• Matthew F. Dixon
  • Igor Halperin
  • Paul Bilokon


This chapter introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. How it has been adopted is heavily fragmented by the academic disciplines underpinning the applications. We view some key mathematical examples that demonstrate the nature of machine learning and how it is used in practice, with the focus on building intuition for more technical expositions in later chapters. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Such arguments are developed further in later chapters. This chapter also introduces reinforcement learning for finance and is followed by more in-depth case studies highlighting the design concepts and practical challenges of applying machine learning in practice.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Matthew F. Dixon
    • 1
  • Igor Halperin
    • 2
  • Paul Bilokon
    • 3
  1. 1.Department of Applied MathematicsIllinois Institute of TechnologyChicagoUSA
  2. 2.Tandon School of EngineeringNew York UniversityBrooklynUSA
  3. 3.Department of MathematicsImperial College LondonLondonUK

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