Predicting bankruptcy of companies listed on the Stock Exchange using the artificial neural network
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Being informed of capital markets companies financial situation is one of the shareholders and economic analysts perturbation. Thus, financial market analysts and researchers were looking for methods to predict capital markets companys future conditions. This research is finding a model to predict bankruptcy of stock exchanges markets companies with using the artificial neural network. In this research we used Zemijewski financial ratios with one macro economic variable to predict companies bankruptcy. Population of study was selected from the accepted companies in Irans stock and exchanges organization. Financial ratios have been extracted from companies financial statement in a five years period between 2010 and 2014, finally we choose 84 companies that divided to salubrious and bankrupt equal number in each. We used multi-layer perceptron (MLP) with back propagation algorithm to create predictor model and data analysis. The network has been trained once with financial ratios and again with additional macro economic variable to confirm that the accuracy of network model will increase by additional macro economic variable. Ultimately the designed model in total mode has 92.95 percent of accuracy and 85 percent correct prediction of bankrupted companies for one year earlier of bankruptcy.
Keywords:
Language:
Persian
Published:
Journal of Investment Knowledge, Volume:7 Issue: 26, 2018
Pages:
277 to 296
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