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

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  • 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.

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  • 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
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    Cited by:

    1. Banerjee, Anirban & Roy, Prince, 2023. "High-frequency traders’ evolving role as market makers," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    2. NIdhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2022. "When is the Order to Trade Ratio fee effective?," Working Papers 8, xKDR.
    3. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    4. Zijian Shi & John Cartlidge, 2024. "Neural stochastic agent‐based limit order book simulation with neural point process and diffusion probabilistic model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    5. 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.
    6. Zijian Shi & John Cartlidge, 2023. "Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology," Papers 2303.00080, arXiv.org.

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    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

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