Skip to main content
SpringerLink
Log in

Improved expert selection model for forex trading

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Deshpande M, Karypis G. Evaluation of techniques for classifying biological sequences. In: Proceedings of Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 2002, 417–431

    Chapter  Google Scholar 

  2. Duskin O, Feitelson D G. Distinguishing humans from robots in web search logs: preliminary results using query rates and intervals. In: Proceedings of the Workshop on Web Search Click Data. 2009, 15–19

    Google Scholar 

  3. Kadous MW, Sammut C. Classification of multivariate time series and structured data using constructive induction. Machine Learning, 2005, 58(2): 179–216

    Article  Google Scholar 

  4. Liu X, Zhang P Z, Zeng D J. Sequence matching for suspicious activity detection in anti-money laundering. In: Proceedings of IEEE ISI International Workshops on Intelligence and Security Informatics. 2008, 50–61

    Chapter  Google Scholar 

  5. Tan P N, Kumar V. Discovery of web robot sessions based on their navigational patterns. Data Mining and Knowledge Discovery, 2002, 6(1): 9–35

    Article  MathSciNet  Google Scholar 

  6. Wang J G, Huang J Z, Guo J F, Lan Y Y. Recommending high-utility search engine queries via a query-recommending model. Neurocomputing, 2015, 167(1): 195–208

    Article  Google Scholar 

  7. Huang X, Cheng H, Li R H, Qin L, Yu J X. Top-k structural diversity search in large networks. Proceedings of the VLDB Endowment, 2013, 6(13): 1618–1629

    Article  Google Scholar 

  8. Hutter M. On the foundations of universal sequence prediction. In: Proceedings of International Conference on Theory and Applications of Models of Computation. 2006, 408–420

    Chapter  Google Scholar 

  9. Kaj L N. Application of a simple likelihood ratio approximant to protein sequence classification. Bioinformatics, 2006, 22(23): 2865–2869

    Article  Google Scholar 

  10. Wang J L, Zhao P L, Hoi S C H. Cost-sensitive online classification. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(10): 2425–2438

    Article  Google Scholar 

  11. Zhu Z X, Xiao J, Li J Q, Wang F X, Zhang Q F. Global path planning of wheeled robots using multi-objective memetic algorithms. Integrated Computer-Aided Engineering, 2015, 22(4): 387–404

    Article  Google Scholar 

  12. Mao R, Xu H L, Wu W B, Li J Q, Li Y, Lu M H. Overcoming the challenge of variety: big data abstraction, the next evolution of data management for aal communication systems. IEEE Communications Magazine, 2015, 53(1): 42–47

    Article  Google Scholar 

  13. Wang J L, Zhao P L, Hoi S C H, Jin R. Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering, 2006, 26(3): 698–710

    Article  Google Scholar 

  14. Mao R, Zhang P H, Li X L, Liu X, Lu M H. Pivot selection for metricspace indexing. International Journal of Machine Learning and Cybernetics, 2016, 7(2): 311–323

    Article  Google Scholar 

  15. Lu M H, Tang Y N, Sun R C, Wang T F, Chen S P, Mao R. A real time displacement estimation algorithm for ultrasound elastography. Computers in Industry, 2015, 69(1): 61–71

    Article  Google Scholar 

  16. Li J Q, Liu C C, Liu B, Mao R, Wang Y C, Chen S, Yang J J, Pan H, Wang Q. Diversity-aware retrieval of medical records. Computers in Industry, 2015, 69(1): 81–91

    Article  Google Scholar 

  17. Zheng K, Zhou X F, Fung P C, Xie K X. Spatial query processing for fuzzy objects. The VLDB Journal, 2012, 21(5): 1–23

    Article  Google Scholar 

  18. Littlestone N, Warmuth M K. The weighted majority algorithm. In: Proceedings of Symposium on Foundations of Computer Science. 1989, 256–261

    Chapter  Google Scholar 

  19. Littlestone N. Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. In: Proceedings of Symposium on Foundations of Computer Science. 1987, 68–77

    Google Scholar 

  20. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 1958, 65(6): 386

    Article  Google Scholar 

  21. Valiant L G. A theory of the learnable. Communications of the ACM, 1984, 27(11): 1134–1142

    Article  MATH  Google Scholar 

  22. Blum A. Separating distribution-free and mistake-bound learning models over the boolean domain. In: Proceedings of Symposium on Foundations of Computer Science. 1990, 211–218

    Google Scholar 

  23. Widrow B, Hoff M E. Adaptive Switching Circuits. Cambridge: MIT Press, 1988

    Google Scholar 

  24. Wei L, Keogh E. Semi-supervised time series classification. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 748–753

    Chapter  Google Scholar 

  25. Xi X P, Keogh E, Shelton C, Wei L, Ratanamahatana C A. Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine learning. 2006, 1033–1040

    Google Scholar 

  26. Lewis D D. Naive (bayes) at forty: the independence assumption in information retrieval. In: Proceedings of European Conference on Machine Learning. 1998, 4–15

    Google Scholar 

  27. Yakhnenko O, Silvescu A, Honavar V. Discriminatively trained markov model for sequence classification. In: Proceedings of IEEE International Conference on Data Mining. 2005, 498–505

    Google Scholar 

  28. Eban E, Birnbaum A, Shalev-Shwartz S, Globerson A. Learning the experts for online sequence prediction. In: Proceedings of the International Conference on Machine learning. 2012, 125–133

