Brain-inspired evolving neuro-fuzzy system for financial forecasting and trading of the s&p500 index

  • Authors:

  • Weng Luen Ho;Whye Loon Tung;Chai Quek

  • Affiliations:

  • Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore

  • Venue:
  • PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
  • Year:
  • 2010

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Abstract

An interday financial trading system with a predictive model empowered by a novel brain-inspired evolving Mamdani-Takagi-Sugeno Neural-Fuzzy Inference System (eMTSFIS) is proposed in this paper. The eMTSFIS predictive model possesses synaptic mechanisms and information processing capabilities of the human hippocampus, resulting in a more robust and adaptive forecasting model as compared to existing econometric and neural-fuzzy techniques. The trading strategy of the proposed system is based on the moving-averages-convergence/divergence (MACD) principle to generate buy-sell trading signals. By introducing forecasting capabilities to the computation of the MACD trend signals, the lagging nature of the MACD trading rule is addressed. Experimental results based on the S&P500 Index confirmed that eMTSFIS is able to provide highly accurate predictions and the resultant system is able to identify timely trading opportunities while avoiding unnecessary trading transactions. These attributes enable the eMTSFIS-based trading system to yield higher multiplicative returns for an investor.