Competition, Division and Unity
The Impact of Market Structures on Trading Quality

Chengcheng Qu

Doctoral Thesis in Business Administration at Stockholm University, Sweden 2024

Competition, Division and Unity

The Impact of Market Structures on Trading Quality
Chengcheng Qu
Academic dissertation for the Degree of Doctor of Philosophy in Business Administration at
Stockholm University to be publicly defended on Monday 4 March 2024 at 13.00 in Lecture
Room 19 (Lärosal 19), 2nd floor, House 2, Campus Albano, Albanovägen 18.
Abstract

The financial market operates as an ecosystem, involving diverse yet interconnected marketplaces and participants. Market
design, intricately interacting with technology, regulation, and competition, shapes how participants adapt their trading
behavior and therefore influences market performance. This dissertation investigates how market microstructure impacts
the behavior of fast and slow traders, the incentive for liquidity provision and liquidity demand, and the competition between
exchanges for gaining order flow.
Article I examines a strategic solution to the concern that fast arbitrageurs make liquidity provision more costly bypicking off quotes before market makers have time to revise them. The solution in question was to prohibit proprietary
traders from engaging in liquidity taking, which prevents fast arbitrageurs from sniping market makers’ stale quotes.
The results reveal that the trading ban mitigated adverse selection costs and narrowed the bid-ask spread. Market makers
experienced higher profits and quoted higher volumes at better prices. The ban successfully eliminated over half of crossexchange toxic arbitrage trades.
Article II evaluates the market quality effects of market fragmentation. The study leverages a quasi-natural experiment,
which occurred when the Swiss stock markets suddenly transitioned from fragmentation to centralization in July 2019,
following the breakdown of EU-Switzerland equivalence rules. Because this event was unrelated to technologicaldevelopments, and did not disrupt trading, it establishes a firm ground for identifying the effect of fragmentation. The key
finding is that greater market fragmentation improves market liquidity, as captured by bid-ask spreads and depth, while
it does not impact market efficiency. These results align with theoretical predictions stating that market fragmentation
improves liquidity through quote competition across exchanges.
Article III studies the role of restrictions on the minimum tick size and the minimum lot size for determining transaction
costs at the futures market. The arrangement of minimum tick and lot sizes by regulators constrains trading at discrete prices
and quantities. The study demonstrates, both theoretically and empirically, a trade-off between the restrictions of discrete
price and discrete quantity. Given this tradeoff, a futures exchange can minimize futures transaction costs by choosing the
optimal futures price. That is, when the futures price is high, it is also relatively more continuous, and the futures quantity
is relatively more discrete, compared to the case when the futures price is low. In other words, when the futures price ishigh rather than low, the lot size restriction causes relatively more friction than the tick size restriction and vice versa. The
empirical results strongly support the model.
Keywords: Market microstructure, latency arbitrage, fragmentation, tick size, lot size, liquidity, adverse selection,market efficiency.
Stockholm 2024
http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-225463
ISBN 978-91-8014-641-8
ISBN 978-91-8014-642-5

Stockholm Business School
Stockholm University, 106 91 Stockholm

COMPETITION, DIVISION AND UNITY

Chengcheng Qu

Competition, Division and
Unity
The Impact of Market Structures on Trading Quality

Chengcheng Qu

©Chengcheng Qu, Stockholm University 2024
ISBN print 978-91-8014-641-8
ISBN PDF 978-91-8014-642-5
Cover image: ©Annie Spratt/Unsplash
Printed in Sweden by Universitetsservice US-AB, Stockholm 2024

To my younger self

Acknowledgments

When I am finishing this dissertation, my reflection brings me back to thenostalgic Stockholm summer in 2018, when I took the first step down thisroad. It is only now that I have come to realize how little foresight I had of therelentless path filled with twists and turns that awaited ahead, and how muchfurther I have traveled than I ever imagined in navigating this extended route.
As I have reached this point, at the completion of this dissertation and as the
person I am today, I am compelled to express my profound gratitude to themany individuals, both near and far, for having sustained me through all theunforeseen challenges, recurring detours, and troubled uncertainties.

