Abstract
In this study, a novel adaptive learning algorithm for feed-forward network based on optimized instantaneous learning rates is proposed to predict stock market movements. In this new algorithm, the optimized adaptive learning rates are used to adjust the weight changes dynamically. For illustration and testing purposes the proposed algorithm is applied to two main stock price indices: S&P 500 and Nikkei 225. The experimental results reveal that the proposed algorithm provides a promising alternative to stock market prediction.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yu, L., Wang, S., Lai, K.K. (2005). A Novel Adaptive Learning Algorithm for Stock Market Prediction. In: Deng, X., Du, DZ. (eds) Algorithms and Computation. ISAAC 2005. Lecture Notes in Computer Science, vol 3827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11602613_45
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DOI: https://doi.org/10.1007/11602613_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30935-2
Online ISBN: 978-3-540-32426-3
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