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Determination of Temporal Stock Investment Styles via Biclustering Trading Patterns

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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.

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Funding

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).

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Authors and Affiliations

  1. School of Computer Science, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Jianjun Sun

  2. School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Qinghua Huang

  3. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shanxi, People’s Republic of China

    Xuelong Li

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  1. Jianjun Sun
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  2. Qinghua Huang
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  3. Xuelong Li
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Correspondence to Qinghua Huang.

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Sun, J., Huang, Q. & Li, X. Determination of Temporal Stock Investment Styles via Biclustering Trading Patterns. Cogn Comput 11, 799–808 (2019). https://doi.org/10.1007/s12559-019-9626-9

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