My deepest appreciation goes to my dedicated supervisors for their patient
guidance and invaluable mentorship throughout the entirety of my doctoral
research. Their expertise and mentorship have been instrumental in shaping
this dissertation. I am deeply indebted to my primary supervisor, Björn
Hagströmer, for the exceptional commitment of time and effort invested inevery facet of my academic and professional journey. I am extremely grateful
to my secondary supervisor, Lars Nordén, who has been a constant source of
support ensuring the success of this project and my growth as a researcher.
My gratitude extends to Michał Dzieliński for his committement and insights
contributing to the meetings, discussions and feedback.

I would like to express my gratitude to Darya Yuferova, Richard Payne,
Dagfinn Rime, and Jarkko Peltomäki for their consutrctive comments andsuggestions that have enhanced this dissertation. I am sincerely thankful forthe enriching experience of working alongside Caihong Xu in ourcollaboration in teaching and research. I would like to thank all the faculty at
the Finance Section for their help, especially Iñaki Rodriguez Longarela, AiJun Hou, and Sara Jonsson for forstering my skills as an educator with theteaching opportunities.

This journey would have been incomplete without my incredible friends
and their companionship have been my pillars of strength. Thank you,
Fatemeh, for the countless moments of shared laughter, support, andunderstanding. For nearly two decades, Hanyu has been a cherishedcompanion sharing in my challenges and celebrations despite the geographic
distance. Thank you, Xiaoan, Zeshu for being the amazing individuals youare, and for making this adventures brighter and more fulfilling. I am also
thankful for the the shared memories with my friends: Ester, Petter, Anton,
Ian, Dong, Amir, Qinglin, Alexandar, and Mengyu.

I extend my sincere gratitude to all who have offered me comfort, strength,
and hope when I needed it most. Special thanks to Yoshimi Fukumura for
hosting the accountability writing group. Words fall short of expressing myappreciation to Susumu Hirasawa, who creates the inspiring soundtrackaccompanying my writing journey. My heartfelt thanks also go to my felinecompanions, Paper and Picco, for adding warmth and joy to the long hours ofworking.

Lastly, this dissertation would not have been possible without my parents
and their constant understanding and support.

Chengcheng Qu
Stockholm, January 2024

ii

Contents

Introduction ..................................................................................................... 1
Article I: Latency Arbitrage and Market Liquidity .......................................... 13
1

Introduction ...................................................................................................... 14

2

Previous Research ........................................................................................... 17
2.1

Latency Arbitrage........................................................................................ 17

2.2

Restrictions on Latency Arbitrage ............................................................... 19

2.3

Alternative Liquidity Protections ................................................................... 21

3

Institutional Details ........................................................................................... 21

4

Empirical Evidence ........................................................................................... 23
4.1

Impacts on Liquidity .................................................................................... 23

4.2

Impacts on Proprietary Trading................................................................... 27

4.3
5

Impacts on Toxic Arbitrage .......................................................................... 29
Conclusion and Discussion............................................................................... 34

References.................................................................................................................. 36
Tables ......................................................................................................................... 38
Figures ........................................................................................................................ 50
Appendix ..................................................................................................................... 55

Article II: Market Fragmentation, Liquidity, and Eﬀiciency ............................ 61
1

Introduction ...................................................................................................... 62

2

Institutional Details and Identification Strategy ........................................... 66

3

Empirical Framework .................................................................................... 70
3.1

Sample ....................................................................................................... 70

3.2

Data ........................................................................................................... 70

3.3

Market Quality Measures.......................................................................... 72

3.4

Matched Sample ....................................................................................... 75

3.5

Fragmentation Measures .......................................................................... 76

3.6
4
4.1

Market Quality Effects............................................................................... 79

4.2

Difference-in-Differences Regression Results .......................................... 80

4.3

Cross-Sectional Variation in the Liquidity Results .................................. 81

4.4
5

Descriptive Statistics ................................................................................. 77
Results ............................................................................................................ 79

Cross-Sectional Variation in the Eﬀiciency Results ............................... 83
Conclusion ....................................................................................................... 84

References ................................................................................................................ 86

