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Advanced Techniques for Financial Distress Prediction

Author

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  • Lee-Wen Yang

    (Department of Accounting, Chaoyang University of Technology, Taichung City 413310, Taiwan)

  • Nguyen Thi Thanh Binh

    (Department of Accounting, Chaoyang University of Technology, Taichung City 413310, Taiwan)

  • Jiang Meng Yi

    (Department of Accounting, Chaoyang University of Technology, Taichung City 413310, Taiwan)

Abstract

This study compares Logit, Probit, Extreme Value, and Artificial Neural Network (ANN) models using data from 2012 to 2024 in the Taiwan electronics industry. ANN outperforms traditional models, achieving 98% accuracy in predicting financial distress. Two robust distress signals are identified: Return on Assets (threshold: 7.03%) and Total Asset Growth (threshold: −9.05%). The nonlinear impacts of financial distress on variables are analyzed, with a focus on contextual considerations in decision-making. These findings bring attention to the importance of utilizing advanced techniques like ANN for improved predictive accuracy, offering profound clarification for risk assessment and management.

Suggested Citation

  • Lee-Wen Yang & Nguyen Thi Thanh Binh & Jiang Meng Yi, 2025. "Advanced Techniques for Financial Distress Prediction," Forecasting, MDPI, vol. 8(1), pages 1-19, December.
    • Handle: RePEc:gam:jforec:v:8:y:2025:i:1:p:2-:d:1829596
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