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Evaluation of the operational efficiency of internal capital markets in state-owned enterprises using big data

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  • Zhao, Yuting

Abstract

Nonlinear and time-varying factors affect the capital market, making traditional methods of accurate risk prediction challenging. This study employs big data-driven machine learning (ML) algorithms to predict capital market risk. By utilizing historical data from the internal capital market (ICM) of state-owned enterprises (SOEs), the model applies a deep neural network to model the data, optimizing the parameters using particle swarm optimization to enhance prediction accuracy. The results demonstrate the model’s assessment accuracy of 95.67 %, highlighting its high precision and practical applicability in evaluating ICM efficiency in SOEs. This study offers a novel approach for integrating big data and ML algorithms into capital market risk analysis and ICM performance assessment.

Suggested Citation

  • Zhao, Yuting, 2025. "Evaluation of the operational efficiency of internal capital markets in state-owned enterprises using big data," Finance Research Letters, Elsevier, vol. 85(PD).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pd:s1544612325013261
    DOI: 10.1016/j.frl.2025.108068
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    References listed on IDEAS

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