دستگاه ترکیبی برای راه حل مشکلات تبعیض نمونه در پیش بینی ورشکستگی شرکت ها
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|48279||2012||12 صفحه PDF||سفارش دهید||10359 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 8, 15 June 2012, Pages 7512–7523
This paper proposes a new approach to the forecasting of firms’ bankruptcy. Our proposal is a hybrid method in which sound companies are divided in clusters using Self Organized Maps (SOM) and then each cluster is replaced by a director vector which summarizes all of them. Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS). For the test of the model we considered a real setting of Spanish enterprises from the construction sector. With this procedure we intend to overcome the sampling-bias problems that matched-pairs models often suffer. We estimated two benchmark models: a back propagation neural network and a simple MARS model. Our results show that the proposed hybrid approach is much more accurate than the benchmark techniques for the identification of the bankrupt companies.