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Learning representation of stock traders and immediate price impacts

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  • Xie, Wen-Jie
  • Li, Mu-Yao
  • Zhou, Wei-Xing

Abstract

We use 239-day trading-level data for a stock on the Shanghai Stock Exchange, including about 440,000 traders and 1.77 million trading relationships, to study the representation of traders in a trading network using the network representation learning method, and to identify different traders' local outlier factor (LOF). Based on the local outlier factors, traders are divided into two categories: novel and normal. The novel traders' orders have smaller immediate price impact. Our method can be used to characterize and discover the behavior of medium-scale trading networks and provide certain decision support for market investors and regulators.

Suggested Citation

  • Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:ememar:v:48:y:2021:i:c:s1566014120306002
    DOI: 10.1016/j.ememar.2020.100791
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    More about this item

    Keywords

    Machine learning; Network embedding; Trading network; Price impact;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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