Abstract
The paper presents an idea of using an MLP neural network for determining the optimal buy and sell time on a stock exchange. The inputs in the training set consist of past stock prices and a number of technical indicators. The buy and sell moments on the training data that will become the output to the neural network can be determined either automatically or manually by a user on past data. We discuss also the input space transformation and some improvements to the backpropagation algorithms.
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Kordos, M., Cwiok, A. (2011). A New Approach to Neural Network Based Stock Trading Strategy. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_51
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DOI: https://doi.org/10.1007/978-3-642-23878-9_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23877-2
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