Abstract
This paper presents a stop-loss - maximum return (SLMR) trading strategy based on improving the classic moving average technical indicator with neural networks. We propose an improvement in the efficiency of the long term moving average by using the limited recursion in Elman Neural Networks, jointly with hybrid neuro-symbolic neural network, while still fully keeping all the learning capabilities of non-recursive parts of the network. Simulations using Eurostoxx50 financial index will illustrate the potential of such a strategy for avoiding negative asset returns and decreasing the investment risk.
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Marques, N.C., Gomes, C. (2010). Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_55
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DOI: https://doi.org/10.1007/978-3-642-15280-1_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15279-5
Online ISBN: 978-3-642-15280-1
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