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Int. J. Ethics Soc 2026, 8(2): 47-60 |
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Kordmenjiri M, Alikhani R, Maranjory M, Fallah R. Comparison of the Performance of the Water Cycle Algorithm with Metaheuristic and Machine Learning Methods in Predicting Unethical Behaviors and Financial Fraud. Int. J. Ethics Soc 2026; 8 (2) :47-60
URL: http://ijethics.com/article-1-459-en.html
URL: http://ijethics.com/article-1-459-en.html
1- Department of Accounting, Cha.C., Islamic Azad University, Chalus. Iran
2- Department of Accounting, Cha.C., Islamic Azad University, Chalus. Iran ,raziehalikhani@iau.ac.ir
2- Department of Accounting, Cha.C., Islamic Azad University, Chalus. Iran ,
Abstract: (14 Views)
Introduction: Unethical financial behaviors and financial statement fraud are among the fundamental challenges of accounting and auditing systems, leading to the misallocation of resources, reduced investor confidence, and financial crises. The purpose of this study is to compare the performance of the Water Cycle Algorithm with six other methods, including the Genetic Algorithm, Firefly Algorithm, Artificial Bee Colony Algorithm, Logistic Regression, Support Vector Machine, and Decision Tree, in predicting fraudulent financial statements and unethical financial behaviors.
Material and Methods: The statistical population consisted of all companies listed on the Tehran Stock Exchange during the period 2016–2023. The final sample included 30 companies and 218 firm-year observations, of which 28 observations (12.83%) were classified as fraudulent firms and 190 observations (87.17%) as non-fraudulent firms. Using the Shannon entropy method, 15 significant variables were selected from an initial set of 42 variables. Subsequently, seven algorithms with optimized parameter settings were independently executed 30 times each. The performance of the algorithms was evaluated using Accuracy, Precision, Recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The nonparametric Wilcoxon signed-rank test was employed to examine the statistical significance of the differences among the algorithms.
Results: The Water Cycle Algorithm achieved the best performance among all methods, with an F1-score of 0.869 and an AUC of 0.923. It was followed by the Genetic Algorithm (F1 = 0.825, AUC = 0.887), Artificial Bee Colony Algorithm (F1 = 0.818, AUC = 0.879), Firefly Algorithm (F1 = 0.808, AUC = 0.871), Support Vector Machine (F1 = 0.799, AUC = 0.862), Logistic Regression (F1 = 0.782, AUC = 0.846), and Decision Tree (F1 = 0.770, AUC = 0.831). The results of the Wilcoxon test indicated that the differences between the Water Cycle Algorithm and all other methods were statistically significant (p-value < 0.05 for all comparisons). Furthermore, the Water Cycle Algorithm, with a standard deviation of 0.025, demonstrated the highest stability among the algorithms examined.
Conclusion: The Water Cycle Algorithm significantly outperforms other metaheuristic and machine learning methods in predicting unethical financial behaviors and fraudulent financial statements. Therefore, it can serve as an effective tool for auditors, financial analysts, and regulatory authorities.
Material and Methods: The statistical population consisted of all companies listed on the Tehran Stock Exchange during the period 2016–2023. The final sample included 30 companies and 218 firm-year observations, of which 28 observations (12.83%) were classified as fraudulent firms and 190 observations (87.17%) as non-fraudulent firms. Using the Shannon entropy method, 15 significant variables were selected from an initial set of 42 variables. Subsequently, seven algorithms with optimized parameter settings were independently executed 30 times each. The performance of the algorithms was evaluated using Accuracy, Precision, Recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The nonparametric Wilcoxon signed-rank test was employed to examine the statistical significance of the differences among the algorithms.
Results: The Water Cycle Algorithm achieved the best performance among all methods, with an F1-score of 0.869 and an AUC of 0.923. It was followed by the Genetic Algorithm (F1 = 0.825, AUC = 0.887), Artificial Bee Colony Algorithm (F1 = 0.818, AUC = 0.879), Firefly Algorithm (F1 = 0.808, AUC = 0.871), Support Vector Machine (F1 = 0.799, AUC = 0.862), Logistic Regression (F1 = 0.782, AUC = 0.846), and Decision Tree (F1 = 0.770, AUC = 0.831). The results of the Wilcoxon test indicated that the differences between the Water Cycle Algorithm and all other methods were statistically significant (p-value < 0.05 for all comparisons). Furthermore, the Water Cycle Algorithm, with a standard deviation of 0.025, demonstrated the highest stability among the algorithms examined.
Conclusion: The Water Cycle Algorithm significantly outperforms other metaheuristic and machine learning methods in predicting unethical financial behaviors and fraudulent financial statements. Therefore, it can serve as an effective tool for auditors, financial analysts, and regulatory authorities.
Type of Study: Original Article |
Subject:
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Received: 2026/06/2 | Accepted: 2026/07/14 | Published: 2026/07/18
Received: 2026/06/2 | Accepted: 2026/07/14 | Published: 2026/07/18
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© 2026 CC BY 4.0 | International Journal of Ethics and Society
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