Explanation and Evaluation of the Water Cycle Algorithm Metaheuristic Method in Corporate Bankruptcy Prediction
Keywords:
Bankruptcy, Bankruptcy Prediction, Water Cycle AlgorithmAbstract
This study investigates corporate bankruptcy prediction in a competitive environment influenced by various regulatory frameworks. The statistical population comprised companies listed on the Tehran Stock Exchange. Based on the predefined sample selection criteria, data from 329 listed firms were collected and analyzed for the period 2018–2021. The study examined various financial indicators, including cash holdings, assets, liabilities, and profitability, and employed the Water Cycle Algorithm (WCA) to select the most influential features. The results indicated that the Water Cycle Algorithm (WCA) identified fourteen key financial ratios with an accuracy exceeding 97% and a negative predictive value greater than 99%. These ratios were subsequently used as inputs to the prediction model. Furthermore, the results obtained from the Water Cycle Algorithm were evaluated using a confusion matrix comprising four performance metrics: accuracy, precision, sensitivity, and specificity. In addition, to ensure the reliability of the findings, each of the implemented methods was executed multiple times. The results demonstrated that the Water Cycle Algorithm achieved an accuracy rate of 97.86%, indicating strong predictive performance. The findings also suggest that the Water Cycle Algorithm outperformed other approaches, including AutoML and XGBoost.
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Copyright (c) 2025 Kamran Khalili (Author); Mehdi Maranjory; Razieh Alikhani , Yosef Taghipourian (Author)
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