Year: 2025
Type: article
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.
Source: Forecasting
Institution: Chaoyang University of Technology
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7117764431