Foreign-Exchange-Rate Forecasting With Artificial Neural Networks

  • Lean Yu
  • Shouyang Wang
  • Kin Keung Lai

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 107)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Forecasting Foreign Exchange Rates with Artificial Neural Networks: An Analytical Survey

  3. Basic Learning Principles of Artificial Neural Networks and Data Preparation

  4. Individual Neural Network Models with Optimal Learning Rates and Adaptive Momentum Factors for Foreign Exchange Rates Prediction

  5. Hybridizing ANN with Other Forecasting Techniques for Foreign Exchange Rates Forecasting

  6. Neural Network Ensemble for Foreign Exchange Rates Forecasting

  7. Developing an Intelligent Foreign Exchange Rates Forecasting and Trading Decision Support System

    1. Front Matter
      Pages 248-248

About this book

Introduction

The foreign exchange market is one of the most complex dynamic markets with the characteristics of high volatility, nonlinearity and irregularity. Since the Bretton Woods System collapsed in 1970s, the fluctuations in the foreign exchange market are more volatile than ever. Furthermore, some important factors, such as economic growth, trade development, interest rates and inflation rates, have significant impacts on the exchange rate fluctuation. Meantime, these characteristics also make it extremely difficult to predict foreign exchange rates. Therefore, exchange rates forecasting has become a very important and challenge research issue for both academic and ind- trial communities. In this monograph, the authors try to apply artificial neural networks (ANNs) to exchange rates forecasting. Selection of the ANN approach for - change rates forecasting is because of ANNs’ unique features and powerful pattern recognition capability. Unlike most of the traditional model-based forecasting techniques, ANNs are a class of data-driven, self-adaptive, and nonlinear methods that do not require specific assumptions on the und- lying data generating process. These features are particularly appealing for practical forecasting situations where data are abundant or easily available, even though the theoretical model or the underlying relationship is - known. Furthermore, ANNs have been successfully applied to a wide range of forecasting problems in almost all areas of business, industry and engineering. In addition, ANNs have been proved to be a universal fu- tional approximator that can capture any type of complex relationships.

Keywords

algorithms artificial neural networks decision support system ensemble learning forecasting foreign exchange rates genetic algorithms hybrid learning learning modeling

Authors and affiliations

  • Lean Yu
    • 1
  • Shouyang Wang
    • 1
  • Kin Keung Lai
    • 2
  1. 1.Chinese Academy of SciencesBeijingChina
  2. 2.City University of Hong KongKowloonHong Kong

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-71720-3
  • Copyright Information Springer US 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Business and Economics
  • Print ISBN 978-0-387-71719-7
  • Online ISBN 978-0-387-71720-3
  • Series Print ISSN 0884-8289
  • About this book