valuation and high profitability factors show negative congestion levels [3]. - Industries with relatively high congestion levels include machinery, comprehensive services, environmental protection, non-ferrous metals, and beauty care [3]. - The congestion level for the medical biotechnology and beauty care sectors has increased significantly [3].

Core Viewpoint - The market is expected to continue a trend of oscillating upward in the coming week, supported by technical indicators and liquidity metrics [1][2]. Market Indicators - The liquidity shock index for the CSI 300 was 0.30, indicating higher liquidity than the average level over the past year by 0.30 standard deviations [2]. - The PUT-CALL ratio for the SSE 50 ETF options decreased to 0.85, reflecting a reduced caution among investors regarding short-term movements [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 0.82% and 1.40%, respectively, indicating increased trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates saw weekly increases of 0.15% and 0.25%, respectively [2]. - The official manufacturing PMI for China in May was reported at 49.5, matching expectations, while the Caixin manufacturing PMI was lower at 48.3 [2]. Technical Analysis - The Wind All A index broke through the SAR point on June 4, signaling a buy opportunity, with the moving average strength index scoring 207, placing it in the 81.6% percentile since 2021 [2][3]. Market Performance - For the week of June 2 to June 6, the SSE 50 index rose by 0.38%, the CSI 300 index increased by 0.88%, the CSI 500 index grew by 1.6%, and the ChiNext index surged by 2.32% [3]. - The overall market PE (TTM) stands at 19.2 times, which is in the 52.3% percentile since 2005 [3]. Factor Analysis - Small-cap factors performed well, with a crowding degree of 1.05, while low valuation factors had a crowding degree of 0.06 [3]. - The industry crowding degree is relatively high in machinery, comprehensive, retail, environmental protection, and beauty care sectors, with notable increases in beauty care and banking [3].

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of Shenwan First-Level Industry Indices on a daily basis, identifying industries with high or low crowding levels and significant changes in crowding over time[4]. - **Model Construction Process**: The model calculates crowding levels for each industry index daily, based on metrics such as main fund inflows and outflows. It identifies industries with the highest and lowest crowding levels and tracks significant changes in crowding over recent trading days[4]. - **Model Evaluation**: The model provides actionable insights into industry crowding dynamics, helping to identify potential investment opportunities or risks[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 model involves the following steps: 1. Calculate the premium rate of an ETF as the percentage difference between its market price and net asset value (NAV). 2. Compute the Z-score of the premium rate over a rolling window to standardize the deviation. 3. Identify ETFs with extreme Z-scores as potential arbitrage opportunities[5]. - **Model Evaluation**: The model effectively highlights ETFs with significant deviations from their NAV, which may indicate arbitrage opportunities or risks of price corrections[5]. --- Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results were provided for this model[4]. 2. Premium Rate Z-Score Model - No specific numerical backtesting results were provided for this model[5]. --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the report. --- Factor Backtesting Results No specific quantitative factor backtesting results were provided in the report.

