Machine Learning for Asset Management

233 Pages Posted: 13 Dec 2023 Last revised: 25 Mar 2024

See all articles by Joerg Osterrieder

Joerg Osterrieder

University of Twente; Bern Business School

Date Written: November 20, 2023

Abstract

Machine Learning for Asset Management is structured into four distinct parts, each offering a detailed examination of machine learning's role in finance. The book is designed to be a resource for a range of readers, from professionals to students.

Part I establishes the groundwork, introducing the reader to the foundational aspects of machine learning and its historical integration into finance. This section covers early applications and the evolution of machine learning, particularly in financial settings.

Part II shifts to the practical elements of machine learning in finance, including feature engineering, model evaluation, and the implementation of these models in real-world financial scenarios. This section is rich with mathematical formulas and algorithms essential for understanding and applying machine learning in finance.

Part III delves into the primary machine learning techniques—supervised, unsupervised, and reinforcement learning—and their direct applications in asset management. The section is supplemented with relevant case studies and mathematical explanations.

Part IV addresses more complex topics, such as the ethical implications and regulatory challenges of using machine learning in finance, risk management, and the exploration of future trends and technologies in the field.

This comprehensive guide aims to provide readers with a thorough understanding of the intricate relationship between machine learning and finance.

Keywords: machine learning, asset management, quantitative finance, deep learning, reinforcement learning, feature engineering, model evaluation, risk management, algorithmic trading, financial data analysis

JEL Classification: G00

Suggested Citation

Osterrieder, Joerg, Machine Learning for Asset Management (November 20, 2023). Available at SSRN: https://ssrn.com/abstract=4638186 or http://dx.doi.org/10.2139/ssrn.4638186

Joerg Osterrieder (Contact Author)

University of Twente ( email )

Drienerlolaan 5
Departement of High-Tech Business and Entrepreneur
Enschede, 7522 NB
Netherlands

Bern Business School ( email )

Brückengasse
Institute of Applied Data Sciences and Finance
Bern, BE 3005
Switzerland

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