Data Science in Finance: From Theory to Practice

January 16, 2020 | 8:30 AM - 5:30 PM

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Overview

Hear from academics and practitioners on how data science techniques can be used in your investment process. Topics include: using machine learning for stock selection and for risk forecasting, machine learning explainability, predicting returns using textual analysis of news articles, measuring the state of the economy by analyzing business news, using loan applications to predict loan defaults.

Agenda

8:30 AM | REGISTRATION & CHECK-IN

9:00 AM | MACHINE LEARNING FOR STOCK SELECTION

Keywan Rasekhschaffe, Gresham Investment Management

10:00 AM | MACHINE LEARNING FOR RISK FORECASTING

Yuriy Bodjov, CFA, TD Asset Management

11:00 AM | BREAK

11:15 AM | THE INTUITIVE APPEAL OF EXPLAINABLE MACHINES

Solon Barocas, Cornell University, Microsoft Research

12:15 PM | LUNCH

1:15 PM |  WHEN WORDS SWEAT: IDENTIFYING SIGNALS FOR LOAN DEFAULT IN THE TEXT OF LOAN APPLICATIONS

Oded Netzer, Columbia Business School

2:15 PM | BREAK

2:30 PM | PREDICTING RETURNS WITH TEXT DATA

Zheng (Tracey) Ke, Harvard University

3:30 PM | THE STRUCTURE OF ECONOMIC NEWS

Bryan Kelly, Ph.D., Yale School of Management, AQR

4:30 PM | NETWORKING & CATERED RECEPTION

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