![Changes in liquidity and volatility due to the interruption of HFT (minute-wise regressions). This figure illustrates the β1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _1$$\end{document}-coefficient from Eq. (6), i.e., the minute-wise cross-sectional differences between treatment (DAX30) and control group (CAC40), for each minute of the first hour of trading on non-event days (minutes 1 to 60) and the event day (minutes 61 to 120) separated by the red line. Dependent variables are Spread, L1-Volume, Depth(10), Order Imbalance, S.D. Price, and S.D. Midpoint. The purple line represents the median coefficient for non-event days and the dotted lines represent the upper and lower bounds of the 95%-confidence interval](https://www.researchgate.net/publication/363798358/figure/fig1/AS:11431281115894565@1675136499603/Changes-in-liquidity-and-volatility-due-to-the-interruption-of-HFT-minute-wise_Q320.jpg)
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RESEARCH PAPER
The Impact of High-Frequency Trading on Modern Securities
Markets
An Analysis Based on a Technical Interruption
Benjamin Clapham •Martin Haferkorn •Kai Zimmermann
Received: 7 May 2020 / Accepted: 8 June 2022 / Published online: 23 September 2022
ÓThe Author(s) 2022
Abstract High-frequency traders account for a significant
part of overall price formation and liquidity provision in
modern securities markets. In order to react within
microseconds, high-frequency traders depend on special-
ized low latency infrastructure and fast connections to
exchanges, which require significant IT investments. The
paper investigates a technical failure of this infrastructure
at a major exchange that prevents high-frequency traders
from trading at low latency. This event provides a unique
opportunity to analyze the impact of high-frequency trad-
ing on securities markets. The analysis clearly shows that
although the impact on trading volume and the number of
trades is marginal, the effects on liquidity and to a lesser
extent on price volatility are substantial when high-fre-
quency trading is interrupted. Thus, investments in high-
frequency trading technology provide positive economic
spillovers to the overall market since they reduce transac-
tion costs not only for those who invest in this technology
but for all market participants by enhancing the quality of
securities markets.
Keywords High-frequency trading Market quality
Securities markets IT spillover
1 Introduction
The financial services industry currently faces and has
already experienced considerable changes due to digitiza-
tion and the automation of business processes. This is
especially true for the securities trading industry, where the
use of computers and algorithms for the automation of
trading processes has reshaped financial markets into
modern highly technologized places (Gomber and Zim-
mermann 2018). Along with advancements in process
automation related to information retrieval, interpretation,
and processing into investment signals, the speed of trading
in financial markets has dramatically increased and traders
known as high-frequency traders (HFTs) emerged. These
traders pursue specialized business models dedicated to
trading within microseconds and account for a large share
of the market. In Europe, the market share of high-fre-
quency trading (HFT) represents around 35% of the total
equity trading volume after peaking in 2010 with 40%. In
the U.S., the share of HFT is even higher and has settled at
around 50% of total equity trading after a peak in 2009
with about 60% (Zaharudin et al. 2022).
Various studies highlight the importance of HFT for the
efficiency of modern securities markets. Menkveld (2013)
finds that an often pursued strategy of HFTs is market
making, i.e., to provide liquidity to the market on a con-
tinuous basis, allowing others to trade on the basis of
efficient prices throughout the day. Moreover, due to their
speed advantage, HFTs quickly incorporate new informa-
tion into prices (Brogaard et al. 2014) and coordinate pri-
ces across different venues (Haferkorn 2017). However,
Accepted after 3 revisions by Dennis Kundisch.
The views expressed in this article are privately held by the authors
and cannot be attributed to the European Securities and Markets
Authority (ESMA).
B. Clapham (&)K. Zimmermann
Goethe University Frankfurt, Frankfurt, Germany
e-mail: clapham@wiwi.uni-frankfurt.de
K. Zimmermann
e-mail: kzimmermann@wiwi.uni-frankfurt.de
M. Haferkorn
European Securities and Markets Authority (ESMA), Paris,
France
e-mail: risk.analysis@esma.europa.eu
123
Bus Inf Syst Eng 65(1):7–24 (2023)
https://doi.org/10.1007/s12599-022-00768-6
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
exorbitant investments in fast computer systems and
communication infrastructure not only by traders but also
by exchanges are necessary to facilitate the innovation of
trading at minimum latency, which raises the question
whether these investment in information technology (IT)
are economically valuable. In particular, Budish et al.
(2015) claim that recurring investments in ever faster
infrastructure and technology due to the race of being able
to trade at the highest speed currently possible are not
efficient. Yet, research has shown that HFTs fulfill
important intermediary functions which are beneficial for
all market participants, and that HFT contributes to the
efficiency of securities markets (Brogaard et al. 2014;
Hasbrouck and Saar 2013; Menkveld 2013).
Consequently, HFT not only benefits trading firms
investing in this technology (Baron et al. 2012) but can
lead to IT spillover effects (Han et al. 2011) to all market
participants, which makes HFT a relevant research area
from an information systems (IS) perspective. Because data
is continuously available and transparent in securities
markets, spillover effects of IT investments can be ana-
lyzed in this environment and their magnitude can be
determined as suggested by Han et al. (2011). Thus, our
paper aims to add to this research stream by analyzing
potential spillover effects of investments in HFT technol-
ogy on liquidity and volatility of securities markets, which
affect transaction costs of the entire securities trading
industry.
HFTs use special infrastructures and dedicated access
points to the infrastructure of exchanges besides standard
gateways for slower traders that do not require such latency
sensitive connections. On October 2nd, 2017, Deutsche
Bo
¨rse’s electronic trading venue Xetra experienced a
technical failure on their HFT gateway that interrupted the
high-frequency connections, thus making it impossible for
HFTs to communicate with the exchange at low latency,
which effectively stopped HFT. Taking away their speed
advantage, this event severely interfered with the trading
strategies of HFTs and prevented the use of their ultra-fast
trading technology. We exploit this event to empirically
investigate how today’s automated securities markets react
if HFT technology is unavailable in order to measure the
spillover effects of investments in this technology on
overall market quality. Specifically, we investigate how the
sudden interruption of HFT technology influences liquidity
and volatility, two central measures of market quality,
which determine implicit transaction costs for all market
participants.
Our results show that an interruption of HFT signifi-
cantly decreases liquidity of the affected stocks along dif-
ferent dimensions. Thus, securities markets become less
efficient and trading becomes more costly for all market
participants. Also, price volatility significantly increases
leading to higher risks for traders and intermediaries.
Consequently, our results show that HFT and correspond-
ing investments in the necessary infrastructure have sig-
nificant positive spillover effects for the whole securities
trading industry and affect the efficiency of the entire
securities market. Adding to the discussion on resilient
financial markets, we find that securities markets do not
collapse if HFT technology is suddenly unavailable due to
a technical failure.
The remainder of the paper is structured as follows:
Sect. 2presents related research on HFT, investments in
HFT technology, and IT spillover effects in securities
markets. In Sect. 3, we derive our research hypotheses and
introduce the data set. Methodology and results of our
empirical study are described in Sect. 4. Finally, we dis-
cuss the implications of our findings as well as limitations
of our approach in Sect. 5and conclude the paper in
Sect. 6.
2 Background and Related Research
2.1 HFT and Investments in Fast Trading Technology
The securities trading industry has experienced significant
technological changes due to different waves of automa-
tion, which first affected exchanges and then went further
down the value chain from the sell side (i.e., intermediaries
such as brokers and dealers) to the buy side (i.e., institu-
tional investors) (Francioni and Gomber 2017). In partic-
ular, the automation of the buy side led to an innovation of
trading technology and associated trading strategies with
the emergence of algorithmic trading and HFT.
According to the revised European directive on markets
in financial instruments (MiFID II), algorithmic trading
means ‘‘trading in financial instruments where a computer
algorithm automatically determines individual parameters
of orders such as whether to initiate the order, the timing,
price or quantity of the order or how to manage the order
after its submission, with limited or no human interven-
tion’’ (European Parliament and Council 2014). Further,
HFT is defined as a particular kind of algorithmic trading
that is characterized by (1) infrastructure intended to
minimize latency such as co-location, (2) no human
intervention in order initiation, generation, and routing, and
(3) high intraday message volumes (i.e., order, quote, and
cancellation messages) (European Parliament and Council
2014). HFTs regularly employ trading strategies such as
market making or news trading, which have already been
profitable before the emergence of low latency trading
technology (Gomber et al. 2011). However, based on their
superior and faster trading technology, HFTs have devel-
oped these different trading strategies further, which
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provides them with a competitive advantage compared to
traditional traders (Lattemann et al. 2012; Seddon and
Currie 2017).
