This book provides an in-depth assessment of the use of novel statistical approaches and machine learning tools in predicting foreign exchange rate movement.
This book provides an in-depth assessment of the use of novel statistical approaches and machine learning tools in predicting foreign exchange rate movement.
Arif Orçun Söylemez is Associate Professor at the Economics Department of Marmara University, Istanbul, Turkey.
Inhaltsangabe
1. A brief introduction of the global foreign exchange market 2. Prominent structural models for exchange rate determination 3. Nonlinearity of the exchange rates 4. Causes of nonlinearity of the exchange rates 5. Machine learning 6. Comparing the predictive powers of models 7. Should we focus on sign estimations rather than point estimations? 8. Final remarks
1. A brief introduction of the global foreign exchange market 2. Prominent structural models for exchange rate determination 3. Nonlinearity of the exchange rates 4. Causes of nonlinearity of the exchange rates 5. Machine learning 6. Comparing the predictive powers of models 7. Should we focus on sign estimations rather than point estimations? 8. Final remarks
1. A brief introduction of the global foreign exchange market 2. Prominent structural models for exchange rate determination 3. Nonlinearity of the exchange rates 4. Causes of nonlinearity of the exchange rates 5. Machine learning 6. Comparing the predictive powers of models 7. Should we focus on sign estimations rather than point estimations? 8. Final remarks
1. A brief introduction of the global foreign exchange market 2. Prominent structural models for exchange rate determination 3. Nonlinearity of the exchange rates 4. Causes of nonlinearity of the exchange rates 5. Machine learning 6. Comparing the predictive powers of models 7. Should we focus on sign estimations rather than point estimations? 8. Final remarks
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