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

Sponsorship Overview

For over 80 years, CFA Society New York—formerly known as New York Society of Security Analysts (NYSSA)—has been a leading forum for the exchange of information among investment professionals. Our mission is to raise standards of practice in the industry for the ultimate benefit of society. Towards that goal, CFA Society New York content is curated by volunteer practitioner interest groups that strive to shape the future of the industry. A bold line exists between the financial support by organizations to maintain the quality of our programming and the selection of speakers who contribute to dialogue in our community. To that end, a ceaseless commitment to transparency is something we consider foundational to our cause.

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Refunds, Cancellations, and Substitutions

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  • LESS THAN 48 HOURS OR AFTER EVENT HAS STARTED:
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