广发证券资深金工分析师 周飞鹏 SAC: S0260521120003 zhoufeipeng@gf.com.cn 广发证券首席金工分析师 安宁宁 SAC: S0260512020003 anningning@gf.com.cn 广发金工安宁宁陈原文团队 摘要 主要结论： 在前期报告《龙头扩散效应行业轮动之二-优选行业组合构建》中我们构建了月度优选行业轮动组合。而由于部分一级行业暂无 对应ETF作为投资工具，部分时期可能存在无法直接持有特定行业标的的情况。为最大限度获取轮动收益，本报告尝试从选股的角度探讨以 持股来复制或增厚轮动策略收益的方式。 行业个股双驱优选组合月度调仓，2013年以来年化收益33.6%，相对中证500指数年化超额收益 28.3%，IR2.07，相对最大回撤27.8%。 板块行情的底层驱动机制： 受活跃资金挖掘概念主题手法的启发，我们思考板块行情的启动和发展过程或许在微观层面上源自板块内个股上涨的 蔓延与扩散，从最初的行情龙头到概念相关的更多个股，正是行情覆盖范围的逐渐延伸催生了一轮行业上行趋势。我们将此过程称为"龙头扩散效 应"。 收益复制角度： 行业全复制组合收益复制效果最理想，但由于 ...

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].