Workflow
【广发金工】ETF资金流出

Market Performance - The Sci-Tech 50 Index decreased by 1.10% over the last five trading days, while the ChiNext Index increased by 1.38%. The large-cap value and growth indices rose by 1.45% and 1.60%, respectively. The Shanghai 50 Index rose by 1.22%, and the small-cap index represented by the CSI 2000 increased by 0.16%. The beauty and personal care sector, along with non-bank financials, performed well, while the computer and defense industries lagged behind [1]. Risk Premium Analysis - The static PE of the CSI All Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data at two standard deviations above the mean, with notable instances in 2012, 2018, and 2020. As of April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it was 4.08%. The latest reading on January 19, 2024, was 4.11%, marking the fifth instance since 2016 exceeding 4%. As of May 16, 2025, the indicator was at 3.86%, with the two standard deviation boundary at 4.76% [1]. Valuation Levels - As of May 16, 2025, the CSI All Index's PETTM percentile is at 52%. The Shanghai 50 and CSI 300 indices are at 62% and 50%, respectively, while the ChiNext Index is close to 11%. The CSI 500 and CSI 1000 indices are at 31% and 33%, indicating that the ChiNext Index's valuation is relatively low compared to historical levels [2]. Long-term Market Trends - The technical analysis of the Deep 100 Index shows a pattern of bear markets occurring every three years, followed by bull markets. The last bear market began in Q1 2021, with previous declines ranging from 40% to 45%. The current adjustment appears to have sufficient time and space, suggesting a potential upward cycle from the bottom [2]. Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 34.1 billion yuan, while margin financing decreased by approximately 600 million yuan. The average daily trading volume across the two markets was 1.2317 trillion yuan [3]. AI and Machine Learning Applications - The report discusses the use of convolutional neural networks (CNN) to model price and volume data, aiming to predict future prices. The learned features are mapped to industry themes, with a current focus on banking [6].