IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v53y2021i43p4931-4949.html
   My bibliography  Save this article

An agent-based model and detect price manipulation based on intraday transaction data with simulation

Author

Listed:
  • Mohammad Zare
  • Omid Naghshineh A.
  • Erfan Salavati
  • Adel Mohammadpour

Abstract

In this article, we propose an agent-based model for LOB markets, and by simulation, we estimate the model’s parameters. This model has two interesting points. First, we divide the data transaction of 1 day into six parts. Second, we detect price manipulation by using intraday transaction data. To detect price manipulation, we simulate the model once without the price manipulator and once with the price manipulator, and then by using some statistical properties such as skewness we find relations to detect price manipulation.

Suggested Citation

  • Mohammad Zare & Omid Naghshineh A. & Erfan Salavati & Adel Mohammadpour, 2021. "An agent-based model and detect price manipulation based on intraday transaction data with simulation," Applied Economics, Taylor & Francis Journals, vol. 53(43), pages 4931-4949, September.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:43:p:4931-4949
    DOI: 10.1080/00036846.2021.1912282
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2021.1912282
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2021.1912282?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. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    2. Robert A. Jarrow, 2008. "Market Manipulation, Bubbles, Corners, and Short Squeezes," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 6, pages 105-130, World Scientific Publishing Co. Pte. Ltd..
    3. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    4. Rama Cont & Adrien de Larrard, 2013. "Price Dynamics in a Markovian Limit Order Market," Post-Print hal-00552252, HAL.
    5. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    6. Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    7. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
    8. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    9. Hugh Luckock, 2003. "A steady-state model of the continuous double auction," Quantitative Finance, Taylor & Francis Journals, vol. 3(5), pages 385-404.
    10. Tālis J. Putniņš, 2012. "Market Manipulation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 26(5), pages 952-967, December.
    11. Evans, George W, 1986. "A Test for Speculative Bubbles in the Sterling-Dollar Exchange Rate: 1981-84," American Economic Review, American Economic Association, vol. 76(4), pages 621-636, September.
    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. Mohammad Zare & Omid Naghshineh Arjmand & Erfan Salavati & Adel Mohammadpour, 2021. "An Agent‐Based model for Limit Order Book: Estimation and simulation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1112-1121, January.
    2. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    3. Frank Kelly & Elena Yudovina, 2018. "A Markov Model of a Limit Order Book: Thresholds, Recurrence, and Trading Strategies," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 181-203, February.
    4. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    5. Chávez-Casillas, Jonathan A. & Figueroa-López, José E., 2017. "A one-level limit order book model with memory and variable spread," Stochastic Processes and their Applications, Elsevier, vol. 127(8), pages 2447-2481.
    6. Efstathios Panayi & Gareth W. Peters, 2015. "Stochastic simulation framework for the limit order book using liquidity-motivated agents," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-52.
    7. Efstathios Panayi & Gareth Peters, 2015. "Stochastic simulation framework for the Limit Order Book using liquidity motivated agents," Papers 1501.02447, arXiv.org, revised Jan 2015.
    8. Johannes Bleher & Michael Bleher & Thomas Dimpfl, 2020. "From orders to prices: A stochastic description of the limit order book to forecast intraday returns," Papers 2004.11953, arXiv.org, revised May 2021.
    9. Tucker Hybinette Balch & Mahmoud Mahfouz & Joshua Lockhart & Maria Hybinette & David Byrd, 2019. "How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?," Papers 1906.12010, arXiv.org.
    10. Jonathan A. Ch'avez-Casillas & Jos'e E. Figueroa-L'opez, 2014. "One-level limit order book models with memory and variable spread," Papers 1407.5684, arXiv.org, revised Mar 2016.
    11. Weibing Huang & Mathieu Rosenbaum & Pamela Saliba, 2019. "From Glosten-Milgrom to the whole limit order book and applications to financial regulation," Papers 1902.10743, arXiv.org.
    12. Federico Gonzalez & Mark Schervish, 2017. "Instantaneous order impact and high-frequency strategy optimization in limit order books," Papers 1707.01167, arXiv.org, revised Oct 2017.
    13. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    14. Robert A. Jarrow, 2015. "Asset Price Bubbles," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 201-218, December.
    15. Julius Bonart & Martin Gould, 2015. "Latency and liquidity provision in a limit order book," Papers 1511.04116, arXiv.org, revised Jun 2016.
    16. Antoine Jacquier & Hao Liu, 2017. "Optimal liquidation in a Level-I limit order book for large tick stocks," Papers 1701.01327, arXiv.org, revised Nov 2017.
    17. Gareth W. Peters & Efstathios Panayi & Francois Septier, 2015. "SMC-ABC methods for the estimation of stochastic simulation models of the limit order book," Papers 1504.05806, arXiv.org.
    18. Anatoliy Swishchuk, 2020. "Stochastic Modelling of Big Data in Finance," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1613-1630, December.
    19. Anatoliy Swishchuk & Tyler Hofmeister & Katharina Cera & Julia Schmidt, 2017. "General Semi-Markov Model For Limit Order Books," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-21, May.
    20. Maxime Morariu-Patrichi & Mikko S. Pakkanen, 2017. "Hybrid marked point processes: characterisation, existence and uniqueness," Papers 1707.06970, arXiv.org, revised Oct 2018.

    More about this item

    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:taf:applec:v:53:y:2021:i:43:p:4931-4949. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.