Abstract

http://ssrn.com/abstract=924033
 
 

References (40)



 
 

Citations (7)



 


 



Corporate Failure Prediction Modeling: Distorted By Business Groups' Internal Capital Markets?


Nico Dewaelheyns


Lessius University College; KU Leuven - Faculty of Business and Economics (FBE)

Cynthia Van Hulle


KU Leuven - Department of Applied Economics


Journal of Business Finance & Accounting, Vol. 33, No. 5-6, pp. 909-931, June/July 2006

Abstract:     
Most models in the bankruptcy prediction literature implicitly assume companies are stand-alone entities. However, in view of the importance of business groups in Continental Europe, ignoring group ties may have a negative impact on predictive reliability. We find that models encompassing both bankruptcy variables defined at subsidiary level and at group level have a substantially better fit and classification performance. Furthermore we find that the group's support causes improved survival chances for subsidiaries, especially when these subsidiaries belong to the group's core business. Overall our results are consistent with existing theoretical and empirical findings from the internal capital markets literature.

Number of Pages in PDF File: 23

Accepted Paper Series


Date posted: August 13, 2006  

Suggested Citation

Dewaelheyns, Nico and Van Hulle, Cynthia, Corporate Failure Prediction Modeling: Distorted By Business Groups' Internal Capital Markets?. Journal of Business Finance & Accounting, Vol. 33, No. 5-6, pp. 909-931, June/July 2006. Available at SSRN: http://ssrn.com/abstract=924033 or http://dx.doi.org/10.1111/j.1468-5957.2006.00009.x

Contact Information

Nico Dewaelheyns (Contact Author)
Lessius University College ( email )
Department of Business Studies
Korte Nieuwstraat 33
Antwerp, 2000
Belgium
KU Leuven - Faculty of Business and Economics (FBE) ( email )
Naamsestraat 69
Leuven, B-3000
Belgium
Cynthia Van Hulle
KU Leuven - Department of Applied Economics ( email )
Naamsestraat 69
B-3000 Leuven
BELGIUM
32-16-326734 (Phone)
32-16-326732 (Fax)
Feedback to SSRN


Paper statistics
Abstract Views: 976
Downloads: 19
References:  40
Citations:  7

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo4 in 0.407 seconds