پیش‌بینی ورشکستگی بنگاه‌های اقتصادی قابل پذیرش در بورس برق و انرژی با استفاده از اتوماتای یادگیر
محورهای موضوعی : مدیریت صنعتی
Seyed Mahdi Mazhari
1
(Amirkabir University of Technology, School of EE, Power System Analysis Laboratory, Tehran, Iran)
Hassan Monsef
2
(University of Tehran, University College of Engineering, School of ECE, Research Laboratory of Power System Operation and Planning Studies, Tehran, Iran)
Hooman Mirzaei
3
(Amirkabir University of Technology, School of EE, Power System Analysis Laboratory, Tehran, Iran)
کلید واژه: Learning Automata, پیش‌بینی ورشکستگی, Bankruptcy Prediction, بورس اوراق بهادار, نسبت‌های مالی, Financial ratios, شرکت‌های مرتبط با حوزة برق, Firms Related to Power and Energy Industry,
چکیده مقاله :
با توجه به آغاز به کار بورس برق و انرژی در سال 1391، ارائة مشاوره‌های جانبی به سرمایه‌گذاران یکی از اولویت‌های توسعه و پیشرفت این بورس تازه تاسیس، می‌باشد. پیش‌بینی ورشکستگی بنگاه‌های اقتصادی، نه تنها به سرمایه‌گذاران در اولویت‌دهی و جلوگیری از دست رفتن اصل و فرع سرمایه کمک می‌کند، بلکه تاثیر بسزایی در نحوة اعتباردهی و در نتیجه جلوگیری از نابودی بنگاه اقتصادی خواهد داشت. در این مقاله، مسألة پیش‌بینی ورشکستگی بنگاه‌های اقتصادی مرتبط با حوزة برق و انرژی، در محیط شرکت‌های ایران، بررسی می‌گردد. برای این منظور از اطلاعات 200 سال-شرکت، از بین شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران، در سال‌های 1380 تا 1388، استفاده شده است. در کلیة مطالعات تعداد شرکت‌های ورشکسته و غیرورشکسته مساوی در نظر گرفته شده و شرکت‌های ورشکسته بر مبنای مادة 141 قانون تجارت انتخاب شده‌اند. به منظور ایجاد یک رابطة پیشنهادی برای پیش‌بینی ورشکستگی مالی شرکت‌های مرتبط با حوزة برق و انرژی، از یک الگوریتم هوشمند مبتنی بر اتوماتای یادگیر استفاده شده است. مطابق نتایج ارائه شده، دقت مدل پیشنهادی برای داده‌های آموزش حدود 91% و بر روی داده‌های آزمون تقریباً 88% می‌باشد. با توجه آنالیز حساسیت‌های انجام‌شده، می‌توان نتیجه گرفت که مدل پیشنهادی نیازهای فنی و اقتصادی مسأله را ارضاء نموده و می‌تواند به عنوان ابزاری برای پیش‌بینی ورشکستگی شرکت‌ها مورد استفاده قرار گیرد.
In today's world, insurance of productive capital investment and reducing economic risk causes more fundraising and therefore the greatest economic boom cycle. One way to arrive capital investment security is to predict bankruptcy of a business unit. As the Iranian power and energy stock is going to start working by 2012, providing suitable bits of advice to investors would be a priority. This paper proposes a new solution approach for bankruptcy prediction of the Iranian power and energy industries. To do so, an evolutionary algorithm premised on Learning Automata is employed and adapted to the problem. Two sets of firms related to power and energy industries that are listed on the Tehran Stock Exchange (TSE) are selected as the training and test data, respectively. The developed algorithm is conducted on both train and test data, and the efficiency of the proposed method is evaluated via several scenarios. It was practically seen in simulations that the learning automata-based algorithm could achieve an accuracy of 91% and 88% over the train and test data, respectively. Besides these, the same data sets are also conducted by other methods such as MDA and Logit, and the obtained results are compared with reality. The yielded results prove the accuracy as well as the efficiency of the proposed solution technique
1- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
2- Mehrani, S., Mehrani, K., Monsefi Y., Karami, G. (2006). Investigation of the Zmijewski and Shirata Models for bankruptcy prediction of the firms within Tehran Stock Exchange. The Iranian Accounting and Auditing Review, 12(3), 105-131.
