【广发金工】均线情绪修复

Market Performance - The Sci-Tech 50 Index decreased by 1.89% over the last five trading days, while the ChiNext Index increased by 0.22%. The large-cap value index rose by 0.10%, and the large-cap growth index fell by 0.16%. The Shanghai 50 Index declined by 0.46%, and the small-cap index represented by the CSI 2000 dropped by 0.74%. The non-ferrous metals and oil & petrochemical sectors performed well, whereas household appliances and food & beverage sectors 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 to be at two standard deviations above the mean, with notable peaks in 2012, 2018, and 2020. As of April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it rose to 4.08%. The latest reading on January 19, 2024, was 4.11%, marking the fifth instance since 2016 exceeding 4%. As of June 13, 2025, the indicator was at 3.83%, with the two standard deviation boundary at 4.75% [1]. Valuation Levels - As of June 13, 2025, the CSI All Index's P/E TTM percentile was at 54%. The Shanghai 50 and CSI 300 indices were at 62% and 52%, respectively. The ChiNext Index was close to 13%, while the CSI 500 and CSI 1000 indices were at 30% and 22%. The ChiNext Index's valuation is relatively low compared to historical averages [2]. Long-term Market Trends - The technical analysis of the Deep 100 Index indicates a bear market every three years, followed by a bull market. Historical declines ranged from 40% to 45%, with the current adjustment starting in Q1 2021 showing sufficient time and space for a potential upward cycle [2]. Fund Flow and Trading Activity - Over the last five trading days, ETF funds saw an outflow of 17 billion yuan, while margin financing increased by approximately 9.4 billion yuan. The average daily trading volume across both markets was 1.3392 trillion yuan [2]. Neural Network Analysis - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes. The latest recommended themes include non-ferrous metals and banking sectors [9].