机器学习方法在金融决策和时间序列分析中的进展:小组讨论

Nino Antulov-Fantulin, Petter N. Kolm
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引用次数: 0

摘要

机器学习(ML)的进步正在对许多领域产生深远的影响。在本文中,作者介绍了他们在2022年应用机器学习日主持的小组讨论的策划版本,讨论了机器学习的最新进展对金融决策、数据驱动分析和时间序列建模的影响。该小组由金融和ML领域的行业和学术小组成员组成:Robert Almgren, Matthew Dixon, Lisa Huang, Fabrizio Lillo, Mathieu Rosenbaum和Nicholas Westray。在与小组成员的讨论中,作者集中讨论了(1)深度学习的最新发展,如变压器和物理信息神经网络,(2)在金融中应用ML的常见误解和挑战,以及(3)机遇和新的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advances of Machine Learning Approaches for Financial Decision Making and Time-Series Analysis: A Panel Discussion
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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