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GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction

Author

Listed:
  • Amal Al Ali

    (Information Systems Department, University of Sharjah, Sharjah 27272, United Arab Emirates
    These authors contributed equally to this work.)

  • Ahmed M. Khedr

    (Computer Science Department, University of Sharjah, Sharjah 27272, United Arab Emirates
    Mathematics Department, Zagazig University, Zagazig 44519, Egypt
    These authors contributed equally to this work.)

  • Magdi El Bannany

    (College of Business Administration, Umm Al Quwain University, Umm Al Quwain 536, United Arab Emirates
    Department of Accounting and Auditing, Faculty of Business, Ain Shams University, Cairo 11566, Egypt
    These authors contributed equally to this work.)

  • Sakeena Kanakkayil

    (Computer Science Department, University of Sharjah, Sharjah 27272, United Arab Emirates
    These authors contributed equally to this work.)

Abstract

Despite the obvious benefits and growing popularity of Machine Learning (ML) technology, there are still concerns regarding its ability to provide Financial Distress Prediction (FDP). An accurate FDP model is required to avoid financial risk at the lowest possible cost. However, in the Internet era, financial data are exploding, and they are being coupled with other kinds of risk data, making an FDP model challenging to operate. As a result, researchers presented several novel FDP models based on ML and Deep Learning. Time series data is are important to reflect the multi-source and heterogeneous aspects of financial data. This paper gives insight into building a time-series model and forecasting distress far in advance of its occurrence. To build an efficient FDP model, we provide a hybrid model (GALSTM-FDP) that incorporates LSTM and GA. Unlike other previous studies, which established models that predicted distress probability only within one year, our approach predicts distress two years ahead. This research integrates GA with LSTM to find the optimum hyperparameter configuration for LSTM. Using GA, we focus on optimizing architectural aspects for modeling the optimal network based on prediction accuracy. The results showed that our algorithm outperforms other state-of-the-art methods in terms of predictive accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:1:p:38-:d:1075915
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    References listed on IDEAS

    as
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    2. Soumya Ranjan Sethi & Dushyant Ashok Mahadik & Rajkiran V. Bilolikar, 2024. "Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 164-179, January.

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