Automated algorithmic trading: machine learning and agent-based modelling in complex adaptive ...

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UNIVERSITY OF SOUTHAMPTON
Automated Algorithmic Trading:
Machine Learning and Agent-based
Modelling in Complex Adaptive
Financial Markets
by
Ash Booth
Supervisors: Dr. Enrico Gerding & Prof. Frank McGroarty
Examiners: Prof. Alex Rogers & Prof. Dave Cliff
A thesis submitted in partial fulfillment for the
degree of Doctor of Philosophy
in the
Faculty of Business and Law Faculty of Physical Sciences and Engineering
Southampton Management School Electronics and Computer Science
Institute for Complex Systems Simulation
April 2016

UNIVERSITY OF SOUTHAMPTON
ABSTRACT
Faculty of Business and Law Faculty of Physical Sciences and Engineering
Southampton Management School Electronics and Computer Science
Doctor of Philosophy
by Ash Booth
Over the last three decades, most of the world’s stock exchanges have transitioned to
electronic trading through limit order books, creating a need for a new set of models for
understanding these markets. In this thesis, a number of models are described which
provide insight into the dynamics of modern financial markets as well as providing a
platform for optimising trading and regulatory decisions.
The first part of this thesis proposes an autonomous system that uses novel machine
learningtechniquestopredictthepricereturnoverwelldocumentedseasonaleventsand
usesthesepredictionstodevelopaprofitabletradingstrategy. TheDAX, FTSE 100and
S&P 500areexploredforthepresenceofseasonalityeventsbeforeanautomatedtrading
system based on performance weighted ensembles of random forests is introduced and
showntoimprovetheprofitabilityandstabilityoftradingsuchevents. Theperformance
of the models is analysed using a large sample of stocks and the results show that the
system described in this section produces superior results in terms of both profitability
and prediction accuracy compared with other ensemble techniques.
The second part of this thesis explores price impact. For many players in financial mar-
kets, the price impact of their trading activity represents a large proportion of their
transaction costs. This section of the thesis proposes an adaptation of the system in-
troduced in the first part for predicting the price impact of order book events. The
system’s performance is benchmarked using ensembles of other popular regression algo-
rithms including: linear regression, neural networks and support vector regression using
depth-of-book data from the BATS Chi-X exchange. The results show that recency-
weighted ensembles of random forests produce over 15% greater prediction accuracy on
out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.
Finally, a novel procedure for extracting the directional effects of features is proposed
and used to explore the features most dominant in the price formation process.
The final part of this thesis addresses the requirement for testing algorithmic trading
strategies laid out in the Markets in Financial Instruments Directive (MiFID) II by
describinganagent-basedsimulation. Fivetypesofagentoperateinalimitordermarket
iv
producing a model that is able to reproduce a number of stylised market properties
including: clustered volatility, autocorrelation of returns, long memory in order flow,
concave price impact and the presence of extreme price events. The model is found to
be insensitive to reasonable parameter variations. Finally, the model is used to explore
how trading strategy affects the implementation shortfall of trading a large order. A
numberofexecutionstrategieswithvariousordertypes,areevolvedandevaluatedinthe
agent-based market. It is shown that the evolved strategies outperform the simple, well-
known strategies significantly, suggesting that execution strategy plays an important
role in determining the implementation shortfall of trading large orders.1
1This work was supported by an EPSRC Doctoral Training Centre grant (EP/G03690X/1).
For my family
v

