IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v30y2021ics2214635021000393.html
   My bibliography  Save this article

Commonality and contrarian trading among algorithmic traders

Author

Listed:
  • Arumugam, Devika
  • Krishna Prasanna, P.

Abstract

We examine the trading behavior of two heterogeneous groups of Algorithmic Traders (ATs), namely High-Frequency Traders (HFTs) and Buy-side Algorithmic Traders (BATs). We find that these two groups exhibit within-group and between-group commonality in trading volumes, wherein within-group commonality of BATs is higher than that of HFTs. Also, there exists within-group commonality in directional measures of trade execution. We find new evidence of between-group contrarian trading behavior, which is more pronounced among BATs than HFTs. The presence of within-group commonality and between-group contrarian trading among ATs ensures market stability and price discovery.

Suggested Citation

  • Arumugam, Devika & Krishna Prasanna, P., 2021. "Commonality and contrarian trading among algorithmic traders," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    • Handle: RePEc:eee:beexfi:v:30:y:2021:i:c:s2214635021000393
    DOI: 10.1016/j.jbef.2021.100495
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214635021000393
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jbef.2021.100495?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
    ---><---

    References listed on IDEAS

    as
    1. Roman Kozhan & Wing Wah Tham, 2012. "Execution Risk in High-Frequency Arbitrage," Management Science, INFORMS, vol. 58(11), pages 2131-2149, November.
    2. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    3. Benos, Evangelos & Brugler, James & Hjalmarsson, Erik & Zikes, Filip, 2017. "Interactions among High-Frequency Traders," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1375-1402, August.
    4. Péter Kondor, 2009. "Risk in Dynamic Arbitrage: The Price Effects of Convergence Trading," Journal of Finance, American Finance Association, vol. 64(2), pages 631-655, April.
    5. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    6. Chen Yao & Mao Ye, 2018. "Why Trading Speed Matters: A Tale of Queue Rationing under Price Controls," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2157-2183.
    7. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    8. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    9. Zhou, Hao & Kalev, Petko S. & Frino, Alex, 2020. "Algorithmic trading in turbulent markets," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    10. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    11. Takatoshi Ito & Kenta Yamada & Misako Takayasu & Hideki Takayasu, 2012. "Free Lunch! Arbitrage Opportunities in the Foreign Exchange Markets," NBER Working Papers 18541, National Bureau of Economic Research, Inc.
    12. Baron, Matthew & Brogaard, Jonathan & Hagströmer, Björn & Kirilenko, Andrei, 2019. "Risk and Return in High-Frequency Trading," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(3), pages 993-1024, June.
    13. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    14. Brogaard, Jonathan & Hendershott, Terrence & Riordan, Ryan, 2017. "High frequency trading and the 2008 short-sale ban," Journal of Financial Economics, Elsevier, vol. 124(1), pages 22-42.
    15. Andrew G. Haldane, 2012. "The Race to Zero," International Economic Association Series, in: Franklin Allen & Masahiko Aoki & Jean-Paul Fitoussi & Nobuhiro Kiyotaki & Roger Gordon & Joseph E. S (ed.), The Global Macro Economy and Finance, chapter 13, pages 245-270, Palgrave Macmillan.
    16. Nawrocki, David & Viole, Fred, 2014. "Behavioral finance in financial market theory, utility theory, portfolio theory and the necessary statistics: A review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 2(C), pages 10-17.
    17. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    18. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    19. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    20. Filimonov, Vladimir & Bicchetti, David & Maystre, Nicolas & Sornette, Didier, 2014. "Quantification of the high level of endogeneity and of structural regime shifts in commodity markets," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 174-192.
    21. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    22. Jain, Pankaj K. & Jain, Pawan & McInish, Thomas H., 2016. "Does high-frequency trading increase systemic risk?," Journal of Financial Markets, Elsevier, vol. 31(C), pages 1-24.
    23. Malceniece, Laura & Malcenieks, Kārlis & Putniņš, Tālis J., 2019. "High frequency trading and comovement in financial markets," Journal of Financial Economics, Elsevier, vol. 134(2), pages 381-399.
    24. Hasbrouck, Joel & Seppi, Duane J., 2001. "Common factors in prices, order flows, and liquidity," Journal of Financial Economics, Elsevier, vol. 59(3), pages 383-411, March.
    25. Ramiah, Vikash & Xu, Xiaoming & Moosa, Imad A., 2015. "Neoclassical finance, behavioral finance and noise traders: A review and assessment of the literature," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 89-100.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).
    2. Arumugam, Devika & Prasanna, P. Krishna & Marathe, Rahul R., 2023. "Do algorithmic traders exploit volatility?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    3. Chien-Liang Chiu & Paoyu Huang & Min-Yuh Day & Yensen Ni & Yuhsin Chen, 2024. "Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period," Mathematics, MDPI, vol. 12(2), pages 1-22, January.

    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. Anagnostidis, Panagiotis & Fontaine, Patrice, 2020. "Liquidity commonality and high frequency trading: Evidence from the French stock market," International Review of Financial Analysis, Elsevier, vol. 69(C).
    2. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    4. Kang, Jongho & Kang, Jangkoo & Kwon, Kyung Yoon, 2022. "Market versus limit orders of speculative high-frequency traders and price discovery," Research in International Business and Finance, Elsevier, vol. 63(C).
    5. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    6. Ramos, Henrique Pinto & Perlin, Marcelo Scherer, 2020. "Does algorithmic trading harm liquidity? Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    8. Ya‐Kai Chang & Robin K. Chou, 2022. "Algorithmic trading and market quality: Evidence from the Taiwan index futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1837-1855, October.
    9. Töngür, Ünal & Elveren, Adem Yavuz, 2014. "Deunionization and pay inequality in OECD Countries: A panel Granger causality approach," Economic Modelling, Elsevier, vol. 38(C), pages 417-425.
    10. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    11. Kou, Mingting & Yang, Yuanqi & Chen, Kaihua, 2024. "Financial technology research: Past and future trajectories," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 162-181.
    12. Aït-Sahalia, Yacine & Brunetti, Celso, 2020. "High frequency traders and the price process," Journal of Econometrics, Elsevier, vol. 217(1), pages 20-45.
    13. Banerjee, Anirban & Roy, Prince, 2023. "High-frequency traders’ evolving role as market makers," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    14. Andreas Dietrich, 2012. "Does growth cause structural change, or is it the other way around? A dynamic panel data analysis for seven OECD countries," Empirical Economics, Springer, vol. 43(3), pages 915-944, December.
    15. Benos, Evangelos & Brugler, James & Hjalmarsson, Erik & Zikes, Filip, 2017. "Interactions among High-Frequency Traders," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1375-1402, August.
    16. Arumugam, Devika & Prasanna, P. Krishna & Marathe, Rahul R., 2023. "Do algorithmic traders exploit volatility?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    17. Jurich, Stephen N. & Mishra, Ajay Kumar & Parikh, Bhavik, 2020. "Indecisive algos: Do limit order revisions increase market load?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    18. Syamala, Sudhakara Reddy & Wadhwa, Kavita, 2020. "Trading performance and market efficiency: Evidence from algorithmic trading," Research in International Business and Finance, Elsevier, vol. 54(C).
    19. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    20. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.

    More about this item

    Keywords

    Algorithmic trading; High-frequency trading; Commonality; Contrarian; NSE;
    All these keywords.

    JEL classification:

    • 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
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G40 - Financial Economics - - Behavioral Finance - - - General

    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:eee:beexfi:v:30:y:2021:i:c:s2214635021000393. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.