Financial Distress Prediction of Iranian Companies Using Data Mining Techniques

• Authors: Mahdi Moradi , Mahdi Salehi , Mohammad Ebrahim Ghorgani , Hadi Sadoghi Yazdi
• Languages of publication: EN

Abstracts

EN

Decision-making problems in the area of financial status evaluation are considered very important. Making incorrect decisions in firms is very likely to cause financial crises and distress. Predicting financial distress of factories and manufacturing companies is the desire of managers and investors, auditors, financial analysts, governmental officials, employees. Therefore, the current study aims to predict financial distress of Iranian Companies. The current study applies support vector data description (SVDD) to the financial distress prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 3-fold cross-validation to find out the optimal parameter values of kernel function of SVDD. To evaluate the prediction accuracy of SVDD, we compare its performance with fuzzy c-means (FCM).The experiment results show that SVDD outperforms the other method in years before financial distress occurrence. The data used in this research were obtained from Iran Stock Market and Accounting Research Database. According to the data between 2000 and 2009, 70 pairs of companies listed in Tehran Stock Exchange are selected as initial data set.

Keywords

EN

financial distress prediction; Support vector data description; Fuzzy c-mean

• Publisher: Sciendo
• Journal: Organizacija
• Year: 2013
• Volume: 46
• Issue: 1
• Pages: 20-27

Dates

published

2013-01-01

online

2013-02-12

Contributors

author

Mahdi Moradi

• Ferdowsi University of Mashhad, Iran

author

Mahdi Salehi

• Ferdowsi University of Mashhad, Faculty of Economics and Business Administration, Azadi Square, Vakilabad Bolvard, Mashhad City, Khorasan Razavi Province, Iran

author

Mohammad Ebrahim Ghorgani

• East Oil and Gas Company, NIOC, Iran

author

Hadi Sadoghi Yazdi

• Ferdowsi University of Mashhad, Iran

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Identifiers

DOI

10.2478/orga-2013-0003