Tables ........................................................................................................................ 88
Figures....................................................................................................................... 94
Appendix .................................................................................................................... 97

Article III: Tick Size, Lot Size, and Liquidity in Futures Trading .................... 99
1

Introduction .................................................................................................... 100

2Theory and Empirical Framework ................................................................... 104
2.1

A Model of Futures Trading with Tick Size and Lot Size Restrictions ........ 105

2.2

Policy Implications..................................................................................... 110

2.3

Regression Analysis.................................................................................. 111

3

Institutional Setting and Data .......................................................................... 113
3.1

Description of the OMXS Futures Market .................................................. 113

3.2

Data .......................................................................................................... 115

3.3

Variables ................................................................................................... 115

3.4

Descriptive Statistics ................................................................................. 117

4

Regression Results ........................................................................................ 120

5Concluding Remarks ...................................................................................... 121

References................................................................................................................ 124
Figures ...................................................................................................................... 126
Tables ....................................................................................................................... 132
Appendix A................................................................................................................ 134
Appendix B................................................................................................................ 137

Introduction

The financial market plays a pivotal role in bringing market participants
together for liquidity, investment, risk management, and price discovery. Asan ecosystem, the financial market involves diverse yet interconnectedmarketplaces and participants. Market design, intricately interacting withtechnology, regulation, and competition, shapes how participants adapt theirtrading behavior and therefore influences market performance.
One prevailing market design for financial asset trading is the continuous
limit order book adopted across various assets and countries. Despite thesignificant advancements in financial technology, including electronic trading
platforms, algorithmic trading, and high-frequency trading, the fundamental
concept of the continuous limit order book has remained relatively unchanged.
This design traces back to eras of human-driven trading when trades weremanually recorded and organized on paper.
The fundamental idea of a limit order book is to arrange trading with a listof limit orders to buy or sell with specified prices and volume. Whenever anincoming buy order matches the price of existing sell orders, a trade happens,and likewise for an incoming sell order that matches the price of existing buy
orders. The order book updates dynamically as order are entered, canceled,updated, and matched during the trading hours. A critical principle is the priceand time priority, which places the order with favorable price first, then theearliest one before other orders at the same price level. Limit orders are
typically classified as passive and will rest in the book until there is a match1

or cancellation. In contrast, incoming orders that are immediately matched toexisting orders, for example market orders, are classified as aggressive.
In the perpetual evolution of financial markets, the contemporary versionof the continuous limit order book has become more efficient. One of the mostremarkable transformations spurred by technology is the introduction ofelectronic trading. This transformation has brought about significant
enhancements in the speed of trading, allowing the market price to reflect new
information instantly, and a reduction in the costs associated with trading
services as well as transactions. These advancements driven by technology
upgrades have also profoundly changed arbitrage strategies, market-making
activities, and even retail trading.
Another noticeable shift in the evolution of market structure is the increasein competition among trading venues. Bucking the historical trend of regionaland entrant exchanges merging with or being acquired by large national
players, the past two decades have witnessed the emergence of alternative
venues taking order flow from monopolistic national exchanges. It is now
common for the same stock to be traded at multiple venues without being duallisted. Moreover, the intensifying competition extends beyond the realm of
displayed trading at centralized limit order books to various levels of market
transparency, especially dark trading and off-exchange trading.
Adding to the complexity is the variation in trading within the same
exchange. For instance, when considering stocks listed and traded on the same
exchange, the minimal price change, or the tick size, can differ significantly.
Stocks at a low price level can have the minimal tick size as a fraction of one
cent, while other stocks can have tick sizes as ten multiples of cents, or evena greater amount. Since the minimum amount by which the price of a stock
can change is, by definition, one tick, the tick size has important implications
for liquidity provision and trading costs. In this complex trading environment,2digital technology, automated trading, and algorithmic trading have been
instrumental in navigating the substantial challenges.
The ongoing transformations in financial markets have significantly
reduced transaction costs addressing not only explicit costs associated withexchange services but also implicit costs contingent on the timing and decision
when trading. The exchange stands between liquidity demanders andsuppliers, and market makers, the liquidity providers, are positioned between
liquidity investors and informed traders. In traditional market microstructure
model, the micro-scope decision of market makers involves navigating
between these two groups by quoting competitive bid-ask spreads to offset
potential losses to informed traders, who trade only when quoted price isfavorable, with income from liquidity traders, who are only price accepter.
A natural question may arise that, given the observed reduction in market
friction and transaction costs, can financial market push these transformations
further to eliminate frictions entirely and achieve a zero bid-ask spreads? A
closer examination of real financial market, however, reveals that achieving acompletely frictionless financial market is an impractical goal due to inherent
design features within the continuous limit order book.
Budish et al. (2015) demonstrate that, despite speed races to minimize
latency that delays price adjustments, these frictions remain inevitable within
the continuous limit order book. The design of continuous limit order bookcan facilitate fast traders sniping stale quotes with marginally small timepriority. For this reason, even in the absence of potential information
asymmetry, inventory risks, or latency, marker makers can still lose to fast
snipers and charge positive bid-ask spread. The fragmentation of trading
venues introduces another layer of friction for monitoring prices andmanaging orders across venues. Moreover, the minimal tick and lot sizes serveas necessary instruments in operating the order book. Excessively small tick3

