Core Viewpoint - The report discusses the "Leading Stock Diffusion Effect" as a mechanism driving sector trends in the A-share market, emphasizing the importance of constructing optimal investment portfolios based on improved factors like economic conditions and capital flows [1][2][3]. Research Background - The demand for industry-level beta timing has increased due to the development of flexible allocation funds and the growing industry ETF system, making sector rotation a core asset allocation need [6]. - The A-share market has seen accelerated sector rotation, which poses challenges to traditional rotation models, necessitating a reevaluation and improvement of these models [7]. Mechanism of Diffusion Effect - The diffusion effect in the A-share market typically involves capital migrating from core leading stocks to related targets, driven by policy triggers, active capital inflows, cognitive dissemination, and expectation overshoot leading to differentiation [2][16]. - The process includes vertical and horizontal expansions within the industry, market capitalization descent, and valuation arbitrage, ultimately leading to a broader sector rally [17]. Performance of Improved Factors - The report presents improved factors based on the previous discussion, showing significant performance enhancements in the revised SUE and active large order factors, with annualized excess returns of 7.9% and 10.3% respectively [21][22]. - The improved factors demonstrate better stability and lower volatility compared to traditional models, particularly in recent years [64]. Optimal Industry Portfolio - The optimal industry portfolio, constructed using a common condition screening method based on component factors, has shown superior historical performance with an annualized return of 26.0% and an annualized excess return of 19.1% since 2013 [3][64]. - The portfolio has maintained stable excess growth since 2022, with an annualized excess return of 11.7% and a maximum drawdown of 9.2% [74]. Comparison of Multi-Headed Construction Methods - The report compares two multi-headed construction methods: composite factor multi-headed and component factor common condition screening, concluding that the latter offers lower volatility and more stable excess returns [42][64]. - The composite factor multi-headed approach has shown stagnation in excess returns in recent years, while the optimal industry portfolio continues to outperform [53][64].