Professor Matthew Dixon on Machine Learning & Investing : The Good, The Bad & The Ugly

Professor Matthew Dixon on Machine Learning For Investing : The Good, The Bad & The Ugly

Professor Matthew Dixon on Machine Learning & Investing : The Good, The Bad & The Ugly

Machine Learning & Investing : The Good, The Bad & The Ugly. Professor Matthew Dixon is an Assistant Professor of Applied Math and affiliate in the Stuart Business school. Matthew researches applications of machine learning in finance. Matthew began his career as a quant in structured credit trading at Lehman Brothers. Lehman Brothers was prior to a career in consulting for finance and technology firms and pursuing academic research.

Professor Dixon’s prize winning and Intel funded research has led to new approaches, algorithms and software for fintech. In addition to funding from the National Science Foundation and Google. Funding for the purpose of developing new technologies for fintech in partnership with the University of Michigan and Northwestern University. Moreover, in 2020, he released the first textbook on machine learning in finance with Prof. Igor Halperin (NYU and Fidelity Investments).

In conclusion, Matthew is also an associate editor of the AIMS. The Journal of Dynamics and Games. Furthermore Matthew also serves on the board of the (CFA) institute’s New York Quantitative Society. In addition, Matthew has held scientific appointments at Stanford University and UC Davis. Lastly, Matthew holds a PhD in Applied Mathematics from Imperial College, London.

Professor Matthew Dixon on Machine Learning & Investing : The Good, The Bad & The Ugly

Professor Dixon’s paper on Bayesian Networks. Specifically for valuing private companies can be found here.

A Bayesian Approach to Ranking Private Companies Based on Predictive Indicators by Matthew Francis Dixon, Jike Chong :: SSRN

Artificial Intelligence & Machine Learning – Rebellion Research