A wavelet neural network application to predict financial market crises
Forecasting currency crisis is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. Some non-parameter models,such as KLRs single analysis method,have been proposed for obtaining accurate prediction results,in an attempt to remedy the performance of parameter models,such as Probit/Logit method. This paper develops a new approach for predicting currency crisis,namely Wavelet Neural Networks (WNN) model. Using same indicators and a set of data,we analyze the probability of crisis by this approach. According to result,both models are able to signal currency crises reasonably well in-sample,and that the forecasting power of WNN out-of-sample has better performance than ANN.
wavelet neural network forecasting currency crisis
Yin Yu
School of Management,Donghua University,China
国际会议
The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)
上海
英文
519-523
2007-06-04(万方平台首次上网日期,不代表论文的发表时间)