Title: Identifying companies' bankruptcy using an enhanced neural network model: a case study evaluating the bankruptcy of Iranian stock exchange companies

Authors: Shahin Ordikhani; Sara Habibi; Ahmad Reza Haghighi

Addresses: Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran ' School of Engineering, Urmia University, Sero Highway, Oroumieh, West Azerbaijan Province, Oroumieh, Iran ' Department of Mathematics, Technical and Vocational University, Tehran, Iran

Abstract: The purpose of this research is to utilise an enhanced neural network model to anticipate the bankruptcy of stock exchange companies and test the predictive power of this model by considering the concept of misclassification. Misclassifications decrease the accuracy of prediction. Among every one of the structures of the two-layer neural network, the perceptron model with the structure of nine neurons in the input layer and a neuron in the output layer with the Levenberg learning algorithm demonstrated the most predictive power. The neuron structures three, five, and nine were considered to decide the proper characteristics of a two-layer perceptron for anticipating companies' bankruptcy. Among them, the two-layer perceptron with nine neurons in the input layer and one neuron in the output layer identified to has the best performance. The findings demonstrate that applying artificial neural network models amplify financial management for facing with fluctuations and bankruptcy.

Keywords: effective predicting financial rates; bankruptcy; neural network; machine learnings; predictive power.

DOI: 10.1504/IJISE.2021.116927

International Journal of Industrial and Systems Engineering, 2021 Vol.38 No.4, pp.503 - 529

Received: 31 Jul 2019
Accepted: 28 Sep 2019

Published online: 09 Aug 2021 *

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