Summary of Key Points
This news article focuses on the "reconstruction of quantitative investment in the era of large models." Through the insights shared by several industry experts at the Lujiazui Financial Salon, it explores the impact of AI on quantitative research and investment: AI has significantly improved efficiency (such as automating report writing and identifying investment factors), but it has also exacerbated the convergence of strategies and the decline in Alpha (excess returns). The core value of researchers has shifted from performing specific tasks to generating innovative ideas. The competitive focus for quantitative institutions has moved from individual experts to the team's ability to collaborate effectively (i.e., creating organizational Alpha). FOFs (funds of funds) need to use a combination of multiple strategies to navigate market fluctuations. Additionally, AI will widen the gap between skilled individuals, leading to the emergence of "superstars" in the field.
1. AI as an Efficiency Accelerator for Quantitative Research
AI acts like a "super assistant" in quantitative research, helping to process large amounts of data quickly, write reports, and identify investment factors (for example, using GPT-based systems to generate innovative factor expressions). However, it cannot create excess returns on its own. For instance, Xu Gonglei from Southern Fund emphasizes that while AI represents a revolution in efficiency, it also accelerates the degradation of factors—what used to be a profitable strategy may become ineffective within months. As more institutions use AI, strategies become increasingly similar, making it harder to achieve excess returns. In this context, managing Beta (returns that follow the market trend, such as index funds) becomes a core competency, as it is essential to control overall market risk when Alpha is scarce.
2. AI Enhances Efficiency but Cannot Generate Returns on Its Own
Although AI can handle routine tasks, the value lies in human creativity and innovative ideas. He Kang from Huatai Securities notes that while 90% of their in-depth reports are assisted by AI, the essence of their work still relies on human insight. For example, AI can be used to simulate researchers' efforts in identifying factors, but someone must provide guidance on what kind of factors to search for; it can analyze news to extract market sentiment, but someone needs to convert this information into actionable investment signals; and top private equity firms can use AI to simulate strategic interactions with competitors, but someone must come up with innovative ways to utilize these insights (such as converting order flows into investment opportunities). In other words, AI is a tool that requires a clear plan before it can be effectively utilized.
3. How FOFs Cope with Converging Quantitative Strategies
He Yangyang from Bofulizhi Management points out that the scale of quantitative strategies in private equity has grown significantly (accounting for 31.2% of stock-based strategies), but traditional quantitative approaches are becoming less effective due to the similarity of strategies. FOFs should diversify their portfolios by combining quantitative and subjective strategies (e.g., having fund managers select stocks manually) to mitigate risk. They also need to be flexible, adjusting their asset allocation based on market conditions (e.g., expanding into alternative assets when commodity markets are volatile).
4. The Competition in Quantitative Institutions Shifts from Individuals to Team Collaboration
He Zixiu from Finance Stage suggests that while quantitative professionals typically possess high qualifications and incomes, they often lack trust in each other, preferring to repeat existing processes rather than share ideas. This can hinder team collaboration and lead to talent loss. The focus has shifted from individual experts to the team's ability to work together efficiently. Establishing trust and sharing mechanisms is more crucial for success than recruiting top-tier individuals.
5. AI Widens the Gap Between Researchers
Gu Xiaojun from Hande Investment notes that AI allows researchers to spend more time on strategic thinking, potentially increasing efficiency by 5-10 times. However, this also creates a gap between those who can leverage AI effectively (who may be as productive as ten others) and those who cannot. In the future, quantitative institutions may consist of small teams of 3-5 people supplemented by AI assistants. Talent development will focus on practical skills, with an emphasis on using limited funds to develop comprehensive capabilities.
Conclusion
AI has revolutionized quantitative research and investment, but it also brings new challenges, such as converging strategies and declining Alpha returns. To overcome these issues, researchers need to focus on innovative ideas, institutions must improve teamwork, and asset managers should diversify their strategy portfolios. While AI is a powerful tool, those who know how to use it will have a competitive advantage in the future quantitative market.