ScienceDirect Publication: Journal of Empirical Finance
Mon, 30 Sep 2019 13:02:50 GMT language
Publication date: September 2019
Source: Journal of Empirical Finance, Volume 53
Author(s): Samarpan Nawn, Ashok Banerjee
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.