or lot sizes may generate new frictions in the aggregation of information andtrading demands, rather than alleviating them.
In response to these challenges, this dissertation investigates how market
microstructure impacts the behavior of fast and slow traders, the incentive forliquidity provision and liquidity demand, and the competition between
exchanges. The three articles delve into: 1) the trading ban on fast arbitrageurs
to protect liquidity providers from sniping; 2) the impact of fragmentation
versus centralization of displayed trading; and 3) the role of tick and lot sizes
in determining optimal prices and spreads.
Article I examines a strategic solution to the concern that fast arbitrageurs
make liquidity provision more costly by picking off quotes before market
makers have time to revise them. The solution in question was to prohibit
proprietary traders from engaging in liquidity taking, which prevents fast
arbitrageurs from sniping market makers’ stale quotes. The results reveal thatthe trading ban mitigated adverse selection costs and narrowed the bid-ask
spread. Market makers experienced higher profits and quoted higher volumes
at better prices. The ban successfully eliminated over half of cross-exchange
toxic arbitrage trades.
Article II evaluates the market quality effects of market fragmentation. The
study leverages a quasi-natural experiment, which occurred when the Swissstock markets suddenly transitioned from fragmentation to centralization in
July 2019, following the breakdown of Switzerland-EU equivalence rules.
Because this event was unrelated to technological developments, and did not
disrupt trading, it establishes a firm ground for identifying the effect offragmentation. The key finding is that greater market fragmentation improves
market liquidity, as captured by bid-ask spreads and depth, while it does not
impact market efficiency. These results align with theoretical predictions

4

stating that market fragmentation improves liquidity through quotecompetition across exchanges.
Article III studies the role of restrictions on the minimum tick size and theminimum lot size for determining transaction costs at the futures market. The
arrangement of minimum tick and lot sizes by regulators constrains trading at
discrete prices and quantities. The study demonstrates, both theoretically andempirically, a trade-off between the restrictions of discrete price and discrete
quantity. Given this tradeoff, a futures exchange can minimize futurestransaction costs by choosing the optimal futures price. That is, when the
futures price is high, it is also relatively more continuous, and the futures
quantity is relatively more discrete, compared to the case when the futures
price is low. In other words, when the futures price is high rather than low, thelot size restriction causes relatively more friction than the tick size restriction
and vice versa. The empirical results strongly support the model.

Article I: Latency Arbitrage and Market Liquidity
Arbitrage is essential for adjusting pricing errors in financial markets. With
evolving financial markets and technology, prices reflect information more
efficiently and it is more difficult to spot arbitrage opportunities that last longenough for humans to react. However, the few microseconds involved in ordersubmission and processing provide a extremely narrow window of transient
price differences when new information arrives and therefore create fleeting
arbitrage opportunities. These arbitrage opportunities, known as latency
arbitrage, are exclusive to traders who are extremely fast to exploit stale
quotes before market makers withdraw them. Latency arbitrage contributes toeconomically marginal price efficiency but results in substantial liquidity5

