Machine Learning Algorithm Applications in Empirical Finance: A Review of the Empirical Literature

DOI

10.2991/978-94-6463-246-0_38How to use a DOI?

Keywords

machine learning; financial market prediction; bankruptcy prediction; literature review

Abstract

The modern finance industry has many challenges to handle, so advanced tools are applied to assist people in trying to find a solution to those complex problems. Machine learning is a powerful tool that can help researchers tackle difficult issues, including those in the financial industry. In this paper, I reviewed literature focusing on machine learning algorithm applications in empirical finance. I divide the literature review into three sub-sectors: financial market prediction, bankruptcy prediction and credit risk analysis, and other notable aspects. It is found that researchers widely use algorithms like support vector machines and neural networks. Finally, based on my review of the literature, I provide my insights in this area.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaochuan Liao
PY  - 2023
DA  - 2023/09/26
TI  - Machine Learning Algorithm Applications in Empirical Finance: A Review of the Empirical Literature
BT  - Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023)
PB  - Atlantis Press
SP  - 311
EP  - 316
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-246-0_38
DO  - 10.2991/978-94-6463-246-0_38
ID  - Liao2023
ER  -