Title :
A Novel Methodology for Stock Investment Using Episode Mining and Technical Indicators
Author :
Yu-Feng Lin ; Chien-Feng Huang ; Tseng, Vincent S.
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Abstract :
This paper proposes a novel methodology for stock investing using the technique of episode mining and technical indicators. The time-series data of stock price is used for the construction of complex episode events and rules. Our experimental results show that the episode rule mining method not only improves a well-known technical indicator alone, but also assists it in outperforming the benchmark. Based upon the results obtained, we expect this episode mining methodology to advance the research in data mining for finance, and provide an alternative strategy to stock investment in practice.
Keywords :
data mining; financial data processing; investment; stock markets; time series; benchmark outperforming; complex episode events; complex episode rules; episode mining methodology; episode rule mining method; finance data mining; stock investment methodology; stock price; technical indicators; time-series data; Benchmark testing; Data mining; Data models; Erbium; Investments; Prediction algorithms; Stock markets; Cross Validation; Episode Mining; Stock Investing Strategy; Technical Indicators;
Conference_Titel :
Technologies and Applications of Artificial Intelligence (TAAI), 2012 Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4673-4976-5
DOI :
10.1109/TAAI.2012.26