Financial market trading system with a hierarchical coevolutionary fuzzy predictive model

  • Authors:

  • Haoming Huang;Michel Pasquier;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:
  • IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
  • Year:
  • 2009

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Abstract

Financial market prediction and trading presents a challenging task that attracts great interest from researchers and investors because success may result in substantial rewards. This paper describes the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series. A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named Irregular Shaped Membership Function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data.