3- Novbakht, M.R., Sharifi, M. (2010). A neural network based algorithm for bankruptcy prediction of the firms within Tehran Stock Exchange. Industrial Management, 2(4), 163-180.
4- Keats, B.W., Bracker, J.S. (1988). Toward a theory of small firm performance: A conceptual model. American Journal of Small Business, 12, 41-58.
5- Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy.Journal of Accounting Research, 1, 109–131.
6- Aziz, M.A., & Humayon, A.D. (2006). Predicting corporate bankruptcy: Where we stand?. Journal of Corporate Governance, 6 (1), 18-33.
7- Foreman, R.D. (2002). A logistic analysis of bankruptcy within the US local telecommunications industry. Journal of Economics and Business, 2, 1–32.
8- Etemadi, H., Anvary-Rostamy, A.A., Farajzadeh-Dehkordi, H. (2009). A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications, 36, 3199–3207.
9- Shin, K., & Lee, Y. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23(3), 321–328.
10- Varetto, F. (1998). Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking and Finance, 22, 1421–1439.
11- Altman, E.I., Marco, G., Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian Experience). Journal of Banking and Finance, 18, 505–529.
12- Ahn, H., Kim, K.J. (2009). Bankruptcy Prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied soft computing, 9, 599-607.
13- Kim, M., Kang, D. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37, 3373–3379.
14- Park, C., & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23(3), 225–264.
15- Wu, C.H., Tzeng, G.H., Yeong-Jia, G., Fang W.C. (2007). A real-valued genetic algorithm, to optimize the parameters of support vector machine for prediction bankruptcy. Journal of Expert Systems with Application, 32, 397-408.
16- Cochran, J., Darrat, A. F., Elkhal, K. (2006). On bankruptcy of internet companies: an empirical inquiry. Journal of Business Research, 59, 1193-1200.
17- King don, J., Feldman, K. (1995). Genetic algorithms and application to finance. Applied Mathematical Finance, 2, 89-116.
18- Solymani Amiri, G.R. (2003). Financial ratios and insolvency prediction within Tehran Stock Exchange. Financial Investigation, 15, 121-136.
19- Saeedi, A., Aghaei, A. (2010). Predicting financial distress of firms listed in Tehran Stock Exchange using bayesian networks. The Iranian Accounting and Auditing Review, 56, 59-78.
20- Raei, R., Fallah Pour, S. (2004). Neural network based algorithm for bankruptcy prediction. Financial Research, 17, 39-69.
21- Khosh Tenat, M., Ghosuri, M.T. (2005). Comparing financial ratios based on the combined statement of cash flows and accruals and accrual financial ratios based solely on corporate bankruptcy prediction. Experimental Studies on Financial Accounting, 6, 43-61.
22- Nabavi, A., Ahmadi, M., Mahdavi, S. (2010). Investigating in a prediction of firm bankruptcy within logic model. Financial engineering and portfolio management, 1(5), 55-81.
23- Arghavani, H. (2002). Power Exchange. The Fourth Iranian Energy Symposium, 1, 1-4.
24- Narendra, K.S., & Thathachar, K.S. (1989). Learning automata: An introduction. New York, Prentice Hall.
25- Mazhari, S.M., Monsef, H., Lesani, H., Fereidunian, A. (2012). A multi-objective PMU placement method considering measurement redundancy and observability value under contingencies. IEEE Transactions on Power Systems, 99, 1-10; DOI: 10.1109/TPWRS.2012. 2234147.
26- Unsal, C., Kachroo, P., Bay, J.S. (1999). Multiple stochastic learning automata for vehicle path control in an automated highway system. IEEE Transactions on Systems, Man, and Cybernetics, 29, 120-128.
27- Mazhari, S.M., & Monsef, H. (2013). Dynamic sub-transmission substation expansion planning using learning automata. Electric Power Systems Research, 99, 255-266.
