DOES SPEED MATTER? THE ROLE OF HIGH‐FREQUENCY TRADING FOR ORDER BOOK RESILIENCY

• Author: Kai Zimmermann,Martin Haferkorn,Benjamin Clapham
• DOI: http://doi.org/10.1111/jfir.12229
• Date: 01 December 2020
• Published date: 01 December 2020

The Journal of Financial Research Vol. XLIII, No. 4 Pages 933–964 Winter 2020

DOI: 10.1111/jfir.12229

DOES SPEED MATTER? THE ROLE OF HIGH‐FREQUENCY TRADING FOR

ORDER BOOK RESILIENCY

Benjamin Clapham

Goethe University Frankfurt

Martin Haferkorn

European Securities and Markets Authority

Kai Zimmermann

Goethe University Frankfurt

Abstract

We analyze limit order book resiliency following liquidity shocks initiated by large

market orders. Based on a unique data set, we investigate whether high‐frequency

traders are involved in replenishing the order book. Therefore, we relate the net

liquidity provision of high‐frequency traders, algorithmic traders, and human traders

around these market impact events to order book resiliency. Although all groups of

traders react, our results show that only high‐frequency traders reduce the spread

within the first seconds after the market impact event. Order book depth

replenishment, however, takes significantly longer and is mainly accomplished by

human traders’liquidity provision.

JEL Classification: G10, G14, G18

I. Introduction

Since the emergence of highly automated trading desks and fully electronic securities

markets, academics, regulators, and trading firms argue about the direct and indirect

consequences of this technological evolution on modern securities markets. Among the

most controversially discussed issues is the impact of high‐frequency traders (HFTs)

on market quality in open limit order books (Haferkorn 2017). In particular, proponents

of high‐frequency trading (HFT) argue that automated decision making and

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited.

We thank two anonymous referees, Murali Jagannathan (the editor), Peter Gomber, Alex Weissensteiner,

Gunther Wuyts, and participants of the Paris December Finance Meeting, the Annual Meeting of the Swiss

Society for Financial Market Research, and the SAFE Market Microstructure Workshop for valuable comments.

The views expressed in this article are privately held by the authors and cannot be attributed to the European

Securities and Markets Authority (ESMA). Open access funding enabled and organized by Projekt DEAL.

933

© 2020 The Authors. Journal of Financial Research published by Wiley Periodicals LLC on behalf

of The Southern Finance Association and the Southwestern Finance Association

low‐latency infrastructure favor liquidity provision because information evaluation and

the corresponding trading reaction are conducted more efficiently. Therefore, liquidity

increases, which leads to a reduction in implicit transaction costs for all market

participants. The positive effect of HFTs on liquidity holds particularly for HFTs acting

as market makers, which represent the majority of HFTs in terms of trading volume

and order messages (Hagströmer and Nordén 2013). Several studies report the positive

impact of HFT on spreads and order book depth (e.g., Hasbrouck and Saar 2013),

which account for the price and quantity dimension of liquidity. However, little

empirical evidence exists concerning the contribution of HFTs to the third dimension

of liquidity: order book resiliency (e.g., Hasbrouck and Saar 2013), which is the

dynamic characteristic of liquidity representing the recovery of the order book after a

liquidity shock.

1

Especially for sudden drops in liquidity, HFTs can react quicker and

more precisely to such order book changes than other groups of traders. Consequently,

HFTs in particular might contribute to the recovery of liquidity and thus foster order

book resiliency, which leads to increased price efficiency and lower implicit

transaction costs after liquidity shocks.

Based on the debate surrounding the role of HFT for liquidity provision, we study

how different types of traders (i.e., HFTs, algorithmic traders [ATs], and human traders)

replenish liquidity in the order book following a large aggressive order leading to market

impact and a sudden drop in liquidity. Thereby, we aim to reveal the contribution of HFTs to

order book resiliency relative to non‐HFT participants. During and after such events, low‐

latency traders can maximize their speed advantage and benefit from widened spreads and

reduced order book depth. Given that HFTs follow such strategies, other market participants

may profit from increased order book resiliency due to HFT. Moreover, fast liquidity

recovery in terms of spread and depth lowers implicit transaction costs for investors and

ultimately the liquidity component of companies’cost of capital. Therefore, we investigate

the contribution of different types of traders to order book resiliency by using a sample of

large market orders that hit the open limit order book and walk through several order book

levels leading to market impact. In particular, we focus on the net liquidity provision of HFTs

and non‐HFTs around these market impact events to add further evidence on the dynamic

aspect of liquidity. We rely on a proprietary data set provided by Deutsche Boerse, which

enables us to identify HFT as well as algorithmic trading (AT) activity based on

corresponding flags. Thus, we can provide detailed insights on order book resiliency in the

presence of HFTs.

