Profitability Analysis of Listed Companies in the Era of Big Data: Based on the Decision Tree Model

Zhiming Wu; Yong Xiong

doi:10.2991/978-94-6239-719-4_16

<Previous Article In Volume

Next Article In Volume>

Profitability Analysis of Listed Companies in the Era of Big Data: Based on the Decision Tree Model

Authors

Zhiming Wu¹, Yong Xiong²^{, *}

¹Department of Accounting, AnHui Business and Technology College, Hefei, China

²Department of Accounting, Guangzhou College of Technology and Business, Guangzhou, China

^*Corresponding author. Email: xiongyong@gzgs.edu.cn

Corresponding Author

Yong Xiong

Available Online 9 July 2026.

DOI: 10.2991/978-94-6239-719-4_16 How to use a DOI?
Keywords: Machine Learning; Decision Tree Model; Profitability
Abstract: Profitability is a critical indicator highly valued by potential investors. However, this indicator is often constrained by human bounded rationality and management’s manipulative incentives, which compromises the relevance and reliability of financial reporting. This paper selects financial data of Chinese listed companies from the CSMAR database covering the period from 2001 to 2022 as the sample and employs machine learning algorithms to predict their future profitability. The empirical results indicate that machine learning can effectively improve the accuracy of financial forecasting. Specifically, by utilizing the decision tree model, this study provides investors with theoretical support and practical modeling tools for analyzing corporate profitability. Compared with traditional financial forecasting, machine learning algorithms can automatically learn patterns from historical data and capture more complex influencing factors. By analyzing massive multi-source data—including financial and non-financial data, industry information, and real-time market changes—these algorithms facilitate a more comprehensive and dynamic prediction of future financial performance.
Copyright: © 2026 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)

<Previous Article In Volume

Next Article In Volume>

Volume Title: Proceedings of the 2026 6th International Conference on Enterprise Management and Economic Development (ICEMED 2026)
Series: Advances in Economics, Business and Management Research
Publication Date: 9 July 2026
ISBN: 978-94-6239-719-4
ISSN: 2352-5428
DOI: 10.2991/978-94-6239-719-4_16 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Zhiming Wu
AU  - Yong Xiong
PY  - 2026
DA  - 2026/07/09
TI  - Profitability Analysis of Listed Companies in the Era of Big Data: Based on the Decision Tree Model
BT  - Proceedings of the 2026 6th International Conference on Enterprise Management and Economic Development (ICEMED 2026)
PB  - Atlantis Press
SP  - 136
EP  - 147
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-719-4_16
DO  - 10.2991/978-94-6239-719-4_16
ID  - Wu2026
ER  -

download .riscopy to clipboard