Table Of ContentComputational Intelligence
Techniques for Trading and
Investment
Computational intelligence, a sub- branch of artificial intelligence, is a field
which draws on the natural world and adaptive mechanisms in order to study
behaviour in changing complex environments. This book provides an interdisci-
plinary view of current technological advances and challenges concerning the
application of computational intelligence techniques to financial time-s eries fore-
casting, trading and investment.
The book is divided into five parts. The first part introduces the most important
computational intelligence and financial trading concepts, while also presenting
the most important methodologies from these different domains. The second part
is devoted to the application of traditional computational intelligence techniques to
the fields of financial forecasting and trading, and the third part explores the appli-
cations of artificial neural networks in these domains. The fourth part delves into
novel evolutionary-b ased hybrid methodologies for trading and portfolio manage-
ment, while the fifth part presents the applications of advanced computational
intelligence modelling techniques in financial forecasting and trading.
This volume will be useful for graduate and postgraduate students of finance,
computational finance, financial engineering and computer science. Practition-
ers, traders and financial analysts will also benefit from this book.
Christian Dunis is Emeritus Professor of Banking and Finance at Liverpool
John Moores University, UK and Joint General Manager of global risk and new
products at Horus Partners Wealth Management Group SA, Switzerland.
Spiros Likothanassis is Professor and Director at the Pattern Recognition
Labor atory in the Department of Computer Engineering and Informatics at the
University of Patras, Greece.
Andreas Karathanasopoulos is Senior Lecturer in Finance and Risk Manage-
ment at the University of East London, UK.
Georgios Sermpinis is Senior Lecturer in Economics at the University of
Glasgow, UK.
Konstantinos Theofilatos is a Post-Doctoral Researcher in the Department of
Computer Engineering and Informatics at the University of Patras, Greece.
Routledge advances in experimental and computable
economics
Edited by K. Vela Velupillai and Stefano Zambelli
University of Trento, Italy
1 The Economics of Search
Brian and John McCall
2 Classical Econophysics
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Ana Cordeiro dos Santos
4 Computable Foundations for Economics
K. Vela Velupillai
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Edited by Alessandro Innocenti and Angela Sirigu
6 Computational Intelligence Techniques for Trading and Investment
Edited by Christian Dunis, Spiros Likothanassis, Andreas
Karathanasopoulos, Georgios Sermpinis and Konstantinos Theofilatos
Other books in the series include:
Economics Lab
An intensive course in experimental economics
Alessandra Cassar and Dan Friedman
Computational Intelligence
Techniques for Trading and
Investment
Edited by Christian Dunis,
Spiros Likothanassis,
Andreas Karathanasopoulos,
Georgios Sermpinis and
Konstantinos Theofilatos
First published 2014
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
711 Third Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2014 Selection and editorial matter, Christian Dunis, Spiros
Likothanassis, Andreas Karathanasopoulos, Georgios Sermpinis and
Konstantinos Theofilatos; individual chapters, the contributors
The right of the editors to be identified as the authors of the editorial
material, and of the authors for their individual chapters, has been asserted
in accordance with sections 77 and 78 of the Copyright, Designs and
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All rights reserved. No part of this book may be reprinted or reproduced or
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Trademark notice: Product or corporate names may be trademarks or
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British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging in Publication Data
Computational intelligence techniques for trading and investment/edited
by Christian Dunis, Spiros Likothanassis, Andreas Karathanasopoulos,
Georgios Sermpinis and Konstantinos Theofilatos.
pages cm
Includes bibliographical references and index.
1. Investments–Mathematical models. 2. Investment analysis–
Mathematical models. 3. Speculation–Mathematical models. 4.
Computational intelligence. I. Dunis, Christian.
