Is this new forecasting model better than machine learning?
Relevance-based prediction can be used in finance, politics, and sports for more accurate forecasting.
Faculty
Mark P. Kritzman is a Senior Lecturer in Finance at the MIT Sloan School of Management.
Kritzman is also the president and chief executive officer of Windham Capital Management, LLC, and also serves as a senior partner of State Street Associates. He serves on the boards of the Institute for Quantitative Research in Finance and the Investment Fund for Foundations, and on the editorial boards of the Emerging Markets Review, the Financial Analysts Journal, the Journal of Alternative Investments, the Journal of Asset Management, the Journal of Derivatives, and the Journal of Investment Management.
Kritzman has written numerous articles for academic and professional journals, focusing on investing and risk management. He is the author of six books, including Puzzles of Finance and The Portable Financial Analyst.
Kritzman holds an MBA from New York University and a Chartered Financial Analyst designation.
Czasonis, Megan, Mark Kritzman, Cel Kulasekaran, and David Turkington, MIT Sloan Working Paper 6955-23. Cambridge, MA: MIT Sloan School of Management, September 2023. SSRN.
Kinlaw, William, Mark Kritzman, and David Turkington, MIT Sloan Working Paper 6892-23. Cambridge, MA: MIT Sloan School of Management, May 2023.
Kritzman, Mark, Cel Kulasekaran, and David Turkington, MIT Sloan Working Paper 6845-23. Cambridge, MA: MIT Sloan School of Management, April 2023. SSRN.
Czasonis, Megan, Mark Kritzman, and David Turkington. The Journal of Financial Data Science Vol. 5, No. 1 (2023): 27-46. SSRN.
Alsweilem, Khalid, Mark Kritzman, and Malan Rietveld, MIT Sloan Working Paper 6828-22. Cambridge, MA: MIT Sloan School of Management, September 2022.
Kinlaw, William, Mark Kritzman, Michael Metcalfe, and David Turkington, MIT Sloan Working Paper 6730-22. Cambridge, MA: MIT Sloan School of Management, June 2022.
Hong Ru, MFin ’10, PhD ’15, and Juno Wei Chen, MFin ’10, are grateful for the chance to give back to the place where they met and set off on their respective career paths.
Relevance-based prediction can be used in finance, politics, and sports for more accurate forecasting.