Abstract
This study examines the ability of a series of neural networks (MLPs) to predict the five day percentage change in the value of the FTSE 100 Index, during the period June 1995 to December 1996, using technical, fundamental and intermarket data. The primary findings are consistent with a hypothesis that neural network models are capable of detecting structure in the underlying data but indicate that predictive accuracy declines as the time lapse from the model building period increases.
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© 2002 Springer-Verlag Berlin Heidelberg
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Brabazon, A. (2002). Financial Time Series Modelling Using Neural Networks: An Assessment of the Utility of a Stacking Methodology. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_17
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DOI: https://doi.org/10.1007/3-540-45750-X_17
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