Advances of ML Approaches for Financial Decision Making and Time Series Analysis: A Panel Discussion
Antulov-Fantulin, Nino and Kolm, Petter N., Advances of ML Approaches for Financial Decision Making and Time Series Analysis: A Panel Discussion (2022)
Posted: 29 Dec 2022 Last revised: 10 Mar 2023
Date Written: December 17, 2022
Abstract
Advances in machine learning are having profound influence on many fields. In this article, we present a curated version of a panel discussion that we moderated at Applied Machine Learning Days 2022 on the impact of recent advancements in machine learning 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 machine learning: Robert Almgren, Matthew Dixon, Lisa Huang, Fabrizio Lillo, Mathieu Rosenbaum, and Nicholas Westray. In the discussions with the panelists, we focused on (i) the recent developments of deep learning such as NLP-based neural network transformer architectures and physics-informed neural networks, (ii) common misconceptions and challenges in applying machine learning in finance, and (iii) opportunities and new research directions.
Keywords: Deep learning; Machine learning; Portfolio management; Reinforcement learning; Risk management; Trading
JEL Classification: C45, C51, C53, C61, D49, G10, G11, G12, G14
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