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Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling

Author

Listed:
  • Ahmed Amer Abdul-Kareem

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Zaki T. Fayed

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Sherine Rady

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Salsabil Amin El-Regaily

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Bashar M. Nema

    (Department of Computer Science, College of Science, Mustansiriyah University, Baghdad 10001, Iraq)

Abstract

In the realm of financial decision-making, it is crucial to consider multiple factors, among which lies the pivotal concern of a firm’s potential insolvency. Numerous insolvency prediction models utilize machine learning techniques try to solve this critical aspect. This paper aims to assess the financial performance of financial investment firms listed on the Iraq Stock Exchange (ISX) from 2012 to 2022. A Multi-Layer Perceptron predicting model with a parameter optimizer is proposed integrating an additional feature selection process. For this latter process, three methods are proposed and compared: Principal Component Analysis, correlation coefficient, and Particle Swarm Optimization. Through the fusion of financial ratios with machine learning, our model exhibits improved forecast accuracy and timeliness in predicting firms’ insolvency. The highest accuracy model is the integrated MLP + PCA model, at 98.7%. The other models, MLP + PSO and MLP + CC, also exhibit strong performance, with 0.3% and 1.1% less accuracy, respectively, compared to the first model, indicating that the first model serves as a powerful predictive approach.

Suggested Citation

  • Ahmed Amer Abdul-Kareem & Zaki T. Fayed & Sherine Rady & Salsabil Amin El-Regaily & Bashar M. Nema, 2024. "Forecasting Financial Investment Firms’ Insolvencies Empowered with Enhanced Predictive Modeling," JRFM, MDPI, vol. 17(9), pages 1-21, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:424-:d:1483078
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    References listed on IDEAS

    as
    1. Amal Al Ali & Ahmed M. Khedr & Magdi El Bannany & Sakeena Kanakkayil, 2023. "GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction," IJFS, MDPI, vol. 11(1), pages 1-15, February.
    2. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
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