Financial Bankruptcy prediction using artificial neural network and firefly algorithms in companies listed in Tehran Stock Exchange

Subject Areas : Financial engineering

Mahdi Heidary ¹ , Shokrollah Ziari ² , seyed ahmad shayan nia ³ , Alireza Rashidi Kemijan ⁴

1 - Department of industrial management, , Islamic Azad University, Firoozkooh Branch, Firoozkooh, Iran
2 - Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
3 - department of industrial management,, firoozkooh branch, islamic azad university, firoozkooh, iran
4 - Department of industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.

Received: 2020-06-27 Accepted : 2020-10-03 Published : 2021-03-21

Keywords: Firefly Algorithm, Financial Bankruptcy, backpropagation neural network, ,

Abstract :

By anticipating financial turmoil, it is possible to take the necessary precautions before financial distress occurs by managers and investors. This study compares two algorithms for prediction of bankruptcy using Artificial Neural Network (ANN) and Neural network optimized metaheuristic Firefly Algorithm (FA). To run test, first initial values are set for the network weights and biases and then during the optimization process, a population of different weights and biases is generated by FA algorithm. The conversion function used in the output layer is linear and for the middle layer a non-linear sigmoid function is selected. To conduct this research, the data of 79 companies listed on TSE during 2012 to 2015 were collected and analyzed statistically by backpropagation neural network and FA algorithms. The results show that FA, compared to ANN predicted the companies’ bankruptcy much better. Also, FA Algorithm maintains a good correlation between bankrupt and non-bankrupt companies, just like real data.