Binary classification and data analysis for modeling calendar anomalies in financial markets

Hui Hsuan Tung, Chiao Chun Cheng, Yu Ying Chen, Yu Fu Chen, Szu-Hao Huang, An-Pin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

This paper studies on the Day-of-the-week effect by means of several binary classification algorithms in order to achieve the most effective and efficient decision trading support system. This approach utilizes the intelligent data-driven model to predict the influence of calendar anomalies and develop profitable investment strategy. Advanced technology, such as time-series feature extraction, machine learning, and binary classification, are used to improve the system performance and make the evaluation of trading simulation trustworthy. Through experimenting on the component stocks of S&P 500, the results show that the accuracy can achieve 70% when adopting two discriminant feature representation methods, including 'multi-day technical indicators' and 'intra-day trading profile.' The binary classification method based on LDA-Linear Prior kernel outperforms than other learning techniques and provides the investor a stable and profitable portfolios with low risk. In addition, we believe this paper is a FinTech example which combines advanced interdisciplinary researches, including financial anomalies and big data analysis technology.

Original languageEnglish
Title of host publicationProceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-121
Number of pages6
ISBN (Electronic)9781509035557
DOIs
StatePublished - 13 Jul 2017
Event7th International Conference on Cloud Computing and Big Data, CCBD 2016 - Taipa, Macau, China
Duration: 16 Nov 201618 Nov 2016

Publication series

NameProceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016

Conference

Conference7th International Conference on Cloud Computing and Big Data, CCBD 2016
CountryChina
CityTaipa, Macau
Period16/11/1618/11/16

Keywords

  • back-propagation neural networks
  • calendar anomalies
  • day-of-the-week effect
  • linear discriminant analysis
  • support vector machine
  • technical indicators

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  • Cite this

    Tung, H. H., Cheng, C. C., Chen, Y. Y., Chen, Y. F., Huang, S-H., & Chen, A-P. (2017). Binary classification and data analysis for modeling calendar anomalies in financial markets. In Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016 (pp. 116-121). [7979890] (Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCBD.2016.032