Title :
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
Author :
Ang, K.K. ; Quek, C.
Author_Institution :
Sch. of Comput. Eng., Nat. Technol. Univ.
Abstract :
This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model
Keywords :
forecasting theory; fuzzy neural nets; fuzzy reasoning; investment; rough set theory; stock markets; forecasting stock price; neuro-fuzzy inference system; portfolio end values; price difference forecast; price series data; rough set based pseudo outer product; stock market data; stock trading; Artificial intelligence; Artificial neural networks; Data security; Economic forecasting; Fuzzy neural networks; Inference algorithms; Portfolios; Predictive models; Set theory; Stock markets; Forecasting theory; fuzzy neural networks; rough set theory; stock market; time series; Algorithms; Artificial Intelligence; Computing Methodologies; Decision Support Techniques; Forecasting; Fuzzy Logic; Game Theory; Investments; Models, Economic; Neural Networks (Computer); Ownership; Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.875996
