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    Equity trend prediction with neural networks

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    Equity_trend_prediction_with_neural_networks.pdf (241.5Kb)
    Abstract
    This paper presents results of neural network based trend prediction for equity markets. Raw equity exchange data is pre-processed before being fed into a series of neural networks. The use of Self Organising Maps (SOM) is investigated as a data classification method to limit neural network inputs and training data requirements. The resulting primary simulation is a neural network that can prediction whether the next trading period will be, on average, higher or lower than the current. Combinations of pre-processing and feature extracting SOM’s are investigated to determine the more optimal system configuration.
    Citation
    Halliday, R. (2004), Equity trend prediction with neural networks, Research Letters in the Information and Mathematical Sciences, 6, 15-29
    Date
    2004
    Author
    Halliday, R.
    Publisher
    Massey University
    URI
    http://hdl.handle.net/10179/4427
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    • Research Letters in the Information and Mathematical Sciences
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