IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v53y2019icp109-125.html
   My bibliography  Save this article

Do the limit orders of proprietary and agency algorithmic traders discover or obscure security prices?

Author

Listed:
  • Nawn, Samarpan
  • Banerjee, Ashok

Abstract

We investigate the relative roles of limit orders from proprietary algorithmic traders (PAT), agency algorithmic traders (AAT) and non-algorithmic traders (NAT) in the discovery of security prices in National Stock Exchange (NSE) of India. Our results suggest that PAT’s limit orders are most informative, however, AAT and NAT still contribute substantially to price discovery. Contrary to popular belief that algorithmic traders are only interested in large stocks, we find that two algorithmic trading groups together contribute nearly 30%–40% of the price discovery in both small and medium capitalization stocks whereas their combined share of trading volume only ranges between 10%–15% in these stocks. We see that price discovery contribution of PAT’s limit orders increase when we conduct our analysis at longer time gaps. This finding is evidence against the popular notion that HFTs only make prices informative in the very short run. We also find that LOB imbalance of PAT is most informative among three groups of traders and find no evidence to support the popular notion that fast traders often use limit orders to “spoof” market participants about future price movements. However, much of the informativeness of PAT LOB imbalance withers away when PAT places orders opposite to rest of the market suggesting that rather than generating information PAT possibly uses information produced by others.

Suggested Citation

  • Nawn, Samarpan & Banerjee, Ashok, 2019. "Do the limit orders of proprietary and agency algorithmic traders discover or obscure security prices?," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 109-125.
  • Handle: RePEc:eee:empfin:v:53:y:2019:i:c:p:109-125
    DOI: 10.1016/j.jempfin.2019.06.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539819300532
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jempfin.2019.06.003?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. Chakravarty, Sugato, 2001. "Stealth-trading: Which traders' trades move stock prices?," Journal of Financial Economics, Elsevier, vol. 61(2), pages 289-307, August.
    2. Ahn, Hee-Joon & Cheung, Yan-Leung, 1999. "The intraday patterns of the spread and depth in a market without market makers: The Stock Exchange of Hong Kong," Pacific-Basin Finance Journal, Elsevier, vol. 7(5), pages 539-556, December.
    3. Bloomfield, Robert & O'Hara, Maureen & Saar, Gideon, 2005. "The "make or take" decision in an electronic market: Evidence on the evolution of liquidity," Journal of Financial Economics, Elsevier, vol. 75(1), pages 165-199, January.
    4. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    5. Thierry Foucault & Johan Hombert & Ioanid Roşu, 2016. "News Trading and Speed," Journal of Finance, American Finance Association, vol. 71(1), pages 335-382, February.
    6. Harris, Lawrence E. & Panchapagesan, Venkatesh, 2005. "The information content of the limit order book: evidence from NYSE specialist trading decisions," Journal of Financial Markets, Elsevier, vol. 8(1), pages 25-67, February.
    7. G. Geoffrey Booth & Ji-Chai Lin & Teppo Martikainen & Yiuman Tse, 2002. "Trading and Pricing in Upstairs and Downstairs Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1111-1135.
    8. Charles Cao & Oliver Hansch & Xiaoxin Wang, 2009. "The information content of an open limit‐order book," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(1), pages 16-41, January.
    9. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    10. Roger D. Huang, 2002. "The Quality of ECN and Nasdaq Market Maker Quotes," Journal of Finance, American Finance Association, vol. 57(3), pages 1285-1319, June.
    11. Ron Kaniel & Hong Liu, 2006. "So What Orders Do Informed Traders Use?," The Journal of Business, University of Chicago Press, vol. 79(4), pages 1867-1914, July.
    12. Harris, L., 1990. "Liquidity , Trading Rules and Electronic Trading Systems ," Papers 91-8, Southern California - School of Business Administration.
    13. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    14. Dugast, Jérôme & Foucault, Thierry, 2018. "Data abundance and asset price informativeness," Journal of Financial Economics, Elsevier, vol. 130(2), pages 367-391.
    15. Lee, Charles M. C., 1992. "Earnings news and small traders : An intraday analysis," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 265-302, August.
    16. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    17. Samarpan Nawn & Ashok Banerjee, 2019. "Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?," Financial Management, Financial Management Association International, vol. 48(2), pages 641-676, June.
    18. Menkhoff, Lukas & Osler, Carol L. & Schmeling, Maik, 2010. "Limit-order submission strategies under asymmetric information," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2665-2677, November.
    19. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2014. "The Price Impact of Order Book Events," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 47-88.
    20. 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.
    21. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2019. "Price Discovery without Trading: Evidence from Limit Orders," Journal of Finance, American Finance Association, vol. 74(4), pages 1621-1658, August.
    22. Bruno Biais & Thierry Foucault, 2014. "HFT and Market Quality," Bankers, Markets & Investors, ESKA Publishing, issue 128, pages 5-19, January-F.
    23. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    24. Huang, Roger D & Stoll, Hans R, 1994. "Market Microstructure and Stock Return Predictions," The Review of Financial Studies, Society for Financial Studies, vol. 7(1), pages 179-213.
    25. Chan, Louis K C & Lakonishok, Josef, 1995. "The Behavior of Stock Prices around Institutional Trades," Journal of Finance, American Finance Association, vol. 50(4), pages 1147-1174, September.
    26. Hasbrouck, Joel, 1995. "One Security, Many Markets: Determining the Contributions to Price Discovery," Journal of Finance, American Finance Association, vol. 50(4), pages 1175-1199, September.
    27. Gerety, Mason S & Mulherin, J Harold, 1994. "Price Formation on Stock Exchanges: The Evolution of Trading within the Day," The Review of Financial Studies, Society for Financial Studies, vol. 7(3), pages 609-629.
    28. 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.
    29. Badrinath, S G & Kale, Jayant R & Noe, Thomas H, 1995. "Of Shepherds, Sheep, and the Cross-autocorrelations in Equity Returns," The Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 401-430.
    30. Eric K. Kelley & Paul C. Tetlock, 2013. "How Wise Are Crowds? Insights from Retail Orders and Stock Returns," Journal of Finance, American Finance Association, vol. 68(3), pages 1229-1265, June.
    31. Roll, Richard, 1984. "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-1139, September.
    32. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    33. Pascual, Roberto & Escribano, Alvaro & Tapia, Mikel, 2004. "Adverse selection costs, trading activity and price discovery in the NYSE: An empirical analysis," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 107-128, January.
    34. Alexander Kurov, 2008. "Information And Noise In Financial Markets: Evidence From The E‐Mini Index Futures," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 31(3), pages 247-270, September.
    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. NIdhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2022. "When is the Order to Trade Ratio fee effective?," Working Papers 8, xKDR.
    2. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    3. Zijian Shi & John Cartlidge, 2023. "Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology," Papers 2303.00080, arXiv.org.
    4. 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.