Yet, market observers and also researchers criticize the
high investments in ever-faster trading technology for
HFT, which can lead to market concentration and an
inefficient and costly technological arms race (Biais et al.
2015; Budish et al. 2015). From an IS research perspective,
the value of HFT-related IT investments is therefore to be
evaluated. For HFT firms, the profitability of investments
in the underlying trading technology has already been
confirmed (Baron et al. 2012). As pointed out by Kohli and
Grover (2008), research on the economic value of IT
should in particular also consider potential indirect effects
of these investments. Such indirect effects can materialize
in the form of positive spillover effects to either (1) con-
nected downstream industries (Han et al. 2011) or (2) the
industry or sector as a whole. We follow the call by Kohli
and Grover (2008) and investigate whether HFT and
associated investments in low-latency trading technology
lead to positive spillover effects on securities markets, thus
indirectly providing economic value also for traders who
do not invest in HFT technology. Such spillover effects
might materialize because HFTs are able to provide more
and cheaper liquidity to the market and also improve price
discovery due to their technology-based information pro-
cessing and speed advantages (see next subsection for
further details). Moreover, we contribute to the HFT
research agenda for IS scholars proposed by Currie and
Seddon (2017).
In order to assess potential spillover effects of HFT
investments for the entire securities market and the secu-
rities trading industry, securities markets are evaluated
according to different dimensions of market quality. Mar-
ket quality is the general concept used to describe the
(operational) efficiency of securities markets and is regu-
larly assessed along the dimensions liquidity, volatility,
and price discovery. Most empirical studies find a positive
effect of HFT on these dimensions of market quality
(O’Hara 2015).
2.2 The Effects of HFT on Securities Markets
HFT and algorithmic trading are not an entirely new phe-
nomenon in academia. In particular, a growing research
stream around this topic emerged in the finance literature.
In their seminal paper on the impact of algorithmic trading
on the quality of securities markets, Hendershott et al.
(2011) find that the introduction of algorithmic trading on
the New York Stock Exchange enhanced market quality
since algorithmic trading leads to lower bid-ask spreads,
i.e., the difference between the cheapest sell offer and the
highest buy offer. Lower bid-ask spreads allow market
participants to trade at more favorable prices, which
decreases transaction costs and increases the liquidity of a
market. This finding is further supported by Hendershott
and Riordan (2013), who show that algorithmic traders
consume liquidity when bid-ask spreads are narrow and
provide liquidity when they are wide.
With respect to HFTs, which are algorithmic traders that
additionally rely on low latency infrastructure, most
empirical studies show that these traders also provide
additional liquidity to securities markets leading to lower
bid-ask spreads and decreased transaction costs for inves-
tors and intermediaries (Carrion 2013; Zhang and Riordan
2011). The results of Hasbrouck and Saar (2013) further
suggest that increased HFT activity enhances traditional
market quality measures, i.e., HFT leads to decreased
spreads, increased order book depth in terms of tradable
volumes, and lower short-term price volatility. The positive
effect of HFT on liquidity in securities markets results,
among other reasons, from the observation that most HFTs
employ market-making strategies (Hagstro
¨mer and Norde
´n
2013) and that they predominantly submit passive orders,
which provide liquidity to the market (Menkveld 2013).
Moreover, the positive effect of HFT and associated spil-
lover effects on spreads are in line with the model pre-
dictions of Aı
¨t-Sahalia and Sag
˘lam (2017), who state that
speed advantages allow market makers to revise quotes
more efficiently and thereby to reduce inventory costs
allowing them to quote tighter spreads. Furthermore,
research has shown that HFT facilitates price discovery by
incorporating new information into prices more efficiently
and by trading against transitory pricing errors (Brogaard
et al. 2014). Moreover, HFT technology improves price
coordination across different markets by reducing costs and
time to monitor and react to information from the different
markets on which an asset is traded (Haferkorn 2017).
Contrary to the majority of findings for equity markets, Lee
(2015) finds no positive effects of HFT on liquidity and
volatility in futures markets. Specifically, his results indi-
cate that HFTs do not provide additional liquidity but
increase intraday volatility. Also, Shkilko and Sokolov
(2020) observe a positive effect on liquidity when the
speed advantages of the fastest HFTs are removed due to
weather-related interruptions of their microwave networks.
The authors show that these periods are associated with
lower adverse selection and trading costs.
Despite these findings, research on HFT is challenging
since public data feeds do not contain a flag whether an
order book activity or a trade is caused by an algorithm or a
human and whether a trader is using low latency infras-
tructure. Therefore, researchers either have to rely on
proprietary data sets that include HFT flags (e.g., Brogaard
et al. 2014; Menkveld 2013), which impede replication and
comparability of results, or have to use proxies that draw
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on specific trading characteristics to quantify HFT activity
(Haferkorn 2017). Another possibility to infer the impact of
HFT on securities markets is to analyze events that affect
the share or speed of HFT. E.g., previous research analyzed
speed upgrades of trading systems (Wagener et al. 2010;
Riordan and Storkenmaier 2012) or regulatory acts aimed
at limiting HFT activity (Friederich and Payne 2015).
However, all these methods only allow to analyze incre-
mental changes in HFT activity over time or events that
indirectly affect HFT activity.
Our study adds to the finance and information systems
literature on HFT by analyzing the impact of HFT on
securities market quality based on a public data feed for an
event with a sharp and unexpected cut in HFT activity
without the need of any approximation method to quantify
HFT activity. Previous studies either analyze minor chan-
ges in HFT activity over time (e.g., Hasbrouck and Saar
2013) or use regulatory acts aimed at HFT as events (e.g.,
Friederich and Payne 2015), which, however, are known in
advance and thus might be biased by announcement
effects. To the best knowledge of the authors, this is the
first study analyzing the impact of HFT based on a tech-
nical failure that entirely prevents HFTs from trading at
low latency at a major stock exchange. Due to the dis-
ruption of HFT, we are able to derive whether potential
economic spillovers of investments in ultra-fast trading
technology to other market participants and the quality of
securities markets exist.
3 Research Approach
3.1 Research Hypotheses
The large literature body on HFT on the one hand and the
high proportion of HFT relative to overall trading volume
on the other hand emphasize the high academic and prac-
tical interest in this trading technology and its effects on
securities markets (Currie and Seddon 2017). Moreover,
regulatory interventions aimed at HFT in Italy, Germany,
and the European financial market regulation MiFID II
renewed the necessity to clearly evaluate the effect of HFT
on financial markets and potential spillover effects of HFT
investments on the securities trading industry.
Since empirical studies partially come to contradicting
results whether HFT enhances market quality (e.g., Carrion
2013; Hasbrouck and Saar 2013) or not (e.g., Shkilko and
Sokolov 2020; Lee 2015) and because existing studies
regularly analyze incremental changes of HFT activity over
time, we evaluate the impact of HFT on modern securities
markets based on a recent public data set with a sharp and
clear cut-off of HFT activity due to a technical failure
interrupting HFT activity. Consequently, our approach is a
suitable setup to answer our research question whether and
how the sudden interruption of HFT technology influences
liquidity and volatility in securities markets. This allows us
to infer how HFT technology impacts efficiency and
transaction costs in the securities trading industry and to
determine the magnitude of potential spillover effects of
HFT investments.
Although coming to contradicting results, existing
studies that investigate the impact of HFT on market
quality observe that HFT influences liquidity in financial
markets (e.g., Carrion 2013; Shkilko and Sokolov 2020).
Since HFT provides additional order flow to securities
markets, which is generally beneficial because more orders
increase the liquidity of a market (O’Hara 2015), and
because HFTs acting as market makers can quote more
efficiently due to their speed advantage (Aı
¨t-Sahalia and
Sag
˘lam 2017), we hypothesize that liquidity on the
respective exchange deteriorates when HFT activity is
interrupted. In order to avoid the detection of mechanical
patterns due to HFTs being unable to trade (using fast
connections to the exchange), we hypothesize that liquidity
decreases more than trading activity
1
decreases. Specifi-
cally, we hypothesize that liquidity deteriorates along dif-
ferent dimensions, i.e., the bid-ask spread as the most
important measure of implicit transaction costs, the volume
available in the order book (order book depth), and the
imbalance between buy and sell orders (order imbalance).
H1a If HFT activity is interrupted, bid-ask spreads
increase more than trading activity decreases.
H1b If HFT activity is interrupted, order book depth
decreases more than trading activity decreases.
H1c If HFT activity is interrupted, order imbalance
increases more than trading activity decreases.
Moreover, research shows that HFT reduces short-term
volatility in equity markets (Hasbrouck and Saar 2013).
