The Four Horsemen of Machine Learning in Finance

The Four Horsemen of Machine Learning in Finance

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The Four Horsemen of Machine Learning in Finance 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.

Moreover, 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.

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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.

The practice of machine learning in finance has grown somewhat commensurately with both theoretical and computational developments in machine learning.

Early adopters have been the quantitative hedge funds, including Bridgewater Associates, Renaissance Technologies, Worldquant, D.E. Shaw, and Two Sigma who have embraced novel machine learning techniques.

Although there are mixed degrees of adoption and a healthy skepticism exists that machine learning is a panacea for quantitative trading.

In 2015, Bridgewater Associates announced a new artificial intelligence unit.
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Having hired people from IBM Watson with expertise in deep learning. Anthony Ledford, chief scientist at MAN AHL: “It’s at an early stage. We have set aside a pot of money for test trading.

With deep learning, if all goes well, it will go into test trading, as other machine-learning approaches have’”. Winton Capital Management’s CEO David Harding. “People started saying, ‘There’s an amazing new computing technique that’s going to blow away everything that’s gone before’. There was also a fashion for genetic algorithms. Well, I can tell you none of those companies exist today — not a sausage of them”.

Machine Learning in finance as a research area is still nascent, and in important ways incomplete.

Read The Full Paper by Matthew Francis Dixon & Igor Halperin

Machine Learning In Finance : The Four Horsemen of Machine Learning in Finance

Matthew & Igor are two of the smartest Machine Learning minds. Furthermore, Rebellion is honored to publicize their work to our readers!

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Matthew Francis Dixon is a researcher and innovator in the area of mathematical algorithms for prediction, outlier detection and risk.

Author of a Machine Learning in Finance textbook and several Journal papers on algorithms and models for machine learning, blockchain based technologies with applications in fintech. Member of the CFA NY Quant Investing Committee.

Furthermore, Matthew is Editorial Associate for the AIMS Journal of Dynamics & Games. In addition, Deputy Editor of the Journal of Machine Learning in Finance. Co-Founder of Digital Bank Technologies. Chair of the IEEE/ACM Workshop on High Performance Computational Finance (2010-2015). In addition, the Google Summer of Code Mentor for the R Statistical Computing Project, 2017. College of Computing Dean’s Excellence in Research Award (Junior level), 2021.

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Igor Halperin is an Artficial Intelligence quant/researcher with a background in theoretical physics

In addition, Igor has an extensive experience in building cutting-edge statistical and advanced machine learning algorithms. To solve practical problems in finance, especially within portfolio modeling, forecasting models. In addition, optimal control models including reinforcement learning and inverse reinforcement learning.

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