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The application of artificial neural networks to exchange rate forecasting : the role of market microstructure variables / by Nikola Gradojevic and Jing Yang.: FB3-2/100-23E-PDF

Artificial neural networks (ANN) are employed for high-frequency Canada/U.S. dollar exchange rate forecasting.--Abstract This paper examines whether introducing a market microstructure variable (that is, order flow) into a set of daily observations of macroeconomic variables (interest rate, crude oil price) together with an ANN technique can explain Canada/U.S. dollar exchange rate movement better than linear and random walk models. Two statistics are used to compare models: root-mean squared error (RMSE) and the percentage of correctly predicted exchange rate changes (PERC).--Introduction

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Publication information
Department/Agency
  • Bank of Canada.
TitleThe application of artificial neural networks to exchange rate forecasting : the role of market microstructure variables / by Nikola Gradojevic and Jing Yang.
Series title
  • Bank of Canada working paper 1701-9397 2000-23
Publication typeMonograph - View Master Record
Language[English]
FormatDigital text
Electronic document
Other formatsPhysical text-[English]
Note(s)
  • "Artificial neural networks (ANN) are employed for high-frequency Canada/U.S. dollar exchange rate forecasting."--Abstract. "This paper examines whether introducing a market microstructure variable (that is, order flow) into a set of daily observations of macroeconomic variables (interest rate, crude oil price) together with an ANN technique can explain Canada/U.S. dollar exchange rate movement better than linear and random walk models. Two statistics are used to compare models: root-mean squared error (RMSE) and the percentage of correctly predicted exchange rate changes (PERC)."--Introduction.
  • The ISSN (1192-5434) for the print edition has been incorrectly copied in this electronic publication.
  • Résumé en français.
Publishing information
  • Ottawa - Ontario : Bank of Canada December 2000.
Description36p.figs., graphs, references, tables
ISSN1701-9397
Catalogue number
  • FB3-2/100-23E-PDF
Subject terms
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