Analyzing the Effect of Market Regulation using High Frequency Market Data

202 Pages·2013·2.67 MB·English

Analyzing the Effect of Market Regulation using High
Frequency Market Data
Liam Cheung
Doctor of Philosophy
Economics
McGill University
Montreal, Quebec
2013-04-15
A thesis submitted to McGill University in partial
fulfillment of the requirements of the
degree of Doctor of Philosophy in Economics
(cid:13)c Liam Cheung
DEDICATION
This thesis is dedicated to my wife Maria and my children Emma, Thomas and
Matthew.
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ACKNOWLEDGEMENTS
Foremost, Iwouldliketoexpressmysinceregratitudetomyadvisors, Professors
John Galbraith and Sergei Sarkissian for their support of my PhD research. They
bothdemonstratedahighdegreeofpatienceandunderstandingtoanon-traditional
PhDstudent. Theirmotivation,enthusiasmandimmenseknowledgewereinvaluable
in the development of my research.
Besides my advisors, I would like to thank the faculty of the McGill Economics
departmentthathelpedmewithmystudies,especiallyProfessorsFranciscoAlvarez-
Cuadrado and Russell Davidson. Also I thank my fellow students Feng Jiao, Rami
Tabri and Isabelle Galiana for all their motivation, insights and help throughout my
studies.
IwouldliketoacknowledgethesupportofmypartnersandcoworkersatTactico.
LastbutnotleastIwouldliketothankmyfamilyandclosefriendsfortheirsupport
throughout my life.
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ABSTRACT
This thesis uses high frequency data sources to examine subtle effects of market
regulation on measures of market quality. Rapid modernization of market structure
and the occurrence of negative market events present difficulties for market regu-
lators to predict the effects of different market policies and regimes. This thesis
examines three regulatory artefacts of modern securities markets to provide insight
into high frequency effects of regulator policies. First, we examine the effect of mar-
ket fragmentation on market quality. Regulators in modern markets provided for
multiple marketplaces, replacing the legacy monopolies, in the interest of increased
competition. However, many regret the resulting fragmentation of volume. We find
that there are benefits to fragmentation, but that not all securities are uniformly
affected. Second, we examine effects of the implementation of single stock circuit
breakers by US regulators in response to the “flash crash” of 2010. We propose
methods to test the differences in the distribution of the tail of returns between the
periods before and after the introduction of the circuit breakers. Finally, we exam-
ine a controversial facility available in certain futures markets where institutional
traders may delay the publication of block trades over a specified volume threshold.
We find evidence that institutional traders use their private information to attempt
to reduce their exposure but the excess volume is not enough to support a high per-
centage of the block. Block trades are transacted at spreads that are significantly
lower than non-delayed trades even a quarter of the size. Finally, market quality
indicators in the time periods surrounding delayed block trades show no decrease in
market quality. The results and tools developed in this thesis should help regulators
and other market participants evaluate and predict the effects of various regulatory
policies and regimes.
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ABRE´GE´
Cette th`ese utilise des sources de donn´ees `a haute fr´equence pour examiner les
effets subtils de la r´eglementation des march´es sur les mesures de qualit´e du march´e.
Modernisation rapide de la structure du march´e et la de l’occurrence d’´ev´enements
n´egatifs sur le march´e pr´esentent des difficult´es pour les r´egulateurs de march´e de
pr´edire les effets des diff´erentes politiques et r´egimes du march´e. Cette th`ese ex-
amine trois objets de r´egulation des march´es financiers modernes pour donner un
aperu des effets haute fr´equence des politiques de r´egulation. Premi`erement, nous
examinons l’effet de la fragmentation du march´e sur la qualit´e du march´e. Les
r´egulateurs des march´es modernes ont pr´evu pour des multiples march´es, en rem-
plaant les monopoles existants, dans l’int´ert d’avoir plus de concurrence. Nous
constatons qu’il ya des avantages `a fragmentation, mais que tous les titres ne sont
pas uniform´ement affecters. Deuxi`emement, nous examinons les effets de la mise en
uvre de coup-circuits pour les titres individuels des organismes de r´eglementation
des E´tats-Unis en r´eponse `a la “flash crash” de 2010. Nous proposons des m´ethodes
pour tester les diff´erences dans la queue du distribution des rendements entre les
p´eriodes avant et apr`es la introduction des coup-circuits. Enfin, nous examinons
une facilit´e controvers´ee disponible dans certains march´es `a terme o les op´erateurs
institutionnels peuvent retarder la publication des transactions de blocs plus grand
qu’une volume sp´ecifi´e. Nous constatons que les traders institutionnels utilisent
leurs informations priv´ees pour tenter de r´eduire leur exposure mais l’exc`es de vol-
ume n’est pas suffisant pour transiger un pourcentage ´elev´e du bloc. Transactions
de blocs sont n´egoci´es `a des spreads qui sont nettement plus bas que les trades non
retard´ees mme un quart de la volume. Enfin, les indicateurs de qualit´e du march´e
dans les d´elais entourant les bloc ne montrez aucune diminution dans la qualit´e du
march´e. Les r´esultats et les outils d´evelopp´es dans cette th`ese devraient aider les
r´egulateurs et les autres participants du march´e d’´evaluer et de pr´evoir les effets des
diff´erentes politiques et des r´egimes r´eglementaires.
