Andrew W. Lo

About

Andrew W. Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management and the director of MIT's Laboratory for Financial Engineering. He is also a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL), an affiliated faculty of the Department of Electrical Engineering and Computer Science, a member of the Operations Research Center (ORC), and the Institute for Data, Systems, and Society (IDSS), all at MIT. Additionally, he is an external faculty member at the Santa Fe Institute. Lo received his AM and PhD in economics from Harvard University, his BA in economics from Yale University, and graduated from the Bronx High School of Science. His academic career began at the University of Pennsylvania's Wharton School, where he served as an Assistant and Associate Professor. His current research spans several areas including 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, healthcare finance, and deep-tech investing such as fusion energy and advanced manufacturing. Lo has published extensively in academic journals and authored the book 'The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics.' His work has earned numerous awards, including Sloan and Guggenheim Fellowships, the Paul A.…

Research topics

• Economics
• Computer Science
• Artificial Intelligence
• Business
• Finance
• Mathematics
• Statistics
• Physics
• Applied mathematics
• Econometrics

Selected publications

• Spectral factor models

  Journal of Financial Economics · 2021 · 71 citations

  PublisherDOI

• Financing Biomedical Innovation

  Annual Review of Financial Economics · 2022 · 42 citations

  1st authorCorresponding

  We review the recent literature on financing biomedical innovation, with a specific focus on the drug development process and how it may be enhanced to improve outcomes. We begin by laying out stylized facts about the structure of the drug development process and its associated costs and risks, and we present evidence that the rate of discovery for life-saving treatments has declined over time while costs have increased. We make the argument that these structural features require drug developmen…

  PublisherOA PDFDOI

• Introduction to PNAS special issue on evolutionary models of financial markets

  Proceedings of the National Academy of Sciences · 2021 · 41 citations

  Senior authorCorresponding

  At present, there are no criteria to evaluate whether a coronavirus can cause pandemics with severe inflammation or just common colds. We provide a possible answer by considering the virus not only as an infectious agent but as a reservoir of ...It is unclear how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to the strong but ineffective inflammatory response that characterizes severe Coronavirus disease 2019 (COVID-19), with amplified immune activation in diverse…

  DOI

• Predicting clinical trial duration via statistical and machine learning models

  Contemporary Clinical Trials Communications · 2025-04-02 · 2 citations

  articleOpen accessSenior authorCorresponding

  We apply survival analysis as well as machine learning models to predict the duration of clinical trials using the largest dataset so far constructed in this domain. Neural network-based DeepSurv yields the most accurate predictions and we identify key factors that are most predictive of trial duration. This methodology may help clinical researchers optimize trial designs for expedited testing, and can also reduce the financial risk of drug development, which in turn will lower the cost of fundi…

  PublisherDOI

• The Risk, Reward, and Asset Allocation of Nonprofit Endowment Funds

  National Bureau of Economic Research · 2025-07-01 · 2 citations

  reportOpen access1st authorCorresponding

  Using tax filings from 374,351 U.S. nonprofit organizations from 2008 to 2020, we provide the first large-scale analysis of endowment prevalence, function, asset allocation, and returns.Endowment use varies systematically across sectors and revenue models.Organizations with endowments scale mission-related spending more effectively and hedge revenue risk through asset allocation.Yet most endowments underperform passive benchmarks, with the weakest performance concentrated among smaller, self-man…

  PublisherDOI

Frequent coauthors

• Kien Wei Siah

  59 shared

• Jiang Wang

  Taiyuan University of Technology

  57 shared

• Richard T. Thakor

  University of Minnesota

  54 shared

• Chi Heem Wong

  50 shared

• Ruixun Zhang

  43 shared

• Shomesh E. Chaudhuri

  39 shared

• A. Craig MacKinlay

  32 shared

• Mila Getmansky

  27 shared

Awards & honors

• Sloan Fellowship
• Guggenheim Fellowship
• Paul A. Samuelson Award
• Harry M. Markowitz Award
• CFA Institute’s James R. Vertin Award

Similar researchers at Massachusetts Institute of Technology

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• Jonathan A. Parkerh-47
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