Providing Optimal Model to Predict Bankruptcy Using Invasive Weed Algorithm and Evaluating it's Efficiency Compared to Altmen's Model

Document Type : Original Article

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Abstract

Bankruptcy prediction is a topic which affects all countries economic well being. It is vital for all firms to have an accurate model to predict the bankruptcy by default which can pick up the signs of financial distress on time. Therefore, they need a prediction model which can easily recognize the bankruptcy symptoms. For this purpose, this research provides an optimal model to predict bankruptcy by using invasive weed optimization algorithm. The research sample consists of 112 bankrupt and healthy firms, during 2002 to 2012 in accordance with their size and industry type. To evaluate the efficiency of the model invasive weed algorithm-based compared to Altman’s  model, the forenamed models accuracies were evaluated on appropriate prediction of companies’ bankruptcy. The total accuracy of bankruptcy models based invasive weed algorithm and Altman’s  model equal to 97/32%, 54/46% in the event year, 89/28%, and 48/21% in the year prior to event year, and 74/10% and 32/14% in two years prior to the event year, respectively. The results indicate that the model based on invasive weed algorithm can predict the firms’ bankruptcy by higher accuracy in comparison with Altman’s  model.

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Accounting and Auditing Research
Volume 11, Issue 43 - Serial Number 43
Accounting and Auditing Research, Fall 1398, No. 43
October 2019
Pages 41-54

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