Contents
Nomenclature xv
Acknowledgements xx
1 Introduction 1
1.1 Research Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Outline of Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Literature Review 11
2.1 Background - Limit Order Books . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Forecasting Price Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1.1 The Perceptron . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1.2 The Multi-Layer Perceptron . . . . . . . . . . . . . . . . 17
2.2.1.3 Applications in Price Prediction . . . . . . . . . . . . . . 17
2.2.1.4 A Note on Deep Learning . . . . . . . . . . . . . . . . . . 19
2.2.2 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.2.1 The Margin . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2.2 Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2.3 Applications in Price Prediction . . . . . . . . . . . . . . 22
2.2.3 Evolutionary Learning . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.3.1 Chromosomal Representation . . . . . . . . . . . . . . . . 25
2.2.3.2 Fitness Functions . . . . . . . . . . . . . . . . . . . . . . 25
2.2.3.3 Selecting Parents . . . . . . . . . . . . . . . . . . . . . . . 26
2.2.3.4 Producing Offspring . . . . . . . . . . . . . . . . . . . . . 26
2.2.3.5 Applications in Price Prediction . . . . . . . . . . . . . . 28
2.2.4 Predicting with Ensembles . . . . . . . . . . . . . . . . . . . . . . 29
2.2.4.1 Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.4.2 Random Forests . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.4.3 Applications in Price Prediction . . . . . . . . . . . . . . 33
2.2.5 Precautions for Data Mining . . . . . . . . . . . . . . . . . . . . . 35
2.3 The Statistical Properties of Limit Order Markets . . . . . . . . . . . . . 37
2.3.1 Fat-tailed Distribution of Returns . . . . . . . . . . . . . . . . . . 37
2.3.2 Volatility Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.3.3 Autocorrelation of Returns . . . . . . . . . . . . . . . . . . . . . . 38
2.3.4 Long Memory in Order Flow . . . . . . . . . . . . . . . . . . . . . 39
vii
viii CONTENTS
2.3.5 Long Memory in Returns . . . . . . . . . . . . . . . . . . . . . . . 39
2.3.6 Extreme Price Events . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.4 Market Microstructure: Understanding Order Book Dynamics . . . . . . . 41
2.4.1 Price Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.2 Optimal Trade Execution . . . . . . . . . . . . . . . . . . . . . . . 43
2.5 Modelling Limit Order Books . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.5.1 Stochastic Order Book Models . . . . . . . . . . . . . . . . . . . . 45
2.5.2 Agent Based Models . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.5.2.1 Applications in Finance and Economics . . . . . . . . . . 48
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3 Explorations in Forecasting Price Returns - Daily Equities Data 53
3.1 The Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2 Seasonality Effects and Feature Selection. . . . . . . . . . . . . . . . . . . 56
3.2.1 Explorations in Seasonality . . . . . . . . . . . . . . . . . . . . . . 57
3.2.2 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.3 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3 Trading Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.1 Layer 1: The Random Forest Prediction Algorithm . . . . . . . . . 61
3.3.2 Layer 2: Expert Weighting - An Ensemble of Ensembles . . . . . . 63
3.3.3 Layer 3: Signal Filtering and Risk Management . . . . . . . . . . . 64
3.4 Experiments & Empirical Results . . . . . . . . . . . . . . . . . . . . . . . 65
3.4.1 The Base Learning Algorithm . . . . . . . . . . . . . . . . . . . . . 66
3.4.2 Explorations of Ensembles . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.3 Trading System Performance . . . . . . . . . . . . . . . . . . . . . 72
3.4.4 The Effect of Seasonality . . . . . . . . . . . . . . . . . . . . . . . 73
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4 Predicting Equity Market Price Impact Using Market Depth Data 77
4.1 The Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.3 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.3.1 The Base Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.2 An Ensemble of Ensembles . . . . . . . . . . . . . . . . . . . . . . 82
4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.5.1 Parameter Grid Search. . . . . . . . . . . . . . . . . . . . . . . . . 86
4.5.2 The Base Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.5.3 Ensembles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.5.4 Comparative influence of features . . . . . . . . . . . . . . . . . . . 89
4.5.4.1 Feature importance . . . . . . . . . . . . . . . . . . . . . 89
4.5.4.2 Direction of feature influence . . . . . . . . . . . . . . . . 90
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5 An Agent-Based Order Book Model for Automated Trading Algo-
rithms 93
5.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
CONTENTS ix
5.2 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2.1 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2.2 Fat Tailed Distribution of Returns . . . . . . . . . . . . . . . . . . 103
5.2.3 Volatility Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.2.4 Autocorrelation of returns . . . . . . . . . . . . . . . . . . . . . . . 104
5.2.5 Long Memory in Order Flow . . . . . . . . . . . . . . . . . . . . . 107
5.2.6 Concave Price Impact . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.2.7 Extreme Price Events . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6 Conclusions 113
6.1 Implications and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
A Addition details for Chapter 3 117
A.1 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
A.2 Technical Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
B Addition details for Chapter 4 121
B.1 Stock Descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
B.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Bibliography 125