Research Summary

The papers below reflect a small, non-proprietary subset of my research; most of my work involves proprietary systematic strategies.

A consistent theme of my work is structuring large-scale pricing and optimization problems to estimate fair values, generate predictive alpha signals, identify mispricings, and translate these into trading and portfolio decisions with short- to medium-term investment horizons.

This includes developing economically informed pricing models, advanced machine learning techniques, alpha and macro forecasts, risk and transaction cost models, portfolio optimization systems, execution methods, and performance attribution frameworks. My work spans equities, futures/macro, event-driven strategies, and multi-strategy portfolios.

Recently deployed proprietary projects include multi-model machine learning pipelines for regression and classification; relative value frameworks based on persistent and mean-reverting components; nowcasting signals for global futures and FX markets; merger pricing and outcome models; firm-level nowcasting with alternative data; peer groups for relative pricing; transaction cost forecasts; and security-level risk models.

Selected non-proprietary work is listed below. (Full PDF papers available by clicking on the title links.)

Up-to-date PDF copies of my publications may also be available at the Versor Investments Athenaeum.