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Interpretable Machine Learning forFinancial Applications

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Boris Kovalerchuk

    (Central Washington University, Department of Computer Science)

  • Evgenii Vityaev

    (Sobolev Institute of Mathematics, Russian Academy of Sciences)

  • Alexander Demin

    (Ershov Institute of Informatics, Russian Academy of Sciences)

  • Antoni Wilinski

    (WSB University in Gdansk, Department of Finance and Management)

Abstract

This chapter describes machine learning (ML) for financial applications with a focus on interpretable relational methods. It presents financial tasks, methodologies, and techniques in this ML area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, and attribute-based and interpretable relational methodologies. The second part of this chapter presents ML models and practice in finance. It covers the use of ML in portfolio management, design of interpretable trading rules, and discovering money-laundering schemes using the machine learning methodology.

Suggested Citation

  • Boris Kovalerchuk & Evgenii Vityaev & Alexander Demin & Antoni Wilinski, 2023. "Interpretable Machine Learning forFinancial Applications," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 721-749, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_32
    DOI: 10.1007/978-3-031-24628-9_32
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