Episode
LLMs for Equities Feature Forecasting at Two Sigma with Ben Wellington - #736
- Published
- Jun 17, 2025
- Duration seconds
- 3571
- Processing state
failed
Actions
POST https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/llms-for-equities-feature-forecasting-at-two-sigma-with-ben-wellington-736/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/twiml-ai-podcast/llms-for-equities-feature-forecasting-at-two-sigma-with-ben-wellington-736.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
Today, we're joined by Ben Wellington, deputy head of feature forecasting at Two Sigma. We dig into the team’s end-to-end approach to leveraging AI in equities feature forecasting, covering how they identify and create features, collect and quantify historical data, and build predictive models to forecast market behavior and asset prices for trading and investment. We explore the firm's platform-centric approach to managing an extensive portfolio of features and models, the impact of multimodal LLMs on accelerating the process of extracting novel features, the importance of strict data timestamping to prevent temporal leakage, and the way they consider build vs. buy decisions in a rapidly evolving landscape. Lastly, Ben also shares insights on leveraging open-source models and the future of agentic AI in quantitative finance. The complete show notes for this episode can be found at https://twimlai.com/go/736.