Our results show that HFTs contribute significantly to open limit order book

replenishment. Specifically, we find HFTs to be the driving force behind reestablishing

tight spreads within short periods. In contrast, ATs without low‐latency infrastructure

and human traders do not significantly support spread resiliency. The recovery of

bid–ask spreads is accomplished within the first few seconds after a market impact

event, and the largest fraction of widened spread recovers within the first second.

1

Order book resiliency as the third dimension of liquidity is described in early papers on market

microstructure. Black (1971), Kyle (1985), and Harris (2003) describe resiliency as the quick recovery of prices

after market impact events. Building on this, Foucault, Kadan, and Kandel (2005) develop a model of order book

resiliency that defines market resiliency as the spread reversion to its former level after a liquidity shock.

934 The Journal of Financial Research

Human traders, although adapting their submission behavior within the first seconds

after the event, do not significantly affect spread resiliency. When considering the

resiliency of order book depth, however, the results change considerably. HFTs do not

sufficiently replenish order book depth as they predominantly submit small‐volume

orders, focusing on the top of the order book. This also holds for ATs. Depth resiliency,

therefore, is mainly achieved by human traders showing high net liquidity provisions

after market impact events. Therefore, fast liquidity provision by HFTs, which also

prevails after a significant market impact, represents only a specific and limited

contribution to overall order book resiliency. To mitigate the price impact of further

large orders, order book depth must be replenished by various limit orders of relevant

size. As shown in our analysis, this is mainly achieved with the help of human traders

that persistently stay in the order book and offer vast amounts of nontransient liquidity.

Therefore, we show that different types of traders, namely HFTs and human traders,

pursuing different strategies are needed to accomplish order book resiliency in all

dimensions (i.e., spread and depth) in an efficient and fast manner.

II. Related Literature

Liquidity Provision by HFTs

One of the most discussed and analyzed questions regarding HFT is whether and how

HFTs provide liquidity to market participants in different trading situations (Kirilenko

et al. 2017). Liquidity is defined by three dimensions: spread, order book depth, and

order book resiliency (Black 1971; Kyle 1985; Harris 2003). The first part of our

literature review focuses on the impact of HFT on liquidity in terms of spread and order

book depth, followed by a summary of the research on order book resiliency.

Research concerning the relation of HFT and liquidity in terms of spread and

depth is mostly conducted using time‐series regression techniques. Regarding bid–ask

spreads, studies show that HFTs provide liquidity when spreads are wide and consume

liquidity when spreads are tight (Zhang and Riordan 2011; Carrion 2013). In line with

these results, Brogaard, Hendershott, and Riordan (2014) observe that HFTs are more

likely to participate in the order book when bid–ask spreads are wide, trading volume

and price volatility are high, and order book depth is low. Thus, HFTs contribute to

decreasing spreads, which is further observed by Hasbrouck and Saar (2013). These

results are supported by the observation that HFTs mostly follow market‐making

strategies and submit passive (i.e., liquidity‐providing) orders (Hagströmer and

Nordén 2013; Menkveld 2013). Regarding AT in general, Hendershott, Jones, and

Menkveld (2011) find that ATs reduce the bid–ask spread on the New York Stock

Exchange (NYSE). Using a similar data set to ours, Hendershott and Riordan (2013)

confirm this finding.

In contrast to these positive results concerning the impact of HFT on liquidity,

Lee (2015) finds that HFT has no effect on liquidity as spread and depth remain

unaffected. Goldstein, Kwan, and Philip (2018) likewise analyze the contribution of

HFTs to overall liquidity and conclude that liquidity provision by HFTs should not be

overestimated as they provide liquidity in the opposite direction of order imbalance.

935Does Speed Matter?