金工ETF点评：宽基ETF单日净流出26.91亿元，美容护理拥挤度持续高位

• 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]