Lecture on Machine Learning in Finance

Prof Yacine Aït-Sahalia

Princeton University

About the Speaker

Yacine Aït-Sahalia is the Otto A. Hack 1903 Professor of Finance and Economics at Princeton University where he served as the inaugural Director of the Bendheim Center for Finance from 1998 until 2014. He was previously an Assistant Professor (1993-96), Associate Professor (1996-98) and Professor of Finance (1998) at the University of Chicago's Booth School of Business, where he received the Emory Williams Award for Excellence in Teaching in 1995. His research concentrates on financial econometrics, fixed income and derivative securities, optimal portfolio selection, and has been published in leading academic journals. His research contributions in financial econometrics include various methods to estimate and test continuous-time models that are sampled at discrete time intervals, including nonparametric methods, closed-form expansions for the transition density of continuous-time models and various methods to analyze high frequency data with a particular emphasis on the presence of jumps. He recently authored High Frequency Financial Econometrics with Jean Jacod, served as the editor of the Review of Financial Studies, the managing editor of the Journal of Econometrics and an associate editor for Econometrica, the Journal of Finance and the Annals of Statistics. Professor Aït-Sahalia is an elected Fellow of the Econometric Society, the Institute of Mathematical Statistics, the American Statistical Association and the Society for Financial Econometrics. He is also an Alfred P. Sloan Foundation Research Fellow, a Fellow of the Guggenheim Foundation and a Research Associate for the National Bureau of Economic Research. He received his Ph.D. in Economics from the Massachusetts Institute of Technology in 1993 and is a graduate of Ecole Polytechnique in France.

Research Seminar Abstract

First Session

Date: Wednesday, 4 December 2019

The first lecture will describe the main methodologies in machine learning and some applications in finance, including credit scoring, factor models, sentiment analysis and trading, and others.

Second Session

Date: Thursday, 5 December 2019

The second lecture will describe in more details a study of high frequency asset price predictability using machine learning methods.