Applying Neural Networks to Day-to-Day Stock Prediction by Thomas Eskebaek

Overview Background Theories and Strategies Computer Model Theory Implementation Results Conclusion Questions

Background Stock Trading Profit is the goal, foresight the means History Tried for centuries, still no successful method Stock Analysis Foresight is illusive, this offers a hand

Theories and Strategies Fundamental Analysis A stock’s performance can be predicted using intrinsic values, intuition and experience Technical Analysis A stock’s past performance is used to deduce it’s future performance The Random Walk The academics: Stocks are unpredictable

Computer Model Theory Indicators Properties of a stock used by a model to predict future performance Time Horizons The time frame of the prediction Data Selection Data included in the design of the model

Implementation A Neural Network As a tool for the day trader Predicts performance on a daily basis Advantages Introduces randomness Very flexible, easily reconfigured Low computation time

Results 3 Different Performance Parameters Correct prediction of direction of change Up to 76% within a stock class Correct prediction within 10% Between 4% and 8% - pretty close! Correct prediction within 5% Between 0% and 6% - lacks behind! Good results The system has merit, it’s useable Could be better with more different tests

Conclusion Model shows promise Can be used ‘as is’ as a directional predictor System needs consistency check Different configurations could show even better results – more tests needed System merits more research into the method used

Questions?