Imaged financial Ratios and Bankruptcy Prediction using Convolutional Neural Networks

Subject Areas : Financial engineering

abbasali  haghparast ¹ (Department of Accounting, Chabahar International Branch, Islamic Azad University, Chabahar, Iran)
alireza  momeni ² (Department of Accounting, human sciences colege, payam noor university, Tehran, Iran.)
Aziz  Gord ³ (Department of Accounting, Faculty of Management and Accounting, Tehran-Gharb Branch, Payame Noor University, Tehran, Iran.)
fardin  mansoori ⁴ (department of accounting, management and economy college, sistan and balouchestan university, Zahedan, Iran)

Received: 2020-07-18 Accepted : 2020-09-22 Published : 2021-03-21

Keywords: Bankruptcy Prediction, Convolutional neural networks, Imaged financial Ratios,

Abstract :

Convolutional neural networks are being applied to identification problems in a variety of fields, and in some areas are showing higher discrimination accuracies than conventional methods. Hence, in this research, an attempt is made to apply a convolutional neural network to the prediction of corporate bankruptcy. The financial statements ratios has been choice 66 companies that have been delisted from the Iran Stock Market due to de facto bankruptcy as well as the financial statements of 66 listed companies over 2000 to 2019 financial periods. In this method, a set of financial ratios are derived from the financial statements and represented as a grayscale image. The image generated by this process is utilized for training and testing a convolutional neural network. The images for the bankrupt and continuing enterprises classes are used for training the convolutional neural network based on GoogLeNet. The findings shows, in prediction of going concern of firms, Convolutional neural network has predicted with 50 percent of precision. This means that 50 percent of continues firms and 50 percent of bankrupt firms has been predicted precisely.