    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. Lien, Donald & Hung, Pi-Hsia & Lin, Zong-Wei, 2020. "Whose trades move stock prices? Evidence from the Taiwan Stock Exchange," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 25-50.
    2. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid-Ask Spread?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(5), pages 1482-1498.
    3. Park, Seongkyu Gilbert & Ryu, Doojin, 2019. "Speed and trading behavior in an order-driven market," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 145-164.
    4. Arzandeh, Mehdi & Frank, Julieta, 2017. "The Information Content of the Limit Order Book," 7th Annual Canadian Agri-Food Policy Conference, January 11-13, 2017, Ottawa, ON 253251, Canadian Agricultural Economics Society.
    5. Tian, Xiao & Duong, Huu Nhan & Kalev, Petko S., 2019. "Information content of the limit order book for crude oil futures price volatility," Energy Economics, Elsevier, vol. 81(C), pages 584-597.
    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. Pascual, Roberto & Pascual-Fuster, Bartolome & Climent, Francisco, 2006. "Cross-listing, price discovery and the informativeness of the trading process," Journal of Financial Markets, Elsevier, vol. 9(2), pages 144-161, May.
    8. Breedon, Francis & Chen, Louisa & Ranaldo, Angelo & Vause, Nicholas, 2023. "Judgment day: Algorithmic trading around the Swiss franc cap removal," Journal of International Economics, Elsevier, vol. 140(C).
    9. Rzayev, Khaladdin & Ibikunle, Gbenga & Steffen, Tom, 2023. "The market quality implications of speed in cross-platform trading: evidence from Frankfurt-London microwave," LSE Research Online Documents on Economics 119989, London School of Economics and Political Science, LSE Library.
    10. 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).
    11. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    12. Ryan Garvey & Tao Huang & Fei Wu, 2021. "Is faster or slower trading better? An examination of order type execution speed and costs," European Financial Management, European Financial Management Association, vol. 27(2), pages 326-363, March.
    13. Brolley, Michael & Malinova, Katya, 2021. "Informed liquidity provision in a limit order market," Journal of Financial Markets, Elsevier, vol. 52(C).
    14. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid‐Ask Spread?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 101(5), pages 1482-1498, October.
    15. 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.
    16. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2020. "A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage," Economic Modelling, Elsevier, vol. 85(C), pages 57-73.
    17. 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.
    18. 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.
    19. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015.
    20. Anagnostidis, Panagiotis & Fontaine, Patrice & Varsakelis, Christos, 2020. "Are high–frequency traders informed?," Economic Modelling, Elsevier, vol. 93(C), pages 365-383.

    More about this item

    Keywords

    HFT; Limit orders; Quote; Market manipulation;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:empfin:v:53:y:2019:i:c:p:109-125. 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/jempfin .

    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.