Mahdi Moradi, Mahdi Salehi, Hadi Sadoghi Yazdi, Mohammad Ebrahim Gorgani
Ferdowsi University of Mashhad.
National Iranian Oil Company.
DOI: 10.4236/ojacct.2012.12005   PDF    HTML     4,459 Downloads   7,339 Views   Citations

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.

Keywords

Going Concern Prediction; Fuzzy C-Means

Share and Cite:

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, New York, 1981. doi:10.1007/978-1-4757-0450-1
[2]	W. H. Beaver, “Market Prices, Financial Ratios and the Prediction of Failure,” Journal of Accounting Research, Vol. 6, No. 2, 1968, pp. 179-192. doi:10.2307/2490233
[3]	C. Cortes, and V. N. Vapnik, “Support Vector Networks,” Machine Learning, Vol. 20, No. 3, 1995, pp. 273-297. doi:10.1007/BF00994018
[4]	M. J. David and P. W. Robort, “Support Vector Domain Description,” Pattern Recognition Letters, Vol. 20, No. 11-13, 1999,pp.1191-1199. doi:10.1016/S0167-8655(99)00087-2
[5]	G. Finnie and Z. Sun, “R5 Model for Case-Based Reasoning,” Knowledge-Based Systems, Vol. 16, No. 1, 2003, pp. 59-65. doi:10.1016/S0950-7051(02)00053-9
[6]	K. J. Kim, “Financial Time Series Forecasting Using Sup- port Vector Machines,” Neurocomputing, Vol. 55, No. 1-2, 2003, pp. 307-319. doi:10.1016/S0925-2312(03)00372-2
[7]	E. Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, Vol. 23, No. 4, 1968, pp. 589-609. doi:10.1111/j.1540-6261.1968.tb00843.x
[8]	E. Altman, “A Further Empirical Investigation of the Bankruptcy Cost Question,” Journal of Finance, Vol. 39, No. 4, 1984, pp. 1067-1089. doi:10.1111/j.1540-6261.1984.tb03893.x
[9]	W. H. Beaver, “Market Prices, Financial Ratios and the Prediction of Failure,” Journal of Accounting Research, Vol. 6, No. 2, 1968, pp. 179-192. doi:10.2307/2490233
[10]	B. Sowmya and S. Bhattacharya, “Color Image Segmentation Using Fuzzy Clustering Techniques,” IEEE Indicon Conferences, Chennai, 11-13 December 2005, pp. 41-45.
[11]	M. E. Zmijewski, “Methodological Issues Related to the Estimated of Financial Distress Prediction Models,” Journal of Accounting Research, Vol. 22, No. 1, 1984, pp. 59- 82. doi:10.2307/2490859
[12]	J. Ohlson, “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” Journal of Accounting Research, Vol. 18, No. 1, 1980, pp. 109-131. doi:10.2307/2490395
[13]	Y.-S. Ding, X.-P. Song and Y. M. Zen, “Forecasting Financial Condition of Chinese Listed Companies Based on Support Vector Machine,” Expert Systems with Applications, Vol. 34, No. 4, 2008, pp. 3081-3089. doi:10.1016/j.eswa.2007.06.037
[14]	H. Li, J. Sun and B.-L. Sun, “Financial Distress Prediction Based on OR-CBR in the Principle of K-Nearest Neighbors,” Expert Systems with Applications, Vol. 36, No. 1, 2007, pp. 643-659.
[15]	K. R. Solvenia, “Fuzzy C-Means Clustering and Facility Location Problems,” Proceeding of Artificial Intelligence and Soft Computing, Palma de Mallorca, 2006, p. 544.
[16]	M. J. Tax David and P. W. Duin Robert, “Support Vector Data Description,” Machine Learning, Vol. 54, No. 1, 2004, pp. 45-66.
[17]	F. E. H. Tay and L. Cao, “Application of Support Vector Machines in Financial Time Series Forecasting,” Omega, Vol. 29, No. 4, 2001, pp. 309-317. doi:10.1016/S0305-0483(01)00026-3
[18]	Sun and X. Hui, “Financial Distress Prediction Based on Similarity Weighted Voting CBR,” Advanced Data Mining and Applications, Vol. 4093, 2006, pp. 947-958. doi:10.1007/11811305_103
[19]	H. Li and J. Sun, “Majority Voting Combination of Multiple Case-Based Reasoning for Financial Distress Prediction,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 4363-4373. doi:10.1016/j.eswa.2008.05.019
[20]	. Sun and H. Li, “Data Mining Method for Listed Companies’ Financial Distress Prediction,” Knowledge-Based Systems, Vol. 21, No. 1, 2008, pp. 1-5. doi:10.1016/j.knosys.2006.11.003
[21]	W. Chen and Y. Du, “Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model,” Expert Systems with Applications, Vol. 36, No. 2, 2009, pp. 4075-4086. doi:10.1016/j.eswa.2008.03.020
[22]	C. H. Tsai, “Financial Decision Support Using Neural Networks and Support Vector Machines,” Expert Systems, Vol. 25, No. 4, 2008, pp. 380-393.