科技板块出现分化

• The report mentions the construction of the A-share prosperity index, which is based on the Nowcasting target of the year-on-year growth rate of the net profit attributable to the parent company of the Shanghai Composite Index. The index is designed to observe the high-frequency prosperity of A-shares. The current prosperity index is 21.28, which has increased by 15.85 compared to the end of 2023, indicating an upward cycle[29][33][34] - The A-share sentiment index is constructed using market volatility and transaction volume changes, divided into four quadrants. Among these quadrants, only the "volatility up - transaction down" quadrant shows significant negative returns, while the others show significant positive returns. The sentiment index includes bottoming and peaking warning signals. Currently, the bottoming signal indicates bearishness, and the peaking signal also points to bearishness, leading to an overall bearish outlook for the market[36][39][40] - The theme mining algorithm is used to identify investment opportunities in thematic stocks. This algorithm processes news and research report texts, extracts theme keywords, explores relationships between themes and individual stocks, constructs theme active cycles, and builds theme influence factors. Recently, the algorithm has identified semiconductor concept stocks as having high concept heat anomalies, driven by the event of the China Semiconductor Industry Association's announcement regarding chip origin designation[46][47][48] - The index enhancement portfolios for CSI 500 and CSI 300 are mentioned. The CSI 500 enhancement portfolio achieved a return of 1.99% but underperformed the benchmark by 0.38%. Since 2020, the portfolio has generated an excess return of 51.20% relative to the CSI 500 index, with a maximum drawdown of -5.73%. The CSI 300 enhancement portfolio achieved a return of 2.15%, outperforming the benchmark by 0.16%. Since 2020, the portfolio has generated an excess return of 38.68% relative to the CSI 300 index, with a maximum drawdown of -5.86%[46][53][54] - The report utilizes the BARRA factor model to construct ten major style factors for the A-share market, including size (SIZE), beta (BETA), momentum (MOM), residual volatility (RESVOL), non-linear size (NLSIZE), valuation (BTOP), liquidity (LIQUIDITY), earnings yield (EARNINGS_YIELD), growth (GROWTH), and leverage (LVRG). Recent market style analysis shows that liquidity factors are positively correlated with beta, momentum, and residual volatility, while value factors are negatively correlated with beta, residual volatility, and liquidity. From pure factor returns, size factors have high excess returns, while residual volatility shows significant negative excess returns. High beta and high growth stocks performed well recently, while residual volatility and value factors performed poorly[58][59][60] - The report applies factor models for performance attribution analysis of major indices. It highlights that indices like the Shanghai Composite Index, SSE 50, and CSI 300 have significant exposure to size factors due to the market's preference for large-cap stocks, resulting in good performance in style factors. In contrast, indices like CSI 500 and Wind All A have lower exposure to size factors and performed poorly in style factors during the week[66][67][69]