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paper:four_horsemen_of_machine_learning_in_finance

The Four Horsemen of Machine Learning in Finance

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文献基本信息

标题

The Four Horsemen of Machine Learning in Finance

作者

  1. Matthew Francis Dixon, Illinois Institute of Technology
  2. Igor Halperin, New York University (NYU) - NYU Tandon School of Engineering

出版年份

2019

来源

SSRN

关键词

machine learning, asset management, optimal hedging, neural networks, price impact

摘要

Machine Learning has been used in the financial services industry for over 40 years, yet it is only in recent years that it has become more pervasive across investment management and trading. Machine learning provides a more general framework for financial modeling than its linear parametric predecessors, generalizing archetypal modeling approaches, such as factor modeling, derivative pricing, portfolio construction, optimal hedging with model-free, data-driven approaches which are more robust to model risk and capture outliers. Yet despite their demonstrated potential, barriers to adoption have emerged – most of them artifacts of the sociology of this inter-disciplinary field. Based on discussions with several industry experts and the authors' multi-decadal experience using machine learning and traditional quantitative finance at investment banks, asset management and securities trading firms, this position article identifies the major red flags and sets out guidelines and solutions to avoid them. Examples using supervised learning and reinforcement in investment management & trading are provided to illustrate best practices.

引用方式

Dixon, Matthew Francis, and Igor Halperin. “The Four Horsemen of Machine Learning in Finance.” Available at SSRN 3453564 (2019).

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paper/four_horsemen_of_machine_learning_in_finance.txt · 最后更改: 2023/11/10 12:13 由 127.0.0.1

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