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PREFACE AND CONTRIBUTION OF AUTHORS
This thesis is original, unpublished, independent works by the author, Liam
Cheung, except Chapter 3, which is an unpublished joint work with my supervisor
John Galbraith.
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Table of Contents
1 Introduction 1
2 Fragmentation and Market Quality In the Canadian Equity Mar-
kets 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 History and State of Canadian Market Fragmentation . . . . . . . . 10
2.2.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 Canadian Market . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.3 Regulatory Features . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Theory and Empirical Evidence . . . . . . . . . . . . . . . . . . . . . 18
2.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5.1 Measuring Market Fragmentation . . . . . . . . . . . . . . . . 39
2.5.2 Measuring Market Quality . . . . . . . . . . . . . . . . . . . . 47
2.5.3 Estimation Techniques . . . . . . . . . . . . . . . . . . . . . . 51
2.6 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
2.6.1 Single-Stage Regressions on Aggregate Stock Data . . . . . . 61
2.6.2 Two-Stage Regressions on Trade Subsample Data . . . . . . . 63
2.6.3 Analysis of Within-Stock Effects . . . . . . . . . . . . . . . . 71
2.6.4 Matching Analysis . . . . . . . . . . . . . . . . . . . . . . . . 76
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3 Single Stock Circuit Breakers and the Distribution of Equity Re-
turns 82
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
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3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.3 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.5 Inferential Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.5.1 NGARCH-Adjusted Returns . . . . . . . . . . . . . . . . . . 104
3.5.2 Asymmetric Distributions . . . . . . . . . . . . . . . . . . . . 104
3.6 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.6.1 Asymmetric Distributions . . . . . . . . . . . . . . . . . . . . 111
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4 Volume and Price Effects of Delayed Publication Futures Block
Trades 132
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
4.2 Theoretical Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
4.4.1 Volume Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 149
4.4.2 Block Trade Spread Analysis . . . . . . . . . . . . . . . . . . 155
4.4.3 Market Quality Measures During the Delay and Post Publi-
cation Periods . . . . . . . . . . . . . . . . . . . . . . . . . . 157
4.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
4.5.1 Volume Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 158
4.5.2 Block Trade Spread Analysis . . . . . . . . . . . . . . . . . . 166
4.5.3 Publication Delay Effect on Market Quality . . . . . . . . . . 173
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
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List of Tables
2.1 ATS Fee Structure . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Stock Filtering Criteria . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Raw Message File Statistics . . . . . . . . . . . . . . . . . . . . . 27
2.4 aaaa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Sample Filtering Coverage . . . . . . . . . . . . . . . . . . . . . 38
2.6 Market Share by Marketplace . . . . . . . . . . . . . . . . . . . 40
2.7 Market Quality Characteristics by Marketplace . . . . . . . . 41
2.8 Fragmentation Segmentation by Market Capitalization . . . 45
2.9 Fragmentation Segmentation by Share Volume . . . . . . . . 46
2.10 Fragmentation vs. Effective Spread and Price Impact Re-
gression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.11 Fragmentation vs. 5 Minute Volatility and Volatility Ratio
Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.12 Sub-Sample and Full Sample Market Share by Marketplace 65
2.13 Sub-Sample and Full Sample Spread Characteristics . . . . . 65
2.14 Selection Bias Stage 1: Probit Regression . . . . . . . . . . . . 66
2.15 Selection Bias Stage 2: Regression with Probit Estimates on
Effective Spread . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
2.16 Selection Bias Stage 2: Regression with Probit Estimates on
Price Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
2.17 Stock-by-Stock Kernel Matching Results . . . . . . . . . . . . 78
3.1 Bar Data Characteristics . . . . . . . . . . . . . . . . . . . . . . 101
3.2 Unconstrained Estimate Differences . . . . . . . . . . . . . . . 113
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3.3 Constrained Estimate Differences . . . . . . . . . . . . . . . . . 119
3.4 Hill HKKP Alpha Tail Estimate Differences . . . . . . . . . . 126
4.1 Data Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
4.2 Volume Characteristics . . . . . . . . . . . . . . . . . . . . . . . . 148
4.3 Excess Volume Regression Analysis - Pre-Publication Vari-
ables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
4.4 Excess Volume Regression Analysis - Post Publication Vari-
ables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
4.5 Excess Volume Regression Analysis - Other Control Variables162
4.6 Excess Volume as Percentage of Block . . . . . . . . . . . . . . 165
4.7 Spread Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
4.8 Effective Spread Regression Analysis . . . . . . . . . . . . . . . 170
4.9 Realized Spread Regression Analysis . . . . . . . . . . . . . . . 171
4.10 Price Impact Regression Analysis . . . . . . . . . . . . . . . . . 172
4.11 Volume and Spread Characteristics in the 15 Minutes Pre-
ceding and Following Trade Publication . . . . . . . . . . . . . 174
4.12 Volatility Characteristics during Publication Delay and the
Following 15 Minutes . . . . . . . . . . . . . . . . . . . . . . . . . 178
4.13 Summary of Findings . . . . . . . . . . . . . . . . . . . . . . . . . 179
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