DOI: 10.7763/IJCTE.2017.V9.1139
Integrating Physical Expert Systems to Forecast Taiwan Stock Behavior
Abstract—Abstract—This technical analysis includes two parts, the technical indicators and the trends. Many researches have applied technical indicators in the financial decision area, but few researches have applied the wave principle to the trends. The extract N-wave under Elliott Wave characteristics using the back-propagation neural network (BPNN) method is applied in this work. The results showed that integrating RSI and MACD with the N-wave provides better accuracy and profitability prediction than considering only the N-wave physical quantity. RSI represents the short-term disturbance and MACD represents the long-term trends. The results show that long-term and short-term physical quantities also have control ability on the stock market. This study establishes an intelligent model that provides original value retrospect indicators that present more confident recommendations to investors.
Index Terms—Index Terms—Technical analysis, the Elliott wave principle, N-wave technical indicator, back-propagation neural network (BPNN).
Chiu-Chin Chen is with the Department of Information Management, National Penghu University of Science and Technology, 300 Liuhe Rd., Magong, Penghu 880, Taiwan, R.O.C. (e-mail: jennifer@gms.npu.edu.tw).
Chia-Chun Liao was with the Department of Business Administration, National Central University, 300 Zhongda Rd., Zhongli District, Taoyuan City 32001, Taiwan, R.O.C. (e-mail: jonathan.liao.mis@gmail.com).
Cite:Chiu-Chin Chen and Chia-Chun Liao, "Integrating Physical Expert Systems to Forecast Taiwan Stock Behavior," International Journal of Computer Theory and Engineering vol. 9, no. 3, pp. 207-210, 2017.