- citati u SCIndeksu: [3]
- citati u CrossRef-u:[2]
- citati u Google Scholaru:[
 ]
- posete u poslednjih 30 dana:6
- preuzimanja u poslednjih 30 dana:5
|
|
|
2013, vol. 41, br. 4, str. 145-159
|
|
Predviđanje otvaranja stečajnog postupka u Republici Srbiji
Corporate bankruptcy prediction in the Republic of Serbia
Sažetak
Cilj ovog rada je prikaz modela za predviđanje otvaranja stečajnog postupka razvijenih u specifičnim tržišnim uslovima koji vladaju u Republici Srbiji i poređenje njihove preciznosti predviđanja sa, u praksi najčešće korišćenim, Altmanovim Z-score modelom. Mnogi autori iz ove oblasti su razvili modele, ali najčešće u uslovima razvijenih tržišta i privrednog rasta. U radu smo prikazali tri modela koji koriste standardne i određene specifične finansijske pokazatelje, a u cilju predviđanja otvaranja stečajnog postupka u tržištima u razvoju sa karakteristikama recesije. S tim ciljem, na inicijalnom uzorku (130 privrednih društava) smo upotrebili sledeće statističke metode i metode mašinskog učenja: metod logističke regresije, metod stabala odlučivanja i metod veštačkih neuralnih mreža. Na test uzorku (102 privredna društva) smo uporedili preciznost predviđanja novoformiranih modela sa preciznošću predviđanja Altmanovih Z-score modela. Rezultati pokazuju da od pomenuta 3 modela, na nezavisnom test uzorku,jedino model neuralnih mreža pokazuje bolje rezultate u poređenju sa Altmanovim Z-score modelima.
Abstract
The aim of this paper is to present corporate default prediction models constructed in the specific market conditions that prevail in the Republic of Serbia, and to compare their prediction accuracy with the most frequently used model - Altman's Z-score. Many authors have constructed models for the purpose of bankruptcy prediction, but predominantly in stable market conditions or in times of economic growth. We have presented three models that use standard ratios and some specific variables in order to predict corporate bankruptcy in emerging and distressed markets. For that purpose, we have used the following statistical and machine learning methods on a training sample (130 companies): Logistic Regression, Decision Trees and Artificial Neural Networks. Finally, we have compared accuracies of predictions of our models to those of the Altman's Z-score models using an independent hold-out sample (102 companies). Results show that, out of the aforementioned three models, only the one relying on the artificial neural network algorithm performs better when applied on the hold-out sample, compared to Altman's Z-score models.
|
|
|
|
Reference
|
|
|
Altman, E. (2002) Corporate Distress Prediction Models in a Turbulent Economic and Basel II Environment. http://pages.stern.nyu.edu/~ealtman/Corp-Distress.pdf (pristupljeno: 13-01-2013)
|
|
18  
|
Altman, E.I. (1968) Financial rations, discriminant analysis and the predication of corporate bankruptcy. Journal of Finance, 23(4): 589-609
|
|
|
Beaver, W.H., Mcnichols, M.F., Rhie, J. (2005) Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10(1): 93-122
|
|
|
Bharath, S., Shumway, T. (2008) Forecasting Default with the Merton Distance to Default Model. Review of Financial Studies, 21(1): 1339-1369
|
|
|
Boritz, J., Kennedy, D., Sun, J. (2007) Predicting Business Failures in Canada La PréDiction des Faillites D'entreprise au Canada. Accounting Perspectives, 6(2): 141-165
|
|
|
Campbell, J., Hilscher, J., Szilagyi, J. (2008) In Search of Distress Risk. Journal of Finance, 63(1): 2899-2939
|
|
|
Charitou, A., Neophytou, E., Charalambous, C. (2004) Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3): 465-497
|
|
|
Dakovic, R., i dr. (2010) Bankruptcy Prediction in Norway: A Comparison Study. Applied Economics Letters, 17(17): 1739-1746
|
|
|
Dechow, P., i dr. (2011) Predicting Material Accounting Misstatements. Contemporary Accounting Research, 17-82
|
|
2
|
Deventer, D., Imai, K. (2003) Credit risk models and the Basel accords. Singapore: John Wiley & Sons
|
|
|
Jayadev, M. (2006) Predictive Power of Financial Risk Factors: An Empirical Analysis of Default Companies. Vikalpa, 31(3): 45-56
|
|
|
Lee, W. (2006) Genetic Programming Decision Tree for Bankruptcy Prediction. u: Proceedings of the 9th Joint Conference on Information Sciences (JCIS), Kaohsiung: JCIS
|
|
|
Mizdraković, V. (2012) Komparativna analiza ekonomskih aspekata stečaja: Comparative analysis of the economic aspects of bankruptcy. Singipedia - Singidunum University, http://www.singipedia.com/content/3276-Komparativna-analiza-ekonomskihaspekata-ste%C4%8Daja
|
|
|
Nanda, S., Pendharkar, P. (2001) Linear models for minimizing misclassification costs in bankruptcy prediction. International Journal of Intelligent Systems in Accounting, Finance & Management, 10(3): 155-168
|
|
|
Nguyen, H.G. (2005) Using Neutral Network in Predicting Corporate Failure. Journal of Social Sciences, 1(4): 199-202
|
|
2
|
Ohlson, J.A. (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1): 109-131
|
|
|
Santos, M.F., Cortez, P., Pereira, J., Quintela, H. (2006) Corporate bankruptcy prediction using data mining techniques. WIT Transactions on Information and Communication Technologies, 37 (1), pp. 349-357
|
|
|
Sen, T., i dr. (2004) Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks. Journal of Applied Mathematics and Decision Sciences, 8(4): 219-233
|
|
|
Shumway, T. (2001) Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74(1): 103-224
|
|
|
Stanišić, N., Radojević, T., Mizdraković, V., Stanić, N. (2012) Analiza efikasnosti kapitala u kompanijama u Srbiji. Singidunum Journal of Applied Sciences, vol. 9, br. 2, str. 41-49
|
|
|
Youn, H., Gu, Z. (2010) Predict US Restaurant Firm Failures: The Artificial Neural Network Model Versus Logistic Regression Model. Tourism and Hospitality Research, 10(3): 171-187
|
|
|
|
|