_||_1- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
2- Mehrani, S., Mehrani, K., Monsefi Y., Karami, G. (2006). Investigation of the Zmijewski and Shirata Models for bankruptcy prediction of the firms within Tehran Stock Exchange. The Iranian Accounting and Auditing Review, 12(3), 105-131.
3- Novbakht, M.R., Sharifi, M. (2010). A neural network based algorithm for bankruptcy prediction of the firms within Tehran Stock Exchange. Industrial Management, 2(4), 163-180.
4- Keats, B.W., Bracker, J.S. (1988). Toward a theory of small firm performance: A conceptual model. American Journal of Small Business, 12, 41-58.
5- Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy.Journal of Accounting Research, 1, 109–131.
6- Aziz, M.A., & Humayon, A.D. (2006). Predicting corporate bankruptcy: Where we stand?. Journal of Corporate Governance, 6 (1), 18-33.
7- Foreman, R.D. (2002). A logistic analysis of bankruptcy within the US local telecommunications industry. Journal of Economics and Business, 2, 1–32.
8- Etemadi, H., Anvary-Rostamy, A.A., Farajzadeh-Dehkordi, H. (2009). A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications, 36, 3199–3207.
9- Shin, K., & Lee, Y. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23(3), 321–328.
10- Varetto, F. (1998). Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking and Finance, 22, 1421–1439.
11- Altman, E.I., Marco, G., Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian Experience). Journal of Banking and Finance, 18, 505–529.
12- Ahn, H., Kim, K.J. (2009). Bankruptcy Prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied soft computing, 9, 599-607.
13- Kim, M., Kang, D. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37, 3373–3379.
14- Park, C., & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23(3), 225–264.
15- Wu, C.H., Tzeng, G.H., Yeong-Jia, G., Fang W.C. (2007). A real-valued genetic algorithm, to optimize the parameters of support vector machine for prediction bankruptcy. Journal of Expert Systems with Application, 32, 397-408.
16- Cochran, J., Darrat, A. F., Elkhal, K. (2006). On bankruptcy of internet companies: an empirical inquiry. Journal of Business Research, 59, 1193-1200.
17- King don, J., Feldman, K. (1995). Genetic algorithms and application to finance. Applied Mathematical Finance, 2, 89-116.
18- Solymani Amiri, G.R. (2003). Financial ratios and insolvency prediction within Tehran Stock Exchange. Financial Investigation, 15, 121-136.
19- Saeedi, A., Aghaei, A. (2010). Predicting financial distress of firms listed in Tehran Stock Exchange using bayesian networks. The Iranian Accounting and Auditing Review, 56, 59-78.
20- Raei, R., Fallah Pour, S. (2004). Neural network based algorithm for bankruptcy prediction. Financial Research, 17, 39-69.
21- Khosh Tenat, M., Ghosuri, M.T. (2005). Comparing financial ratios based on the combined statement of cash flows and accruals and accrual financial ratios based solely on corporate bankruptcy prediction. Experimental Studies on Financial Accounting, 6, 43-61.
22- Nabavi, A., Ahmadi, M., Mahdavi, S. (2010). Investigating in a prediction of firm bankruptcy within logic model. Financial engineering and portfolio management, 1(5), 55-81.
23- Arghavani, H. (2002). Power Exchange. The Fourth Iranian Energy Symposium, 1, 1-4.
24- Narendra, K.S., & Thathachar, K.S. (1989). Learning automata: An introduction. New York, Prentice Hall.
25- Mazhari, S.M., Monsef, H., Lesani, H., Fereidunian, A. (2012). A multi-objective PMU placement method considering measurement redundancy and observability value under contingencies. IEEE Transactions on Power Systems, 99, 1-10; DOI: 10.1109/TPWRS.2012. 2234147.
26- Unsal, C., Kachroo, P., Bay, J.S. (1999). Multiple stochastic learning automata for vehicle path control in an automated highway system. IEEE Transactions on Systems, Man, and Cybernetics, 29, 120-128.
27- Mazhari, S.M., & Monsef, H. (2013). Dynamic sub-transmission substation expansion planning using learning automata. Electric Power Systems Research, 99, 255-266.