Algorithmic Trading and High Frequency Trading

147 Pages·2017·0.86 MB·English

Algorithmic Trading and High Frequency Trading
Hao Zhou
February 8, 2017
A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy
Centre for Applied Financial Studies
UniSA Business School
University of South Australia
Copyright
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Hao Zhou
February 8, 2017
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Declaration of Authorship
I hereby declare that this thesis contains no material which has been accepted for the award of any other degree
or diploma in any university or other institution and that, to the best of my knowledge this thesis contains no
material previously published or written by another person, except where due reference is made in the text of the
thesis.
Hao Zhou Date: February 8, 2017
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Abstract
This thesis provides one standalone survey essay and three empirical essays on algorithmic trading (AT) and its
effect on market qualities. The survey essay reviews the theoretical, empirical, and policy studies on algorithmic
and high frequency trading. We review the theoretical literature relating to: (1) market maker–taker dynamics,
(2) information content of trades and quotes, and (3) recently incurred or proposed market structural changes.
We aim to provide a comprehensive roadmap for future research by surveying the empirical literature with an
emphasis on how data and causal events can be identified. Our conclusion includes a brief discussion of policy
implications and suggestions for future work.
The first empirical essay investigates the role algorithmic trading on days when the absolute value of the market
return is more than 2%. We find that the abnormal return of a stock is related to the stock’s AT intensity, that
high AT intensity stocks experience less price drops (surges) on days when the market declines (increases) for
more than 2%. This result is consistent with the view that AT minimizes price pressures and mitigates transitory
pricing errors.
The second empirical essay examines algorithmic execution strategy and the effects of algorithmic trading
order imbalances. We find that, ex-ante, algorithmic traders execute their trades according to the prevailing
Volume-Weighted Average Price (VWAP), they are more likely to execute buy (sell) orders when the prevailing
VWAP moves lower (higher) compared to the prevailing stock price. This implies a contrarian strategy which may
mitigate the short-term price trends. Further analyses show that AT order imbalances have a smaller price impact
compared to non-AT order imbalances. These effects are robust on days when the absolute value of the market
return is more than 2%.
The last empirical essay considers the role of algorithmic trading in the price discovery process. We estimate a
state space framework that decomposes stock prices into permanent price series and transient pricing error via state
space frameworks. We find that algorithmic traders contribute more to the permanent price processes and less to
the transient pricing errors compared to other traders. Our results show that AT facilitates the price discovery
process by contributing to permanent price movements. Our results are robust on days when the absolute value of
the market return is more than 2%.
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Acknowledgements
First and foremost, I would like to express my sincere gratitude to my principle supervisor Prof Petko Kalev
for the continuous support of my PhD study and related research, for his patience, motivation, and immense
knowledge. His guidance helped during the research and the writing of this thesis. He was a true source of
inspiration.
I would also like to thank my co-supervisors. I would like thank Dr Michael Burrow for his help in initiating
the PhD candidature. I would like to thank Dr Andy Lian for his support in programming and technical aspects
of the thesis. A special thanks to Ron McIver for his insightful feedback and encouragement.
I am very grateful to University of South Australia for providing financial and facilities support needed to
conduct this study. I would like to thank all the staff members and fellow PhD friends for the stimulating
discussions and their selfless supports. I am grateful to the Australian Securities Exchange, the Securities Industry
Research Centre of Asia-Pacific, and Dr Hui Zheng for providing the novel data for this research. I would like to
also thank Ray Adams, who provides editing assistance on grammar and English expressions.
This thesis has benefited from various comments and suggestions from workshop and conference participants
at the 2012 AFBC meeting in Sydney, the 2014 FMA Europe meeting in Maastricht, the 2014 MFS meeting
in Prague, the 2014 SIRCA Young Researcher Workshop in Sydney, the University of Naples Federico II, the
Frankfurt School of Finance and Management, EMLYON Business School, La Trobe University, and Monash
University.
Last but not least, I would like to thank my parents, my parents-in-law, and my wife for supporting me
spiritually throughout the writing of this thesis and throughout my life in general.
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Contents
Copyright i
Declaration of Authorship ii
Abstract iii
Acknowledgements iv
List of Figures ix
List of Tables x
Abbreviations xi
1 Introduction and overview 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Algorithmic and High Frequency Trading: A Review of the Literature 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Review and policy studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 CT review papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Review papers from broader perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.3 Policy papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Theoretical studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 Market maker–taker dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 Information content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.3 Recently incurred or proposed market structural changes . . . . . . . . . . . . . . . . . . . 19
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2.4 Empirical studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.1 Empirical studies by data identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.1.1 Studies that use CT proxies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.1.2 Studies that classify CT on aggregate . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.1.3 Studies that classify HFT on account level . . . . . . . . . . . . . . . . . . . . . . 29
2.4.2 Causal inferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.4.2.1 Technological upgrades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.4.2.2 Exchange fee changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.2.3 Financial transactions tax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.2.4 Colocations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Discussion on CT and market quality metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5.1 Liquidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5.2 Price discovery and price efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5.3 Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 Algorithmic Trading in Turbulent Markets 44
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 Data and research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.2 Stock and event day selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4 AT intensity and abnormal returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.4.1 Univariate analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.4.2 Multivariate analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4.3 Post-event day analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.4.4 Net effects of AT liquidity demand and supply . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.4.5 Robustness tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
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3.5 AT, news announcements, and market conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.5.1 Matched event day versus non-event day difference-in-differences analysis . . . . . . . . . . 71
3.5.2 AT and news arrivals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.5.3 Causal implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4 Algorithmic Execution Strategy and Order Imbalances 79
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2 Data and event day selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.3 AT execution strategy and the VWAP metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.4 AT and non-AT order imbalances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5 State Space Models for AT and Price Discovery 93
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.3 Data and descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4 The state space model backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4.1 Model representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4.2 Model estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.5 The empirical analysis of AT and price discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.5.1 The state space decomposition of intraday price discovery . . . . . . . . . . . . . . . . . . 103
5.5.2 The price discovery of AT order flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.5.3 Estimation results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.5.4 Robustness test: AT on turbulent days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6 Conclusion 117
6.1 Overview and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
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6.2 Suggestions for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
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List of Figures
2.1 HFT Public Information Demand Analysis via Google Trend. . . . . . . . . . . . . . . . . . . . . . 8
3.1 AT and non-AT Buy Volume by Event Days. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2 AT and non-AT Sell Volume by Event Days. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3 AT and non-AT Trading Volume on Up Days and Down Days. . . . . . . . . . . . . . . . . . . . . 59
4.1 The VWAP, AT, and non-AT of One Event Day and One non-Event Day. . . . . . . . . . . . . . . 82
5.1 Market Capitalisation of Individual Stocks in the ASX. . . . . . . . . . . . . . . . . . . . . . . . . 97
5.2 Intraday Price Series and Hidden Efficient Price Series in One Trading Session. . . . . . . . . . . . 106
5.3 Intraday Return of the Efficient Component vs the Transitory Component in One Trading Session. 107
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