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].

Core Viewpoint - The integration of AI-driven investment strategies, particularly the Smart Beta approach, is seen as a pivotal development in the investment landscape, aiming to balance long-term value investment with quantitative methods [1][5][15] Group 1: Technological and Policy Context - The current era is marked by a convergence of technological evolution and institutional transformation, with generative AI significantly impacting various industries and facilitating the implementation of long-term investment philosophies [3][8] - AI is evolving from enhancing multi-step reasoning capabilities to developing AI Agents capable of executing complex tasks autonomously, marking 2025 as the "Year of AI Agents" [8][10] - National policies are increasingly promoting long-term investment, with new regulations encouraging the entry of long-term capital into the market and advocating for value investing [10][11] Group 2: Role of Quantitative Trading - Quantitative trading serves as both a "lubricant" and "stabilizer" in the market, enhancing resource allocation efficiency and providing liquidity and price discovery mechanisms [4][12] - The evolution of the Chinese market structure, including improved information disclosure and regulatory enforcement, supports a fundamental-driven market mechanism conducive to long-term investment [10][11] Group 3: Smart Beta Strategy - The Smart Beta strategy is positioned as a reliable tool for long-term institutional investors, focusing on systematic modeling of fundamental factors to create transparent and replicable investment tools [15][16] - This strategy emphasizes low turnover, reasonable fees, and high capacity, aligning with the goal of achieving "universal access" for long-term investors [16][15] Group 4: AI Exploration and Future Opportunities - The industry is witnessing a surge in AI research, categorized into academic-driven studies and industrial-level AI development, which involves significant investment and long-term planning [17][18] - Opportunities arising from AI can be divided into application-oriented chances and foundational capability explorations, both of which are crucial for enhancing industry efficiency and addressing fundamental questions about AI's potential [18][19] Group 5: Conclusion and Vision - The current environment presents both uncertainties and structural challenges, yet it also opens up new avenues for development through technological breakthroughs and collaborative efforts [20] - The establishment of AI laboratories, such as WILL, reflects a commitment to exploring the societal value of AI and fostering a culture of responsible innovation within the investment sector [19][20]