Abstract
ABSTRACTSince the sixties, many classification techniques both from statistics and other scientific disciplines have been used to predict corporate failure. Multivariate linear discriminant analysis, however, continues to be one of the main reference methods principally because it is easy to apply and interpret. AdaBoost is a machine learning technique which works by combining a large number of simple classifiers to achieve a high level of accuracy in classification problems. Although the efficiency of this method has been proved in various application fields, it is unknown in the economic-business area. The aim of this study is to present AdaBoost as a classification technique which can be successfully used in failure forecasting. Correspondingly, it has been applied to a sample of 1.180 Spanish firms and has proved to be more accurate than discriminant analysis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
