This thesis investigates the application of arti cial neural networks (ANNs) for forecasting nancial time series (e.g. stock prices). The theory of technical analysis dictates that there are repeating pat- terns that occur in the historic prices of stocks, and that identifying these patterns can be of help in forecasting future price developments. A system was therefore developed which contains several \agents", each producing recommendations on the stock price based on some aspect of technical analysis theory. It was then tested if ANNs, using these recommendations as inputs, could be trained to forecast stock price uctuations with some degree of precision and reliability. The predictions of the ANNs were evaluated by calculating the Pearson correlation between the predicted and actual price changes, and the \hit rate" (how often the predicted and the actual change had the same sign). Although somewhat mixed overall, the empirical results seem to indicate that at least some of the ANNs were able to learn enough useful features to have signi cant predictive power. Tests were performed with ANNs forecasting over di erent time frames, including intraday. The predictive performance was seen to decline on the shorter time scales.
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