Interpretable Machine Learning for Financial Applications

Interpretable Machine Learning for Financial Applications Full article

Source

Machine Learning for Data Science Handbook
Compilation, Springer Nature Switzerland AG. 2023. 975 c. ISBN 9783031246289.

Output data

Year: 2023, Pages: 721-749 Pages count : 29 DOI: 10.1007/978-3-031-24628-9_32

Authors

Kovalerchuk Boris ¹ , Vityaev Evgenii ² , Demin Alexander ³ , Wilinski Antoni ⁴

Affiliations

1	Department of Computer Science, Central Washington University, Ellensburg, USA
2	Sobolev Institute of Mathematics, Russian Academy of Sciences, Novosibirsk, Russia
3	Ershov Institute of Informatics, Russian Academy of Sciences, Novosibirsk, Russia
4	Department of Finance and Management, WSB University in Gdansk, Gdansk, Poland

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.

Kovalerchuk B. , Vityaev E. , Demin A. , Wilinski A.
Interpretable Machine Learning for Financial Applications
In compilation Machine Learning for Data Science Handbook. – Springer Nature Switzerland AG., 2023. – C.721-749. – ISBN 9783031246289. DOI: 10.1007/978-3-031-24628-9_32 Scopus OpenAlex

Published print:	Aug 2, 2023
Published online:	Aug 18, 2023

Scopus:	2-s2.0-85195566957
OpenAlex:	W4385950389

DB	Citing
OpenAlex	2
Scopus	5