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Foreign-Exchange-Rate Forecasting with Artificial Neural Networks

  • Book
  • © 2007

Overview

Authors:
  1. Lean Yu

    1. Chinese Academy of Sciences, Beijing, China

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  2. Shouyang Wang

    1. Chinese Academy of Sciences, Beijing, China

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  3. Kin Keung Lai

    1. City University of Hong Kong, Kowloon, Hong Kong

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  • The book’s modeling framework is multi-level enabling agent of an intelligent foreign-exchange-rate-forecasting methodology. Adding to the methodology is a decision-support system, which can be delivered by both a client/server model and widely-used web technologies
  • Because of the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange-rate forecasting, managers, analysts and technical practitioners in financial institutions across the world will have considerable interest in the book, as well as scholars and graduate students studying financial markets and business forecast
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (15 chapters)

  1. Front Matter

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

    1. Front Matter

      Pages 2-2
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    2. Are Foreign Exchange Rates Predictable? — A Literature Review from Artificial Neural Networks Perspective

      Pages 3-23

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

    1. Front Matter

      Pages 26-26
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    2. Basic Learning Principles of Artificial Neural Networks

      Pages 27-37

    3. Data Preparation in Neural Network Data Analysis

      Pages 39-62

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

    1. Front Matter

      Pages 248-248
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Keywords

About this book

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.

Reviews

From the reviews:

"This monograph consisting of six parts focuses on forecasting exchange rates via artificial neural networks (ANNs) and it is based on the fruit of a very pleasant scientific cooperation between three genuine academic researchers. …The academic researchers together with the business practitioners interested in the recent developments concerning the forecasting foreign exchange rates with ANNs will find in this book an excellent reference." (Vasile Postolica, Zentralblatt MATH, Vol. 1125 (2), 2008)

Authors and Affiliations

  • Chinese Academy of Sciences, Beijing, China

    Lean Yu, Shouyang Wang

  • City University of Hong Kong, Kowloon, Hong Kong

    Kin Keung Lai

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