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Cognitive Computation

, Volume 11, Issue 6, pp 799–808 | Cite as

Determination of Temporal Stock Investment Styles via Biclustering Trading Patterns

  • Jianjun Sun
  • Qinghua HuangEmail author
  • Xuelong Li
Article

Abstract

Due to the effects of many deterministic and stochastic factors, it has always been a challenging goal to gain good profits from the stock market. Many methods based on different theories have been proposed in the past decades. However, there has been little research about determining the temporal investment style (i.e., short term, middle term, or long term) for the stock. In this paper, we propose a method to find suitable stock investment styles in terms of investment time. Firstly, biclustering is applied to a matrix that is composed of technical indicators of each trading day to discover trading patterns (regarded as trading rules). Subsequently a k-nearest neighbor (KNN) algorithm is employed to transform the trading rules to the trading actions (i.e., the buy, sell, or no-action signals). Finally, a min-max and quantization strategy is designed for determination of the temporal investment style of the stock. The proposed method was tested on 30 stocks from US bear, bull, and flat markets. The experimental results validate its usefulness.

Keywords

Machine learning Biclustering Technical analysis Trading rules Investment styles 

Notes

Funding Information

This work was financially supported in part by the National Natural Science Foundation of China under Grant 61571193; in part by the Natural Science Foundation of Guangdong Province, China under Grant 2017A030312006 and Grant 2017A030330247; in part by the Fundamental Research Funds for the Central Universities under Grant 2017MS064; in part by the Science and Technology Program of Guangzhou under Grant 201704020134; and in part by the Project of Science and Technology Department of Guangdong province (Nos. 2016A010101021, 2016A010101022, and 2016A010101023).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science, and Center for OPTical IMagery Analysis and Learning (OPTIMAL)Northwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL)Northwestern Polytechnical UniversityXi’anPeople’s Republic of China
  3. 3.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL)Northwestern Polytechnical UniversityXi’anPeople’s Republic of China

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