محتوای اطلاعاتی شاخصهای سرمایه فکری و عملکرد مالی در پیش‌بینی درماندگی مالی با رویکرد داده‌کاوی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشگاه آزاد اسلامی واحد امارات متحده عربی

2 دانشگاه تربیت مدرس

3 دانشگاه شهید بهشتی

4 دانشگاه آزاد اسلامی واحد اسلامشهر

چکیده

پیش‌بینی درماندگی مالی همواره موردتوجه سهامداران، اعتباردهندگان و مدیران واحدهای تجاری بوده است؛ ازاین‌رو بیش از نیم قرن است که اقتصاددانان، مراکز آموزشی و افراد حرفه‌ای در تلاش هستند تا مدل‌های بهینه‌ای برای پیش‌بینی درماندگی مالی ایجاد کنند؛ در این پژوهش سعی می­شود از مدل‌های درخت تصمیم و مدل تحلیل ممیزی خطی برای پیش‌بینی درماندگی مالی شرکت‌ها استفاده شود و نتایج این دو مدل با یکدیگر مورد­مقایسه قرار گیرد. در این راستا علاوه بر نسبت‌های مالی مرسوم در پیش‌بینی درماندگی مالی، از شاخص‌های مبتنی بر ارزش و سرمایه فکری نیز استفاده شده است. به‌منظور نیل به اهداف بالا، سه فرضیه اصلی تدوین شد. هدف از طرح فرضیه­های پژوهش، بررسی محتوای اطلاعاتی نسبت‌های مالی، شاخص‌های سرمایه فکری و شاخص‌های عملکردی در پیش‌بینی درماندگی مالی شرکت‌های پذیرفته‌شده در «بورس اوراق بهادار تهران» با استفاده از مدل‌های درخت تصمیم وتحلیل ممیزی خطی می باشد . برای آزمون این فرضیه‌ها، نمونه‌ای از میان شرکت‌های پذیرفته‌شده در «بورس اوراق و بهادار تهران» طی سال‌های 1389 تا 1394 انتخاب شد. برای آزمون فرضیه‌ها از نتیجه مدل‌های تحلیل عاملی، درخت تصمیم و تحلیل ممیزی خطی استفاده شد. نتایج حاکی از آن است که پیش‌بینی درماندگی مالی با استفاده از مدل درخت تصمیم امکان‌پذیر است؛ همچنین مدل تحلیل ممیزی با تلفیقی از شاخص نسبت‌های مالی و شاخص عملکردی در پیش‌بینی درماندگی مالی موفق عمل کرده است. نتیجه مقایسه‌ای توانمندی دو مدل نشان می­دهد که اگرچه دقت پیش‌بینی تحلیل ممیزی بر اساس معیارهای ارزیابی و سطح زیر منحنی راک (920/0) بیش از دقت پیش‌بینی درخت تصمیم (901/0) بوده است، اما اختلاف بر اساس آزمون t به لحاظ آماری معنادار نیست (207/0=p)؛ به‌عبارت‌دیگر نتایج حاصل از دو روش بسیار نزدیک به هم است.

عنوان مقاله [English]

Information Content of Intellectual Capital and Financial Performance Indicators in Financial Prediction by Data Mining Approach

نویسندگان [English]

  • Rasoul Tahmasbi 1
  • Ali Asghar Anvary Rostamy 2
  • Seyed Jalal Sadeghi Sharif 3
  • Abbas Khorshidi 4
1 Islamic Azad University
2 Tarbiat Modares University
3 Shahid Beheshti University
4 Islamic Azad University
چکیده [English]

Financial distress prediction is always a concern for shareholders, creditors and business unit managers. Hence, various models proposed to predict financial distress. In this research, decision tree model and linear multiple discriminant analysis model have been used to predict financial distress of companies. In addition, the results of these two models are compared with each other. In this regard, it has been tried to use intellectual capital and value-based performance in addition to conventional financial ratios in predicting financial distress. To achieve the objectives, three hypotheses formulated. The purpose of the research hypotheses is to investigate the information content of intellectual capital and performance indicators and financial ratios in predicting financial distress in Tehran Stock Exchange companies using decision tree models, linear multiple discriminant analysis and the combined model derived from factor analysis - linear multiple discriminant analysis and the factor analysis is the decision tree. To test these hypotheses, a sample selected from Tehran Stock Exchange companies during the years 2010-2010. The results indicate that the prediction of financial distress is possible using a decision tree, but in the decision tree model, intellectual and functional capital indicators do not have information content to predict financial distress. The linear multiple discriminant analysis model has been successful in combining financial ratios and performance indicators in the prediction of financial distress. Comparison of the capabilities of the two models indicated that although the accuracy of predictive analysis of the linear multiple discriminant analysis based on the assessment criteria (competing table) and the level below the rock curve (0.920) was higher than the decision tree (0.901), but based on the t-test, This difference is not significant (p = 0.207). In other words, the results of the two methods are very close.

