Efficiency analysis using the machine learning algorithms: model development and verification

Şafak Sönmez Soydaş (), Yusuf Kalkan (), Alper Veli Çam () and Abdulkadir Barut ()
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Şafak Sönmez Soydaş: Gumushane University
Yusuf Kalkan: Gumushane University
Alper Veli Çam: Gumushane University
Abdulkadir Barut: Türkiye Harran University

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 4, No 10, 3187-3209

Abstract: Abstract Recently, machine learning (ML) algorithms have been employed intensively in the field of finance as in all sectors. The issues such as financial distress prediction, bank credit risk calculation, etc., have been analyzed using ML algorithms. This study aimed to determine firm performance with the data envelopment analysis (DEA) method, sensitivity analysis, and ML algorithms and analyze the efficiency of companies via artificial neural networks (ANNs), support vector machines (SVMs), and logistic regression (LR) classification algorithms. In the study, first, 10 financial ratios were categorized into two parts, such as output and input, and efficiency scores were determined in MS Excel software. The obtained scores were included in the ML algorithm as a categorical dependent variable. Secondly, the data were extracted and included in the analysis software as 80% training and 20% test data, and the accuracy of ML algorithms was tested. Lastly, a comparative analysis of the estimation and classification algorithms of active and inactive companies was conducted. As a result of the analysis, the best classification prediction was seen as the ANN algorithm. SVM and LR algorithms also made an acceptable level of classification prediction. It was expected that the study would have contributed to the literature in terms of testing the companies whose efficiency scores were determined by the DEA method with ML techniques and determining which technique was more successful.

Keywords: Data envelopment analysis; Business efficiency; Artificial neural networks; Support vector machine; Logistic regression (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02114-w

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