Computational Intelligence Paradigms in Economic and Financial Decision Making

183 Pages·2016·6.663 MB·English

by  Marina Resta (auth.)

Intelligent Systems Reference Library 99
Marina Resta
Computational
Intelligence Paradigms
in Economic and
Financial Decision
Making
Intelligent Systems Reference Library
Volume 99
Series editors
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
Lakhmi C. Jain, University of Canberra, Canberra, Australia, and
University of South Australia, Adelaide, Australia
e-mail: [email protected]
About this Series
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and developments in all aspects of Intelligent Systems in an easily accessible and
well structured form. The series includes reference works, handbooks, compendia,
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More information about this series at http://www.springer.com/series/8578
Marina Resta
Computational Intelligence
Paradigms in Economic
and Financial Decision
Making
123
Marina Resta
DIEC
University of Genova
Genova
Italy
ISSN 1868-4394 ISSN 1868-4408 (electronic)
Intelligent Systems Reference Library
ISBN978-3-319-21439-9 ISBN978-3-319-21440-5 (eBook)
DOI 10.1007/978-3-319-21440-5
LibraryofCongressControlNumber:2015950866
SpringerChamHeidelbergNewYorkDordrechtLondon
©SpringerInternationalPublishingSwitzerland2016
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To my parents Vincenzo and Franca,
my husband Stefano and my son Antonio,
without whose loving support this
would not have happened.
Preface
This book presents a number of applications of computational intelligence
paradigms, with a focus on economic and financial decision-making. In this con-
text, the book includes tools like self-organizing maps (SOM) and their variants,
elastic maps and elements of complex network theory.
Thisbookoncomputationalintelligenceisthoughtforbothstudentsatgraduate
level and practitioners dealing with practical application of computational intelli-
gence, and it does not necessarily require a deeper background in artificial intel-
ligenceandmathematics.Inanintroductoryperspective,infact,thefirstpartofthe
book is devoted to provide basic notions and mathematical foundation for the
computational tools that will be used in the second part of the same book.
The intention of the book is not to provide thorough attention to all computa-
tional intelligence paradigms and algorithms, but to give an overview of the most
popular and frequently used models, for these models are provided with a number
of applications with discussion. In addition the book provides insights into many
new developments to tempt the interested reader. In this perspective the material
can be useful to graduate students and researchers who want a broader view of
various paradigms of computational intelligence.
The bookisorganizedinto two parts.Part Iprovidesashort introductiontothe
different paradigms of computational intelligence including: self-organizing maps
(Chap. 1), complex networks (Chap. 2), and elastic maps (Chap. 3). Part II covers
the application of different paradigms, and it can be read in any order. The fol-
lowing topics are included: Chapter 4 introduces the use of SOM variants for the
simulationofmarketpricemodeling;Chapter5analyzestheuseofelasticmapsto
define the risk profile of financial investments; Chapter 6 discusses how
self-organizing maps and their enhancements can be helpful to identify hubs and
communities in financial markets; Chapter 7 employs network paradigms to study
the financial balance sheets of health care providers; Chapter 8 focuses on an
application of self-organizing maps to explore the behavior of a population's
mortality rate and life expectancy. Finally, Chap. 9 uses SOM to discover a firm's
vii
viii Preface
clusters, analyzing data from micro-territories inside a city’s boundaries, trying to
exploit possible development policies.
As a finalremark,it is necessarytothank a numberof people who have helped
toproducethisbook.Firstofall,IamdeeplyindebtedtoProfessorLakhmiJainto
whom I address very warm thanks, for trusting in me and giving this challenging
opportunity. Also, many thanks to my parents, Vincenzo and Franca, my husband
Stefano,andmysonAntonio,withouttheirsupportandloveitwouldhavenotbeen
possible to write this book.
Genova Marina Resta
July 2015
Contents
Part I Theoretical Framework
1 Yet Another Introduction to Self-Organizing Maps . . . . . . . . . . . . 3
1.1 Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 The Basic Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Stopping Criteria and Convergence Measures. . . . . . . . . . . . . . . 6
1.4 Output Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5 SOM Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5.1 SOM Batch. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5.2 Topological Structures in SOMs. . . . . . . . . . . . . . . . . . . 14
1.5.3 Neural Gas and Growing Neural Gas . . . . . . . . . . . . . . . 15
1.5.4 Topology Representing Networks. . . . . . . . . . . . . . . . . . 15
1.5.5 Self-Organizing Surface . . . . . . . . . . . . . . . . . . . . . . . . 16
1.5.6 Evolving Self-Organizing Map. . . . . . . . . . . . . . . . . . . . 16
1.5.7 Growing Hierarchical SOM. . . . . . . . . . . . . . . . . . . . . . 17
1.6 Putting SOM at Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2 Networks Analysis and Beyond. . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Classical Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Lattice Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4 Scale-Free Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.1 Degree Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.2 Power-Law Distribution in Real-World Networks. . . . . . . 26
2.4.3 Barabasi–Albert Model . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5 The Configuration Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6 Small-World Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.7 Measuring the Robustness of Networks. . . . . . . . . . . . . . . . . . . 30
2.7.1 Average Shortest Path Length . . . . . . . . . . . . . . . . . . . . 31
2.7.2 Clustering Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . 31
ix
x Contents
2.7.3 Hierarchical Modularity. . . . . . . . . . . . . . . . . . . . . . . . . 31
2.7.4 Assortativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.7.5 Degree Correlation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.8 Centrality Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 Elastic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 A Formal Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 How Elastic Maps Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4 Available Algorithm Implementations. . . . . . . . . . . . . . . . . . . . 44
Part II Applications
4 SOM Variants for the Simulation of Market Price Modeling . . . . . 49
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Voronoi Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.3 An Application to Financial Markets: Main Settings. . . . . . . . . . 59
4.4 Experimental Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.5 Conclusions and Outlooks for Future Works . . . . . . . . . . . . . . . 67
5 Elastic Maps to Define the Risk Profile of Financial
Investments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.1.1 Strategic Asset Allocation . . . . . . . . . . . . . . . . . . . . . . . 70
5.1.2 Tactical Asset Allocation. . . . . . . . . . . . . . . . . . . . . . . . 70
5.1.3 Stock Picking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 Portfolio Selection Within the Markowitz Framework. . . . . . . . . 72
5.3 Case Study: The General Framework . . . . . . . . . . . . . . . . . . . . 76
5.4 Stocks Picking with Elastic Maps. . . . . . . . . . . . . . . . . . . . . . . 76
5.4.1 Maps Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.4.2 Building Securities Portfolios with Elastic Maps. . . . . . . . 77
5.5 Selection with Fundamental Analysis . . . . . . . . . . . . . . . . . . . . 85
5.5.1 Data and Preprocessing. . . . . . . . . . . . . . . . . . . . . . . . . 85
5.5.2 The Formation of the Portfolio. . . . . . . . . . . . . . . . . . . . 86
5.6 Comparison Between the Methods . . . . . . . . . . . . . . . . . . . . . . 90
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6 Hubs and Communities of Financial Assets with Enhanced
Self-Organizing Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.2 Value at Risk: An Introductory Guide. . . . . . . . . . . . . . . . . . . . 95
6.3 Algorithmic Settings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.3.1 Self-Organizing Maps. . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.3.2 The VaRSOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98