中金2026年展望 | 量化策略：随“集”应变

Core Viewpoint - The report explores whether the advantages of quantitative investment strategies can be sustained in the A-share market environment of 2026, highlighting the cyclical switching between "consensus" and "divergence" market conditions as a key determinant of strategy effectiveness [2][3][5]. Market Environment and Strategy Effectiveness - The A-share market is expected to enter a "central uplift platform period" after returning from historical lows, driven by the long-term trend of market institutionalization and the recovery of incremental funds, particularly from ETFs [3][38]. - The report identifies "institutional holding concentration" as a core indicator linking macro market patterns with micro Alpha sources, suggesting that increased concentration indicates a shift to "consensus" markets, while decreased concentration favors "divergence" markets [2][26][30]. Market Outlook for 2026 - The overall market environment for 2026 is assessed as optimistic, with a focus on structural opportunities due to attractive risk premiums and the absence of extreme overheating [4][44]. - The report anticipates a shift in investment strategies from capturing short-term opportunities in "divergence" markets to identifying core trends in "consensus" markets, particularly with the emergence of AI as a new investment theme [11][41]. Alpha Sources and Market Modes - The evolution of Alpha sources is linked to market modes, with "trading Alpha" being more effective in "divergence" markets and "cognitive Alpha" in "consensus" markets [17][25]. - "Trading Alpha" focuses on capturing short-term pricing inefficiencies, while "cognitive Alpha" emphasizes accurate predictions of future fundamentals [18][19]. Market Concentration Dynamics - High market concentration reflects a consensus-driven environment that rewards depth in research, while low concentration indicates a divergence-driven environment that favors breadth in strategy [27][28]. - The report constructs a market concentration index based on the top holdings of public funds, indicating stronger consensus when the index is high and greater divergence when it is low [30][31]. Investment Strategy Recommendations - In the anticipated "central uplift platform period," strategies that effectively combine depth (through alternative data and machine learning) with breadth (systematic capture of rotation opportunities) are expected to perform better [42][41]. - The report suggests that quantitative strategies may continue to outperform average active equity funds due to their ability to adapt to complex market conditions [42][43].