Machine Learning for Investor Modelling and Recommender Systems 

Recommender systems are an emerging tool in the realm of financial services. There is a growing need for recommender systems designed for investors that incorporate client demographics, preferences and behaviors, and be supported with proper policy and regulation to protect financial agents from inappropriate recommendations. 

Current state-of-the-art recommender systems utilize a vast amount of user-item interaction data to infer users’ preferences. While it is essential to consider individual investors' preferences, this should not be the only objective of the model. Financial recommender systems must consider the risk and return of a recommended asset or portfolio relative to the existing investor portfolio and preferences, as well as future predictions of the asset. This is mainly because financial products differ from other products and online services. For financial products, it is crucial to consider the risk and return of a recommended portfolio relative to clients' risk tolerance and financial preferences. Further, some recommender systems may not be targeted only at investors, but also institutional investors such as financial advisors who require new tools to oversee a broad array of clients. Therefore, recommender systems for financial products represent a unique challenge compared to other recommendation domains.

Investor demographics and trading behaviours can directly influence the guidance recommender systems provide, and thus the accurate understanding of investors through modelling is paramount. Recommender systems utilising investor modelling can target different end-users, such as clients, financial advisors, dealerships, regulators, etc. Incorporating investor modelling into recommender systems designed for clients, households, advisors and dealerships is pivotal to the future of financial decision-making. 

Currently, there is a gap between industry and academia in the area of ML in investor modelling and recommender systems. This workshop will bring together academic and industry participants working on state-of-the-art research in designing recommender systems that incorporate investor preferences, demographics, historical and predicted trading behaviors, and financial details. This workshop builds on previous workshops on machine learning for investor modelling where we identified recommender systems as an emerging area of quantitative behavioral finance. This workshop reaches more broadly to bring together researchers and industry participants working on new methodologies in AI and recommender systems and share their recent results in this fast-moving field.

Key dates

• Paper submission deadline: 13th October, 2023

• Author notification: 27th October, 2023

• Workshop: 27th November, 2023

Organizing Committee

Dr Igor Halperin (Fidelity Investments, Massachusetts)

Dr Svitlana Vyetrenko (JPMorgan Chase & Co, New York)

Mr Thomas J. De Luca (The Vanguard Group, Pennsylvania)

Prof Alberto Rossi (Georgetown University, Washington DC)

Prof Yongjae Lee (Ulsan National Institute of Science and Technology, South Korea)

Prof John R.J. Thompson (University of British Columbia, Canada) (Lead Organizer)