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【广发金工】融资余额增加
广发金融工程研究·2025-07-06 09:03

Core Viewpoint - The recent market performance shows mixed results across various indices, with the ChiNext Index rising while the STAR 50 Index slightly declined, indicating a divergence in sector performance and potential investment opportunities in specific areas [1][2]. Group 1: Market Performance - Over the last five trading days, the STAR 50 Index decreased by 0.35%, while the ChiNext Index increased by 1.50%. The large-cap value stocks rose by 1.94%, and large-cap growth stocks increased by 1.78%. The Shanghai 50 Index saw a rise of 1.21%, and the small-cap stocks represented by the CSI 2000 increased by 0.53% [1]. - Steel and building materials sectors performed well, while the computer and non-bank financial sectors lagged behind [1]. Group 2: Risk Premium and Valuation Levels - The risk premium, measured as the inverse of the static PE of the CSI All Index minus the yield of 10-year government bonds, reached 4.17% on April 26, 2022, and 4.08% on October 28, 2022, indicating a market rebound potential. As of January 19, 2024, the indicator was at 4.11%, marking the fifth occurrence since 2016 of exceeding 4% [1]. - As of July 4, 2025, the CSI All Index's PE TTM percentile was at 61%, with the Shanghai 50 and CSI 300 at 67% and 60%, respectively. The ChiNext Index is close to 20%, indicating a relatively low valuation level compared to historical averages [2]. Group 3: Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 21.2 billion yuan, while margin financing increased by approximately 19.7 billion yuan. The average daily trading volume across the two markets was 1.4136 trillion yuan [4]. Group 4: Technical Analysis and AI Modeling - The long-term technical analysis of the Deep 100 Index suggests a cyclical pattern of bear and bull markets every three years, with significant declines observed in previous cycles. The current adjustment phase, which began in the first quarter of 2021, appears to have sufficient time and space for a potential upward cycle [2]. - A convolutional neural network model has been developed to analyze price and volume data, mapping learned features to industry themes, with a focus on banking and non-ferrous metals sectors [3][9].