Narrow Results

This study suggests time-based clustering models integrating change-point detection and neural networks, and applies them to financial time series forecasting. The basic concept of the proposed models is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage, the clustering neural network modeling stage, is to detect successive change points in the dataset, and to forecast change-point groups with backpropagation neural networks (BPNs). In this stage, three change-point detection methods are applied and compared. They are: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with BPNs. Through the application to financial time series forecasting, we compare the proposed models with a neural network model alone and, in addition, determine which of three change-point detection methods performs better. Furthermore, we evaluate whether the proposed models play a role in clustering to reflect the time. Finally, this study examines the predictability of the integrated neural network models based on change-point detection.

Few studies have considered the information in the frequency domain to detect the structural breaks in financial markets. This paper provides a mixed model that integrates BEMD, AFD and Chow test to study the fluctuation characteristics of the soybean futures price. Due to the application of AFD, the mixed model can detect the structural breaks of the price fluctuation through obtaining the high-resolution information in the frequency domain. According to our results, in general, the soybean futures price in China is mainly determined by IMF11, followed by IMF9, IMF8 and IMF7, and there exist many structural breaks of different IMFs. Interestingly, national macro-controls have opposite effects at IMF8 and IMF11. The results show the effectiveness of the proposed mixed model for detecting structural breaks of financial markets in frequency-domain by revealing the impact of external events on the soybean futures price at multi-scales.