Exchange rate prediction with non-numerical information

Exchange rate prediction is an important yet challenging problem in financial time series analysis. Although the historical exchange rates can provide valuable information, other factors will also affect the prediction significantly. These factors could be numerical or non-numerical ones, which are related to politics, economics, military, or even market psychology. Previous automatic exchange rate prediction merely considers numerical data (or simply the historical rates) for predicting the next day value. In this paper, we show how to utilize and combine many related factors, both numerical and non - numerical factors, for exchange rate prediction. With an example on forecasting exchange rate between US dollar and Japanese yen, we investigate how to exploit the information from non-numerical factors. We then engage a novel integrated approach which successfully combines information obtained from both numerical and non-numerical factors. We show how to quantify the non-numerical fundamental information, provide details steps on how to construct single predictors on different kinds of information separately, and finally describe how to integrate these separate predictors. Experimental results showed that our method can achieve the Directional Symmetry (DS) accuracy of 86.96%, which is much higher than only exploiting numerical information.