Author(s)

Peng Dong ¹

Affiliation(s)

¹ School of Business, Stevens Institute of Technology, Hoboken, New Jersey, 7030, United States

Corresponding Author

Peng Dong

ABSTRACT

This study aims to develop a financial performance prediction model for listed companies based on multi-source data and machine learning algorithms to address the financial uncertainty faced by enterprises in complex economic environments. The decline in financial performance will affect the internal operations and financial stability of the company, affecting its investors, creditors, and other stakeholders. Building an efficient financial performance forecasting system can help companies identify potential operational risks early, provide data support for management to develop more accurate response strategies, and avoid or mitigate the negative impact of financial difficulties. This article proposes a prediction framework based on multi-source data, and constructs Logit regression model and support vector machine model for prediction experiments through multi-dimensional financial indicator preprocessing. Research has found that there is a certain gap in prediction accuracy between the two models, with support vector machine models performing better in identifying companies with deteriorating financial performance, especially in dealing with complex and multi-source data, demonstrating strong robustness and adaptability. This article also reveals the potential and limitations of current machine learning algorithms in financial performance prediction. Although support vector machines have better predictive performance than Logit regression, there is still room for improvement in accuracy in practical applications. Future research will consider integrating more non-financial data, such as industry trends, market sentiment, etc., and further improving the predictive performance of the model by optimizing algorithm structure and parameters. We hope to provide more scientific and forward-looking support for the financial management and decision-making of enterprises, laying a solid foundation for enhancing their ability to cope with uncertain risks.

KEYWORDS

Based on multi-source data, financial performance prediction, support vector machine, Logit regression model, machine learning algorithm, manufacturing listed companies, risk warning, data analysis

CITE THIS PAPER

Peng Dong, Research on Financial Performance Prediction Model of Listed Companies Based on Multi Source Data and Machine Learning Algorithms. Population, Resources & Environmental Economics (2025) Vol. 6: 8-13. DOI: http://dx.doi.org/10.23977/pree.2025.060102.

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