This can be explained by the observation that HFTs, which
employ market making strategies, mitigate the price impact
of multiple orders in the same direction and even provide
liquidity in times of market stress, which weakens price
fluctuations (Brogaard et al. 2018). On the other hand,
HFTs also contribute to an acceleration of volatility in case
of market-wide extreme price movements known as (mini)
flash crashes (Brogaard et al. 2018; Kirilenko et al. 2017).
Therefore, it is relevant to analyze how a sudden inter-
ruption of HFT affects stock market volatility. Because
HFTs might influence price volatility and midpoint
1
We measure trading activity along different dimensions and
account for the number and volume of transactions as well as for
the number of order book interactions (number of submissions and
number of quote updates).
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10 B. Clapham et al.: The Impact of High-Frequency Trading..., Bus Inf Syst Eng 65(1):7–24 (2023)
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volatility differently due to their frequent order updates that
lead to faster and more frequent changes of the order book
and the midpoint between best bid and best ask (Haferkorn
2017), we distinguish between trade price and midpoint
volatility and hypothesize:
H2a If HFT activity is interrupted, price volatility
increases more than trading activity decreases.
H2b If HFT activity is interrupted, midpoint volatility
increases more than trading activity decreases.
3.2 Data Set and Institutional Background
To assess the impact of HFT on market quality, we analyze
a technical failure at Deutsche Bo
¨rse’s electronic trading
platform Xetra that interrupted HFT connections, and thus
made it impossible for HFTs to trade at low latency.
Specifically, there was an outage of high-frequency ses-
sions offering low latency connections to Xetra on October
2nd, 2017.
2
At 09:00:22, Deutsche Bo
¨rse published the
following message on their news board: ‘‘Please be aware
that due to technical problems we are currently experi-
encing a failure (connectivity over HF [high frequency]
sessions) in the Xetra market’’ (Deutsche Bo
¨rse 2017a). At
10:01:03, the technical failure was officially resolved
(Deutsche Bo
¨rse 2017b). We analyze the time frame from
the beginning of continuous trading
3
to 10:00:00 and
exclude the opening auction as it started before the official
announcement of the technical difficulties. The exclusion
of the opening auction is also reasonable because HFTs are
less active in auctions (European Securities and Markets
Authority 2014). Moreover, we stop our analysis at
10:00:00 since the connections may have already been
reestablished a few seconds before the news was officially
published.
For the empirical investigations, we use Refinitiv Tick
History data including highly granular trade and order book
information. The trade data contains executed trades
together with information on price and volume time-
stamped to the microsecond. The order book information
contains price limit and order volume at the respective
limit for both sides of the book, i.e., bid and ask, for the
limits one to ten. Once the order book is updated (caused
by an order update
4
or a trade execution), a new observa-
tion is saved on a microsecond level, which allows us to
analyze HFT activity. Due to the fact that HFTs predom-
inantly operate in highly liquid stocks (European Securities
and Markets Authority 2014), the constituents of the blue
chip index DAX30 traded on Xetra are analyzed in this
study.
Deutsche Bo
¨rse’s electronic trading platform Xetra is an
order-driven market with an open limit order book and the
main venue for trading DAX30 stocks. Since we apply a
difference-in-differences (DiD) approach to analyze the
effects of an absence of HFT activity, we use the DAX30
constituents as the treatment group (which consists of 30
stocks) and the constituents of the highly correlated French
CAC40 index as the control group (which consists of 40
stocks). A list of all stocks analyzed in this study is pro-
vided in Table 5 in the Supplementary file1. The stocks of
the control group are predominantly traded on the main
market Euronext Paris and serve as reference to exclude
any confounding effects. Euronext Paris is also highly
comparable to Xetra in terms of market design and trading
hours, which strengthens the fit of the control group.
Moreover, both indices have a comparable industry cov-
erage and the stocks share a similar European macroeco-
nomic dependency due to the close geographic proximity.
Therefore, the constituents of the CAC40 are suitable to
control for macroeconomic news and other confounding
effects. Finally, and important within the context of our
study, also the amount of HFT actvity on Xetra and
Euronext Paris is comparable (European Securities and
Markets Authority 2014). Because stocks from different
European or even non-European markets do not fulfill
these requirements or are not as comparable to DAX30
stocks as the stocks of the French CAC40 index, they do
not qualify for the control group. The use of DAX30 and
CAC40 stocks as treatment and control group in a DiD-
setup has also been applied in other empirical studies and
has been proven to work well (e.g., Gomber et al. 2016b;
Clapham et al. 2021).
Our observation window covers the first hour of trading
on the event day October 2nd, 2017 as well as the first hour
of trading on the two previous (September 18th and 25th)
and consecutive (October 9th and 16th) Mondays. We use
Mondays instead of just the previous and following trading
day to account for the day-of-the-week effect that has been
found in financial markets (Dubois and Louvet 1996).
Specifically, we use every order book update and trade as
reported by Refinitiv Tick History (so-called tick-by-tick
analysis) for DAX30 and CAC40 stocks traded on Xetra
2
The event date October 2nd, 2017 was a regular trading day with
otherwise ‘‘normal’’ trading conditions, relatively low market
volatility on the event day and the days before and after as measured
by the volatility index VDAX, and no increased number of (ad-hoc)
news according to Thomson Reuters and Bloomberg.
3
Continuous trading for each stock starts at 09:00:00 plus a random
end of the opening auction of up to 30 s. Deutsche Bo
¨rse confirmed
that the outage of high-frequency connections already started slightly
before 09:00:00.
4
Order updates result from new orders entering the market or from
modifications or deletions of existing orders.
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B. Clapham et al.: The Impact of High-Frequency Trading..., Bus Inf Syst Eng 65(1):7–24 (2023) 11
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and Euronext for the first hour of continuous trading on the
five mentioned trading days. This results in 307,525 trades
and more than 7.3 million order book updates being
included in our analysis.
4 Empirical Study
4.1 Market Quality Measurement and Variable
Operationalization
Market quality is the key concept to evaluate the opera-
tional efficiency of financial markets and usually involves
the dimensions liquidity and volatility (Harris 2003).
Consequently, we focus on different measures of these two
dimensions of market quality in order to assess the impact
of the sudden and unanticipated interruption of HFT on
market quality. With this analysis, we provide insights
whether the investments in HFT technology lead to a
positive spillover for the entire securities market by
increasing market quality.
Liquidity is one of the core concepts of market quality
since it determines implicit transaction costs for investors.
It can be measured along different dimensions and empir-
ical studies regularly analyze the bid-ask spread, repre-
senting transaction costs for small orders, and order book
depth, which indicates how much liquidity denoted in euro
volume is offered by passive orders on both sides of the
book (Chordia et al. 2001). Specifically, we use the relative
quoted spread
5
, which is the quoted bid-ask spread (i.e., the
difference between the best bid and best ask) divided by the
midpoint (i.e., the price in between best bid and best ask) as
shown in Eq. (1). The subscripts iand trepresent stock and
point in time respectively. Using the relative instead of the
absolute spread is meaningful in order to account for the
different price levels of the stocks. Throughout the paper,
we report the relative quoted spread in basis points (bps)
6
.
Relative Quoted Spreadi;t¼BestAski;tBestBidi;t
Midpointi;t
ð1Þ
Regarding order book depth, we use two different mea-
sures. First, L1-Volume (see Eq. (2)) represents the euro
volume available at the best bid and ask. Therefore, this
measure indicates how much volume can be traded
immediately without further market impact in terms of
worse prices than the current best bid and ask. Second, we
rely on the Depth(10) measure proposed by Degryse et al.
(2015) in order to measure liquidity that is provided on
deeper levels of the order book, i.e., at worse prices than
the current best bid and ask, but still within an appropriate
range of ten basis points (bps) around the current midpoint.
The calculation of the Depth(10) measure is provided in
Eq. (3). The subscript lindicates the order book level.
Order book levels and the euro volume provided on these
levels are taken into account as long as the respective
level’s bid (ask) limit is larger (smaller) than ten bps below
(above) the current midpoint, which represents the fair
value of a stock.
L1-Volumei;t¼BestAski;tQuantityBestAsk
i;t
þBestBidi;tQuantityBestBid
i;t
ð2Þ
Depth Askð10Þi;t
¼X
L
l¼1
PriceAsk
l;i;tQuantityAsk
l;i;t1PriceAsk
l;i;t\Midpointi;tð1þ10bpsÞ
fg
;
Depth Bidð10Þi;t
¼X
L
l¼1
PriceBid
l;i;tQuantityBid
l;i;t1PriceBid
l;i;t[Midpointi;tð110bpsÞ
fg
;
Depthð10Þi;t
¼Depth Askð10Þi;tþDepth Bidð10Þi;t
ð3Þ
Moreover, we analyze order imbalance similar to Chordia
et al. (2002) in order to evaluate whether asymmetries in
the order book change when HFT is interrupted. Order
imbalances in either direction, i.e., excess interest to buy or
to sell a stock, imply lower levels of liquidity. We calculate
order imbalance based on the difference in trading interest
revealed in the order book. Specifically, we measure
imbalances in the amount of buy and sell order volume
based on the difference between the euro volume on both
sides of the order book that is close to the midpoint (i.e.,
within ten bps in line with the Depth(10) measure).
