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

Zhiming Wu and Yong Xiong ()
Additional contact information
Zhiming Wu: AnHui Business and Technology College, Department of Accounting
Yong Xiong: Guangzhou College of Technology and Business, Department of Accounting

A chapter in Proceedings of the 2026 6th International Conference on Enterprise Management and Economic Development (ICEMED 2026), 2026, pp 136-147 from Springer

Abstract: 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.

Keywords: Machine Learning; Decision Tree Model; Profitability (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-719-4_16

Ordering information: This item can be ordered from
http://www.springer.com/9789462397194

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

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().