Identification of Important News for Exchange Rate Modeling
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.
KeywordsExchange Rate Time Series Data Domain Knowledge Euro Area News Article
- 3.Zhang, D., Simoff, S., Debenham, J.: Exchange rate modelling using news articles and economic data. In: 18th Australian Joint Conference on Artificial Intelligence. (2005)Google Scholar
- 4.Yu, T., Jan, T., Debenham, J., Simoff, S.J.: Incorporate domain knowledge into support vector machine to classify price impacts of unexpected news. In: Australasian Data Mining Conference. (2005)Google Scholar
- 5.Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D., Allan, J.: Language models for financial news recommendation. In: Ninth International Conference on Information and Knowledge Management (CIKM), Washington (2000) 389–396Google Scholar
- 6.Hautsch, N., Hess, D.: Bayesian learning in financial markets-testing for the relevance of information precision in price discovery. Journal of Financial and Quantitative Analysis (2005)Google Scholar
- 8.Zhang, D., Simoff, S.: Informing the curious negotiator: Automatic news extraction from the internet. In: Australasian Data Mining Conference, Cairns, Australia (2004)Google Scholar