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The Journal of Financial Data Science

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Advances of Machine Learning Approaches for Financial Decision Making and Time-Series Analysis: A Panel Discussion

Nino Antulov-Fantulin and Petter N. Kolm
The Journal of Financial Data Science Spring 2023, jfds.2023.1.123; DOI: https://doi.org/10.3905/jfds.2023.1.123
Nino Antulov-Fantulin
is the head of research at Aisot Technologies AG and a senior researcher at ETH Zürich in Zürich, Switzerland, and is a visiting research scientist at the Courant Institute of Mathematical Sciences at New York University in New York, NY
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Petter N. Kolm
is a clinical full professor and the director of the Mathematics in Finance Master’s Program at the Courant Institute of Mathematical Sciences at New York University in New York, NY
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Abstract

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.

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The Journal of Financial Data Science: 5 (1)
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Advances of Machine Learning Approaches for Financial Decision Making and Time-Series Analysis: A Panel Discussion
Nino Antulov-Fantulin, Petter N. Kolm
The Journal of Financial Data Science Mar 2023, jfds.2023.1.123; DOI: 10.3905/jfds.2023.1.123

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Advances of Machine Learning Approaches for Financial Decision Making and Time-Series Analysis: A Panel Discussion
Nino Antulov-Fantulin, Petter N. Kolm
The Journal of Financial Data Science Mar 2023, jfds.2023.1.123; DOI: 10.3905/jfds.2023.1.123
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