costs. Aquilina, Budish, and O’Neill (2022) estimate that eliminating latency
arbitrage would reduce liquidity costs by 16.7%.
This paper investigates one possible solution that protects liquidity
provision from toxic latency arbitrage by prohibiting proprietary traders fromliquidity taking at the Aquis Exchange from February 2016. Proprietary
traders are mainly high-frequency trading firms that manage their own moneyat their own risk in conducting arbitrage, liquidity provision, or both. After theban, they could only trade passively as liquidity providers. The ban had
immediate and sharp impacts, as aggressive proprietary trading dropped toalmost zero the day after the ban was announced.
Existing studies indicate that latency arbitrage can be toxic for liquidityand demonstrate the relation between toxic arbitrage and transaction costs.
Budish et al. (2015) point out that, even in the absence of asymmetric
information and inventory risks, the rents liquidity providers pay to snipers inlatency arbitrage are equal to the liquidity costs borne by investors. Therefore,
latency arbitrage exacerbates costs for liquidity provision and bid-ask spreads.
Foucault et al. (2017) shows that a higher toxic portion of latency arbitrage
drives up liquidity costs. These predictions suggest that restricting toxic
arbitrage alleviates adverse selection risk and therefore reduces bid-ask
spreads.
Utilizing a difference-in-difference setup, this study compares the liquidity
changes on Aquis for the most traded stocks in the European markets to thesame stock traded on exchanges without the ban. The results show that roundtrip transaction costs, measured as the bid-ask spread, narrowed by 11% andthe total trade-based transaction costs, measured as effective spread, remained
unchanged. A breakdown of the effective spread reveals a 21% drop in
adverse selection costs and a 66% increase in profits for liquidity provision.
Market makers thus earned higher profits and quoted higher volumes at
6

favorable prices. As a gauge for adverse selection under symmetric
information, toxic arbitrage trades decreased by more than half.
This paper is the first to compare liquidity change after banning aggressive
proprietary trading. The findings extend the literature on toxic arbitrage withnovel empirical evidence from a natural experiment. This paper also adds tothe literature on the efficiency and complexity of passive liquidity protection
mechanisms, including studies on the speed bump, by estimating the direct
liquidity impact of limiting sniping. Additionally, this study extends theliterature on exchange differentiation, especially exchanges that match
liquidity providers with liquidity traders at lower adverse selection risks andliquidity costs.

Article II: Market Centralization, Liquidity, and Efficiency
One of the most notable trends in the equity markets in the United Statesand Europe over the past two decades is the transition from nearlymonopolistic national exchanges to a fragmented marketplace, where
numerous trading venues compete for order flow trading the same stock. In
the United States, the primary exchanges, where the equities are listed,
represents merely 46% of the lit trading volume in May 2021, on average.
Theoretical studies present mixed effects of fragmentation on market quality.
On the one hand, fragmentation can boost competition between exchanges,
innovations in technology, and cross-venue order routing. On the other hand,fragmentation can introduce challenges of higher adverse selection risks andextra data feeds expenses.
This paper analyzes the market quality effects of the sharp reversion fromfragmentation to centralization in lit trading of Swiss equities following thelapsing of the Switzerland-EU stock exchange equivalence. On July 1, 2019,7

the market share of the Swiss Exchange (SIX) in the total exchange-based
volume in trading Swiss stocks increased from approximately 70% to nearly
100%. This sudden centralization was exogenously due to a negotiation
breakdown between Switzerland and European Union (EU) on a large web of
economic agreements which include the equivalence status of Switzerlandwith EU. When the equivalence lapsed, Switzerland banned trading of Swissstocks at EU exchanges, leaving the Swiss Exchange (SIX) the only exchange
to trade shares of Swiss companies. The nature of this event allows us tocleanly isolate the effect of fragmentation on market quality.
The identification strategy compares the large- and mid-cap Swiss equities
with comparable German stocks from the same industry and with a similarlevel of bid-ask spreads, while not subject to the regulatory change infragmentation. The results demonstrate that the reduction in market
fragmentation leads to significant reductions in market liquidity. Market
efficiency is unaffected.
Specifically, market depth deteriorated significantly when the market
transformed from fragmentation to centralization. The impacts are strong for
depth measured both ex ante, the amount required to move prices by 10 basis
points (bps), and ex post, the price impact over the 10 seconds after a trade.
Market tightness became worse as quoted bid-ask spread and effective spreadincreased. In cross-sectional analysis, stocks with the highest fragmentation in
trading volume exhibit no additional liquidity effects benchmarked to the restof the stocks. In contrast, stocks with the highest fragmentation in depth
exhibit stronger reductions in market depth and those with the highest
fragmentation in quoted spread exhibit stronger reductions in market
tightness. The evidence aligns with theoretical predictions on a positive
relation between lit market fragmentation and market depth by Foucault andMenkveld (2008).

