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Authors: Svitlana Galeshchuk 1 and Sumitra Mukherjee 2

Affiliations: 1 Ternopil National Economic University and Université Grenoble Alpes, Ukraine ; 2 Nova Southeastern University, United States

ISBN: 978-989-758-220-2

Keyword(s): Neural Networks, Deep Learning, Convolution Networks, Exchange Rate Prediction, Emerging Markets.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Accurate prediction of exchange rates is critical for devising robust monetary policies. Machine learning methods such as shallow neural networks have higher predictive accuracy than time series models when trained on input features carefully crafted by domain knowledge experts. This suggests that deep neural networks, with their ability to learn abstract features from raw data, may provide improved predictive accuracy with raw exchange rates as inputs. The preponderance of research focuses on developed currency markets. The paucity of research in emerging currency markets, and the crucial role that stable currencies play in such economies, motivates us to investigate the effectiveness of deep networks for exchange rate prediction in emerging markets. Literature suggests that the Efficient Market Hypothesis, which posits that asset prices reflect all relevant information, may not hold in such markets because of extraneous factors such as political instability and governmental interven tions. This motivates our hypothesis that inclusion of carefully chosen macroeconomic factors as input features may improve the predictive accuracy of deep networks in emerging currency markets. This position paper proposes novel input features based on currency clusters and presents our method for investigating the hypothesis using exchange rates from developed as well as emerging currency markets. (More)

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Paper citation in several formats:
Galeshchuk S. and Mukherjee S. (2017). Deep Learning for Predictions in Emerging Currency Markets.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 681-686. DOI: 10.5220/0006250506810686

@conference{icaart17,
author={Svitlana Galeshchuk and Sumitra Mukherjee},
title={Deep Learning for Predictions in Emerging Currency Markets},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={681-686},
publisher={ScitePress},
organization={INSTICC},
doi={10.5220/0006250506810686},
isbn={978-989-758-220-2},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Deep Learning for Predictions in Emerging Currency Markets
SN - 978-989-758-220-2
AU - Galeshchuk S.
AU - Mukherjee S.
PY - 2017
SP - 681
EP - 686
DO - 10.5220/0006250506810686

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