Improved expert selection model for forex trading

doi:10.1007/s11704-017-6472-3

Front. Comput. Sci.

2018, Vol. 12

Issue (3) : 518-527 https://doi.org/10.1007/s11704-017-6472-3

RESEARCH ARTICLE

Improved expert selection model for forex trading

Jia ZHU¹, Xingcheng WU¹, Jing XIAO¹(

), Changqin HUANG¹, Yong TANG¹, Ke Deng²

¹. School of Computer Science, South China Normal University, Guangzhou 510631, China
². School of Computer Science and Information Technology, Royal Melbourne Institute of Technology, Melbourne 3000, Australia

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Abstract

Online prediction is a process that repeatedly predicts the next element in the coming period from a sequence of given previous elements. This process has a broad range of applications in various areas, such as medical, streaming media, and finance. The greatest challenge for online prediction is that the sequence data may not have explicit features because the data is frequently updated, which means good predictions are difficult to maintain. One of the popular solutions is to make the prediction with expert advice, and the challenge is to pick the right experts with minimum cumulative loss. In this research, we use the forex trading prediction, which is a good example for online prediction, as a case study. We also propose an improved expert selection model to select a good set of forex experts by learning previously observed sequences. Our model considers not only the average mistakes made by experts, but also the average profit earned by experts, to achieve a better performance, particularly in terms of financial profit. We demonstrate the merits of our model on two real major currency pairs corpora with extensive experiments.

Keywords online learning expert selection forex prediction

Corresponding Author(s): Jing XIAO

Just Accepted Date: 28 March 2017 Online First Date: 27 November 2017 Issue Date: 02 May 2018

Cite this article:

Jia ZHU,Xingcheng WU,Jing XIAO, et al. Improved expert selection model for forex trading[J]. Front. Comput. Sci., 2018, 12(3): 518-527.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6472-3
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I3/518

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