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Deep Learning-Based Model for Financial Distress Prediction

Author

Listed:
  • Mohamed Elhoseny

    (Mansoura University
    University of Sharjah)

  • Noura Metawa

    (University of Sharjah
    Mansoura University)

  • Gabor Sztano

    (Corvinus University of Budapest)

  • Ibrahim M. El-hasnony

    (Mansoura University)

Abstract

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.

Suggested Citation

  • Mohamed Elhoseny & Noura Metawa & Gabor Sztano & Ibrahim M. El-hasnony, 2025. "Deep Learning-Based Model for Financial Distress Prediction," Annals of Operations Research, Springer, vol. 345(2), pages 885-907, February.
    • Handle: RePEc:spr:annopr:v:345:y:2025:i:2:d:10.1007_s10479-022-04766-5
    DOI: 10.1007/s10479-022-04766-5
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    References listed on IDEAS

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