Exponent back propagation neural network forecasting for financial cross-correlation relationship
作者：
Highlights：
• A new neural network (EBPNN) is developed.
• An approach to cross-correlations prediction between financial time series.
• Empirical research is performed in testing the forecasting effect of EBPNN.
• Forecasting long-term cross-correlations by training short-term cross-correlations.
• The proposed model is advantageous in increasing the forecasting precision.
摘要
•A new neural network (EBPNN) is developed.•An approach to cross-correlations prediction between financial time series.•Empirical research is performed in testing the forecasting effect of EBPNN.•Forecasting long-term cross-correlations by training short-term cross-correlations.•The proposed model is advantageous in increasing the forecasting precision.
论文关键词：Forecast,Cross-correlation,Neural network,Financial time series,Exponential type function
论文评审过程：Received 21 February 2015, Revised 30 December 2015, Accepted 31 December 2015, Available online 27 January 2016, Version of Record 11 February 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.045
