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http://www.springerlink.com/content/u63526m626567405/

Identification of Important News for Exchange Rate Modeling
書籍シリーズ	IFIP International Federation for Information Processing
出版社	Springer Boston
ISSN	1571-5736 (Print) 1861-2288 (Online)
巻	Volume 217/2006
書籍	Artificial Intelligence in Theory and Practice
DOI	10.1007/978-0-387-34747-9
著作権	2006
ISBN	978-0-387-34654-0
DOI	10.1007/978-0-387-34747-9_49
ページ	475-482

Identification of Important News for Exchange Rate Modeling

Debbie Zhang, Simeon J. Simoff, and John Debenham

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.