5
Besides quoted spreads, other studies on market quality often also
analyze effective spreads (e.g., Chordia et al. 2001; O’Hara 2015).
The quoted spread averages the bid-ask spread over a given period,
and thus, provides an indication of the implicit transaction costs
during a given period independent from trade executions. The
effective spread, in contrast, is only calculated once a trade occurs by
multiplying the difference between trade price and midpoint by two.
Since traders might trade strategically when bid-ask spreads are
narrow, the quoted spread can overestimate implicit transaction costs.
However, the dependence of the effective spread on actual trades and
a potential strategic behavior of traders is the reason why we do not
use the effective spread in this study but focus on the quoted spread.
Compared to other traders, HFTs are particularly strong at trading
strategically when bid-ask spreads are narrow (e.g., Hendershott and
Riordan 2013). Consequently, effective spreads would mechanically
rise when HFT technology is unavailable although not necessarily
leading to worse spreads for other market participants. This does not
hold for quoted spreads, which are only determined by liquidity
supply. Therefore, relying on quoted spreads allows us to analyze
spillover effects of HFT without potential biases or mechanical
adjustments in effective spreads.
6
To get spreads in bps, the relative quoted spread shown in Eq. (1)is
multiplied by 10,000.
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Possible values of the order imbalance measure specified in
Eq. (4) range between zero and one.
Order Imbalancei;t¼jDepth Askð10Þi;tDepth Bidð10Þi;tj
Depthð10Þi;t
ð4Þ
Volatility is the second dimension of market quality ana-
lyzed in our empirical study. We differentiate between
trade price volatility (S.D.Price) and midpoint volatility
(S.D.Midpoint). As shown in Eq. (5), trade price volatility
is measured by the standard deviation of trade prices pi;tin
a given time interval Tdivided by the average trade price
pi;tin the same time interval to obtain the measure in rel-
ative terms to account for different price levels of the
analyzed stocks. The variable nrepresents the number of
trades (midpoints) in time interval T. Midpoint volatility is
computed identically except that pi;trepresents the mid-
point of best bid and best ask and not trade prices. Due to
the fast quoting behavior in today’s automated securities
markets and particularly the fast quoting behavior of HFTs,
it is of interest to differentiate between these two volatility
measures (Haferkorn 2017). As the relative spread, both
measures for volatility are provided in bps throughout the
paper.
S:D:Pricei;T¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pðpi;tpi;tÞ2
n1
q
pi;t
ð5Þ
In order to rule out that our observations regarding changes
in liquidity and volatility are only driven by mechanical
changes in trading activity due to the potential absence of
HFTs despite their possibility to revert to slower connec-
tions, we also incorporate measures for trading activity in
our analysis. Trading activity is regularly measured via the
number of trades and the euro volume traded in a given
period (Chordia et al. 2001). In particular with the emer-
gence of HFTs, who often update their orders leading to a
large number of orders relative to the number of executed
trades, the number of quotes, i.e., changes of best bid and/
or best ask, and the number of order submissions in a given
time interval are also analyzed (Hasbrouck and Saar 2013).
In order to be able to run regression analyses with
contemporaneous observations, we aggregate all measures
of liquidity, volatility, and trading activity into one-minute
intervals. This means, we average all liquidity measures in
a given one-minute interval and sum up the observed
number of trades, quotes, order submissions, as well as the
trading volume, which indicate trading activity. Both
volatility measures are calculated based on all observations
of prices respectively midpoints in a certain one-minute
interval. This results in 20,990 observations in total for the
70 stocks and 60 min on five trading days.
7
Due to order
book data issues for three CAC40 stocks (ACCP.PA,
BNPP.PA, and UNBP.AS), market quality measures that
depend on full order book information (i.e., number of
submissions, Depth(10), and order imbalance) could not be
calculated for 180 observations. Thus, the final data set to
investigate these measures consists of 20,810 observations.
4.2 Methodology
For the analysis of our research hypotheses, we rely on two
different methodologies to (i) study the effects of an
interruption of HFT over time and (ii) to identify the
overall effect of an interruption of HFT on liquidity and
volatility. In the first part of the analysis, we investigate the
minute-wise differences between treatment (i.e., DAX30
stocks traded on Xetra) and control group (i.e., CAC40
stocks traded on Euronext) during the trading hour where
the technical failure existed following the approach of
Battalio and Schultz (2011). This analysis allows us to
observe whether the impact of the interruption of HFT is
particularly strong during a specific period, whether the
market gradually adapts to the new situation, or whether
the impact is constant during the observation period.
Specifically, we run cross-sectional regressions for each
minute of the first hour of trading on the event day and the
non-event days (see Eq. (6)).
Yi¼a0þb1DAXiþbcCiþeið6Þ
The dependent variable Yiaccounts for each liquidity,
volatility, and trading activity parameter introduced in the
previous section, where irepresents the respective stock.
The dummy variable DAXihas a value of one if the specific
stock is in the treatment group (i.e., is a constituent of the
DAX30 and traded on Xetra, thus being affected by the
interruption of HFT) and takes a value of zero otherwise.
Ciis a vector of control variables commonly applied in
market microstructure research comprising log market cap,
the reciprocal of the closing price, log trading volume in
euro, and range volatility (i.e., the daily high price divided
by the low price) (Hendershott et al. 2011; Gresse 2017).
8
All control variables are computed on a daily basis for each
of the five days under investigation (one event day, four
control days). The variable eiequals the idiosyncratic error
term. Standard errors are clustered by stock. The variable
of interest is b1, which explains the difference in market
7
There are are five extended opening auctions of 2 min in the sample
that reduce the theoretical maximum number of observations of
21,000 by 10.
8
Due to the high correlation of trading volume and the number of
trades, log trading volume is not included in the regressions where
Trades and Volume are the dependent variables.
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quality between treatment and control group for each
minute of the period under investigation. We plot b1and
the corresponding upper and lower bounds of its 95%-
confidence interval for each minute and market quality
variable to illustrate the differences between treatment and
control group during the interruption of HFT during the
first trading hour on the event day.
In the second part of the analysis, we rely on a DiD-
appoach to identify the overall effect of an interruption of
HFT technology during the entire observation period.
Because the technical failure leading to an interruption of
HFT on Xetra on October 2nd, 2017 represents a natural
experiment arising from an exogenous event, the DiD-ap-
proach is a suitable research method to analyze the effects
of this event on market quality (Wooldridge 2013). In the
case at hand, the DiD-analysis consists of a treatment and a
control group, which are observed on event and non-event
days. However, only the treatment group is affected by the
exogenous event on the event day. The DiD-methodology
allows a clear analysis of the effect of a treatment (here the
exogenous and unanticipated interruption of HFT on Xetra)
since it cancels out potential confounding effects such as
trends in the treatment group over the observation period
and permanent differences between both groups (Imbens
and Wooldridge 2009). Moreover, this methodology is
commonly used to assess the impact of new regulations,
market design variations, or changes in trading technology
on the quality of securities markets (e.g., Gomber et al.
2016b; Clapham et al. 2021; Hendershott et al. 2011). Yet,
the referenced studies apply the DiD-approach to analyses
based on daily data whereas this study is based on intraday
data. In additional tests reported together with other
robustness checks in Sect. 4.4, we confirm that the com-
mon trends assumption of the DiD-approach (Angrist and
Pischke 2008) holds for our sample despite the use of
intraday data. Thereby, this study shows that the DiD-ap-
proach can also be used in case of observations at intraday
frequency. Our regression setup for the DiD-approach is
implemented as shown in Eq. (7):
Yi;t¼a0þb1ðEventtDAXiÞþb2ðDay DAXiÞ
þb3Eventtþb4Day þbcCi;tþbkMinutetþei;t
ð7Þ
Again, Yi;taccounts for each liquidity, volatility, and
trading activity parameter, where irepresents the respec-
tive stock and tthe respective one-minute observation
interval. The dummy variable DAXihas a value of one if
the specific stock is in the treatment group and takes a
value of zero otherwise. Eventtis also a dummy variable
and indicates whether a one-minute interval belongs to one
of the trading days without technical problems (zero) or
whether it is on the treatment day where HFT was not
possible on Xetra (one). In order to control for potential
time trends and trend-driven differences between treatment
and control group in the data, we also include a linear time
trend ðDay DAXiÞin the regression. Moreover, we add
common market microstructure controls (Ci, same as
before) and control for each minute of the analyzed first
hour of trading on the observed days by adding dummy
variables for each minute (Minutet). Minute controls are
included since market quality parameters change over the
trading day and also vary within the first hour of trading
(McInish and Wood 1992). The variable ei;trepresents the
idiosyncratic error term. We derive the results of the panel
regression relying on stock fixed effects (FE) estimators to
cancel out stock specific time-constant and unobserved
effects as suggested by Wooldridge (2002). Therefore, a
single DAXidummy is not considered in the regression
setup because it does not vary over time and would be
dropped due to FE.
