Matthew Dixon, Ph.D, FRM

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Matthew Dixon, Ph.D, FRM

Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF.  Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets.  He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.

October 2020


Modernizing Investment Research: Using AI to Derive Insights

Active managers face three headwinds – declining fees, outflows, and the increasingly difficult task of delivering consistent alpha in a competitive market. Our industry has the opportunity to address these issues by modernizing investment research. The Society of Quantitative Analysts (SQA) and CFA Society New York are pleased to present a series of timely panel discussions on this opportunity. Each featured panel will focus, respectively, on one of four key steps identified as fundamental to the modernization of investment research.

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Fintech Thought Leadership Group, Modernizing Investment Research Series, Quantitative Investing, Virtual Events & Programming
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