Abstract
Decision-making problems in the area of financial status evaluation have been considered very important. Making incorrect decisions in firms is very likely to cause financial crises and distress. Predicting going concern of factories and manufacturing companies is the desire of managers, investors, auditors, financial analysts, governmental officials, employees. This research introduces a new approach for modeling of company’s behavior based on Fuzzy Clustering Means (FCM). Fuzzy clustering is one of well-known unsupervised clustering techniques, which allows one piece of data belongs to two or more clusters. 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. Our experimental results showed that FCM approach obtains good prediction accuracy in developing a financial distress prediction model. Also, in effective features determination test the results show that features based on cash flows play more important role in clustering two classes.
Highlights
The empirical literature of going concern prediction has recently gained further momentum and attention from financial institutions
Specially Treated (ST) companies are considered as companies in financial distress and those never specially treated are regarded as healthy ones
Results of algorithm test based on fuzzy clustering indicate that the model would cluster going concern data by using data in the year of financial distress, one two years be for financial distress with 96.67%, 85.19% and 77.74% respectively for going concern firms
Summary
The empirical literature of going concern prediction has recently gained further momentum and attention from financial institutions. Among financial distress forecasting methods, discriminant analysis was the dominant method for predicting corporate failure from 1966 until the early part of the 1980s [7,8,9]. It gained wide popularity due to its ease of use and interpretation Both linear and quadratic discriminant analyses are sensitive to deviation from multivariate normality [10]. Since the 1990s, neural networks have been the most widely used techniques in developing quantitative bankruptcy prediction [13], in particular, the approximation or classification powers of the MLP trained by the backpropagation algorithm [14]. Many studies compared the neural networks backpropagation algorithm with the statistical methods and found neural networks backpropagation outperforms the other statistic methods, such as Multivariate Discriminant Analysis (MDA) [15]. The Radial Basis Function Network (i.e., RBFN), have been widely used in a large number of fields, such as classification problems [17], function approximations [18] and management sciences
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