4.3 Results
4.3.1 Descriptive Statistics
Before discussing the results of our regression analyses, we
will first focus on the descriptive statistics of our data. As
described in the data set section, we aggregate all variables
into one-minute intervals for the following analysis.
Table 1reports descriptive statistics for the four liquidity
and two volatility measures analyzed in this study. In this
table, liquidity and volatility measures are averaged sepa-
rately across DAX30 stocks traded on Xetra and CAC40
stocks traded on Euronext as well as separately across days
with HFT on Xetra (non-event) and the day where HFT on
Xetra was interrupted (event). Also, the table shows per-
centage-wise changes in liquidity and volatility for each
group of stocks between the first hour of trading on non-
event days and the event day. Moreover, it includes a
descriptive DiD-calculation, which shows the difference
between the average changes within the two groups of
stocks that serves as a first indicator for the effects of an
interruption of HFT on securities market quality. Detailed
descriptive statistics of the whole data set are provided in
Tables 6 and 7 in the Supplementary file1, which provide
further support for the comparability of treatment and
control group.
With an average spread of 4.90 bps on days where HFT
is possible, the constituents of the DAX30 are equally
liquid as the stocks of the CAC40, which have an average
relative spread of 4.95 bps. During the interruption of HFT
connections on Xetra on October 2nd, 2017, however, the
average spread of DAX30 stocks rises by 17.4% to 5.75
bps while the average spread of CAC40 stocks marginally
decreases by 3.7%. Taking the difference between the
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respective changes in these two groups, which leads to a
descriptive DiD-result based on means, we see that the
average spread of stocks where HFT is not possible is
21.1% higher than the average spread of stocks where HFT
takes place. Consequently, this is a first indication that HFT
and the underlying low-latency trading infrastructure might
increase liquidity in terms of relative spreads and, thus,
lead to a positive economic spillover of this technology to
other market participants and market quality as a whole.
Regarding the two measures L1-Volume and Depth(10)
quantifying different dimensions of order book depth, we
also see that liquidity deteriorates since less quoted volume
is available in the order book when HFTs are absent. Order
volume at the top of the order book (L1-Volume) on
average decreases by 9.1% whereas the euro volume ten
bps around the midpoint (Depth10) even decreases by
24.0% compared to CAC40 stocks. Most severely, order
imbalance of DAX30 stocks increases by 32.5% when HFT
is interrupted compared to the control group traded on
Euronext where HFT is still possible. Again, these
descriptive results suggest that investments in the under-
lying infrastructure and technology of HFT lead to spil-
lover effects to securities markets and market participants
since the market is more liquid and thus operationally more
efficient if HFT technology is available.
Looking at the two volatility measures, we observe an
increase for DAX30 stocks traded on Xetra from the non-
event days to the event day while volatility for CAC40
stocks even declines. This results in 16.8% higher trade
price volatility and 7.3% higher midpoint volatility on
average when HFTs are suddenly absent. Consequently,
this first descriptive analysis provides several indications
that HFT positively affects market quality by increasing
liquidity and decreasing volatility. Moreover, the descrip-
tive analysis reveals that the non-event means for all ana-
lyzed variables are highly comparable between DAX30
stocks traded on Xetra and CAC40 stocks traded on
Euronext, which reinforces the suitability of our selected
control group.
4.3.2 Minute-Wise Regression Analysis
The results of the minute-wise regressions (see Fig. 1)
show that the interruption of HFT significantly affects
liquidity in terms of increased spreads and decreased order
book depth measured by Depth(10). Bid-ask spreads are
particularly high in the first minutes of the interruption of
HFT and then gradually decrease. Yet, spreads remain
significantly higher than the median bid-ask spread in the
first hour of trading on non-event days throughout the
entire period during which HFT was not possible. Also,
Depth(10) is significantly lower on the event day indicating
that less volume is available in the order book. Similarly to
the spread, order imbalance is significantly higher in the
first 15 min of the interruption of trading and then gradu-
ally decreases, thereby not always being significantly dif-
ferent from the non-event median as the lower bound of the
95%-confidence interval touches the non-event median
several times. The effect on the volume available at the top
of the order book (L1-Volume) is less pronounced.
Although it is also slightly lower in almost every minute of
the interruption of HFT, the non-event median is still
within the 95%-confidence interval. Consequently, the
analysis based on minute-wise regressions reveals a sig-
nificant negative effect on most liquidity dimensions if
HFT technology is unavailable.
Looking at the two volatility measures, we observe that
the variance of the minute-wise cross-sectional differences
between treatment (DAX30) and control group (CAC40)
substantially rises. Because the majority of coefficients is
above the non-event median, a slight increase in volatility,
particularly in trade price volatility, is visible. However,
this effect is less pronounced than for the liquidity mea-
sures and the coefficients are not significantly different
Table 1 Descriptive results for
changes in liquidity and
volatility due to the interruption
of HFT
Spread,S.D.Price, and
S.D.Midpoint are reported in
bps, L1-Volume and Depth(10)
are reported in 1,000 euro
Variable Group Non-Event Event %Change DiD
Spread DAX30 4.90 5.75 17.4% 21.1%
CAC40 4.95 4.76 -3.7%
L1-Volume DAX30 101.00 86.98 -13.9% -9.1%
CAC40 82.37 78.38 -4.8%
Depth(10) DAX30 813.85 585.09 -28.1% -24.0%
CAC40 649.91 623.14 -4.1%
Order Imbalance DAX30 0.21 0.28 33.2% 32.5%
CAC40 0.20 0.20 0.7%
S.D. Price DAX30 2.06 2.36 14.6% 16.8%
CAC40 1.80 1.76 -2.2%
S.D. Midpoint DAX30 2.12 2.19 3.7% 7.3%
CAC40 2.08 2.00 3:6%
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from the non-event median but rather fluctuate around it.
Consequently, the effect of an interruption of HFT on
volatility is not so strong that it consistently affects
volatility in each one-minute interval of the observation
period.
To answer our hypotheses and to avoid the detection of
mechanical effects due to the potential absence of one
group of traders
9
, also changes in trading activity need to
be evaluated. The results of the minute-wise regression
analyses of trading activity are provided in Fig. 2 in the
Supplementary file1. As the results show, no significant
effect on trading activity is observable due to the inter-
ruption of HFT technology. Thus, this analysis provides
first supporting evidence for all three liquidity-related
hypotheses H1a to H1c, although there is weaker evidence
for changes in order book depth measured by L1-Volume.
A significant effect on trade price and midpoint volatility
(H2a and H2b) cannot be found based on this analysis.
Minute
Spread (in bps)
02468
DAX coefficient for Spread
0
10
20
30
40
50
60
70
80
90
100
110
120
Minute
L1−Volume (in 1,000 euro)
−150 −50 0 50 100
DAX coefficient for L1−Volume
0
10
20
30
40
50
60
70
80
90
100
110
120
Minute
Depth(10) (in 1,000 euro)
−400 −200 0 100 300
DAX coefficient for Depth(10)
0
10
20
30
40
50
60
70
80
90
100
110
120
Minute
Order Imbalance
0.0 0.1 0.2 0.3
DAX coefficient for Order Imbalance
0
10
20
30
40
50
60
70
80
90
100
110
120
Minute
S.D. Price (in bps)
−2 −1 01234
DAX coefficient for S.D. Price
0
10
20
30
40
50
60
70
80
90
100
110
120
Minute
S.D. Midpoint (in bps)
−2 −1 0123
DAX coefficient for S.D. Midpoint
0
10
20
30
40
50
60
70
80
90
100
110
120
Fig. 1 Changes in liquidity and volatility due to the interruption of
HFT (minute-wise regressions). This figure illustrates the b1-coeffi-
cient from Eq. (6), i.e., the minute-wise cross-sectional differences
between treatment (DAX30) and control group (CAC40), for each
minute of the first hour of trading on non-event days (minutes 1 to 60)
and the event day (minutes 61 to 120) separated by the red line.
