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Computational data analysis techniques in economics and finance /

The vast volume of financial data that exists and the globalization of financial markets create new challenges for researchers and practitioners in economics and finance. Computational data analysis techniques can contribute significantly within this context, by providing a rigorous analytic framewo...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Doumpos, Michael, Zopounidis, Constantin, Gaganis, Chrysovalantis
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Nova Publishers, [2015]
Colección:Studies in financial optimization and risk management.
Temas:
Acceso en línea:Texto completo

MARC

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245 0 0 |a Computational data analysis techniques in economics and finance /  |c Michael Doumpos, Constantin Zopounidis and Chrysovalantis Gaganis, editors. 
264 1 |a New York :  |b Nova Publishers,  |c [2015] 
300 |a 1 online resource. 
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490 1 |a Studies in Financial Optimization and Risk Management 
500 |a Includes bibliographical references and index. 
588 |a Description based on print version record and CIP data provided by publisher. 
504 |a Includes bibliographical references and index. 
505 0 |a COMPUTATIONAL DATA ANALYSIS TECHNIQUES IN ECONOMICS AND FINANCE; COMPUTATIONAL DATA ANALYSIS TECHNIQUES IN ECONOMICS AND FINANCE; Library of Congress Cataloging-in-Publication Data; Contents; Preface; Chapter 1: Optimum Currency Areas within the US and Canada: A Data Analysis Approach; Abstract; 1. Introduction; 2. Methodology; 2.1. Correspondence Analysis; 2.2. Hierarchical Cluster Analysis; 2.3. Data; 3. Empirical Estimation; 3.1. Applying Correspondence Analysis; 3.2. Hierarchical Cluster Analysis; Conclusion; References. 
505 8 |a Chapter 2: Directional Forecasting in Financial Time Series Using Support Vector Machines: The USD/EURO Exchange RateAbstract; 1. Introduction; 2. Support Vector Machines; 2.1. Linear Separable Case; 2.2. Error Tolerant SVM; 2.3. Kernel Methods; 3. Data Analysis; 3.1. Data Collection and Kernel Selection; 3.2. Sensitivity Analysis; Acknowledgment; Conclusion; Appendix; References; Chapter 3: Inflation Forecasting in Greece Using a Neuro-Fuzzy System; Abstract; 1. Introduction; 1.1. Related Research; 2. Model Presentation; 2.1. ANFIS Architecture; 2.2. Learning Algorithm of ANFIS. 
505 8 |a 2.3. Data and Model Parameters4. Model Performance Evaluations; Conclusion; References; Chapter 4: The Value of IPRs and Competitiveness Regarding FDI: Linear and Non-Linear Analysis; Abstract; 1. Introduction; 2. FDI Background; 3. Competition Background; 4. IPR Background; 5. Competition Policy and the Exercise of Intellectual Property Rights; 6. Data and Methodology; 7. Empirical Results; 7.1. Linear Results; 7.2. Non-Linear Results; Conclusion; Appendices; References; Cases; Chapter 5: The Modelling of Maintenance Cost: The Case of Container-Ships in Dry-Dock; Abstract; 1. Introduction. 
505 8 |a 2. Literature Review2.1. Prior Research on Operating and Maintenance Costs; 2.2. Containerships and Their Costs; 3. Hypotheses; 3.1. Age; 3.2. Size; 3.3. Stay Days; 3.4. Market Conditions; 3.5. Owners' Negotiating Capacity; 4. Data and Methodology; 4.1. Data Collected; 4.2. Methodology; 5. Discussion of Results; Conclusion; References; Chapter 6: Discounted Cash Flows through Expertons in Business Valuation Process; Abstract; 1. Introduction; 2. Preliminaries; 2.1. Literature Review in Business Valuation; 2.2. Fuzzy Methodology in Business Valuation; 3. Fuzzy Methodology for Uncertainty. 
505 8 |a 3.1. Confidence Intervals3.2. Fuzzy Numbers; 3.3. Triangular Fuzzy Numbers; 3.4. Fuzzy Subsets; 3.5. Expertons; 4. Application on Business Valuation; 5. Discounted Cash Flows; 5.1. n- Number of Years; 5.2. CF- Cash Flow; 5.3. k- Discount Rate; 5.4. Vn- Residual Value; Result and Conclusion; References; Chapter 7: Leverage Premium in a Southern European Frame; Abstract; 1. Introduction; 2. Literature Review; 3. Data Collection and Sample Derivation; 4. Methodology; 4.1. Dependent Variable; 4.2. Independent Variables; 5. Empirical Results; 5.1. Full Sample Level; 5.1.1. One Factor Model. 
520 |a The vast volume of financial data that exists and the globalization of financial markets create new challenges for researchers and practitioners in economics and finance. Computational data analysis techniques can contribute significantly within this context, by providing a rigorous analytic framework for decision-making and support, in areas such as financial times series analysis and forecasting, risk assessment, trading, asset management, and pricing. The aim of this edited volume is to present, in a unified context, some recent advances in the field, covering the theory, the methodologies, 
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650 0 |a Finance  |x Statistical methods. 
650 0 |a Quantitative research. 
650 6 |a Économie politique  |x Méthodes statistiques. 
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700 1 |a Doumpos, Michael. 
700 1 |a Zopounidis, Constantin. 
700 1 |a Gaganis, Chrysovalantis. 
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