Computational Intelligence Paradigms in Economic and Financial Decision Making [electronic resource] /
Resta, Marina.author.
The book focuses on a set of cutting-edge research techniques, highlighting the potential of soft computing tools in the analysis of economic and financial phenomena and in providing support for the decision-making process. In the first part the textbook presents a comprehensive and self-contained introduction to the field of self-organizing maps, elastic maps and social network analysis tools and provides necessary background material on the topic, including a discussion of more recent developments in the field. In the second part the focus is on practical applications, with particular attention paid to budgeting problems, market simulations, and decision-making processes, and on how such problems can be effectively managed by developing proper methods to automatically detect certain patterns. The book offers a valuable resource for both students and practitioners with an introductory-level college math background.
Description based upon print version of record.
Preface; Contents; List of Figures; List of Tables; Part I Theoretical Framework ; 1 Yet Another Introduction to Self-Organizing Maps; 1.1 Background; 1.2 The Basic Algorithm; 1.3 Stopping Criteria and Convergence Measures; 1.4 Output Visualization; 1.5 SOM Variants; 1.5.1 SOM Batch; 1.5.2 Topological Structures in SOMs; 1.5.3 Neural Gas and Growing Neural Gas; 1.5.4 Topology Representing Networks; 1.5.5 Self-Organizing Surface; 1.5.6 Evolving Self-Organizing Map; 1.5.7 Growing Hierarchical SOM; 1.6 Putting SOM at Work; 2 Networks Analysis and Beyond; 2.1 Introduction; 2.2 Classical Networks
2.3 Lattice Network2.4 Scale-Free Networks; 2.4.1 Degree Distribution; 2.4.2 Power-Law Distribution in Real-World Networks; 2.4.3 Barabasi--Albert Model; 2.5 The Configuration Model; 2.6 Small-World Networks; 2.7 Measuring the Robustness of Networks; 2.7.1 Average Shortest Path Length; 2.7.2 Clustering Coefficients; 2.7.3 Hierarchical Modularity; 2.7.4 Assortativity; 2.7.5 Degree Correlation; 2.8 Centrality Measures; 3 Elastic Maps; 3.1 Introduction; 3.2 A Formal Description; 3.3 How Elastic Maps Work; 3.4 Available Algorithm Implementations; Part II Applications
4 SOM Variants for the Simulation  of Market Price Modeling4.1 Introduction; 4.2 Voronoi Maps; 4.3 An Application to Financial Markets: Main Settings; 4.4 Experimental Results; 4.5 Conclusions and Outlooks for Future Works; 5 Elastic Maps to Define the Risk Profile  of Financial Investments; 5.1 Introduction; 5.1.1 Strategic Asset Allocation; 5.1.2 Tactical Asset Allocation; 5.1.3 Stock Picking; 5.2 Portfolio Selection Within the Markowitz Framework; 5.3 Case Study: The General Framework; 5.4 Stocks Picking with Elastic Maps; 5.4.1 Maps Visualization
5.4.2 Building Securities Portfolios with Elastic Maps5.5 Selection with Fundamental Analysis; 5.5.1 Data and Preprocessing; 5.5.2 The Formation of the Portfolio; 5.6 Comparison Between the Methods; 5.7 Conclusion; 6 Hubs and Communities of Financial Assets with Enhanced Self-Organizing Maps; 6.1 Introduction; 6.2 Value at Risk: An Introductory Guide; 6.3 Algorithmic Settings; 6.3.1 Self-Organizing Maps; 6.3.2 The VaRSOM; 6.4 Discussion Case; 6.5 Conclusion; 7 Financial Landscapes of Health Care Providers; 7.1 Introduction
8.2.2 The Lee--Carter Model
Includes bibliographical references at the end of each chapters.
The book focuses on a set of cutting-edge research techniques, highlighting the potential of soft computing tools in the analysis of economic and financial phenomena and in providing support for the decision-making process. In the first part the textbook presents a comprehensive and self-contained introduction to the field of self-organizing maps, elastic maps and social network analysis tools and provides necessary background material on the topic, including a discussion of more recent developments in the field. In the second part the focus is on practical applications, with particular attention paid to budgeting problems, market simulations, and decision-making processes, and on how such problems can be effectively managed by developing proper methods to automatically detect certain patterns. The book offers a valuable resource for both students and practitioners with an introductory-level college math background.
Computational intelligence.
Artificial intelligence.
Operations research.
Decision making.
Computational Intelligence.
Artificial Intelligence.
Operations Research/Decision Theory.