Title :
Stock trading using PSEC and RSPOP: a novel evolving rough set-based neuro-fuzzy approach
Author :
Ang, K.K. ; Quek, C.
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore
Abstract :
This paper presents a novel evolving rough-set based neuro-fuzzy approach in stock trading using the method of forecasting stock price difference. The proposed pseudo self-evolving cerebellar (PSEC) algorithm and the rough set-based pseudo outer-product (RSPOP) algorithm are used to construct a novel evolving rough set-based neuro-fuzzy system as the underlying forecast modeling tool to identify the fuzzy sets and fuzzy rules respectively. The proposed price difference forecast model is then incorporated with a forecast bottleneck free trading decision model to investigate achievable trading profit. Experimental results on real world stock market data showed that the proposed stock trading model with the evolving rough set-based neuro-fuzzy price difference forecast yielded significantly higher profits than the trading model without forecast.
Keywords :
cerebellar model arithmetic computers; commerce; forecasting theory; fuzzy neural nets; fuzzy set theory; pricing; rough set theory; stock markets; forecast modeling; fuzzy rule; fuzzy set; pseudoself-evolving cerebellar; rough set-based neurofuzzy system; rough set-based pseudoouter-product; stock market; stock price difference forecasting; stock trading; trading profit; Computational intelligence; Economic forecasting; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Inference algorithms; Neural networks; Predictive models; Stock markets; Training data;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554804