8

Market efficiency, measured as the absolute autocorrelation of intraday
returns and absolute deviation of the variance ratio 10-second and 1-second
returns from unity, show no significant change with the reduction infragmentation. The finding is contrary to the predictions in Chen and Duffie(2021). In their model of lit market fragmentation, trader aggressiveness tends
to be higher in fragmented markets because cross-market price impact is noninstantaneous; in a non-fragmented environment, instead, traders arecompelled to shift from order splitting across venues to order shredding overtime. The practice of order shredding delays trading and therefore is associatedwith lower eﬀiciency.

Article III: Tick Size, Lot Size, and Liquidity in Futures Trading
Futures are standardized and heavily regulated contracts. These features
enable futures trading in highly liquid secondary markets. However, some of
the regulations might induce frictions, causing unnecessarily low market
liquidity and high trading costs. As one of the features of a limit order book,the minimal tick size and lot size constrain trading at discrete grids of prices
and quantities. The minimal tick size sets the lowest rents for liquidity
provision and the incentive for market participants to undercut the best pricecompeting for price priority. The minimal lot size sets how close traders canalign the trading volume with their optimal positions for arbitrage, hedging,or speculation.
This paper studies the role of restrictions on the minimum tick size and theminimum lot size for determining transaction costs at the futures market. We
extend the models of Budish et al. (2015) and Li and Ye (2021) for the futures
market to illustrate how the minimum tick size and lot size restrictions affectfutures trading and transaction costs. The model consists of three stages in

9which the regulator, the futures exchange, and traders sequentially makedecisions. In equilibrium, the futures exchange chooses the optimal futures
price to minimize the transaction costs faced by traders given the tick and lot
size. The model expresses the futures bid-ask spread, given tick size and lot
size restrictions, as a function of futures volatility and trading activity by bothinformed and uninformed traders. Empirical results from the Nasdaq OMXS
30 index futures data strongly support the model.
The model further presents the bid-ask spread as two components, one
driven by the tick size and one by the lot size. When the futures price is highrather than low, the lot size restriction causes relatively more friction than thetick size restriction and vice versa. Therefore, the optimal futures pricebalances these two components in a trade-off between the restrictions of
discrete price and discrete quantity. From a policy perspective, in a futures
market where both restrictions are present, it is important for a futures
exchange to take the trade-off into account when considering whether toperform an index “split”, or change the index multiplier in index futures
markets to change the futures price.

10Reference
Aquilina, M., Budish, E., and O’Neill, P. (2022). Quantifying the highfrequency trading “arms race”. Quarterly Journal of Economics, 137 (1), 493–
564.

Budish, E., Cramton, P., and Shim, J. (2015). The high-frequency trading arms
race: Frequent batch auctions as a market design response. Quarterly Journalof Economics, 130 (4), 1547–1621.

Foucault, T., Kozhan, R., and Tham, W. W. (2017). Toxic arbitrage. Review
of Financial Studies, 30 (4), 1053–1094.

Foucault, T. and Menkveld, A. J. (2008). Competition for order flow and smart
order routing systems. Journal of Finance, 63(1):119–158.
Chen, D. and Duﬀie, D. (2021). Market fragmentation. American Economic
Review, 111 (7): 2247-74.

Li, S., and Ye, M. (2022). Discrete price, discrete quantity, and the optimal
nominal price of a stock. Working Paper.

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