Interpretable Machine Learning forFinancial Applications

Boris Kovalerchuk (), Evgenii Vityaev, Alexander Demin and Antoni Wilinski
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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

A chapter in Machine Learning for Data Science Handbook, 2023, pp 721-749 from Springer

Abstract: 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.

Keywords: Finance time series; Relational machine learning; Visual knowledge discovery; Decision tree; Neural network; Deep neural network; Success measure; Portfolio management; Stock market; Trading rules (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_32

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DOI: 10.1007/978-3-031-24628-9_32

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