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Applying Neural Networks to Day-to-Day Stock Prediction

Applying Neural Networks to Day-to-Day Stock Prediction

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Applying Neural Networks to Day-to-Day Stock Prediction

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

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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. Questions?