Core Viewpoint - The A-share market is expected to continue its consolidation next week, influenced by technical indicators and upcoming holiday-related risk aversion among investors [1][2]. Market Analysis - The liquidity shock indicator for the CSI 300 index was 1.13 on Friday, lower than the previous week (2.63), indicating current market liquidity is 1.13 times above the average level of the past year [2]. - The put-call ratio for the SSE 50 ETF options decreased to 0.94 from 1.03, 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 SSE Composite Index and Wind All A were 0.85% and 1.40%, respectively, indicating a decrease in trading activity, positioned at the 58.47% and 68.54% percentiles since 2005 [2]. Macro Factors - The RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.2% and 0.52%, respectively [2]. - Historical data shows that from May 2005 onwards, the probability of the SSE Composite Index, CSI 300, CSI 500, and ChiNext Index rising in the latter half of May was 45%, 45%, 50%, and 47%, with average gains of -0.1%, -0.02%, 0.67%, and 1.71% [2]. Event-Driven Factors - The US stock market experienced a downward trend last week, with the Dow Jones, S&P 500, and Nasdaq indices reporting weekly returns of -2.47%, -2.61%, and -2.47%, respectively [2]. - The People's Bank of China conducted a 500 billion yuan MLF operation on May 23, with a one-year term, resulting in a net injection of 375 billion yuan for May, marking the third consecutive month of increased liquidity [2]. Technical Analysis - The Wind All A index broke below the SAR point on May 23, but the moving average strength index remains above average, indicating no bottoming pattern has emerged [2]. - The current market score based on the moving average strength index is 154, positioned at the 62.5% percentile since 2021 [2]. Performance Overview - For the week of May 19-23, the SSE 50 index fell by 0.18%, the CSI 300 index also decreased by 0.18%, the CSI 500 index dropped by 1.1%, and the ChiNext index declined by 0.88% [3]. - The overall market PE (TTM) stands at 19.0 times, which is at the 50.6% percentile since 2005 [3]. Factor Crowding Observations - The crowding degree for low valuation factors has decreased, with small-cap factor crowding at 0.91, low valuation factor crowding at 0.25, high profitability factor crowding at -0.23, and high profitability growth factor crowding at -0.03 [3]. - Industry crowding is relatively high in machinery equipment, comprehensive, retail, environmental protection, and automotive sectors, while transportation and non-ferrous metals sectors have seen a significant increase in crowding [3].

Investment Rating - The report indicates a neutral outlook for the industry, expecting overall returns to be within -5% to 5% compared to the CSI 300 index over the next six months [16]. Core Insights - The report highlights a significant net outflow of 4.942 billion yuan from broad-based ETFs in a single day, with notable inflows into specific ETFs such as the Sci-Tech 50 ETF (+240 million yuan) and the A500 Index ETF (+23 million yuan) [6]. - The industry crowding monitoring model shows that sectors like light industry manufacturing, beauty care, and textile apparel are currently crowded, while sectors such as electronics, steel, non-bank financials, home appliances, and social services have lower crowding levels, suggesting potential investment opportunities [4]. - The report emphasizes the importance of monitoring ETF products for potential arbitrage opportunities while being cautious of possible pullback risks [5]. Fund Flow Analysis - Broad-based ETFs experienced a net outflow of 4.942 billion yuan, with the top three inflows being the Sci-Tech 50 ETF (+240 million yuan), the Sci-Tech Board 50 ETF (+58 million yuan), and the A500 Index ETF (+23 million yuan) [6]. - The industry-themed ETFs saw a net inflow of 1.278 billion yuan, with the top three inflows being military industry leader ETFs (+473 million yuan), national defense ETFs (+443 million yuan), and military ETFs (+430 million yuan) [6]. - Style strategy ETFs had a net outflow of 328 million yuan, with the top three inflows being dividend ETFs (+73 million yuan), low volatility dividend ETFs (+58 million yuan), and low volatility dividend 50 ETFs (+43 million yuan) [6]. - Cross-border ETFs faced a net outflow of 1.937 billion yuan, with the top three inflows being Hong Kong non-bank ETFs (+47 million yuan), Hong Kong dividend index ETFs (+46 million yuan), and Nasdaq ETFs (+36 million yuan) [6]. Industry Crowding and Fund Movement - The report notes significant changes in fund flows across various sectors, with major outflows from electronics (-3.881 billion yuan), machinery equipment (-3.310 billion yuan), and coal (-436 million yuan) [14]. - Conversely, sectors like electric equipment (+1.369 billion yuan) and pharmaceutical biology (+263 million yuan) saw net inflows, indicating a shift in investor sentiment [14]. - The report provides a heatmap of industry crowding over the past 30 trading days, indicating varying levels of investor interest across sectors [12].

