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Autor(en): 
  • Shouyang Wang
  • Lean Yu
  • Kin Keung Lai
  • Foreign-Exchange-Rate Forecasting with Artificial Neural Networks 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  August 2007  
    Genre:  Wirtschaft / Recht 
     
    Applications of Mathematics / Artificial Intelligence / B / Computational Mathematics and Numerical Analysis / Computer mathematics / Decision Making / Economics and Finance / Economics, Mathematical / Finance / Finance & accounting / Finance, general / macroeconomics / Macroeconomics and Monetary Economics / Macroeconomics/Monetary Economics//Financial Economics / Management decision making / Monetary Economics / Numerical analysis / Operational research / Operations Research / Operations Research and Decision Theory / Operations Research/Decision Theory / Quantitative Finance
    ISBN:  9780387717197 
    EAN-Code: 
    9780387717197 
    Verlag:  Springer Nature EN 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 23 mm 
    Gewicht:  682 gr 
    Seiten:  316 
    Illustration:  XXIII, 316 p. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      
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