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A TSK type fuzzy rule based system for stock price prediction

2006; Elsevier BV; Volume: 34; Issue: 1 Linguagem: Inglês

10.1016/j.eswa.2006.08.020

1873-6793

Pei‐Chann Chang, Chen-Hao Liu,

Neural Networks and Applications

In this paper, a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. The fuzzy rule based model is tested on the Taiwan Electronic Shares from the Taiwan Stock Exchange (TSE). Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors with accuracy close to 97.6% in TSE index and 98.08% in MediaTek. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.