Andrea Frazzini

Andrea Frazzini is an Adjunct Associate Professor of Finance at the Leonard N. Stern School of Business, New York University, where he teaches MBA courses in Behavioral Finance and Hedge Fund Strategies. He is also a Principal at AQR Capital Management, LLC, in Greenwich, Connecticut, integrating academic research with practical investment strategies.

• PhD in Economics, Yale University (2005)
• MSc in Economics, London School of Economics and Political Science (2001)
• BS in Economics, University of Rome III (2000)

His research centers on Behavioral Finance, Asset Pricing, and Asset Management, exploring investor behavior, market anomalies, and portfolio construction. Key contributions include the Betting Against Beta (BAB) model, Quality Minus Junk (QMJ) factor, Risk Parity, and the Dumb Money effect, all grounded in empirical analysis of market inefficiencies and investor psychology.

The 15 most recent articles reflect a consistent focus on factor investing, behavioral anomalies, and institutional investor behavior. They span topics such as leverage constraints, school ties in investing, earnings announcements, and trading costs, demonstrating a strong integration of theory and real-world data. Keywords include Asset Pricing, Behavioral Finance, and Quantitative Investing, with subfields covering BAB, QMJ, risk parity, and investor sentiment.

His scientific awards highlight the impact of his work:

• Smith Breeden Distinguished Paper Prize
• Roger F. Murray Prize
• Swiss Finance Institute Outstanding Paper Award
• Global Investors Award, Best Paper in Asset Pricing
• DFA Prize for Capital Markets and Asset Pricing
• Chicago Quantitative Alliance First Prize (twice)
• Bernstein Fabozzi/Jacobs Levy Awards
• BSI Gamma Foundation Grant

Frazzini has advised no named students in the provided text, but his research has been supported by major industry grants and collaborations, particularly through AQR Capital Management. His work on trading costs utilized nearly a trillion dollars of live trading data, indicating substantial institutional backing. He actively disseminates research through a public data library, fostering transparency and reproducibility in quantitative finance.

While no formal lab or academic research team is mentioned, his work at AQR suggests collaboration within a large quantitative investment team. His public data sets on BAB, QMJ, HML Devil, and global factors are widely used in academia and industry, amplifying the reach of his research.