RT Journal Article
SR Electronic
T1 Advances of Machine Learning Approaches for Financial Decision Making and Time-Series Analysis: A Panel Discussion
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP jfds.2023.1.123
DO 10.3905/jfds.2023.1.123
A1 Nino Antulov-Fantulin
A1 Petter N. Kolm
YR 2023
UL https://pm-research.com/content/early/2023/03/14/jfds.2023.1.123.abstract
AB Advances in machine learning (ML) are having profound influence on many fields. In this article, the authors present a curated version of a panel discussion that they moderated at Applied Machine Learning Days 2022 on the impact of recent advancements in ML on decision making, data-driven analysis, and time-series modeling in finance. The panel consisted of industry and academic panelists in the field of finance and ML: Robert Almgren, Matthew Dixon, Lisa Huang, Fabrizio Lillo, Mathieu Rosenbaum, and Nicholas Westray. In the discussions with the panelists, the authors focused on (1) the recent developments of deep learning such as transformer and physics-informed neural networks, (2) common misconceptions and challenges in applying ML in finance, and (3) opportunities and new research directions.