UID:
almahu_9949285313202882
Umfang:
XVIII, 207 p. 43 illus.
,
online resource.
Ausgabe:
1st ed. 2009.
ISBN:
9783642004957
Serie:
Lecture Notes in Economics and Mathematical Systems, 623
Inhalt:
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 example where 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.
Anmerkung:
Motivation -- Analytical Outlook -- Foreign Exchange Market Predictability -- Equilibrium Relationships -- Market Efficiency Concepts -- Views from Complexity Theory -- Conclusions -- Exchange Rate Forecasting with Support Vector Machines -- Statistical Analysis of Daily Exchange Rate Data. -- Support Vector Classification -- Description of Empirical Study and Results -- Exchange Rate Hedging in a Simulation/Optimization Framework -- Preferences over Probability Distributions -- Problem Statement and Computational Complexity -- Model Implementation -- Simulation/Optimization Experiments -- Contributions of the Dissertation -- Exchange Rate Forecasting with Support Vector Machines -- References -- Exchange Rate Hedging in a Simulation/Optimization Framework.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783642005367
Weitere Ausg.:
Printed edition: ISBN 9783642004940
Sprache:
Englisch
DOI:
10.1007/978-3-642-00495-7
URL:
https://doi.org/10.1007/978-3-642-00495-7
