The growing complexity of many real world problems is one of the biggest challenges of our time. The area of international finance is one prominent examplewhere decision making is often fraud to mistakes, and tasks such as forecasting, trading and hedging exchange rates seem to be too difficult to expect correct or at least adequate decisions. From the high complexity of the foreign exchange market and related decision problems, the author derives the necessity to use tools from Machine Learning and Artificial Intelligence, e.g. Support Vector Machines, and to combine such methods with sophisticated financial modelling techniques. The suitability of this combination of ideas is demonstrated by an empirical study and by simulation. INDICE: Part I: Introduction: Motivation; Analytical Outlook.- Part II: Foreign Exchange Market Predictability: Equilibrium Relationships, Market Efficiency Concepts; Views from Complexity Theory; Conclusions.- Part III: Exchange Rate Forecasting with Support Vector Machines: Introduction; Statistical Analysis of Daily Exchange Rate Data; Support Vector Classification; Description ofEmpirical Study and Results.- Part IV: Exchange Rate Hedging in a Simulation/Optimization Framework: Introduction; Preferences Over Probability Distributions; Problem Statement and Computational Complexity; Model Implementation; Simulation/Optimization Experiments.- Part V: Contributions of the Dissertation: Exchange Rate Forecasting with Support Vector Machines; Exchange Rate Hedging in a Simulation/Optimization Framework.
- ISBN: 978-3-642-00494-0
- Editorial: Springer
- Encuadernacion: Rústica
- Páginas: 205
- Fecha Publicación: 01/05/2009
- Nº Volúmenes: 1
- Idioma: Inglés