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Interpretable Machine Learning for Financial Applications Научная публикация

Сборник

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

Вых. Данные

Год: 2023, Страницы: 721-749 Страниц : 29 DOI: 10.1007/978-3-031-24628-9_32

Авторы

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

Организации

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
В сборнике 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

Опубликована в печати:	2 авг. 2023 г.
Опубликована online:	18 авг. 2023 г.

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

БД	Цитирований
OpenAlex	2
Scopus	5