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

Author

Listed:
  • Zhiming Wu

    (AnHui Business and Technology College, Department of Accounting)

  • Yong Xiong

    (Guangzhou College of Technology and Business, Department of Accounting)

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.

Suggested Citation

  • Zhiming Wu & Yong Xiong, 2026. "Profitability Analysis of Listed Companies in the Era of Big Data: Based on the Decision Tree Model," Advances in Economics, Business and Management Research,, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-719-4_16
    DOI: 10.2991/978-94-6239-719-4_16
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