Dependent variables are Spread,L1-Volume,Depth(10),
Order Imbalance,S.D.Price, and S.D.Midpoint. The purple line
represents the median coefficient for non-event days and the dotted
lines represent the upper and lower bounds of the 95%-confidence
interval
9
Although HFTs cannot trade using their regular high-frequency
gateways and infrastructure, they can still trade via slower connec-
tions to the exchange.
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4.3.3 DiD-Regression Analysis
In order to evaluate the overall effect of the interruption of
HFT technology on liquidity and volatility, we perform a
DiD-regression as described in Eq. (7). The DiD-regres-
sion allows us to attribute changes in market quality to the
sudden interruption of HFT since the control group filters
out potential confounding effects that affect both DAX30
and CAC40 stocks and which would otherwise bias our
results. Therefore, we are able to assess whether there are
positive spillover effects of HFT investments on the quality
of securities markets.
Table 2provides the results of the DiD-regression for
the analyzed liquidity and volatility measures that quantify
different dimensions of market quality. Our main variable
of interest is the interaction term Event DAX (abbreviated
as DiD), which is the DiD-coefficient and thus represents
the impact of a sudden interruption of HFT on the
respective market quality measure. For all four liquidity
measures, we observe a statistically significant negative
effect when HFT is suddenly interrupted. Relative spreads
for DAX30 stocks increase by 1.08 bps compared to reg-
ular trading days when HFT technology is available. Rel-
ative to a spread of 4.90 bps for DAX30 stocks when HFT
is possible (see non-event value for Xetra in Table 1), the
increase of 1.08 bps due the interruption of HFT is also
economically relevant since it equals an increase in
implicit transaction costs by 22% for (smaller) trades that
are executed at the best bid or best ask.
Besides increased spreads, also order book depth mea-
sured by Depth(10) and L1-Volume worsens significantly
when HFT is interrupted. Sufficient order book depth is
particularly relevant to mitigate the price impact and the
resulting implicit transaction costs of larger orders. Again,
the reduction of order book depth is economically relevant
as Depth(10) decreases by 203,947 euro (25%) compared
to 813,850 euro (see Table 1) when HFT is possible. L1-
Volume representing the passive volume quoted at the top
of the order book decreases by 11,291 euro when HFTs are
unable to trade at low latency. Yet, the coefficient is only
significant at the 10%-level and the corresponding R2is
relatively low. Consequently, the evidence regarding the
effect on L1-Volume is less pronounced, which is in line
with the results of the minute-wise regression analysis (see
Fig. 1) and the non-significant effect of a robustness test
based on matched pairs (see Sect. 4.4 and Table 8 in the
Supplementary file1). Moreover, we observe a significant
increase in order book imbalance when HFTs are unable to
trade at low latency amounting to more than 31% relative
to the non-event value. This shows that markets are more
vulnerable to price shocks due to sudden excess demand or
excess supply when HFT is unavailable, which is in line
with Brogaard et al. (2018).
The results for changes in volatility, which are depicted
in the last two columns of Table 2, show that trade price
volatility significantly increases when HFT is interrupted.
Although the minute-wise changes are not significantly
different from the non-event median (see Fig. 1), the
overall effect of the unavailability of HFT technology leads
to significantly increased trade price volatility when taking
the whole interruption period into account. Consequently,
HFT technology and the liquidity providing trading
strategies of many HFTs seem to absorb short-term price
changes and thus reduce trade price volatility, which again
is valuable for all market participants since lower volatility
reduces risk and transaction costs in securities markets.
Yet, the effect on midpoint volatility is not significant.
Since HFTs are highly active in the order book with fre-
quent order submissions and cancellations, this result is
surprising at first glance. However, this typical behavior of
HFTs directly serves as explanation for this results since
there is a trade-off between the positive influence of HFTs
reducing short-term volatility and the increased flickering
of the order book due to HFTs’ frequent order updates.
When HFT is suddenly interrupted, these two effects might
cancel out, which can explain the non-significant result for
midpoint volatility while we do see an effect on trade price
volatility.
The other reported coefficients predominantly serve to
control for general differences between the event day and
non-event days (Event), differences over time that apply to
both DAX30 and CAC40 stocks (Day), and a potential time
trend (Day Dax) in the differences between DAX30 and
CAC40 stocks. They are important to rule out that these
effects influence our DiD-variable of interest, but are not
relevant to answer our research question. Therefore, these
variables are not discussed in detail. The same applies to
the control variables.
In order to rule out that the observed effects on liquidity
and volatility result from mechanical effects due to changes
in trading activity caused by the interruption of HFT, we
also conduct the DiD-analysis for the trading activity
measures. The results are reported in Table 3and clearly
show that the interruption of HFT connections did not
significantly change any of the four measures of trading
activity. The DiD-coefficient for trading volume is even
positive although not significant. These findings can be
explained by the fact that traders who usually act as HFTs
do not completely lose the connection to Xetra. They are
only unable to trade at low latency using the usual high-
frequency connections to the exchange and can thus con-
tinue trading via connections to the market that offer the
regular speed. Consequently, the results of the DiD-re-
gression strongly support that HFT technology increases
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stock market liquidity along different dimensions (H1a to
H1c) and thus leads to lower transaction costs for all
market participants. Moreover, we find support for
increased trade price volatility when HFT is interrupted
(H2a). Hypothesis H2b regarding midpoint volatility needs
to be rejected, most likely due to the ambivalent nature of
HFT with respect to order book and midpoint changes.
Having identified the overall impact of an interruption of
HFT on liquidity and volatility, we need to evaluate the
effect size by comparing it to potentially mechanical
effects of an interruption of HFT on trading activity. This
allows us to determine the excess effect on liquidity and
trading volume in addition to potential changes in trading
activity and to derive final conclusions regarding our
hypotheses. Although the DiD-analysis revealed no sig-
nificant effects of an interruption of HFT on trading
activity, we still use the corresponding coefficients as a
conservative approach to determine the net effect of HFTs’
low-latency technology on liquidity and volatility. Specif-
ically, we compare the percentage change of trading
activity and liquidity/volatility measures using the respec-
tive DiD-coefficient and the corresponding non-event mean
value. The results of this analysis are reported in Table 4.
As already discussed, all DiD-coefficients for the dif-
ferent liquidity measures and trade price volatility are
significant. The analysis provided in Table 4further shows
a substantial effect of an interruption of HFT net of
changes in trading activity, particularly for the liquidity
measures spread (net increase by 11% to 15%), Depth(10)
(net decrease by 14% to 18%), and order imbalance (net
increase by 21% to 25%). The net effect is calculated by
subtracting the percentage change of market quality vari-
ables based on the DID-coefficient from Eq. (7) from the
percentage change in the number of trades and quotes.
10
The effect on L1-Volume is less strong (net decrease up to
5%), which is in line with the previous analyses. One likely
explanation for the smaller impact of an interruption of
HFT on L1-Volume is that without HFT technology, two-
sided liquidity-providing orders are submitted with wider
spreads, but not necessarily with substantially lower vol-
ume. Consequently, the whole order book moves further
away from the midpoint and liquidity-providing orders now
rest on relatively lower levels of the order book compared
to days on which HFT is possible. While L1-Volume
captures the (slightly lower) volume at the new best bid and
ask, and thus does not decrease that substantially, it is of
completely other quality because of the larger distance to
the midpoint representing the fair value of a stock.
Taken together, the net-effect analysis shows a positive
spillover of HFT technology on securities markets by
leading to higher levels of liquidity. This holds for relative
spreads, thus confirming Hypothesis H1a, order book
imbalance (H1c), and at least partially for order book depth
(H1b). Consequently our results clearly show that HFT
increases liquidity along different dimensions.
Table 2 Regression results explaining changes in liquidity and volatility due to the interruption of HFT based on Eq. (7)
Spread L1-Volume Depth(10) Order Imb. S.D. Price S.D. Midpoint
Event -1.469 -15.535 -35.327 -46.464 -0.174 -0.189
(-1.226) (-1.307) (-7.055)(-7.490)(-2.116)(-2.337)
Day 0.738 6.894 -2.272 0.278 0.027 0.045
(1.650) (1.692)(-1.063) (0.057) (1.209) (2.146)
DiD 1.080 -11.291 -203.947 0.067 0.284 0.113
(Event DAX) (6.689)(-1.663)(-4.446) (6.359 ) (2.036) (0.889)
Time Trend 0.014 -2.707 -17.973 0.001 -0.065 -0.058
(Day DAX) (0.320) (-1.282) (-1.783) (0.322) (-2.013)(-1.920)
Controls Yes Yes Yes Yes Yes Yes
Observations 20,990 20,990 20,810 20,810 20,990 20,990
R20.572 0.091 0.469 0.297 0.170 0.171
Adjusted R20.569 0.085 0.466 0.293 0.165 0.165
F Statistic 415.326 31.155 272.654 130.500 63.967 63.996
Spread,S.D.Price, and S.D.Midpoint are in bps, L1-Volume and Depth(10) are in 1,000 euro. Event is dummy variable being one for the trading
day on which HFT was interrupted, DAX is a dummy variable being one if a stock is a constituent of the DAX30, and Day represents the different
observation days
Note: t-statistics in parentheses; p\0.1; p\0.05; p\0.01
10
Changes in the number of trades and quotes are chosen as
reference because they represent the strongest effect on trading
activity in terms of executed trades and order book activity,
respectively, thus being the most conservative references.
