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Thursday, January 24 | 8:30 AM – 5:30 PM

Online registration is closed. Walk-ins are welcome.

Fees: Members: $495 | Nonmembers: $595

SQA and CFANY’s Fintech Leadership Group Present

Data Science in Finance: Looking Beyond the Hype

Host: Fintech Leadership Group & SQA

Data Science is blossoming in the financial industry and literature. More and more financial firms are introducing machine learning systems to forecast markets and trade. Academics are astounded by “unprecedented out-of-sample return prediction” ability of ML and are setting а “new standard for accuracy in measuring risk premia.”[1]  They find that “in designing and pricing securities, constructing portfolios, and risk management… deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.”[2]  At the same time, “rapid empirical success in this field currently outstrips mathematical understanding.”[3]

Join us to learn from leading academics and practitioners about Data Science applications in finance and to understand what’s behind these techniques and why they work so well.

[1] Shihao Gu, Bryan Kelly and Dacheng Xiu ”Empirical Asset Pricing via Machine Learning.” Chicago Booth Research Paper No. 18-04

[2] J. B. Heaton, N. G. Polson and J. H. Witte “Deep Learning in Finance.” arXiv:1602.06561v3 [cs.LG] 14 Jan 2018

[3] Sanjeev Arora “Mathematics of Machine Learning: An introduction.” https://www.cs.princeton.edu/~arora/


Date & Time:

Thursday, Jan. 24, 2019
8:30 am -5:30 pm


CFA Society New York Conference Center
1540 Broadway Suite 1010


Early Bird Pricing
( For the first 100 people to register)

Member: $425
Nonmember: $525

Regular Pricing

Member: $595
Nonmember: $695

Who Would Be Interested

Portfolio Managers
Practitioners interested in how machine learning, artificial intelligence, and other data science techniques can be used in financial and other sectors.

8:30 AM | Registration and Breakfast

9:00 – 10:00 AM

“What is Machine Learning and Deep Learning?”

Sanjeev Arora, Princeton University

10:00 – 11:00 AM

“Hierarchical and Hybrid Neural Networks Models for Time Series Forecasting.”

Slawek Smyl, Uber Technologies

11:00 – 11:15 AM | Coffee break

11:15 – 12:15 PM

“Can Machines Learn Finance?” **

Bryan Kelly, Yale University and AQR

12:15 – 1:15 PM | Lunch

1:15 – 2:15 PM

“Open Source Random Variables: Building a Prediction Web” **

Peter Cotton, JP Morgan and Stanford University

2:15 – 3:15 PM

“Mathematicians helping art conservators and art historians.”

Ingrid Daubechies, Duke University

3:15 – 3:30 PM | Coffee break

3:30 – 4:30 PM

“Challenge for Finance: AI Interpretability”

Ken Perry, Consultant in Risk and Quantamental Investing, CRO, formerly of Och Ziff

4:30  PM | Cocktails & Networking Reception

** Presentations will only be available at the event; no post-event video will be offered

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

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