Organizacija, Volume (46), No (1), Year (2013-1) , Pages (20-27)

Title : ( Financial Distress Prediction of Iranian Companies by Using Data Mining Techniques )

Authors: Mahdi Moradi , Mahdi Salehi , Mohammad Ebrahim Ghorgani , Hadi Sadoghi Yazdi ,

Citation: BibTeX | EndNote

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. 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. In addition, 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. The data used in this research was 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

Going concern prediction; Support vector data description; Fuzzy
برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@article{paperid:1032300,
author = {Moradi, Mahdi and Salehi, Mahdi and Mohammad Ebrahim Ghorgani and Sadoghi Yazdi, Hadi},
title = {Financial Distress Prediction of Iranian Companies by Using Data Mining Techniques},
journal = {Organizacija},
year = {2013},
volume = {46},
number = {1},
month = {January},
issn = {1318-5454},
pages = {20--27},
numpages = {7},
keywords = {Going concern prediction; Support vector data description; Fuzzy c-mean},
}

[Download]

%0 Journal Article
%T Financial Distress Prediction of Iranian Companies by Using Data Mining Techniques
%A Moradi, Mahdi
%A Salehi, Mahdi
%A Mohammad Ebrahim Ghorgani
%A Sadoghi Yazdi, Hadi
%J Organizacija
%@ 1318-5454
%D 2013

[Download]