A Study of TAIEX Futures High-frequency Trading by using EEMD-based Neural Network Learning Paradigms

• Main Authors: Huang, Sven Shih Hao, 黃仕豪
• Other Authors: Shiau, Yuo Hsien
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/45455658200913105573

碩士 === 國立政治大學 === 應用物理研究所 === 102 === Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.