Forecasting Financial Performance: A Comparative Study of Machine Learning Models in Accounting

DOI

10.2991/978-94-6463-538-6_33How to use a DOI?

Keywords

Financial performance; accounting; machine learning model

Abstract

This paper explores the application of different machine learning models in financial performance prediction and makes a comparative study. Machine learning models play an important role in financial performance prediction, and the commonly used models include linear regression, decision tree, random forest, support vector machines, and neural networks. Linear regression is suitable for handling linear relationships, but has limited effect for predicting complex nonlinear patterns. Decision trees and random forests are able to handle nonlinear relationships and are robust to feature selection and data incompleteness. Support vector machines perform well in handling high-dimensional data and non-linear patterns, while neural networks have the ability to process complex patterns and large-scale data. The choice of suitable model depends on the dataset, prediction target and business scenario, and to evaluate the model performance, accuracy, recall and F1 score. With the development of technology and the enrichment of data, we can expect more advanced models to apply in the field of accounting and finance in the future. To sum up, machine learning models provide a powerful tool for financial performance prediction, but in practical application, appropriate models need to be selected according to the requirements and data characteristics.

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.

Download article (PDF)

TY  - CONF
AU  - Ze Tang
PY  - 2024
DA  - 2024/10/01
TI  - Forecasting Financial Performance: A Comparative Study of Machine Learning Models in Accounting
BT  - Proceedings of the 4th International Conference on Economic Development and Business Culture (ICEDBC 2024)
PB  - Atlantis Press
SP  - 285
EP  - 292
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-538-6_33
DO  - 10.2991/978-94-6463-538-6_33
ID  - Tang2024
ER  -