Visual Explorations in Finance

Self-organizing maps (SOM) have proven to be of significant economic value in the areas of finance, economic and marketing applications. As a result, this area is rapidly becoming a non-academic technology. This book looks at near state-of-the-art SOM applications in the above areas, and is a multi-authored volume, edited by Guido Deboeck, a leading exponent in the use of computational methods in financial and economic forecasting, and by the originator of SOM, Teuvo Kohonen. The book contains chapters on applications of unsupervised neural networks using Kohonen's self-organizing map approach.

Edited by Guido Deboeck, a leading exponent in the use of computation intelligence methods in finance and economic forecasting, and by the originator of SOM, Teuvo Kohonen. Their intention is to share knowledge and experience that has been gathered by the many contributing authors and editors in applying advanced analytical tools for more effective information and knowledge management. The inclusion of an 8 page colour section makes this book unique, colourful and exciting to read. Each chapter contains examples, exercises and solutions, perfectly suited to aid self-study. All arguments in the text are carefully crafted to promote understanding and enjoyment for the reader. It is an appropriate, interesting and entertaining text for those who require a rounded course as well as for those who wish to continue with further studies in algebra

Contenu
1: Applications.- 1 Let Financial Data Speak for Themselves.- 2 Projection of Long-term Interest Rates with Maps.- 3 Picking Mutual Funds with Self-Organizing Maps.- 4 Maps for Analyzing Failures of Small and Medium-sized Enterprises.- 5 Self-Organizing Atlas of Russian Banks.- 6 Investment Maps of Emerging Markets.- 7 A Hybrid Neural Network System for Trading Financial Markets.- 8 Real Estate Investment Appraisal of Land Properties using SOM.- 9 Real Estate Investment Appraisal of Buildings using SOM.- 10 Differential Patterns in Consumer Purchase Preferences using Self-Organizing Maps: A Case Study of China.- 2: Methodology, Tools and Techniques.- 11 The SOM Methodology.- 12 Self-Organizing Maps of Large Document Collections.- 13 Software Tools for Self-Organizing Maps.- 14 Tips for Processing and Color-coding of Self-Organizing Maps.- 15 Best Practices in Data Mining using Self-Organizing Maps.- Notes.- Author Index.