HG4515.2.C66 2014
332.60285′63–dc23
2013036599
ISBN: 978-0-415-63680-3 (hbk)
ISBN: 978-0-203-08498-4 (ebk)
Typeset in Times New Roman
by Wearset Ltd, Boldon, Tyne and Wear
‘To my family’ Christian Dunis
‘Dedicated to my daughter Kalliopi–Klelia’ Spiros Likothanassis
‘To my family’ Andreas Karathanasopoulos
‘To my beloved Fountouki’ Georgios Sermpinis
‘To my family and my beloved Zoi-Triantafyllia’ Konstantinos
Theofilatos
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Contents
List of figures ix
List of tables xi
List of contributors xiii
Preface xv
Acknowledgements xix
PART I
Introduction 1
1 Computational intelligence: recent advances, perspectives
and open problems 3
KONSTANTINOS THEOFILATOS, EFSTRATIOS GEORGOPOULOS,
SPIROS LIKOTHANASSIS AND SEFERINA MAVROUDI
2 Financial forecasting and trading strategies: a survey 22
CHARALAMPOS STASINAKIS AND GEORGIOS SERMPINIS
PART II
Trading and investments with traditional computational
intelligence techniques 41
3 Hidden Markov models: financial modelling and
applications 43
SOVAN MITRA
4 Adaptive filtering on forecasting financial derivatives
indices 66
CHRISTOS DIMITRAKOPOULOS, ANDREAS
KARATHANASOPOULOS, GEORGIOS SERMPINIS AND
SPIROS LIKOTHANASSIS
viii Contents
PART III
Trading and investments with artificial neural networks 79
5 Modelling and trading the corn–ethanol crush spread with
neural networks 81
CHRISTIAN L. DUNIS, JASON LAWS, PETER W. MIDDLETON
AND ANDREAS KARATHANASOPOULOS
6 Trading decision support with historically consistent neural
networks 116
HANS- JöRG VON METTENHEIM
PART IV
Trading and investments with hybrid evolutionary
methodologies 131
7 Advanced short-t erm forecasting and trading deploying
neural networks optimized with an adaptive evolutionary
algorithm 133
KONSTANTINOS THEOFILATOS, THOMAS AMORGIANIOTIS,
ANDREAS KARATHANASOPOULOS, GEORGIOS SERMPINIS,
EFSTRATIOS GEORGOPOULOS AND SPIROS LIKOTHANASSIS
8 Using argumentation and hybrid evolutionary multi- model
partitioning algorithms for efficient portfolio construction 146
NIKOLAOS SPANOUDAKIS, KONSTANTINA PENDARAKI AND
GRIGORIOS BELIGIANNIS
PART V
Trading and investments with advanced computational
intelligence modelling techniques 175
9 Forecasting DAX 30 using support vector machines and
VDAX 177
RAFAEL ROSILLO, JAVIER GINER AND DAVID DE LA FUENTE
10 Ensemble learning of high- dimensional stock market data 192
MANOLIS MARAGOUDAKIS AND DIMITRIOS SERPANOS
Index 216
Figures
1.1 The MLP neural network architecture 6
1.2 The RNN architecture 6
1.3 The HONN architecture 8
1.4 The RBF neural network architecture 9
1.5 Meta- heuristic algorithms flow chart 13
3.1 A diagram of a two-s tate Markov model with state transitions
depicted by arrows 44
3.2 Graphs for the S&P 500 index 1976–1996 57
3.3 Graphs for the Nikkei 225 index 1976–1996 58
3.4 Graph of Bank of England interest rates (%) for each month
from October 1980 to December 2008 59
3.5 Graph of interest rates states for each month from October
1980 to December 2008 60
5.1 The corn–ethanol crush CBOT daily closing prices 89
5.2 Histogram of corn–ethanol spread return series 90
5.3 A single output, inter-c onnected MLP model 96
5.4 Elman RNN architecture with two neurons/nodes for the
hidden layer 97
5.5 Second order HONN with three inputs 99
6.1 Modelling with the HCNN. The initial hidden states can also
be trained 118
8.1 The structure of the hybrid evolutionary multi- model
partitioning algorithm used for financial time series
forecasting 158
8.2 Mapping from a fixed dimensional internal representation to a
variable length NARMAX parameter vector. The resulting
order is n(p, q, r) = (3, 2, 3) 159
8.3 The fitness of each candidate model is the model conditional pdf 160
8.4 The PORTRAIT system architecture 163
8.5 The investment process using the PORTRAIT tool 164
8.6 A screenshot of the PORTRAIT tool 165
8.7 The bear market context, the moderate investor role and the
specific context that combines them returns for all eight years 167
