Call for papers
Accepted papers:
Using Machine Learning to Predict Investors’ Switching Behaviour [Paul Nixon, Evan Gilbert]
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Diversification-Enhancing Contrastive Learning [Youngbin Lee, Yejin Kim, Joohwan Hong, Yongjae Lee]
Links: TBA
Papers should be submitted on CMT3 by 13th October, 2023
Submission URL: https://cmt3.research.microsoft.com/RECSYS2023/Submission/Index
We invite research papers on Machine Learning for Investor Modeling. The scope and topics include (broadly defined and not limited to) on machine learning (ML) models for recommender systems and investor modelling that:
provide real-time and long-term decisions support systems for investments (securities, residential, commercial, etc.), loans and debt consolidation, insurance, real estate, etc.
act as automatic or semi-automatic assistive artificial intelligences (AI), such as a Robo-advisor or Advisor-in-the-loop (hybrid), for clients, advisors, institutional investors, etc.
considers investment behavioral biases of clients
are used to gain human attention for financial behavior interventions
incorporate virtual data assistants for more natural interactions with investors
improve financial literacy to guide financial decision-making and minimize errors detrimental to stated investment goals
incorporate equitable recommendations through machine learning constrained by fairness
utilize sentiment analysis or large language models to analyze phone calls, textual correspondence, or any other alternative data sources for conversational recommender systems
are informed by market events including news, social media, etc.
ethical AI in the realm of recommender systems
reduce operating costs
We also invite tutorials and introductory papers to bridge the gap between academia and the financial industry:
Overview of Industry Challenges
Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.
Algorithmic Tutorials
Short tutorials from academic researchers that explain current solutions to challenges related to the technical areas mentioned above, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.
Submission Guidelines
All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template (or, ACM LaTeX templates, use the sigconf template). Submissions are limited to 4-8 content pages, including all figures and tables but excluding references.
Following the conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed.
Accepted Papers
Papers that are accepted will be presented as oral presentations, depending on schedule constraints. Abstracts of accepted papers will be posted on the workshop website but will not be archived online by the workshop. Accepted papers will be invited to submit full manuscripts to the Special Issue on "Statistical and Machine Learning for Investor Modelling" in the Journal of Behavioral Finance.