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Overview
Co-Hosted by: CFA Society New York & SQA
In Partnership with FinTech Leadership Group & Quantitative Investing Group
We are thrilled to bring you the fourth installation of our popular annual Data Science conference, this time in an all-virtual format. The theme this year — Learning from Machine Learning – dives into new ML techniques and applications to different areas of investing. We will also explore the issue of ML interpretability.
Registration Options
Single Day Pass
- Nonmembers: $125
2-Day Combo
- Nonmembers: $200
Agenda
9:30 AM | WELCOMING REMARKS
Lilian Quah, CFA, Managing Director, Portfolio Manager, Head of Quantitative Research, Epoch Investment Partners
9:35 AM | KEYNOTE SPEAKER – Alternative Data in Investment Management: Usage, Challenges and Valuation*
Introduced by Lilian Quah, CFA, Epoch Investment Partners
Petter Kolm, Director of the Mathematics in Finance Master’s Program, New York University
*This section will not be recorded
10:30 AM | KEYNOTE SPEAKER – The Colour of Finance Words
Introduced by Peg DiOrio, CFA, Voya Investment Management
Diego Garcia, BBA, PhD, Chair of the Finance Division and Burridge Endowed Chair in Finance at the Leeds School of Business, University of Colorado at Boulder.
11:15 AM | KEYNOTE SPEAKER – Machine Learning in Quantitative Investing
Introduced by Christos Koutsoyannis, Atlas Ridge Capital
Vladimir Zdorovtsov,Ph.D.,Senior Vice President, Director, Global Equity Research, Acadian
12:00 PM | CLOSING REMARKS
Lilian Quah, CFA, Managing Director, Portfolio Manager, Head of Quantitative Research, Epoch Investment Partners
9:30 AM | WELCOMING REMARKS
Carole K. Crawford, CFA, Founder and Managing Partner, fincap360
9:35 AM | KEYNOTE SPEAKER – Fundamental Analysis Via Machine Learning
Introduced by Carole K. Crawford, CFA, fincap360
Haifeng You, CFA, Hong Kong University of Science and Technology
10:30 AM | KEYNOTE SPEAKER – The Changing Economics of Knowledge Production
Introduced by Inna Okounkova
Simona Abis, Ph.D., Columbia Business School
11:15 AM | PANEL DISCUSSION – Data Science in Finance: Applications and Strategy for the Future
Introduced by Ken D’Souza,CFA, QMA
Moderator
Lan Cai, CFA, Deputy Chief Investment Officer, PBUCC
Panelists
Irina Bogacheva, Head of Quantitative Research, Franklin Templeton Investment Solutions
Peg DiOrio, CFA, Head of Quantitative Equity Portfolio Management, Voya Investment Management
Stacie L. Mintz, CFA, Managing Director, Co-Head of the Quantitative Equity team and Portfolio Manager, QMA
Lilian Quah, CFA, Managing Director, Portfolio Manager, Head of Quantitative Research, Epoch Investment Partners
12:00 PM | CLOSING REMARKS
Ken D’Souza,CFA, Portfolio Manager, QMA
Agenda
Alternative Data in Investment Management: Usage, Challenges and Valuation*
Petter Kolm, Director of the Mathematics in Finance Master’s Program, New York University
*This section will not be recorded
The Colour of Finance Words
Diego Garcia, Chair of the Finance Division and Burridge Endowed Chair in Finance at the Leeds School of Business, University of Colorado at Boulder.
Machine Learning in Quantitative Investing
Vladimir Zdorovtsov, Senior Vice President, Director, Global Equity Research, Acadian
Fundamental Analysis Via Machine Learning
Haifeng You, Hong Kong University of Science and Technology
The Changing Economics of Knowledge Production
Simona Abis, Columbia Business School
Panel Discussion | Data Science in Finance: Applications and Strategy for the Future
Peg DiOrio, CFA, Head of Head of Quantitative Equity Portfolio Management, Voya Investment Management
Stacie L. Mintz, CFA, Managing Director, Co-Head of the Quantitative Equity team and Portfolio Manager, QMA
Lilian Quah, CFA, Managing Director, Portfolio Manager, Head of Quantitative Research, Epoch Investment Partners
Additional Details
Learning Outcomes
- Discussion of best AI techniques
- Assessment of Machine learning in Finance
- Integration of Data Science in the investment process
- New applications of machine learning and natural language processing techniques in finance
- Opportunities and challenges associated with the use of alternative data in investing
- The potential impact of big data technologies on labor markets and the investment management industry in particular
About SQA
The Society of Quantitative Analysts (SQA) is a not-for-profit organization that focuses on education and communication to support members of the quantitative investment community. SQA has hosted educational events in NYC since 1965. The principal mission of SQA is to encourage the dissemination and discussion of leading-edge ideas and innovations, including analytical techniques and technologies for investment research and management. There is more information about SQA and its history on our website: www.sqa-us.org