High‐Frequency Trading and the Execution Costs of Institutional Investors

Abstract

This paper studies whether high‐frequency trading (HFT) increases the execution costs of institutional investors. We use technology upgrades that lower the latency of the London Stock Exchange to obtain variation in the level of HFT over time. Following upgrades, the level of HFT increases. Around these shocks to HFT institutional traders’ costs remain unchanged. We find no clear evidence that HFT impacts institutional execution costs.

Order imbalance and selling aggression under a shorting ban: Evidence from the UK

“…Third, as our data is not free of informational event and it employs before-after study, it benefits the academia through exposing a previously understudied kind of short selling (during bans or otherwise) by investigating causal efficiency of ban regimes. Interestingly, while Brogaard, Hendershott, Hunt, and Ysusi (2014) show that high frequency short sale orders constrict market liquidity, this study finds worsening of liquidity when short sales are restricted as well.…”

The impact of latency sensitive trading on high frequency arbitrage opportunities

“…However, Jain and McInish (2012) find that HFT increases tailrisk in Japan, while Boehmer et al (2014) in their global study report that HFT increases short-term volatility, leading to further negative externalities in the market, as modelled by Biais et al (2012). Brogaard et al (2014b) find following infrastructure upgrades on the LSE, the associated increase in HFT activity does not affect institutional trader costs, while Van Kervel and Menkveld (2016) document that institutional transaction costs increase (decrease) when HFTs trade in the same (opposite) direction as institutional investors who execute a package of trades through order-splitting strategies on the Nasdaq OMX Sweden. Conversely, Toth et al (2015) find order splitting does not appear to change with the rise of algorithmic trading on the LSE.…”

Computerized and High-Frequency Trading

“…Therefore, proxies for algorithmic trading and the HFT portion thereof have been developed. These include the rate of electronic message traffic normalized by trading volume as used by Hendershott, Jones and Menkveld (2011) and Viljoen, Westerholm, and Zheng (2014), the use of proprietary data to identify specific HFTs in the data as in Brogaard, Hendershott, Hunt, and Ysusi (2014), or the use of account-level trade-by-trade data on certain contracts and schemes for classifying traders into various high-frequency categories, based on their trading volume and inventory management; see Hendershott and Riordan (2012), Brogaard, Hendershott and Riordan (2013) and Baron, Brogaard and Kirilenko (2012).…”