Research Summary

The materials on this page reflect only a small, non-proprietary subset of my research. The majority of my work involves confidential systematic investment research that cannot be shared publicly.

My research focuses on systematic and statistical arbitrage investment strategies that translate predictive signals into robust portfolio construction and trading decisions. The work integrates economic structure, statistical modeling, machine learning, and large-scale data analysis to generate forecasts for returns, risk, liquidity, and related quantities across horizons ranging from seconds to months. I also work on optimization problems that arise in portfolio construction and trading. This research spans four systematic portfolio areas: equities, futures/macro, event-driven strategies, and their multi-strategy combination. While carrying out this research at Versor, I collaborate with and support a team of more than thirty researchers.

Recent projects include building multi-model machine-learning pipelines for regression and classification forecasts, constructing alpha signals for global futures and FX markets, nowcasting firm fundamentals using alternative data, constructing market-relevant peer groups, decomposing returns into persistent and mean-reverting components, developing forecasts of merger outcomes, predicting global equity transaction costs, and enhancing security-level risk models.

A consistent theme of my work is structuring large forecasting and optimization problems to identify predictive signals and translate them into robust portfolio construction and systematic trading strategies.

In addition, I produce some non-proprietary work that is listed below.

For up-to-date PDF copies of my publications, please also look at the Versor Investments Athenaeum.