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Identification of Important News for Exchange Rate Modeling

  • Debbie Zhang
  • Simeon J. Simoff
  • John Debenham
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 217)

Abstract

Associating the pattern in text data with the pattern with time series data is a novel task. In this paper, an approach that utilizes the features of the time series data and domain knowledge is proposed and used to identify the patterns for exchange rate modeling. A set of rules to identify the patterns are firstly specified using domain knowledge. The text data are then associated with the exchange rate data and pre-classified according to the trend of the time series. The rules are further refined by the characteristics of the pre-classified data. Classification solely based on time series data requires precise and timely data, which are difficult to obtain from financial market reports. On the other hand, domain knowledge is often very expensive to be acquired and often has a modest inter-rater reliability. The proposed method combines both methods, leading to a “grey box” approach that can handle the data with some time delay and overcome these drawbacks.

Keywords

Exchange Rate Time Series Data Domain Knowledge Euro Area News Article 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Debbie Zhang
    • 1
  • Simeon J. Simoff
    • 1
  • John Debenham
    • 1
  1. 1.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia

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