Modeling and Determining the Power of Working Capital Management in Predicting Corporate Financial Bankruptcy Using Artificial Intelligence Algorithm

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Azizi Sedighe

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Journal Paper

Paper Information

Journal: FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES) Year:2021 | Volume:14 | Issue:51 Page(s): 171-190

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Title

Modeling and Determining the Power of Working Capital Management in Predicting Corporate Financial Bankruptcy Using Artificial Intelligence Algorithm

Author(s)

Azizi Sedighe | Issue Writer Certificate

Keywords

Financial Bankruptcy

Working Capital Management

Artificial Intelligence Algorithm

Abstract

The main purpose of this study is to model and determine the ability of Working Capital Management in predicting Financial Bankruptcy of companies using Artificial Intelligence Algorithms. The statistical population of the study consists of 120 companies listed on the Tehran Stock Exchange during the years 2008-2019. In order to achieve the objectives of the research, first by studying previous research in the field of financial distress, 12 financial ratios affecting Financial Bankruptcy have been selected. After calculating the ratios, the mean comparison test was used to consider the ratios that have a significant difference between the two bankrupt and non-bankrupt financial groups for calculation in the forecasting models, which showed that all 12 variables are suitable for use in the models. Then, in order to evaluate the ability of Working Capital Management in predicting companies' Financial Bankruptcy, to compare research models with and without Working Capital Management variable based on five models of multilayer perceptron neural network, support vector machine, decision tree, logistic regression and multiple audit analysis is performed. The results of comparing bankruptcy prediction models showed that the multilayer perceptron neural network model has the highest power in predicting companies in terms of Financial Bankruptcy and soundness compared to other models. The results of comparing the models showed that with the development of the research model, by entering the Working Capital Management variable, the training error of the multilayer perceptron neural network model is reduced to 0. 036 and the accuracy of the model is increased to 75%.

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APA: Copy

Azizi, Sedighe. (2021). Modeling and Determining the Power of Working Capital Management in Predicting Corporate Financial Bankruptcy Using Artificial Intelligence Algorithm. FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), 14(51 ), 171-190. SID. https://sid.ir/paper/951422/en

Vancouver: Copy

Azizi Sedighe. Modeling and Determining the Power of Working Capital Management in Predicting Corporate Financial Bankruptcy Using Artificial Intelligence Algorithm. FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)[Internet]. 2021;14(51 ):171-190. Available from: https://sid.ir/paper/951422/en

IEEE: Copy

Sedighe Azizi, “Modeling and Determining the Power of Working Capital Management in Predicting Corporate Financial Bankruptcy Using Artificial Intelligence Algorithm,” FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), vol. 14, no. 51 , pp. 171–190, 2021, [Online]. Available: https://sid.ir/paper/951422/en

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