Matthew S Rothman Millennium Head of Statistical Arbitrage Strategies, MIT Sloan School of Management Faculty

Matthew S. Rothman occupies a dual leadership position bridging institutional asset management and academic finance. As Head of Statistical Arbitrage Strategies and Head of Quantitative Advisory Services at Millennium Management—one of the industry's largest multi-strategy hedge funds—Rothman directs the development of systematic investment strategies and delivers quantitative research and portfolio advisory services across the firm's investment platform. This role, which he has held since July 2020, places him at the center of Millennium's quantitative strategies business during a period of significant evolution in systematic investing.

In parallel with his industry responsibilities, Rothman maintains an active faculty position as Senior Lecturer in Finance at the MIT Sloan School of Management, a role he has held since 2017. His teaching portfolio focuses on graduate-level instruction in quantitative investing, asset pricing, and systematic portfolio management—subjects directly informed by his three decades of institutional experience. This academic engagement reflects a notable trend among senior hedge fund executives: maintaining scholarly involvement to influence next-generation quants and researchers while remaining actively deployed in capital markets.

Rothman's career trajectory reflects progressive advancement through the quantitative finance hierarchy at systemically important institutions. Before Millennium, he served as Managing Director at Goldman Sachs and held the position of Head of Global Quantitative Equity Research at Credit Suisse. His earlier roles included Director of Quantitative Global Macro Research at Acadian Asset Management, Global Head of Quantitative Equity Research at both Barclays Capital and Lehman Brothers, and senior quantitative research positions at Sanford C. Bernstein and Goldman Sachs Asset Management. This progression demonstrates deep expertise across multiple market regimes and institutional cultures.

His technical specialization spans the full spectrum of modern quantitative finance: statistical arbitrage, factor modeling, portfolio construction methodologies, machine learning applications in financial markets, and global macro systematic strategies. This breadth positions him to advise institutional investors navigating the ongoing transition from traditional factor-based approaches to machine learning-enhanced systematic strategies. His visibility at major institutional conferences, including the 2026 MIT Sloan Investment Conference and Cornell Financial Engineering AI & Future of Finance Conference, underscores his role as a recognized authority for both hedge fund managers and academic researchers monitoring trends in quantitative investing.