Stock Market Trading Rules Discovery Based on Biclustering Method

Yun Xue, Zhiwen Liu, Jie Luo, Zhihao Ma, Meizhen Zhang, Xiaohui Hu and Qiuhua Kuang

Mathematical Problems in Engineering, 2015, vol. 2015, 1-13

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.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2015/849286.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2015/849286.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:849286

DOI: 10.1155/2015/849286

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().