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We also observe a positive net effect on trade price
volatility indicating that an interruption of HFT increases
stock market volatility. The net effect on midpoint
volatility is not substantially different from zero and also
the direction of the effect is not robust (see Sect. 4.4),
which can be explained by the opposing effects of HFT on
midpoint volatility as already discussed. With respect to
our second hypothesis, we thus provide evidence that the
interruption of HFT increases trade price volatility up to
7% net of changes in trading activity (H2a) but does not
lead to significant changes in midpoint volatility (H2b).
In summary, our analysis shows that HFT significantly
improves the quality of securities markets. While we do not
find any changes in trading activity that can be traced back
to an interruption of HFT technology, we do find a positive
effect of HFT on liquidity and also on trade price volatility.
If HFT is suddenly interrupted, three of the four analyzed
liquidity measures significantly deteriorate and trade price
volatility increases. Most importantly, all these effects are
substantially larger than any of the insignificant changes in
trading activity so that they do not mechanically result
from the potential absence of some traders. However, the
impact of HFT on liquidity is more pronounced than its
effect on trade price volatility. For the latter, we only find
significant evidence based on the entire interruption period
but not when looking at the individual one-minute obser-
vations. In summary, our findings show that investments in
HFT infrastructure and technology provide positive eco-
nomic spillover effects for the entire securities market
since HFT enhances market quality and reduces transaction
costs for all market participants.
4.4 Robustness Checks
We also perform different robustness checks to validate the
results of our analyses. First, we test whether the basic
Table 3 Regression results
explaining changes in trading
activity due to the interruption
of HFT based on Eq. (7)
Trades,Quotes, and
Submissions are in number of
occurrences, Volume is in 1,000
euro. Event is dummy variable
being one for the trading day on
which HFT was interrupted,
DAX is a dummy variable being
one if a stock is a constituent of
the DAX30, and Day represents
the different observation days
Note: t-statistics in parentheses;
p\0.1; p\0.05; p\0.01
Trades Volume Quotes Submissions
Event -1.469 -15.535 -35.327 -46.464
(-1.226) (-1.307) (-7.055)(-7.490)
Day 0.738 6.894 -2.272 0.278
(1.650) (1.692)(-1.063) (0.057)
DiD -1.188 4.885 -7.870 -8.722
(Event DAX) (-0.907) (0.301) (-1.258) (-1.007)
Time Trend -1.320 -16.506 -4.752 -6.960
(Day DAX) (-2.441)(-2.525)(-1.778)(-1.233)
Controls Yes Yes Yes Yes
Observations 20,990 20,990 20,990 20,810
R20.069 0.046 0.138 0.132
Adjusted R20.063 0.040 0.132 0.126
F Statistic 23.452 15.283 49.746 46.856
Table 4 Effect size of changes in market quality net of changes in trading activity
Trades Volume Quotes Submissions
Non-Event Value 11.06 177.24 117.86 181.49
DiD-Coefficient 1:19 4.89 7:87 8:72
Percentage Change 10:74%2.76% 6:68%4:81%
Spread L1-Volume Depth(10) Order Imb. S.D. Price S.D. Mid
Non-Event Value 4.90 101.00 813.85 0.21 2.06 2.12
DiD-Coefficient 1.08 211.29 2203.95 0.07 0.28 0.11
Percentage Change 22.03% 11:18%25:06%31.63% 13.81% 5.34%
Net Effect (Trades) 11.29% 0:44%14:32%20.89% 3.07% 5:40%
Net Effect (Quotes) 15.35% 4:50%18:38%24.95% 7.13% 1:34%
Non-Event Value provides market quality and trading activity measures for DAX30 stocks on non-event days. DiD-Coefficient reports the b1-
coefficient from Eq. (7). Percentage Change is calculated by comparing the DiD-coefficient with the corresponding non-event value. Net Effect
provides the percentage change for each market quality measure net of the change in trading activity measured by the number of trades and
quotes. Coefficients printed in bold are significant at the 10%-level
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assumption of the DiD-approach, i.e., the existence of a
common trend in treatment and control group before the
treatment (Angrist and Pischke 2008), holds in our case.
This is particularly relevant because different from previ-
ous studies (e.g., Gomber et al. 2016b; Clapham et al.
2021; Hendershott et al. 2011), this study applies the DiD-
analysis to intraday data. Fig. 3 in the Supplementary file1
plots the market quality and trading activity measures for
DAX30 and CAC40 stocks averaged for each minute of the
first hour of trading on non-event days and the event day.
The plots provide strong support for a common trend of
DAX30 and CAC40 stocks in the non-event period, espe-
cially for the liquidity measures spread and order imbal-
ance as well as for the two volatility measures. The
common trend assumption also holds for the two order
book depth measures Depth(10) and L1-Volume except for
a jump in DAX30 stocks (which is not observable for
CAC40 stocks) in the minutes 15 to 20. Common trends are
also visible for the trading activity measures number of
trades, quotes, submissions, and trading volume.
To provide further evidence for the assumption of
common trends to hold for our sample, we also conduct a
formal test as suggested by Autor (2003) and Pischke
(2005). Therefore, we interact the treatment variable with
time dummies (in our case for every minute of the pre-
event period) and leave out the interaction term for the last
pre-event period to test whether the differences between
treatment and control group in earlier non-event periods
deviate from the last pre-event period. If treatment and
control group follow common trends, the majority of
interaction terms should be insignificant. The analysis
shows that this is the case for our sample. For the analyzed
liquidity and volatility measures, the average share of
insignificant non-event interaction terms is 85.0% (min
66.1%, max 94.9%) while it is 76.3% (min 62.7%, max
83.1%) for the trading activity measures. Thus, the DiD-
assumption of common trends is fulfilled for our sample
despite the short observation period and the use of intraday
data.
Second, since the control group of CAC40 stocks is
larger than the treatment group of DAX30 stocks, we
repeat the DiD-analysis using one-to-one nearest-neighbor
matching without replacement as suggested by Davies and
Kim (2009). With this methodology, the most similar
CAC40 stock is assigned to each DAX30 stock, which
results in equally sized treatment and control group, each
consisting of 30 stocks.
11
Following Davies and Kim
(2009), we match stocks according to market capitalization
and stock price as of the event day.
12
The findings of this
analysis based on matched stocks from the control to the
treatment group strongly support our results. The results
are highly comparable to the results of the DiD-regressions
based on the full sample both in terms of effect size and
significance (see Tables 8 and 9 in the Supplementary
file1). Only the negative effect on L1-Volume is not sig-
nificant based on the matched pairs sample. Yet, this is in
line with the results of the minute-wise regressions and the
relatively low significance of the effect for the full sample.
Moreover, also the effect size net of changes in trading
activity is highly comparable between our initial results
and the matched sample (see Table 10 in the Supplemen-
tary file1). We only observe a sign change for the
insignificant effect of HFT on midpoint volatility. For all
other measures, the impact of HFT on market quality net of
changes in trading activity is even slightly more pro-
nounced when comparing the results for the matched
sample with those for the full sample.
Third, we investigate whether the impact of an inter-
ruption of HFT on market quality differs for large and
small stocks. Therefore, we divide the matched sample into
thirds according to market capitalization and run the DiD-
regression separately for each subset of stocks. As shown
in Table 11 in the Supplementary file1, the results for the
different subsets according to market capitalization are
qualitatively similar while the effect of an interruption of
HFT on volatility is mainly driven by the medium third
group. Yet, although the stocks in our sample significantly
vary in size (market capitalization of the largest DAX30
stock is 18 times larger than that of smallest DAX30 stock
in our sample), they still represent the largest and most
liquid German stocks.
