@article{461,
  author = {Mohamed El Ghourabi, Amira Dridi, Fedya Telmoudi},
  title = {Component; Rough Set Theory, Gaussian Case Based Reasoning- clustering, Real Valued Genetic Algorithm, Support Vector Machine, Financial Stress Index, Value at Risk},
  journal = {International Journal of Information Studies },
  year = {2011},
  volume = {3},
  number = {2},
  doi = {},
  url = {},
  abstract = {Financial crises are perceived as shocking events, several researchers concentrated on the identification of stressed and stable periods in order to take strategic decisions on time. In this paper, a new hybrid approach is proposed to deal with the prediction of the Value at Risk (VaR). Based on financial variables from bank’s balance sheets as input data , this approach integrate Rough Set Theory (RST), Gaussian Case Based Reasoning- clustering (GCBR-Clustering), Real valued Genetic Algorithm (RGA) with Support Vector Machines (SVM) in order to classify stressed and non stressed periods and therefore determine the VaR . The RST-GCBR Clustering-RGASVM combination is justified by a high accuracy rate which reaches 96.551% in cluster 1and 100% in cluster 2.},
}