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Authors: Algirdas Maknickas and Nijolė Maknickienė

Affiliation: Vilnius Gediminas Technical University, Lithuania

ISBN: 978-989-758-157-1

Keyword(s): Ensembles, EVOLINO, Finance, Forecasting, Investment Portfolio, Orthogonality.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Multi-Agent Intelligent Systems and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The chaotic and largely unpredictable conditions that prevail in exchange markets are of considerable interest to speculators because of the potential for profit. The creation and development of a support system using artificial intelligence algorithms provides new opportunities for investors in financial markets. Therefore, the authors have developed a support system that processes historical data, makes predictions using an ensemble of EVOLINO recurrent neural networks, assesses these predictions using a composition of high-low distributions, selects an orthogonal investment portfolio, and verifies the outcome on the real market. The support system requires multi-core hardware resources to allow for timely data processing using an MPI library-based parallel computation approach. A comparison of daily and weekly predictions reveals that weekly forecasts are less accurate than daily predictions, but are still accurate enough to trade successfully on the currency markets. Information o btained from the support system gives investors an advantage over uninformed market players in making investment decisions. (More)

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Paper citation in several formats:
Maknickas, A. and Maknickienė, N. (2015). Investment Support System using the EVOLINO Recurrent Neural Network Ensemble.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 138-145. DOI: 10.5220/0005600901380145

@conference{ncta15,
author={Algirdas Maknickas. and Nijolė Maknickienė.},
title={Investment Support System using the EVOLINO Recurrent Neural Network Ensemble},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA: NCTA, (ECTA 2015)},
year={2015},
pages={138-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005600901380145},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA: NCTA, (ECTA 2015)
TI - Investment Support System using the EVOLINO Recurrent Neural Network Ensemble
SN - 978-989-758-157-1
AU - Maknickas, A.
AU - Maknickienė, N.
PY - 2015
SP - 138
EP - 145
DO - 10.5220/0005600901380145

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