نوع مقاله : مقاله پژوهشی
نویسندگان
1 سرمایه گذاری میراث فرهنگی و گردشگری ایران،کارشناس سرمایه گذاری و نظارت،کارشناسی ارشد مدیریت مالی
2 سازمان بورس و اوراق بهادار، معاون نظارت بر نهادهای مالی،دکترای مدیریت مالی
چکیده
کلیدواژه‌ها
عنوان مقاله [English]
نویسندگان [English]
The prediction of financial distress as a way of preventing it has drawn the attention of firms’ managers. In this regard, the deployment of financial ratios and specifically firm’s cash flow statement is well-known in statistical models. The main issue in this study is to see whether there is a meaningful relationship between the firm’s financial situation and its cash flow pattern and can it help us in predicting a financial distress.
In this study, we investigate the impact of cash flow pattern for companies accepted in Tehran Stock Exchange during the period of 2006 to 2015 (focusing data gathered from 162 selected companies). Consequently, the inability of repaying bank debt for companies is considered as the criterion of financial distress and those without this inability are not financially distressed. For this purpose, financially distressed companies are separated from those which are not distressed for the period of 2006 – 2010 and by using the result of this separation we investigate the prediction power of financial ratios used in our research: current assets to total assets, total debt to total assets, sales to total assets, operating profits to total sales, natural logarithm of assets and interest to gross profit by implementing neural network method. After confirming the effect of these ratios on financial situation of companies, by the use of these ratios and using neural network, the financial situation of companies from 2011 to 2015 is predicted by Logistic regression. The results show that the mentioned ratios have the prediction power with %88 of observations, therefore, there is a meaningful correlation between financially distressed and non-distressed in terms of Cash Flow pattern
کلیدواژه‌ها [English]
16. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4),589–609.
17. Beaver, W. (1966). Financial ratios as predictors of failures, empirical research in accounting, selected studies. Supplement to the Journal of Accounting Research, 5(4), 71–127.
18. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, Empirical Research in Accounting: Selected Studies,, 71-111.
19. Bruwer, B. S. (2005). Cash flow patterns in listed South African industrial companies. Meditari Accountancy Research, 13(1),, 17-1.
20. Cortés, E., Gámez, M., García Rubio, N., & Elizondo, D. (April 2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems 45, 110-122.
21. Deakin, E. B. (1972). A discriminant analysis of predictors of Business failure. Journal of Accounting Research, 10 (1), 167-179.
22. Diar, l., & A.E, A. (2006). Bankruptcy Predictio, Where we stand? Journal of Corporate Governance, Vol 6, No. 1, Vol 6, No. 1.
23. Dimitras, Zanakis & Zopounidis. (1996). A survey of business failure with an emphasis on prediction methods and industrial application. European Journal of Operational Research, 90, 487–513.
24. Farajzadeh Dehkordi, H., Etemadi, H., & Anvary Rostamy, A. (2009). A Genetic programing model for bankruptcy predictionL Empirical evidence from Iran. pp. 3199-3207.
25. Goldberg, D. (1989). Genetic algorithms in search optimization and machine.
26. Gordon. (1971). Toward theory of financial distress. The journal of finance, Vol.36 , PP.1347-56.
27. Gup , B., Samson, W., Dugan, M., & kim, M. (1993). An Analysis of Patterns from the Statement of Cash Flows. financial Practice and Education, 8.
28. Horrigan, J. o. (1968). A short history of Financial ratio analysis. The Accounting Review,, 284-294.
29. Jantadej. (2006). Using the Combinations of Cash Flow Components. ph.D. the Graduate Collage at the University of Nebraska.
30. Jones, F. (1987). Current technique in bankruptcy prediction. Accounting literature, 6,131-164.
31. K. King don, J. Feldman. (1995). Genetic Algorithms and application to finance.
32. Kumar, R. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques”. European Journal of Operational Research, 180 No.180.
33. McKee, T., & Lensberg, T. (2002). Genetic prigraming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research , 138,436-451.
34. O' Leary, D. (1998). Using neural network to predict corporate failure. international journal of intelligent System in Accounting Finance and Managemenr, 7(3),187-197.
35. Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
36. Shin K, Lee Y. (2002). A Genetic Algorithm Application in Bankruptcy prediction modeling. Expert systems with application, 1-8.
37. Stickney, ,. C., Brown, P. R., & Wahlen, J. M. ((2004).). (A Strategic Perspective): Thomson South. Financial Reporting and Statement Analysis, 10.
38. Tmari, M. (1966). fainancial ratios as a mean of forcasting bankruptcy. management international review، vol.1.no.4., 20.
39. Varetto, F. (2007). Genetic algorithms applications in the analysis of insolvency risk. journal of Banking and finance, Vol.22,pp1421-1439.
40. Ward, T. J. ( (1994)). Cash Flow Information and the Prediction of Financially Distressed Mining, Oil and Gas Firms: A Comparative Study . Journal of Applied Business Research, Volume 10, Number 3: 78-87.
41. Zavgren. (1983). the prediction of corporate failure : the atate of. journal of accounting literature, vol.2.1-38.
42. Zopounidis, c., & Dimitras, A. (1998). Multicriteria decision aid methods for the prediction of business failure. Kluwer Academic Punlishers.
)