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Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy
September 2010
MarquesGomes2010b
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.
In proceedings
Nuno Marques
Yaxin Bi, Mary-Anne Williams
Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management
LNCS
Springer
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6291
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http://www.springerlink.com/content/1141554573008721/
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Nuno Marques, Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy, in: Yaxin Bi and Mary-Anne Williams (eds), Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management, LNCS, Springer, Vol. 6291, (http://www.springerlink.com/content/1141554573008721/), September 2010.
<a href="/people/members/view.php?code=9ac1f1682cb91b20d9fbfb8e659b0819" class="author">Nuno Marques</a>, <b>Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy</b>, in: Yaxin Bi and Mary-Anne Williams (eds), <u>Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management</u>, LNCS, <a href="http://www.springer.com" title="Link to external entity..." target="_blank" class="publisher">Springer</a>, Vol. 6291, (<a href="http://www.springerlink.com/content/1141554573008721/" target="_blank">url</a>), September 2010.
@inproceedings {MarquesGomes2010b, author = {Nuno Marques}, editor = {Yaxin Bi and Mary-Anne Williams}, title = {Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy}, booktitle = {Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management}, series = {LNCS}, publisher = {Springer}, volume = {6291}, url = {http://www.springerlink.com/content/1141554573008721/}, 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.}, month = {September}, year = {2010}, }
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