宽德投资冯鑫：AI时代的指数化投资——量化投资与长期价值投资的融合

Core Viewpoint - The integration of AI-driven investment transformation, long-term policy orientation, and the responsibility of the domestic quantitative investment industry presents both challenges and opportunities for institutional managers committed to a long-term perspective [1][16]. Group 1: Era Background - The current era is characterized by a convergence of technological evolution and institutional transformation, with generative AI fundamentally altering various industries and providing new tools for long-term value investment [1]. - The development of AI is progressing from enhancing multi-step reasoning capabilities (L2) to achieving "perception-planning-execution" closed-loop capabilities (L3), marking 2025 as the "Year of AI Agents" [2]. Group 2: Policy and Market Dynamics - National policies are reinforcing a long-term orientation, with new regulations encouraging long-term capital market entry, advocating value investment, and standardizing algorithmic trading [4]. - The A-share market is undergoing positive structural changes, with improved information disclosure, regulatory enforcement, and investor composition, creating a foundation for sustainable long-term investment [5]. Group 3: Role of Quantitative Trading - Quantitative trading plays a crucial role in enhancing resource allocation efficiency and market stability, acting as both a "lubricant" and "stabilizer" in financial markets [6]. - Research indicates that quantitative trading can provide liquidity and price discovery, thereby improving overall market efficiency [6]. Group 4: Smart Beta Strategy - The Smart Beta strategy aims to serve long-term institutional capital by providing a reliable long-term allocation tool that combines long-termism with a tool-oriented approach [10]. - This strategy emphasizes a systematic modeling of fundamental factors, focusing on long-term value characterization while adhering to the principles of objectivity and discipline in quantitative investment [10][11]. Group 5: AI Exploration and Future Opportunities - The industry is increasingly embracing AI, with research categorized into interest-driven academic AI studies and more challenging industrial-grade AI development [12]. - Opportunities in the AI era can be divided into application-oriented real opportunities and foundational capability exploration, with the latter focusing on the potential of intelligent systems [13]. Group 6: Conclusion and Call to Action - The current environment presents a unique opportunity for active participation in shaping the future, emphasizing the importance of long-term commitment and practice over short-term certainty [16][17].