Workshop on Machine Learning for Investor Modelling
Description
The scope and topics of this workshop are:
- Machine learning to model investor behaviours that are
- informed by market events including news, social media, etc.,
- guided by financial advisors with differing discretionary licenses, and
- designed for use in robo- and hybrid-advisor applications.
- Sentiment analysis of investor and financial advisor communications using different datasets (phone calls, textual correspondence, or any other alternative data sources)
- The role of AI/Decision Support Systems in Fintech designed to support retail investors in making better financial decisions.
- Machine Learning for human, robo, or hybrid financial advice and related areas.
- An intersection between behavioural finance and machine learning, broadly defined.
‘Investors’ are broadly defined and includes retail and institutional investors as well as financial advisors and brokers.
Behavioural finance is the study of how the psychology of investors affects personal investment and market outcomes. Based on investment behaviours, there is a need for investment dealerships and banks to develop tools that provide automated support for retail investors and financial advisors in selecting, managing, and evaluating investment portfolios. The difficulty in designing these tools lies in the specification of the complex mathematical structure that models investor behaviour in the presence of market conditions. Machine learning offers a data-driven approach with less specification of the structure of the data generating process.
Financial industry has recently started using machine learning in personal finance applications, such as risk modelling, return forecasting, portfolio construction, financial distress prediction, and so forth. Growing in this body of work is using machine learning techniques to analyze retail investor trading behaviours. The growing interest is marked by a recent special issue titled “Artificial Intelligence for Behavioral Finance” in the Journal of Behavioral and Experimental Finance. The application of machine learning and AI in modelling investor behaviours is a natural partnership. This partnership has many unearthed areas of research, that includes:
- Analyzing transactional data using AI,
- Designing AI-based financial services,
- AI and the role of financial advisors,
- Analyzing the behaviours of financial analysts and effect of decisions on market conditions, and
- Documentation of AI for client communication and regulators.
Understanding investor behaviours is paramount to each of these research areas, particularly in designing effective robo-tools for investors, advisors, dealerships, banks, and regulators. Particular interest lies in learning about investor behaviours during the 2020 market crash informed by their previous behaviours. This workshop will bring together theoretical and industrial machine learners, quantitative finance experts, financial industry practitioners, and fintech entrepreneurs to share their understanding of how AI can be employed to better model investor behaviours and guide the next steps in the research path of behavioural finance and machine learning.
List of Speakers
- Thomas DeLuca, The Vanguard Group
- Eduardo Fontes, Wealth.com
- Igor Halperin, Fidelity Investments
- Ivan Indriawan, University of Adelaide
- Tomasz Kaczmarek, Poznań University of Economics and Business
- Yongjae Lee, Ulsan National Institute of Science and Technology (UNIST)
- Jonathan Lerner, BlackRock
- Jonathan Li, University of Ottawa
- Adam Metzler, Wilfrid Laurier University
- Aaron Miles, LPL Financial
- Jithin Pradeep, The Vanguard Group
- Alberto Rossi, Georgetown University
- Alik Sokolov, University of Toronto
- Akshay Vaghani, Citigroup
- Svitlana Vyetrenko, JP Morgan Chase
- Tina Ruiwen Wang, The Vanguard Group
- Yu Yu, BlackRock
- Thaleia Zariphopoulou, The University of Texas at Austin
Schedule
| 09:00 to 09:30 |
Yongjae Lee, Ulsan National Institute of Science and Technology (UNIST) Location:Online |
| 09:30 to 10:00 |
Yu Yu, BlackRock Location:Online |
| 10:00 to 10:30 |
Thomas DeLuca, Vanguard Location:Online |
| 10:30 to 11:00 |
Coffee Break
|
| 11:00 to 11:45 |
Aaron Miles, LPL Financial Location:Online |
| 11:45 to 12:30 |
Jithin Pradeep, The Vanguard Group, Tina Ruiwen Wang, The Vanguard Group |
| 12:30 to 12:40 |
Group Photos
|
| 12:40 to 14:00 |
Lunch
|
| 14:00 to 15:00 |
Thaleia Zariphopoulou, University of Texas at Austin Location:Online |
| 15:00 to 15:30 |
Coffee Break
|
| 15:30 to 16:00 |
Tomasz Kaczmarek, Poznań University of Economics and Business Location:Online |
| 16:00 to 16:30 |
Akshay Vaghani, Citigroup Location:Online |
| 16:30 to 17:00 |
Ivan Indriawan, University of Adelaide Location:Online |
| 17:00 to 18:30 |
Reception
|
| 09:00 to 09:45 |
Alberto Rossi, Georgetown University Location:Online |
| 09:45 to 10:30 |
Adam Metzler, Wilfrid Laurier University |
| 10:30 to 11:00 |
Coffee Break
|
| 11:00 to 11:45 |
Data Science for Client Engagement: From correlation to causation
Jonathan Lerner, BlackRock Location:Online |
| 11:45 to 12:30 |
Alik Sokolov, University of Toronto |
| 12:30 to 14:00 |
Lunch
|
| 14:00 to 14:45 |
Eduardo Fontes, Wealth.com Location:Online |
| 14:45 to 15:30 |
Igor Halperin, Fidelity Investments Location:Online |
| 15:30 to 16:00 |
Coffee Break
|
| 16:00 to 16:30 |
Jonathan Li, University of Ottawa Location:Online |
| 16:30 to 17:00 |
Svitlana Vyetrenko, JP Morgan AI Research |
