SQA and CFA Society of New York: 5th Annual Data Science in Finance Conference: Frontiers in Investment Data Science

5th Annual Data Science in Finance Conference: Frontiers in Investment Data Science

Day One, January 19th, 2022:

9 - 10am: Paper Presentation on Applied ML Research

•  Introduction to FinBERT, a finance-specific deep learning algorithm for extracting textual information.
• Presenter: Professor Allen Huang, Associate Dean, School of Business and Management, ​Associate Professor, Hong Kong University of Science and Technology.
• Discussant: Dr. Adam Kelleher, Chief Data Scientist for Research, Barclays Investment Bank

10 - 11am: Paper Presentation on Data Science for Robo-Advising

• An overview of the need for and use of AI in robo-advice, including the personalization of recommendations, issues around generating and maintaining trust in automated financial advice, and the future of effective human/robot interactions.
• Presenter: Dr. Marie Briere, Amundi Research, Paris Dauphine University, Université Libre de Bruxelles
• Paper: Bianchi and Briere, 2021

11am – Noon: Paper Presentation on The Impact of Data Science on Financial Markets

• An assessment of the impact of big data on market informativeness for large vs. small firms and on lowering the cost of capital for large firms.
• Presenter: Professor Maryam Farboodi, Jon D. Gruber Career Development Professor, Assistant Professor, Finance, MIT Sloan School of Management
• Papers: Farboodi et al, 2018 and Begenau et al, 2021

Day Two, January 20th, 2022:

9 – 10:15am: Keynote Lecture: Introduction to Causality

• Introduction to the fundamentals of causal inference, an emerging field in machine learning that goes beyond correlational patterns to improve decision-making. Causal inference methods rely on patterns generated by stable and robust causal mechanisms and promises to address fundamental challenges in machine learning such as generalizability, interpretability, and bias. Cause and effect can be captured in a formal graphical model (causal graph) and answered systematically using available data. The DoWhy Python library implements a four-step causal modeling framework for analyzing decision-making tasks.
• Speaker: Dr. Emre Kiciman, Senior Principal Researcher, Microsoft Research and Dr. Amit Sharma, Principal Researcher, Microsoft Research India

10:15 – 11am: Case Study of Causal Inference in Financial Markets

• CausaLens is pioneering Causal AI, "a new category of intelligent machines that can reason about the world the way humans do, through cause-and-effect relationships and with imagination." Introduction to their approach and tools, including causalNet, a modeling system that extracts the causal drivers from large datasets and marries them with human domain knowledge. Dr. Sipos will highlight use cases in asset management.
• Presenter: Dr. Maksim Sipos, Co-Founder and CTO, CausaLens

11am – Noon: Panel Discussion on What It Is Really Like To Implement Data Science in Investment Management

• The panel will address the following questions:                                        
  • Have we established best practices for data acquisition, storage, and processing?
  • Should you build your own investment data platform? If so, how do you do it?
  • Could we move towards a common data platform?
  • As data science roles become more specialized, how do you build the right team?
  • 2021 has been declared the “Year of MLOps”; what does this mean for our industry?

      

Speakers:

• Dr. Jeff Meli, Head of Research, Barclays Investment Bank
• Carson Boneck, Chief Data Officer, Balaysny Asset Management, L.P.
• Dr. Dan Duggan, Senior Data Scientist, Goldman Sachs
• Sanne de Boer, Director of Quantitative Equity Research, Voya
• Moderated by Lilian Quah, Head of Quantitative Research, Epoch Investment Partners, Inc.

Pricing:

• Members: $10 for both days
• Non-members: $200 for both days | $125 for one day
• Students: $25 for both days | $15 per day (SQA only)