IDEAS home Printed from https://ideas.repec.org/p/cfr/cefirw/w0232.html

Invariance of buy-sell switching points

Author

Listed:
  • Kyoung-hun Bae

    (Ulsan National Institute of Science and Technology)

  • Albert S. Kyle

    (Robert H. Smith School of Business, University of Maryland)

  • Eun Jung Lee

    (Hanyang University)

  • Anna Obizhaeva

    (New Economic School)

Abstract

Define the number of buy-sell “switching points” as the number of times that individual traders change the direction of their trading. Based on the hypothesis that switching points take place in business time, market microstructure invariance predicts that the aggregate number of switching points is proportional to the 2=3 power of the product of dollar volume and volatility. Using trading data from the Korea Exchange (KRX) from 2008 to 2010, we estimate the exponent to be 0.675 with standard error of 0.005. Invariance explains about 93% of the variation in the logarithm of the number of switching points each month across stocks. Most of the variation represents changes in the number of accounts trading the stock and not the number of switching points per account.

Suggested Citation

  • Kyoung-hun Bae & Albert S. Kyle & Eun Jung Lee & Anna Obizhaeva, 2016. "Invariance of buy-sell switching points," Working Papers w0232, Center for Economic and Financial Research (CEFIR).
    • Handle: RePEc:cfr:cefirw:w0232
    as

    Download full text from publisher

    File URL: http://www.cefir.ru/papers/WP232.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Benoit Mandelbrot & Howard M. Taylor, 1967. "On the Distribution of Stock Price Differences," Operations Research, INFORMS, vol. 15(6), pages 1057-1062, December.
    2. Torben G. Andersen & Oleg Bondarenko & Albert S. Kyle & Anna Obizhaeva, 2016. "Intraday Trading Invariance in the E-mini S&P 500 Futures Market," Working Papers w0229, New Economic School (NES).
    3. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    4. Anna, Petrenko, . "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(01).
    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. Kyoung-hun Bae & Albert S. Kyle & Eun Jung Lee & Anna A. Obizhaeva, 2020. "Invariance of Buy-Sell Switching Points," Working Papers w0273, New Economic School (NES).
    2. J. Doyne Farmer & Laszlo Gillemot & Fabrizio Lillo & Szabolcs Mike & Anindya Sen, 2004. "What really causes large price changes?," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 383-397.
    3. Saswat Patra & Malay Bhattacharyya, 2021. "Does volume really matter? A risk management perspective using cross‐country evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 118-135, January.
    4. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    5. Albert S. Kyle & Anna A. Obizhaeva & Yajun Wang, 2020. "Industrial Organization, Order Internalization, and Invariance," Working Papers w0274, New Economic School (NES).
    6. Laura Eslava & Fernando Baltazar-Larios & Bor Reynoso, 2022. "Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model," Papers 2211.17220, arXiv.org.
    7. Kashyap, Ravi, 2019. "The perfect marriage and much more: Combining dimension reduction, distance measures and covariance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    8. Frédéric Bucci & Fabrizio Lillo & Jean-Philippe Bouchaud & Michael Benzaquen, 2020. "Are trading invariants really invariant? Trading costs matter," Post-Print hal-02323318, HAL.
    9. Meenakshi Venkateswaran & B. Wade Brorsen & Joyce A. Hall, 1993. "The distribution of standardized futures price changes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(3), pages 279-298, May.
    10. Xin Ling, 2017. "Normality of stock returns with event time clocks," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57, pages 277-298, April.
    11. Michele Caraglio & Fulvio Baldovin & Attilio L. Stella, 2021. "How Fast Does the Clock of Finance Run?—A Time-Definition Enforcing Stationarity and Quantifying Overnight Duration," JRFM, MDPI, vol. 14(8), pages 1-15, August.
    12. Robert J. Elliott & Carlton-James U. Osakwe, 2006. "Option Pricing for Pure Jump Processes with Markov Switching Compensators," Finance and Stochastics, Springer, vol. 10(2), pages 250-275, April.
    13. Scalas, Enrico, 2006. "The application of continuous-time random walks in finance and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 225-239.
    14. Jonas Hanetho, 2023. "Deep Policy Gradient Methods in Commodity Markets," Papers 2308.01910, arXiv.org.
    15. Sandrine Jacob Leal & Mauro Napoletano, 2017. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading," Post-Print hal-01768876, HAL.
    16. Torben G. Andersen & Oleg Bondarenko & Albert S. Kyle & Anna Obizhaeva, 2016. "Intraday Trading Invariance in the E-mini S&P 500 Futures Market," Working Papers w0229, Center for Economic and Financial Research (CEFIR).
    17. Ata Türkoğlu, 2016. "Normally distributed high-frequency returns: a subordination approach," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 389-409, March.
    18. Albert S. Kyle & Anna Obizhaeva, 2016. "Market Microstructure Invariance: A Dynamic Equilibrium Model," Working Papers w0228, New Economic School (NES).
    19. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    20. Chang, Carolyn W. & S.K. Chang, Jack & Lim, Kian-Guan, 1998. "Information-time option pricing: theory and empirical evidence," Journal of Financial Economics, Elsevier, vol. 48(2), pages 211-242, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:cfr:cefirw:w0232. 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: Julia Babich (email available below). General contact details of provider: https://edirc.repec.org/data/cefirru.html .

    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.