Learning representation of stock traders and immediate price impacts

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

Emerging Markets Review, 2021, vol. 48, issue C

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.

Keywords: Machine learning; Network embedding; Trading network; Price impact (search for similar items in EconPapers)
JEL-codes: C1 C19 C45 C61 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ememar:v:48:y:2021:i:c:s1566014120306002

DOI: 10.1016/j.ememar.2020.100791

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