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Stock Market Trading Rules Discovery Based on Biclustering Method

Author

Listed:
  • Yun Xue
  • Zhiwen Liu
  • Jie Luo
  • Zhihao Ma
  • Meizhen Zhang
  • Xiaohui Hu
  • Qiuhua Kuang

Abstract

The prediction of stock market’s trend has become a challenging task for a long time, which is affected by a variety of deterministic and stochastic factors. In this paper, a biclustering algorithm is introduced to find the local patterns in the quantized historical data. The local patterns obtained are regarded as the trading rules. Then the trading rules are applied in the short term prediction of the stock price, combined with the minimum-error-rate classification of the Bayes decision theory under the assumption of multivariate normal probability model. In addition, this paper also makes use of the idea of the stream mining to weaken the impact of historical data on the model and update the trading rules dynamically. The experiment is implemented on real datasets and the results prove the effectiveness of the proposed algorithm.

Suggested Citation

  • Yun Xue & Zhiwen Liu & Jie Luo & Zhihao Ma & Meizhen Zhang & Xiaohui Hu & Qiuhua Kuang, 2015. "Stock Market Trading Rules Discovery Based on Biclustering Method," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, March.
    • Handle: RePEc:hin:jnlmpe:849286
    DOI: 10.1155/2015/849286
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    Cited by:

    1. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    2. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages for Fossil Fuel and Renewable Energy Stocks," Documentos de Trabajo del ICAE 2018-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.

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