Petter Kolm

Professor, Courant Institute of Mathematical Sciences NYU Courant

Petter Kolm is the Director of the Mathematics in Finance Master’s Program and Clinical Professor at the Courant Institute of Mathematical Sciences, New York University and the Principal of the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group's hedge fund.  Petter has coauthored four books: Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich.  

Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Financial Data Science (JFDS), Journal of Investment Strategies (JoIS), Journal of Machine Learning in Finance (JMLF), and Journal of Portfolio Management (JPM). He is an Advisory Board Member of Betterment (one of the largest robo-advisors) and Alternative Data Group (ADG). Petter is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and Scientific Advisory Board Member of Artificial Intelligence Finance Institute (AIFI).

As a consultant and expert witness, Petter has provided his services in areas including alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization w/ transaction costs and taxes, quantitative and systematic trading, risk management, robo-advisory and investing, smart beta strategies, transaction costs, and tax-aware investing

Digital Week - Thursday (EST)

Thursday, June 25th, 2020

12:05 PM Greedy online classification of persistent market states using realized intraday volatility features

  • Determining which hidden state a new observation belongs to without the need to parse historical observations
  • Clustering temporal features while explicitly penalizing jumps between states by fixed-cost regularization team
  • Obtaining a higher accuracy than the correctly specified maximum likelihood estimator 

Check out the incredible speaker line-up to see who will be joining Petter.

Download The Latest Agenda