Group Meeting, Interest Group, Interest Group Meetings, Quantitative Investing, Virtual Events & Programming

Quantitative Investing Group Meeting

Friday, September 11 | 12:00 PM - 1:30 PM

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Overview


Join us for this interactive session to hear directly from the authors of Machine Learning in Finance: From Theory to Practice, Matthew Dixon and Igor Halperin. This book introduces machine learning methods in finance and includes Python code examples. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.

This 90 minute session will include plenty of time for Q&A with the author.

Machine Learning in Finance: From Theory to Practice
https://www.amazon.com/Machine-Learning-Finance-Theory-Practice/dp/3030410676

Group Description

Speakers

Matthew Dixon, PhD, FRM
Stuart School of Business, Illinois Institute of Technology

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.


Igor Halperin, PhD
Fidelity Investments – NYU Tandon