Artificial Intelligence in Financial Markets Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics /

As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic a...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Dunis, Christian L. (Editor ), Middleton, Peter W. (Editor ), Karathanasopolous, Andreas (Editor ), Theofilatos, Konstantinos (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Palgrave Macmillan UK : Imprint: Palgrave Macmillan, 2016.
Edición:1st ed. 2016.
Colección:New Developments in Quantitative Trading and Investment,
Temas:
Business enterprises-Finance.
Financial services industry.
Financial risk management.
Social sciences-Mathematics.
Artificial intelligence.
Corporate Finance.
Financial Services.
Risk Management.
Mathematics in Business, Economics and Finance.
Artificial Intelligence.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 1. A Review of Applications of Artificial Intelligence in Financial Domain
  • SECTION I: Financial Forecasting and Trading
  • 2. Trading the FTSE100 Index - 'Adaptive' Modelling and Optimisation Techniques
  • 3. Modelling, Forecasting and Trading the Crack - A Sliding Window Approach to Training Neural Networks
  • 4. GEPTrader: A new Standalone Tool for Constructing Trading Strategies with Gene Expression Programming
  • SECTION II: ECONOMICS
  • 5. Business Intelligence for Decision Making in Economics
  • 6. An automated literature analysis on data mining applications to credit risk assessment
  • SECTION III: CREDIT RISK ANALYSIS
  • 7. Intelligent credit risk decision support: architecture and implementations
  • 8. Artificial Intelligence for Islamic Sukuk Rating Predictions
  • SECTION IV: PORTFOLIO MANAGEMENT, ANALYSIS AND OPTIMISATION
  • 9. Portfolio selection as a multiperiod choice problem under uncertainty: an interation-based approach
  • 10. Handling model risk in portfolio selection using a Multi-Objective Genetic Algorithm
  • 11. Linear regression versus fuzzy linear regression - does it make a difference in the evaluation of the performance of mutual fund managers?

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