We investigate how money market news headlines can be used to forecast intraday currency exchange rate movements. The innovation of the approach is that, unlike analysis based on quantifiable information, the forecasts are produced from text describing the current status of world financial markets, as well as political and general economic news. In contrast to numeric time series data textual data contains not only the effect (e.g., the dollar rises against the Deutschmark) but also the possible causes of the event (e.g., because of a weak German bond market). Hence improved predictions are expected from this richer input. The output is a categorical forecast about currency exchange rates: the dollar moves up, remains steady or goes down within the next one, two or three hours respectively. On a publicly available commercial data set the system produces results that are significantly better than random prediction. The contribution of this research is the smart modeling of the prediction problem enabling the use of content rich text for forecasting purposes
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