High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market
We analyse the intraday behaviour of high-frequency traders (HFTs) and its impact on aspects of market quality such as liquidity, price discovery and excess volatility. For that, we use a unique transactions data set for four UK stocks, over the period of a randomly selected week. Our data identifies the counterparties to each transaction, enabling us to track the trading behaviour of individual HFTs. We first find that HFTs differ significantly from each other in terms of liquidity provision: while some HFTs mostly consume liquidity (ie trade more ‘aggressively’) by primarily executing trades via market orders, others mostly supply liquidity (ie trade more ‘passively’) by primarily executing trades via limit orders. To examine how trading behaviour is related to these patterns of liquidity provision, we split the HFTs in two groups, according to their trade aggressiveness, and examine the behaviour and impact of each group separately. We find that the ‘passive’ HFTs follow a trading strategy consistent with market making and as such their trades have alternating signs and are independent of recent (ten-second) price changes. By contrast, ‘aggressive’ HFTs exhibit persistence in the direction of their trades and trade in line with the recent (ten-second) price trend. We then explore the relationship between HFT activity and market quality. We find that both higher price volatility and lower spreads cause HFT activity to increase. We suggest a number of reasons as to why this might be so. Finally, we use a tick time specification to examine the impact of HFT activity on price discovery (ie information-based volatility) and noise (ie excess volatility). We find that while HFTs have a higher information-to-noise contribution ratio than non-HFTs, there are instances where this is accompanied by a large absolute noise contribution.
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- Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
- Michael J. Barclay & Terrence Hendershott & D. Timothy McCormick, 2003. "Competition among Trading Venues: Information and Trading on Electronic Communications Networks," Journal of Finance, American Finance Association, vol. 58(6), pages 2637-2666, December.
- Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011.
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- Hendershott, Terrence & Jones, Charles M. & Menkveld, Albert J., 2008. "Does algorithmic trading improve liquidity?," CFS Working Paper Series 2008/41, Center for Financial Studies (CFS).
- Hasbrouck, Joel, 1991. "The Summary Informativeness of Stock Trades: An Econometric Analysis," Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 571-595.
- Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
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- Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2009. "Rise of the machines: algorithmic trading in the foreign exchange market," International Finance Discussion Papers 980, Board of Governors of the Federal Reserve System (U.S.).
- Hasbrouck, Joel, 1993. "Assessing the Quality of a Security Market: A New Approach to Transaction-Cost Measurement," Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 191-212.
- Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
- Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June. Full references (including those not matched with items on IDEAS)
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