5 Discussion
5.1 Contribution to Research
5.1.1 IS Literature
With our research, we add to the IS-literature in manifold
ways. We contribute to the long standing question raised by
Han et al. (2011) whether IT-systems can be beneficial not
only for the companies that invest in them but also for firms
in the same industry that do not directly invest in such
systems. In this paper, we show that the IT-systems used by
HFTs indeed have a positive effect on the overall market
(measured by market quality) and consequently all market
participants. This comes despite the fact that only a subset
11
The 30 CAC40 stocks included in the matched pairs sample are
denoted by * in Table 5 in the Supplementary file1.
12
The results do not change if we use the first or the last control day
as reference.
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of market participants, i.e., HFTs, invest in such IT-sys-
tems. Therefore, these investments and trading systems
provide a positive spillover to the rest of the market. With
this finding, we follow the call of Kohli and Grover (2008)
and contribute to the proposed research thrust on the
indirect and intangible paths of economic value of IT. In
this regard, our paper increases the understanding of the
effects of HFT and the underlying technology on the
securities trading industry and adds to the call for IS-re-
lated research on HFT by Currie and Seddon (2017).
5.1.2 Finance Literature
Based on an event with a technical disruption of the ultra-
fast connections to the exchange that suddenly interrupted
HFT in a securities market, we contribute to the research
stream of HFT and market quality in the finance literature.
Our results confirm the results of previous studies (e.g.,
Hasbrouck and Saar 2013) and indicate positive effects of
HFT on liquidity and volatility measured along a variety of
dimensions. The positive impact on volatility, however, is
less distinct than the positive impact on liquidity. Our
methodology has two major advantages compared to
existing empirical studies. First, the connectivity failure for
HFTs allows us to investigate a sharp and unanticipated cut
in HFT activity, which enables us to analyze market quality
with and without HFT activity. Previous studies either
analyze incremental changes in HFT activity over time
(e.g., Hasbrouck and Saar 2013) or conduct event studies
based on regulatory acts aimed to limit HFT (e.g., Frie-
derich and Payne 2015), which, however, are known in
advance and thus might be biased by announcement
effects. Second, we are able to use a public data feed but
still do not need to rely on any proxy for HFT activity.
Moreover, our results show that markets do not collapse
when HFTs as a significant group of market participants
lose their primary market access. Nevertheless, trading
becomes more costly for all market participants and
volatility increases when HFTs are unable to trade at low
latency.
5.2 Practical Implications
The results of our analysis are highly relevant for investors,
market operators, and regulators alike. The trading strate-
gies of HFTs are not only profitable for those traders that
have the ability to use low latency infrastructure, but our
results show that HFT activity also enhances market
quality for all participants in financial markets and the
securities trading industry as a whole. Thus, other institu-
tional investors and also retail investors benefit from the
liquidity provided by HFTs since lower spreads and higher
order book depth decrease transaction costs. In particular,
our results show that spreads increase by 22% (15% net of
changes in trading activity) and depth decreases by 25%
(18% net of changes in trading activity) when HFT tech-
nology is unavailable, which is highly relevant from an
economic point of view since these are the major deter-
minants of implicit transaction costs that traders and
investors have to bear. Despite these huge cost savings and
improvements in liquidity, sufficient and resilient protec-
tion mechanisms need to be in place to curb the risk of
potential negative effects of HFT such as flash crashes,
e.g., by using circuit breakers, which most regulators
demand exchanges to implement (Gomber et al. 2016a).
From the perspective of market operators, our results
emphasize the importance of HFTs for the liquidity of a
market. Since a liquid market is necessary to compete with
other trading venues, market operators should continue to
invest in low latency infrastructure and fast trading systems
in order to attract HFTs’ order flow. Nevertheless,
exchange operators need to be aware that the market
quality of their trading platform (and thus its attractiveness)
depends on a potentially small group of HFT firms, which
could create a disadvantageous dependency for the
exchange. Our results also suggest that market regulation
which potentially limits HFT activity should be designed
with caution since our results show that HFT activity
enhances market quality. Yet, also potential harmful effects
of HFTs need to be taken into consideration. Further, the
analyzed event impressively illustrates the role that oper-
ational risk plays in today’s financial markets. With new
technologies emerging, regulators should also consider
these developments in their regulatory approach to ensure
safe and resilient markets.
5.3 Limitations and Future Research
We are aware that our empirical study has some limita-
tions. First, we rely on a DiD-approach to exclude any
confounding effects using the stocks of the CAC40 index
as control group. Although DAX30 and CAC40 show
common trends in the non-event period and the stocks as
well as both markets Xetra and Euronext are highly com-
parable in several dimensions, there could still be other
effects besides the sudden interruption of HFT that only
affect German stocks and not the control group of French
stocks or vice versa. Second, we rely on the first hour of
trading on four different Mondays where HFT was possible
as control period. Nevertheless, there might be other
specific differences on these days although we have
checked that there were no other major announcements or
events and included several controls in our analyses.
However, since the first hour of trading exhibits signifi-
cantly other market quality parameters than other hours
throughout the trading day (McInish and Wood 1992), a
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B. Clapham et al.: The Impact of High-Frequency Trading..., Bus Inf Syst Eng 65(1):7–24 (2023) 21
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comparison with the first trading hour on other days is
necessary and Mondays appear to be the best choice given
the existence of the day-of-the-week effect in financial
markets (Dubois and Louvet 1996). Finally, our observa-
tions are based on highly liquid blue chip stocks and a short
observation window of one hour for the interruption of
HFT at a particular stock exchange. Thus, our findings
might not be completely generalizable regarding other
stock segments and markets although our results are
comparable to the findings of other academic studies on
HFT.
Most importantly, our results need to be put in relation
to potential adverse effects of HFT and its underlying
technology that are not part of our study. For instance,
although HFTs initially trade in the opposite direction of
sudden and extreme price movements, they support an
acceleration of such events in case extreme price move-
ments persist for a longer time or affect multiple securities,
which can lead to so-called mini flash crashes (Brogaard
et al. 2018). This behavior of HFTs was also visible during
the Flash Crash in May 2010 where major U.S. stock
market indices plunged and recovered within minutes
(Kirilenko et al. 2017). Moreover, HFT and the speed of
trading can have negative implications for information
production since informed traders have less time to trade
on their information, which they, e.g., obtained via research
(Baldauf and Mollner 2020). Therefore, and as HFTs try to
detect and join such informed trading, prices become more
efficient in the short run since private information is
incorporated into market prices more quickly. But infor-
mational rents have to be shared so that institutional traders
might have less incentives to engage in costly research and
information production, which could lead to less efficient
prices in the long run (Van Kervel and Menkveld 2019).
Despite the large literature stream on HFT, there are still
many interesting directions and opportunities for future
research. For example, future work could focus on the
interconnectedness of primary markets and alternative
trading venues due to HFT in order to investigate whether
and how a technical problem regarding HFT on one
exchange might affect trading and market quality on
alternative trading venues. In addition, taking into account
the limitation that we only analyze European securities
markets, future research could investigate technical prob-
lems with HFT in U.S. markets, which show even higher
levels of HFT activity than European markets. Finally,
future research could investigate how the interruption of
HFT due to a technical failure affects price discovery and
information transmission in securities markets.
6 Conclusion
Besides regulatory changes, technological advancements
and the automation of trading processes have led to major
transformations of the securities trading industry and
securities markets in Europe as well as in other jurisdic-
tions. In particular, the emergence and rise of HFT, which
accounts for around 35% of the equity trading volume in
Europe and around 50% in the U.S. (Zaharudin et al.
2022), has gained academic and regulatory attention. We
want to contribute to the discussion on whether HFT is
beneficial for financial markets and whether the huge
investments in necessary low latency infrastructure have
positive spillover effects for the entire market and all
market participants due to improvements in market quality.
Unlike existing studies on HFT, we analyze a technical
failure at a major stock exchange that prevents HFTs from
trading at low latency in order to draw conclusions about
the impact of HFT on securities market quality and the
securities trading industry. This event allows us to analyze
a sharp and unexpected cut-off of HFT activity rather than
to investigate incremental changes in HFT activity over
time. Moreover, we can rely on a public data feed without
the need to approximate HFT activity. Showing how
securities markets react when HFT technology is suddenly
interrupted due to a technical failure, our results confirm
the positive impact of HFT on liquidity and trade price
volatility. Consequently, HFT and associated investments
in the necessary infrastructure lead to more efficient mar-
kets and lower transaction costs for all market participants,
which confirms the value of this technology for securities
markets and the existence of a positive spillover effect of
investments in low-latency infrastructure for the entire
securities trading industry.
Funding Open Access funding enabled and organized by Projekt
DEAL.
Supplementary InformationThe online version contains
supplementary material available at https://doi.org/10.1007/s12599-
022-00768-6.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
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use, you will need to obtain permission directly from the copyright
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