IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v63y2024i4d10.1007_s10614-023-10367-6.html
   My bibliography  Save this article

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

Author

Listed:
  • Ao Kong

    (Nanjing University
    Nanjing University of Finance and Economics)

  • Robert Azencott

    (University of Houston)

  • Hongliang Zhu

    (Nanjing University)

  • Xindan Li

    (Nanjing University)

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.

Suggested Citation

  • Ao Kong & Robert Azencott & Hongliang Zhu & Xindan Li, 2024. "Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1401-1429, April.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10367-6
    DOI: 10.1007/s10614-023-10367-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10367-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10367-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ao Kong & Hongliang Zhu & Robert Azencott, 2021. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 416-438, April.
    2. Jang, Jeewon & Kang, Jangkoo, 2019. "Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 222-247.
    3. Jun Pan & Darrell Duffie, 2001. "Analytical value-at-risk with jumps and credit risk," Finance and Stochastics, Springer, vol. 5(2), pages 155-180.
    4. Kapadia, Nishad & Zekhnini, Morad, 2019. "Do idiosyncratic jumps matter?," Journal of Financial Economics, Elsevier, vol. 131(3), pages 666-692.
    5. Tim Bollerslev & Viktor Todorov, 2011. "Tails, Fears, and Risk Premia," Journal of Finance, American Finance Association, vol. 66(6), pages 2165-2211, December.
    6. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    7. Jarrow, Robert A & Rosenfeld, Eric R, 1984. "Jump Risks and the Intertemporal Capital Asset Pricing Model," The Journal of Business, University of Chicago Press, vol. 57(3), pages 337-351, July.
    8. Boudt, Kris & Petitjean, Mikael, 2014. "Intraday liquidity dynamics and news releases around price jumps: Evidence from the DJIA stocks," Journal of Financial Markets, Elsevier, vol. 17(C), pages 121-149.
    9. Van Ness, Bonnie & Van Ness, Robert & Yildiz, Serhat, 2021. "Private information in trades, R2, and large stock price movements," Journal of Banking & Finance, Elsevier, vol. 131(C).
    10. Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    11. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.
    12. Michael J. Brennan & Sahn-Wook Huh & Avanidhar Subrahmanyam, 2018. "High-Frequency Measures of Informed Trading and Corporate Announcements," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2326-2376.
    13. Jiang, George J. & Lo, Ingrid & Verdelhan, Adrien, 2011. "Information Shocks, Liquidity Shocks, Jumps, and Price Discovery: Evidence from the U.S. Treasury Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(2), pages 527-551, April.
    14. Armand Joulin & Augustin Lefevre & Daniel Grunberg & Jean-Philippe Bouchaud, 2008. "Stock price jumps: news and volume play a minor role," Papers 0803.1769, arXiv.org.
    15. Ni, Xiaoran & Peng, Qiyuan & Yin, Sirui & Zhang, Ting, 2020. "Attention! Distracted institutional investors and stock price crash," Journal of Corporate Finance, Elsevier, vol. 64(C).
    16. Yanjian Zhu & Zhaoying Wu & Hua Zhang & Jing Yu, 2017. "Media sentiment, institutional investors and probability of stock price crash: evidence from Chinese stock markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(5), pages 1635-1670, December.
    17. Ymir Mäkinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2033-2050, December.
    18. Gao, Ya & Han, Xing & Li, Youwei & Xiong, Xiong, 2019. "Overnight momentum, informational shocks, and late informed trading in China," International Review of Financial Analysis, Elsevier, vol. 66(C).
    19. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    20. Su, Zhifang & Bao, Haohua & Li, Qifang & Xu, Boyu & Cui, Xin, 2022. "The prediction of price gap anomaly in Chinese stock market: Evidence from the dependent functional logit model," Finance Research Letters, Elsevier, vol. 47(PB).
    21. Prodromou, Tina & Westerholm, P. Joakim, 2022. "Are high frequency traders responsible for extreme price movements?," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 94-111.
    22. Xu, Yongxin & Xuan, Yuhao & Zheng, Gaoping, 2021. "Internet searching and stock price crash risk: Evidence from a quasi-natural experiment," Journal of Financial Economics, Elsevier, vol. 141(1), pages 255-275.
    23. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    24. Riordan, Ryan & Storkenmaier, Andreas & Wagener, Martin & Sarah Zhang, S., 2013. "Public information arrival: Price discovery and liquidity in electronic limit order markets," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1148-1159.
    25. Chee Kian Leong, 2016. "Credit Risk Scoring with Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 423-446, March.
    26. Faten Lakhal, 2008. "Stock market liquidity and information asymmetry around voluntary earnings disclosures," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 4(1), pages 60-75, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ao Kong & Robert Azencott & Hongliang Zhu & Xindan Li, 2020. "Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis," Papers 2011.04939, arXiv.org, revised Feb 2021.
    2. Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen, 2023. "Jump forecasting in foreign exchange markets: A high‐frequency analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 578-624, April.
    3. Ao Kong & Hongliang Zhu & Robert Azencott, 2021. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 416-438, April.
    4. Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
    5. Zhou, Haigang & Zhu, John Qi, 2019. "Firm characteristics and jump dynamics in stock prices around earnings announcements," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    6. Caporin, Massimiliano & Poli, Francesco, 2022. "News and intraday jumps: Evidence from regularization and class imbalance," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    7. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    8. Radu LUPU & Iulia LUPU & Tanase STAMULE & Mihai ROMAN, 2022. "Entropy as Leading Indicator for Extreme Systemic Risk Events," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 58-73, December.
    9. Boudt, Kris & Petitjean, Mikael, 2014. "Intraday liquidity dynamics and news releases around price jumps: Evidence from the DJIA stocks," Journal of Financial Markets, Elsevier, vol. 17(C), pages 121-149.
    10. Christensen, Kim & Oomen, Roel & Podolskij, Mark, 2010. "Realised quantile-based estimation of the integrated variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 74-98, November.
    11. Jan Hanousek & Evzen Kocenda & Jan Novotny, 2014. "Price jumps on European stock markets," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 14(1), pages 10-22, March.
    12. Bjursell, Johan & Gentle, James E. & Wang, George H.K., 2015. "Inventory announcements, jump dynamics, volatility and trading volume in U.S. energy futures markets," Energy Economics, Elsevier, vol. 48(C), pages 336-349.
    13. repec:hal:journl:peer-00732538 is not listed on IDEAS
    14. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    15. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    16. Chen, Chin-Ho, 2019. "Downside jump risk and the levels of futures-cash basis," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    17. Cui, Xin & Sensoy, Ahmet & Nguyen, Duc Khuong & Yao, Shouyu & Wu, Yiyao, 2022. "Positive information shocks, investor behavior and stock price crash risk," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 493-518.
    18. Doureige J. Jurdi, 2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    19. Eric Jacquier & Cedric Okou, 2013. "Disentangling Continuous Volatility from Jumps in Long-Run Risk-Return Relationships," CIRANO Working Papers 2013s-14, CIRANO.
    20. Olivier Scaillet & Adrien Treccani & Christopher Trevisan, 2020. "High-Frequency Jump Analysis of the Bitcoin Market," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 209-232.
    21. Su, Zhifang & Bao, Haohua & Li, Qifang & Xu, Boyu & Cui, Xin, 2022. "The prediction of price gap anomaly in Chinese stock market: Evidence from the dependent functional logit model," Finance Research Letters, Elsevier, vol. 47(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10367-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.