The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Sequential Probabilistic System for Bankruptcy Data Classification
Abstract
In the last decade, the development of bankruptcy prediction models has been one of important issues in accounting and corporate finance research fields. Indeed, bankruptcy is a critical event that yields important loss to management, shareholders, employees, and also to government. Statistical methods such as discriminant analysis, logistic and probit models were widely used for developing bankruptcy prediction systems. However, statistical-based approaches are assumes strong assumptions including linearity of the relationship among dependent and independent variables, normality of the errors which limit their applicability in bankruptcy real world problems. Recently, machine learning and soft computing techniques including artificial neural networks, support vector machines, and evolutionary intelligence have brought forth new alternatives in solving nonlinear problems with applications in bankruptcy prediction. The purpose of this chapter is to present a sequential probabilistic system for bankruptcy data classification to help manager in making decisions.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|