Andrew W. Lo

Faculty

Andrew W. Lo

About

Andrew W. Lo is the Charles E. and Susan T. Harris Professor, a Professor of Finance, and the Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management.

His current research spans five areas: evolutionary models of investor behavior and adaptive markets, systemic risk and financial regulation, quantitative models of financial markets, financial applications of machine-learning techniques and secure multi-party computation, and healthcare finance.  Recent projects include: deriving risk aversion, loss aversion, probability matching, and other behaviors as emergent properties of evolution in stochastic environments; constructing new measures of systemic risk and comparing them across time and systemic events; applying spectral analysis to investment strategies to decompose returns into fundamental frequencies; and developing new statistical tools for predicting clinical trial outcomes, incorporating patient preferences into the drug approval process, and accelerating biomedical innovation via novel financing structures.

Lo has published extensively in academic journals (see http://alo.mit.edu) and his most recent book is Adaptive Markets: Financial Evolution at the Speed of Thought.  His awards include Batterymarch, Guggenheim, and Sloan Fellowships; the Paul A. Samuelson Award; the Eugene Fama Prize; the IAFE-SunGard Financial Engineer of the Year; the Global Association of Risk Professionals Risk Manager of the Year; the Harry M. Markowitz Award; the Managed Futures Pinnacle Achievement Award; one of TIME’s “100 most influential people in the world”; and awards for teaching excellence from both Wharton and MIT. His book Adaptive Markets has also received a number of awards, listed here. He is a Fellow of Academia Sinica; the American Academy of Arts and Sciences; the Econometric Society; and the Society of Financial Econometrics.

Lo is also a principal investigator at the MIT Computer Science and Artificial Intelligence Laboratory, an affiliated faculty member of the MIT Department of Electrical Engineering and Computer Science, an external faculty member of the Santa Fe Institute, and a research associate of the National Bureau of Economic Research. He is a member of the New York Federal Reserve Board’s Financial Advisory Roundtable, FINRA’s Economic Advisory Committee, the National Academy of Sciences Board on Mathematical Sciences and Their Applications, Beth Israel Deaconess Medical Center’s Board of Overseers, and the boards of Roivant Sciences and the Whitehead Institute for Biomedical Research.

Lo holds a BA in economics from Yale University and an AM and PhD in economics from Harvard University.

Honors

Lo wins Jamieson Prize

Pinnacle Achievement Award Given to Lo

GARP honors Lo as Risk Manager of the Year

Lo’s book wins PROSE Award

Markowitz Award given to Lo

Publications

"A Portfolio Approach to Accelerate Therapeutic Innovation in Ovarian Cancer."

Chaudhuri, Shomesh, Katherine Cheng, Andrew W. Lo, Shirley Pepke, Sergio Rinaudo, Lynda Roman, and Ryan Spencer. Journal of Investment Management. Forthcoming.

"Acceleration of Rare Disease Therapeutic Development: A Case Study of AGIL-AADC."

Das, Sonya, Samuel Huang, and Andrew W. Lo. Drug Discovery Today. Forthcoming.

"Book Review."

Lo, Andrew W. Review of Reinventing Capitalism in the Age of Big Data, by Viktor Mayer Schönberger and thomas Ramge. Science, Forthcoming.

"Dynamic Alpha: A Spectral Decomposition of Investment Performance Across Time Horizons."

Chaudhuri, Shomesh E. and Andrew W. Lo. Management Science. Forthcoming.

"Estimation of Clinical Trial Success Rates and Related Parameters."

Wong, Chi Heem, Kien Wei Siah, and Andrew W. Lo. Biostatistics. Forthcoming.

Healthcare Finance.

Lo, Andrew W. and Shomesh Chaudhuri. Forthcoming.

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Press

Using data science to forecast clinical trial outcomes

MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process

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Ideas Made to Matter

Machine learning helps predict clinical trial outcomes

Using artificial intelligence to enhance data on clinical trial outcomes can help biomedical firms, investors, and — ultimately — patients.

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