Daily stock market forecast from textual web data

Our aim is to predict stock markets using information contained in articles published on the Web, mostly textual articles appearing in the leading and influential financial newspapers. From those articles the daily closing values of major stock market indices in Asia, Europe and America are predicted. Textual statements contain not only the effect but also why it happened. A prediction system has been built that uses data mining techniques and sophisticated keyword tuple counting and transformation to produce periodically forecasts in stock markets. Exploiting textual information in addition to numeric time series data increases the quality of the input, hence improved predictions are expected. The forecasts are available in real-time via the Internet Web site. The system's accuracy for this difficult but also extremely challenging application is highly promising.