    Google Scholar 

  29. Cesa-Bianchi N, Freund Y, Haussler D, Helmbold D P, Schapire R E, Warmuth M K. How to use expert advice. Journal of the ACM, 1997, 44(3): 427–485

    Article  MathSciNet  MATH  Google Scholar 

  30. Foster D P, Vohra R V. A randomization rule for selecting forecasts. Operations Research, 1993, 41(4): 704–709

    Article  MATH  Google Scholar 

  31. Abernethy J, Bartlett P, Rakhlin A. Multitask learning with expert advice. In: Proceedings of International Conference on Computational Learning Theory. 2007, 484–498

    Google Scholar 

  32. Avidan S. Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 261–271

    Article  Google Scholar 

  33. Yu C N J, Joachims T. Learning structural svms with latent variables. In: Proceedings of the 26th Annual International Conference on Machine Learning. 2009, 1169–1176

    Google Scholar 

  34. Ron D, Singer Y, Tishby N. The power of amnesia: learning probabilistic automata with variable memory length. Machine Learning, 1996, 25(2): 117–149

    Article  MATH  Google Scholar 

  35. Zhao P, Hoi S, Zhuang J. Active learning with expert advice. In: Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence. 2013, 704–713

    Google Scholar 

  36. Zhu J, Yang J, Xiao J, Huang C Q, Zhao G S, Tang Y. Online prediction for forex with an optimized experts selection model. In: Proceedings of Asia-Pacific Web Conference. 2016, 371–382

    Google Scholar 

  37. Zheng K, Huang Z, Zhou O Y Zhou X F. Discovering the most influential sites over uncertain data: a rank based approach. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(12): 2156–2169

    Article  Google Scholar 

  38. Zheng K, Su H, Zheng B L, Shang S, Xu J, Liu J, Zhou X F. Interactive top-k spatial keyword queries. In: Proceedings of the 31st IEEE International Conference on Data Engineering. 2015, 423–434

    Google Scholar 

  39. Vapnik V N. The nature of statistical learning theory. IEEE Transactions on Neural Networks, 1995, 8(6): 1564–1564

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Guangdong Province, China (2015A030310509), the National Natural Science Foundation of China (Grant Nos. 61370229, 61272067, 61303049), the S&T Planning Key Projects of Guangdong Province (2014B010117007, 2015B010109003, 2015A030401087, 2016A030303055, 2016B030305004, and 2016B010109008) and the S&T Projects of Guangzhou Municipality, China (201604010003).

Author information

Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, 510631, China

    Jia Zhu, Xingcheng Wu, Jing Xiao, Changqin Huang & Yong Tang

  2. School of Computer Science and Information Technology, Royal Melbourne Institute of Technology, Melbourne, 3000, Australia

    Ke Deng

Authors
  1. Jia Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingcheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changqin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ke Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Xiao.

Additional information

Jia Zhu is currently an associate professor in the School of Computer Science, South China Normal University, China. He finished his postdoctoral fellowship at United Nations University, Macau, China. Prior to that, he received his PhD degree from The University of Queensland, Australia in 2013 and his BS and MS degrees from Bond University, Australia in 2004 and 2006, respectively. His research interests include machine learning and information retrieval. He has published several papers on top conferences and journals, such as Information Sciences and WWW.

Xingcheng Wu is currently a postgraduate student of the School of Computer Science, South China Normal University, China, supervised by Dr. Jia Zhu. His research interests include data mining, machine learning, and cloud computing.

Jing Xiao is a professor in the School of Computer Science at South China Normal University, China. She received her PhD degree from the School of Computing, National University of Singapore, Singapore in 2005. She is currently the vice-chair of the ACM and IEEE Guangzhou sections and a senior member of China Computer Federation (CCF). Her research interests include data mining, recommendation systems, and evolutionary computation algorithms.

Changqin Huang is a professor in the School of Information Technology in Education, South China Normal University, China. He is currently a Guangdong specially appointed professor (Pearl River Scholar) and a senior member (E200014100S) of China Computer Federation (CCF). He received his PhD degree in computer science and technology from Zhejiang University, China in 2005. He has published more than 80 research papers in international journals and conferences. His research interests include service computing, cloud computing, semantic web, and education informationization.

Yong Tang is a professor and the dean of the School of Computer Science at South China Normal University, China. He also serves as the director of the Services Computing Engineering Research Center of Guangdong Province. He has published more than 200 papers and books. He is also a distinguished member and the vice director of the Technical Committee on Collaborative Computing of China Computer Federation (CCF). Moreover, he served as the general or program committee co-chair of more than ten international and national conferences. His current research interests include knowledge and data engineering, temporal database, cooperative computing, social network services, and big data applications.

Ke Deng is currently a lecturer in RMIT University, Australia. He was awarded a PhD degree in computer science at The University of Queensland, Australia in 2007 with focus on data and knowledge engineering. He was a researcher in Huawei Noah Ark’s Research Lab, Hong Kong, China from 2013 to 2015. He was also a postdoctoral research fellow at CSIRO ICT Center in 2007 and an ARC Australian Postdoctoral Fellow from 2010 to 2012.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, J., Wu, X., Xiao, J. et al. Improved expert selection model for forex trading. Front. Comput. Sci. 12, 518–527 (2018). https://doi.org/10.1007/s11704-017-6472-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-017-6472-3

Keywords

Search

Navigation