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Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series

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Abstract

In this study, we propose a new framework to analyze the stock-specific mictrotrading patterns preceding stock price jumps, which should be useful for financial regulation or investment decisions. Using high-frequency trading data, the key step of our framework is to extract a set of core features to distinguish the prejump trading patterns of various stocks taking into account of the temporal information within the feature trajectories. We adopt 10 liquidity measures and 30 technical indicators to generate a high-dimensional candidate feature trajectory set and use a combination of the time-series-based mutual information and the minimum-Redundancy Maximum-Relevancy technique to perform the feature selection. A clustering analysis is then adopted to identify the outlier stocks with idiosyncratic prejump trading patterns. In the end, an application case is conducted based on the level-2 data of 189 constituent stocks of the China Security Index 300 to illustrate the viability of our proposed methodology. Comparison results show that the features we selected has higher capacity to identify the similarity among trading trajectories than those without considering temporal feature information, which provides more reliable features in detecting the outlier trading patterns.

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Data Availability

The data that support the findings of this study are provided by Shanghai Wind Information Co., Ltd. (http://www.wind.com.cn).

Code availability

The code is available upon request.

Notes

  1. In Lee and Mykland (2008), the numerator of the second term of $C_n$ was incorrect. There should be \(\log(4 \pi)\) instead of \(\log\pi\). We use the corrected formula. It has also been noted in some studies, for example, in Gilder, Shackleton and Taylor (2014). We thank Ping-Chen Tsai for pointing out this issue.

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Funding

This work was supported by China Postdoctoral Science Foundation (2021M701670), Jiangsu Planned Projects for Postdoctoral Research Funds (2021K357C), National Natural Science Foundation of China (71720107001, U1811462, 72071103, 72001104), Fundamental Research Funds for the Central Universities(0118/14370107),and Humanities and Social Science Fund of the Ministry of Education (17YJA790101).

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Authors and Affiliations

  1. School of Management and Engineering, Nanjing University, Nanjing, 210093, China

    Ao Kong, Hongliang Zhu & Xindan Li

  2. School of Finance, Nanjing University of Finance and Economics, Nanjing, 210023, China

    Ao Kong

  3. Department of Mathematics, University of Houston, TX, 77204, USA

    Robert Azencott

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  1. Ao Kong
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All authors contributed to the study conception and design. All authors read and approved the final manuscript.

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Correspondence to Xindan Li.

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Kong, A., Azencott, R., Zhu, H. et al. Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series. Comput Econ 63, 1401–1429 (2024). https://doi.org/10.1007/s10614-023-10367-6

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