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Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
- Source :
- IEEE Transactions on Neural Networks. 17:1301-1315
- Publication Year :
- 2006
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2006.
-
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.
- Subjects :
- Volume-weighted average price
Neuro-fuzzy
Computer Networks and Communications
Computer science
computer.software_genre
Computing Methodologies
Profit (economics)
Decision Support Techniques
Pattern Recognition, Automated
Fuzzy Logic
Game Theory
Computer Science::Computational Engineering, Finance, and Science
Artificial Intelligence
ComputerApplications_MISCELLANEOUS
Econometrics
Trading strategy
Investments
Algorithmic trading
Ownership
Pairs trade
General Medicine
Computer Science Applications
Models, Economic
Portfolio
Stock market
Neural Networks, Computer
Data mining
Rough set
computer
Decision model
Algorithms
Software
Forecasting
Subjects
Details
- ISSN :
- 19410093 and 10459227
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks
- Accession number :
- edsair.doi.dedup.....b6e9d19e94c4aa316074561464669f33