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Table of Contents
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
7.2 The Financial Statements of Public Italian Healthcare Providers: 7.3 The Methodology: Motivation and Description; 7.3.1 The Minimum Spanning Tree Filtering Procedure; 7.3.2 The Planar Maximally Filtered Graph; 7.3.3 The Directed Bubble Hierarchical Tree; 7.4 Results Discussion; 7.4.1 Retrieving Information from Networks; 7.4.2 Cluster Analysis for the MST Network; 7.4.3 Cluster Analysis for the PMFG Network; 7.4.4 Cluster Analysis for the DBHT Network; 7.5 Conclusion; 8 Using SOM for Mortality Projection; 8.1 Background; 8.2 Mortality Trends and Related Issues; 8.2.1 Actuarial Notations
8.2.2 The Lee
Carter Model
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
7.2 The Financial Statements of Public Italian Healthcare Providers: 7.3 The Methodology: Motivation and Description; 7.3.1 The Minimum Spanning Tree Filtering Procedure; 7.3.2 The Planar Maximally Filtered Graph; 7.3.3 The Directed Bubble Hierarchical Tree; 7.4 Results Discussion; 7.4.1 Retrieving Information from Networks; 7.4.2 Cluster Analysis for the MST Network; 7.4.3 Cluster Analysis for the PMFG Network; 7.4.4 Cluster Analysis for the DBHT Network; 7.5 Conclusion; 8 Using SOM for Mortality Projection; 8.1 Background; 8.2 Mortality Trends and Related Issues; 8.2.1 Actuarial Notations
8.2.2 The Lee
Carter Model