Investment Rating - The industry is rated as "Neutral," indicating that the expected overall return in the next six months is between -5% and 5% compared to the CSI 300 index [13]. Core Insights - The report highlights significant capital outflows from broad-based ETFs, totaling 1.679 billion yuan in a single day, with notable inflows into specific ETFs such as the Shanghai 50 ETF and the CSI 300 ETF [6][11]. - The report emphasizes the crowdedness of certain sectors, particularly textiles, media, and pharmaceuticals, while suggesting lower crowdedness in electronics and petrochemicals, which may present investment opportunities [4][10]. - The report also identifies potential arbitrage opportunities in specific ETFs based on the Z-score model, while cautioning about the risks of potential corrections in these assets [5]. Summary by Sections Capital Flow - Broad-based ETFs experienced a net outflow of 1.679 billion yuan, with the top three inflows being the Shanghai 50 ETF (+143 million yuan), CSI 300 ETF (+101 million yuan), and ChiNext ETF (+93 million yuan) [6]. - Industry-themed ETFs saw a minor net outflow of 17 million yuan, with significant inflows into military-related ETFs [6]. - Style strategy ETFs had a net outflow of 128 million yuan, while cross-border ETFs faced a substantial outflow of 2.067 billion yuan [6]. Industry Crowdedness Monitoring - The report constructed a model to monitor the crowdedness of various sectors, indicating that textiles, beauty care, and light industry are currently crowded, while electronics and petrochemicals are less so [4]. - Recent capital flows show increased allocation to automotive, home appliances, and banking sectors, while reducing exposure to computers, basic chemicals, and defense industries [4]. ETF Product Focus Signals - The report suggests monitoring specific ETFs for potential investment opportunities based on historical data and Z-score analysis, while also highlighting the need to be cautious of potential corrections [5][12].

- The industry crowding monitoring model was constructed to monitor the daily crowding levels of Shenwan primary industry indices. The model identifies industries with high crowding levels, such as textiles, beauty care, and light manufacturing, while industries like media and electronics show lower crowding levels. It also tracks significant daily changes in crowding levels for industries like environmental protection, food & beverage, and real estate[4] - The Z-score premium rate model was developed to screen ETF products for potential arbitrage opportunities. This model uses rolling calculations to identify ETFs with significant deviations from their intrinsic value, providing signals for potential trades while warning of possible price corrections[5] - The industry crowding monitoring model highlights that defense, non-bank finance, and environmental protection sectors saw significant inflows of main funds, while sectors like automobiles, electrical equipment, and basic chemicals experienced outflows. Over the past three days, coal, beauty care, and banking sectors were favored, while computing, electronics, and electrical equipment were reduced[4] - The Z-score premium rate model provides ETF signals, including top inflows for ETFs like Sci-Tech 50 ETF (+5.77 billion yuan) and Sci-Tech 100 Index ETF (+2.27 billion yuan), while ETFs like Shanghai 50 ETF (-4.86 billion yuan) and ChiNext ETF (-3.46 billion yuan) saw significant outflows[6][7] - The industry crowding monitoring model's evaluation indicates its effectiveness in identifying crowded sectors and tracking fund flows, aiding investors in understanding market dynamics[4] - The Z-score premium rate model is evaluated as a useful tool for identifying arbitrage opportunities in ETFs, though it requires caution due to potential risks of price corrections[5] - The industry crowding monitoring model's testing results show significant fund flow changes in various sectors, such as coal (+4.28 billion yuan over three days) and computing (-129.02 billion yuan over three days)[14][15] - The Z-score premium rate model's testing results include ETF fund flow data, such as Sci-Tech 50 ETF (+5.77 billion yuan) and Shanghai 50 ETF (-4.86 billion yuan)[6][7]

