Metrika

  • 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

Sadržaj

članak: 1 od 1  
2013, vol. 41, br. 4, str. 145-159
Predviđanje otvaranja stečajnog postupka u Republici Srbiji
Univerzitet Singidunum, Poslovni fakultet, Beograd

e-adresanstanisic@singidunum.ac.rs, vmizdrakovic@singidunum.ac.rs, gknezevic@singidunum.ac.rs
Ključne reči: tržišta u razvoju; Altmanov Z-score; modeli predviđanja stečaja; finansijski pokazatelji; metoda veštačkih neuralnih mreža
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.
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)
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
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
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
 

O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/industrija41-4024
objavljen u SCIndeksu: 23.12.2013.
metod recenzije: dvostruko anoniman