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].

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 who adhere to a long-term perspective [2][4]. Group 1: Era Background - The current era is characterized by a convergence of technological evolution and institutional transformation, with generative AI significantly altering various industries and providing new tools for long-term value investment [4]. - AI is evolving from enhancing multi-step reasoning capabilities (L2) to developing AI Agents (L3) that possess "perception-planning-execution" closed-loop capabilities, marking 2025 as the "Year of AI Agents" [4]. Group 2: Market Dynamics - In overseas markets, AI-assisted research has become mainstream, with hedge fund managers leading the adoption of large models to optimize investment research processes [5]. - National policies are reinforcing a long-term orientation, with new guidelines encouraging long-term capital entry into the market and advocating for value investment [5]. - The A-share market is undergoing positive structural changes, with continuous optimization in information disclosure, regulatory enforcement, and investor structure, creating a foundation for sustained long-term investment [5][6]. Group 3: Quantitative Investment Strategies - The Smart Beta strategy is positioned to meet the needs of long-term institutional capital, aiming to provide a reliable long-term allocation tool that combines long-termism with a tool-oriented approach [12][13]. - Smart Beta strategies emphasize a tool-oriented approach, focusing on systematic modeling of fundamental factors to create understandable, replicable, and assessable allocation tools [13]. - The design principles of Smart Beta strategies include high capacity, low turnover, and reasonable fees, supporting institutional investors in achieving long-term allocations [13]. Group 4: AI Development and Research - The industry is embracing AI, with research categorized into interest-driven academic AI studies and more challenging industrial-grade AI development, which requires significant investment and long-term planning [17][18]. - The establishment of the Wizard Intelligence Learning Lab (WILL) reflects the commitment to exploring the future of intelligence, emphasizing the importance of AI's social value [19]. Group 5: Conclusion and Call to Action - The current environment presents both uncertainty and structural challenges, but also opens up opportunities for innovation and development [22]. - The emphasis is on participation and construction rather than observation, highlighting the belief that worthwhile endeavors are often based on long-term faith and practice [22][23].

近日，在2025年中金财富主办的"新科技、新金融、新生态"财富管理发展论坛上，宽德投资联合创始人冯鑫发表了《AI时代的指数化投资：量化投资与长期价值 投资的融合》主题演讲，他围绕人工智能驱动下的投资变革、政策导向的长期化趋势，以及本土量化投资行业的责任与定位，分享了对时代背景与行业走向的深 度思考。 冯鑫拥有20多年量化投资实战经验，曾在SAC、BNP Paribas等国际一流投资机构任基金经理，2013年回国创建宽德投资。宽德投资过往业绩优异，如今已跻身国内 量化私募第一梯队。 精彩观点： 我们正站在一个技术演进与制度转型交汇的关键时点。以生成式AI为代表的新一轮技术浪潮正在深刻改变各行各业，也为长期投资理念的落地提供了新的工 具。 国内外多项研究已经表明，量化交易作为专业的交易者之一，其常驻性与广谱性特征，使其在市场中能够承担着"润滑剂"与"稳定器"的双重角色。 长期资金的崛起、理性投资的回归、AI能力的进步，重构了投资逻辑与市场生态。对于坚持长期视角的机构管理人而言，这既是巨大的挑战，更是难得的时代 机遇。 真正具备生命力的量化投资产品，既要能服务当下，也要具备面向未来的适应能力。智慧选股（Smart ...