IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v42y2017icp31-38.html
   My bibliography  Save this article

Evidence of algorithmic trading from Indian equity market: Interpreting the transaction velocity element of financialization

Author

Listed:
  • Dubey, Ritesh Kumar
  • Chauhan, Yogesh
  • Syamala, Sudhakara Reddy

Abstract

Technology and innovation have been the driving forces behind financialization across the globe. One such technological advent, in the pursuit for minimizing the risk and maximizing the return and in order to adhere to the financial sector changes, is Algorithmic Trading (AT). Though AT is being used extensively across the world, there is a lack of academic research on the evidence of AT in most of the markets. The lack of evidence stems from the ambiguity in definitions of AT and High Frequency Trading (HFT) and their usage interchangeably. The lack of evidence also hinders the understanding and interpretation of the impact of ever-increasing unprecedented growth in the velocity of financial transactions on the social machinery of global economies. We take advantage of the clear definition and identification of AT in the Indian equity market to provide evidence of AT and interpreting it as the transaction velocity element of financialization. We also attempt to decipher the impact of AT, symbolizing the transaction velocity element of financialization, on the price discovery process.

Suggested Citation

  • Dubey, Ritesh Kumar & Chauhan, Yogesh & Syamala, Sudhakara Reddy, 2017. "Evidence of algorithmic trading from Indian equity market: Interpreting the transaction velocity element of financialization," Research in International Business and Finance, Elsevier, vol. 42(C), pages 31-38.
  • Handle: RePEc:eee:riibaf:v:42:y:2017:i:c:p:31-38
    DOI: 10.1016/j.ribaf.2017.05.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531916302161
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ribaf.2017.05.014?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. Donald MacKenzie, 2006. "An Engine, Not a Camera: How Financial Models Shape Markets," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262134608, April.
    2. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    3. Avanidhar Subrahmanyam, 2013. "Algorithmic trading, the Flash Crash, and coordinated circuit breakers," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 13(3), pages 4-9, September.
    4. repec:dau:papers:123456789/11664 is not listed on IDEAS
    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. Lee, Kuan-Hui, 2011. "The world price of liquidity risk," Journal of Financial Economics, Elsevier, vol. 99(1), pages 136-161, January.
    7. 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.
    8. Fabian Muniesa, 2014. "The Provoked Economy: Economic Reality and the Performative Turn," Post-Print halshs-00989576, HAL.
    9. Lesmond, David A., 2005. "Liquidity of emerging markets," Journal of Financial Economics, Elsevier, vol. 77(2), pages 411-452, August.
    10. Kelejian, Harry H. & Mukerji, Purba, 2016. "Does high frequency algorithmic trading matter for non-AT investors?," Research in International Business and Finance, Elsevier, vol. 37(C), pages 78-92.
    11. Johannes Prix & Otto Loistl & Michael Huetl, 2007. "Algorithmic Trading Patterns in Xetra Orders," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 717-739.
    12. 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.
    13. Lagoarde-Segot, Thomas, 2015. "Diversifying finance research: From financialization to sustainability," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 1-6.
    14. Fabian Muniesa, 2014. "The Provoked Economy," Post-Print halshs-01113022, HAL.
    15. Nidhi Aggarwal & Susan Thomas, 2014. "The causal impact of algorithmic trading on market quality," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2014-023, Indira Gandhi Institute of Development Research, Mumbai, India.
    16. Mark Lang & Karl V. Lins & Mark Maffett, 2012. "Transparency, Liquidity, and Valuation: International Evidence on When Transparency Matters Most," Journal of Accounting Research, Wiley Blackwell, vol. 50(3), pages 729-774, June.
    17. Eric Budish & Peter Cramton & John Shim, 2015. "Editor's Choice The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1547-1621.
    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. Karkowska, Renata & Palczewski, Andrzej, 2023. "Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Ritesh Kumar Dubey & A. Sarath Babu & Rajneesh Ranjan Jha & Urvashi Varma, 2022. "Algorithmic Trading Efficiency and its Impact on Market-Quality," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 381-409, September.
    3. 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).
    4. 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).

    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. Ritesh Kumar Dubey & A. Sarath Babu & Rajneesh Ranjan Jha & Urvashi Varma, 2022. "Algorithmic Trading Efficiency and its Impact on Market-Quality," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 381-409, September.
    2. 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.
    3. 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).
    4. Craig W. Holden & Stacey Jacobsen & Avanidhar Subrahmanyam, 2014. "The Empirical Analysis of Liquidity," Foundations and Trends(R) in Finance, now publishers, vol. 8(4), pages 263-365, December.
    5. NIdhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2022. "When is the Order to Trade Ratio fee effective?," Working Papers 8, xKDR.
    6. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    7. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    8. Angerer, Martin & Neugebauer, Tibor & Shachat, Jason, 2023. "Arbitrage bots in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 262-278.
    9. Yang, Haijun & Ge, Hengshun & Luo, Ying, 2020. "The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity," Research in International Business and Finance, Elsevier, vol. 53(C).
    10. Oliver Linton & Soheil Mahmoodzadeh, 2018. "Implications of High-Frequency Trading for Security Markets," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 237-259, August.
    11. Ben Ammar, Imen & Hellara, Slaheddine & Ghadhab, Imen, 2020. "High-frequency trading and stock liquidity: An intraday analysis," Research in International Business and Finance, Elsevier, vol. 53(C).
    12. Lagoarde-Segot, Thomas, 2017. "Financialization: Towards a new research agenda," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 113-123.
    13. Mousumi Bhattacharya & Sharad Nath Bhattacharya & Sumit Kumar Jha, 2022. "Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data," Australian Journal of Management, Australian School of Business, vol. 47(2), pages 251-272, May.
    14. Foucault, Thierry & Moinas, Sophie, 2018. "Is Trading Fast Dangerous?," TSE Working Papers 18-881, Toulouse School of Economics (TSE).
    15. 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).
    16. 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).
    17. 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).
    18. Chordia, Tarun & Miao, Bin, 2020. "Market efficiency in real time: Evidence from low latency activity around earnings announcements," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    19. Le, Anh Tu & Le, Thai-Ha & Liu, Wai-Man & Fong, Kingsley Y., 2020. "Multiple duration analyses of dynamic limit order placement strategies and aggressiveness in a low-latency market environment," International Review of Financial Analysis, Elsevier, vol. 72(C).
    20. Karolis Liaudinskas, 2022. "Human vs. Machine: Disposition Effect among Algorithmic and Human Day Traders," Working Paper 2022/6, Norges Bank.

    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:riibaf:v:42:y:2017:i:c:p:31-38. 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: http://www.elsevier.com/locate/ribaf .

    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.