The Future of AI Innovation in Equity Trading and Investing
Last Friday, our CEO, Paul White, joined Renee Yao, CIO and Founder of Ivy Capital, at Cornell’s Duffield Engineering campus for a Rebellion Research Fund Managers fireside chat. The conversation focused on one of the most important questions facing the investment industry today: What is the future of AI innovation for equity trading and investing?
During the discussion, Paul shared his perspective on how AI is changing the way equity traders and investors approach research and generate alpha. Historically, the formula was relatively straightforward: Data + People + Compute = P&L. Today, that equation is evolving to Data + Intelligence + Compute = P&L. This shift does not mean that people are becoming less important. Instead, AI is becoming a tool that complements human expertise, allowing teams to work with larger amounts of information, accelerate research, and unlock opportunities for nonlinear growth.
Data has always been critical to quantitative investing, but with AI, the need for high-quality, clean, and well-curated data is even greater. Data must also be well-documented so that it can be effectively used by both people and AI agents. AI can help streamline the process of preparing and evaluating data; for example, what once took more than a month to onboard and evaluate a new data trial can now take as little as 24 hours. With comprehensive documentation and automatically generated features, AI can further accelerate the research process.
The next step is turning that data into intelligence and ideas. Historically, researchers would read papers and textbooks, network, and explore new concepts to develop research ideas. Today, that process is increasingly becoming People + AI → Ideas, with people guiding AI agents to explore information and connect it to broader investment objectives. Our Idea 2 Alpha project is one example: analyzing a new research paper, which would previously be measured in months, can now take around five hours. This shift also changes the skills needed to work effectively with AI. Technical skills such as coding and modeling remain important, but understanding business objectives and knowing how to reliably leverage AI are becoming increasingly more valuable.
Of course, scaling these capabilities also requires managing the cost of compute. As AI enables teams to conduct more research, controlling cloud and token costs becomes increasingly important. We have been working with vendors to develop tools that provide real-time budgeting limits and spending controls, while also capturing data that can be used to analyze spending and ROI. The goal is not simply to use more AI, but to use it efficiently and strategically.
Ultimately, AI is changing how quantitative teams work, but people remain at the center of the process. By combining data, intelligence, and compute, teams can accelerate the path from information to ideas and create new opportunities in equity trading and investing.
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