کلیدواژه‌ها [English]

  • .Information content- Intellectual capital- Financial performance- Financial distress- Data mining

مراجع

  1. Ahangari, M. (2011). Using decision tree algorithm to predict bankrupted companies and not-bankrupted companies accepted in Tehran Stock Exchange. 1s National Conference on Computer and Information Technology Students, Tehran, Iran (In Persian).
  2. Alfaro, E., Garcia, N., Gamez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110-122.
  3. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
  4. Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2014). Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model.
  5. Anvari Rostami, A. A., & Seraji, H. (2005). Measuring intellectual capital and examining the relationship between intellectual capital and market value of shares of companies in Tehran Stock Exchange. Review of accounting and auditing, 39, 49-62 (In Persian).
  6. Asgarnezhad Nouri, B., & Soltani, M. (2016). Designing a bankruptcy prediction model based on account, market and macroeconomic variables (Case Study: Cyprus Stock Exchange). Iranian Journal of Management Studies, 9(1), 125-147 (In Persian).
  7. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 4, 71-111.
  8. Chalaki, P. (2017). Review the effect of Management Ability on the Financial Distress, with an emphasis on Financial Flexibility in Tehran Stock Exchange listed companies. Master Thesis, Orumiey, Iran (In Persian).
  9. Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167-179.
  10. Dehkordi, H. F. (2007). Application of Genetic Algorithms in Bankruptcy Forecast Modeling. Master Thesis in Accounting, Tarbiat Modares University, Tehran, Iran (In Persian).
  11. Esmaeilzadeh, A. & Shakeri, H. (2015). Prediction of financial distress among companies accepted in Tehran Stock Exchange using simple Bayesian network and comparing it with data envelopment analysis, 6(22), 1-28 (In Persian).
  12. Etemadi, H., Rostamy, A. A. A., & Dehkordi, H. F. (2009). A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications, 36(2), 3199-3207.
  13. Fadaeinezhad, M.E., & Eskandari, R. (2011). Designing and explaining the bankruptcy prediction model of companies in Tehran Stock Exchange. Accounting Research, 3(9), 38-55 (In Persian).
  14. Fulmer, J. G., Moon, J. E., Gavin, T. A., & Erwin, J. M. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11), 25-37.
  15. Grice, J. S., & Ingram, R. W. (2001). Tests of the generalizability of Altman's bankruptcy prediction model. Journal of Business Research, 54(1), 53-61.
  16. Janer, J. (2011). Bankruptcy prediction and its advantages. Empirical Evidence from SMEs in the French Hospitality Industry, 3, 1-117.
  17. Liao, Q., & Mehdian, S. (2016). Measuring financial distress and predicting corporate bankruptcy: An index approach. Review of Economic and Business Studies, 9(1), 33-51.
  18. Lord, J. Weech-Maldonado, R., & Davlyatov, G. (2017). Predicting Financial Distress in Nursing Homes: An Application of the Altman Z-Score Model. Innovation in Aging, 1, 185-186.
  19. Mansourfar, Gh., Ghayour, F. & Asadi, M. (2016). The effect of modifying the quality of profit in predicting the financial distress of accepted companies in Tehran Stock Exchange. Financial Management Strategy, 4(4), 25-44 (In Persian).
  20. Mansourfar, Gh., Ghayour, F. & Lotfi, B. (2015). The Ability of Support Vector Machine (SVM) in Financial Distress Prediction. Empirical Research in Accounting, 5(1), 177-195 (In Persian).
  21. Mogharrebi, N., Masihi, M., Tahernezhad, A., & Fereydouni, F. (2014). Investigating the Relationship between Values Added of Intellectual Capital and Bankruptcy Among Iranian Petrochemical and Petroleum Industry Companies Accepted in Tehran Stock Exchange. Islamic azad university UAE Branch, UAE (in Persian).
  22. Nadeem, M., De Silva, T.-A., & Kayani, U. N. (2016). Predicting corporate financial distress for New Zealand listed firms using intellectual capital indicators. New Zealand Journal of Applied Business Research, 14(2), 1-15.
  23. Nikbakht, M.R., & Sharifi, M. (2010). Financial Bankruptcy Prediction of Tehran Stock Exchange by Using Artificial Neural Networks. Journal of Industrial Management, 2(4), 163-180 (In Persian).
  24. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
  25. Ramooz, N., & Mahoudi, M. (2017). The Prediction of the Risk of Financial Bankruptcy Using Hybrid Model in Tehran Stock Exchange. Financial Management Strategy, 5(1), 51-75.
  26. Rezende, F. F., Montezano, R. M. d. S., Oliveira, F. N. d., & Lameira, V. d. J. (2017). Predicting financial distress in publicly-traded companies. Revista Contabilidade & Finanças, 28(75), 390-406.
  27. Salehi, N., & Azimi Yancheshmeh, M. (2016). Comparative Investigate of Hazard Model and traditional Models for Bankruptcy Predication. Quarterly Financial Accounting Journal, 8(30), 94-121 (In Persian).
  28. Shirata, C. Y. (1998). Financial ratios as predictors of bankruptcy in Japan: an empirical research. Tsukuba College of Technology Japan, 1(1), 1-17.
  29. Shiri, M., & Tabarestani, M. (2012). Prediction of financial distress base on Multivariate Discriminant and effectiveness of efficiency score in improvement of model. Management Research in Iran, 16(1), 149-168 (In Persian).
  30. Springate, G. L. (1978). Predicting the possibility of failure in a Canadian firm: A discriminant analysis. Simon Fraser University.
  31. Sun, L., & Shenoy, P. P. (2007). Using Bayesian networks for bankruptcy prediction: Some methodological issues. European Journal of Operational Research, 180(2), 738-753.
  32. Yalcin, N., Bayrakdaroglu, A., & Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1), 350-364.
  33. Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Business Finance & Accounting, 12(1), 19-45.
  34. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 59-82.

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