Building at the edge
of finance & ML.

I am a Master of Finance student at Fordham University specializing in the intersection of quantitative modeling and machine learning. My work focuses on building systems that translate unstructured data into a measurable edge within prediction markets and alternative asset classes.

Currently focused on:

• NLP & Mention Event Trading — Developing an algorithm that uses Markov chains and NLP to forecast vocabulary shifts in FOMC speeches. By predicting high-probability word mentions, the model identifies and trades mispriced “mention event” contracts on prediction markets before public realization.
• Event-Driven Prediction Markets — Building ML models to forecast EPS beats. Instead of traditional long/short equity, I target event-driven contracts where the Polymarket consensus diverges significantly from statistical probability.
• Alternative Alpha — Exploring niche domains where raw probability meets market mechanics, including the development of predictive models for horse racing.