Group 1 - The core viewpoint of the article suggests that the A-share market is likely to maintain a range-bound fluctuation in the upcoming week, influenced by historical trends and current market indicators [1][2]. - The liquidity shock indicator for the CSI 300 index was reported at 2.63, indicating that current market liquidity is 2.63 times higher than the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 1.03, reflecting a reduced level of caution among investors regarding the short-term performance of the SSE 50 ETF [2]. Group 2 - The five-day average turnover rates for the SSE Composite Index and the Wind All A Index were 0.89% and 1.45%, respectively, indicating increased trading activity compared to historical levels [2]. - The RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.59% and 0.42%, respectively [2]. - In April, new RMB loans amounted to 280 billion, significantly lower than the consensus expectation of 764.44 billion and the previous value of 3.64 trillion [2]. - The M2 money supply grew by 8% year-on-year, surpassing the consensus expectation of 7.54% and the previous value of 7% [2]. Group 3 - Historical data shows that the probability of major A-share indices rising in the latter half of May is relatively low, with the SSE Composite Index, CSI 300, and ChiNext Index having average increases of -0.1%, -0.02%, and 1.71%, respectively [2]. - The Wind All A Index recently broke through the SAR reversal indicator on April 21, indicating a potential upward trend [2]. - The current market score based on the moving average strength index is 209, placing it in the 82.9 percentile since 2021 [2]. Group 4 - The A-share market experienced a recovery last week, with the SSE 50 Index rising by 1.22%, the CSI 300 Index by 1.12%, and the ChiNext Index by 1.38% [3]. - The overall market PE (TTM) stands at 19.0 times, which is at the 51.2 percentile since 2005 [3]. - The factor crowding metrics indicate a stable environment, with small-cap factor crowding at 0.91 and low valuation factor crowding at 0.53 [3].

[Table_Title] 金 金融工程点评 [Table_Message]2025-05-16 风险提示：本报告结论完全基于公开历史数据，建议关注的行业指数与 ETF 产品基于 构建的量化模型，仅供大家参考阅读，不构成任何投资建议。 金工 ETF 点评：宽基 ETF 单日净流出 52.58 亿元；标普油气、电池 ETF 可关注 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 一、资金流向 二、行业拥挤度监测 ◼ 通过构建行业拥挤度监测模型，对申万一级行业指数的拥挤度进行每日监测， 前一交易日交通运输、纺织服饰、美容护理拥挤度靠前，相比较而言，传媒、 社会服务、房地产、电子、电力设备的拥挤度水平较低，建议关注。此外，农 林牧渔、汽车单日拥挤度变动较大。从主力资金流动来看，前一交易日主力 资金流入医药生物、美容护理、煤炭，流出计算机、电子、非银金融。近三个 交易日主力资金增配美容护理、医药生物、煤炭，减配电子、计算机、国防 军工。 三、ET ...

Investment Rating - The report indicates a neutral outlook for the industry, expecting returns to be within -5% to 5% compared to the CSI 300 index over the next six months [15]. Core Insights - The report highlights significant capital outflows from broad-based ETFs, totaling 9.22 billion yuan in a single day, with notable inflows into specific ETFs such as the ChiNext 50 ETF and the STAR Market Index ETFs [6][14]. - The industry crowding index shows that sectors like defense and military, textiles and apparel, and beauty care are currently crowded, while real estate and food and beverage sectors are less crowded, suggesting potential investment opportunities [4][12]. - The report emphasizes the importance of monitoring ETF products for potential arbitrage opportunities while being cautious of possible corrections in the underlying assets [5]. Summary by Sections Capital Flow - Broad-based ETFs experienced a net outflow of 9.22 billion yuan, with the top three inflows being ChiNext 50 ETF (+0.84 billion yuan), STAR Market Index ETF by Huitianfu (+0.48 billion yuan), and STAR Market Index ETF by Huatai-PB (+0.42 billion yuan) [6]. - The top three outflows were from CSI 300 ETF (-1.55 billion yuan), SSE 50 ETF (-0.93 billion yuan), and CSI 1000 ETF (-0.845 billion yuan) [6]. Industry Crowding Monitoring - The report constructed a crowding index model to monitor the crowding levels of various sectors, indicating that defense and military, textiles and apparel, and beauty care are currently crowded, while real estate and food and beverage sectors are less so [4]. - Recent capital flows show significant inflows into beauty care, pharmaceutical biology, and basic chemicals, while outflows were noted in defense and military, computer, and electronics sectors [4]. ETF Product Signals - The report suggests monitoring specific ETF products for potential arbitrage opportunities based on the Z-score model, while also advising caution regarding potential corrections in these products [5]. - The report lists several ETFs with significant capital movements, highlighting the need for investors to pay attention to these signals for potential investment strategies [14].