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Authors: Marius Kinderis ; Marija Bezbradica and Martin Crane

Affiliation: Dublin City University, Ireland

Keyword(s): Bitcoin, Blockchain, Sentiment Analysis, NLP.

Abstract: Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract meaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted huge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed in this project involves using data mining techniques: mining text documents such as news articles and tweets try to infer the relationship between information contained in such items and cryptocurrency price direction. The Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model which comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success of the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings reported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations when compared to other existing models in the market. (More)

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Paper citation in several formats:
Kinderis, M.; Bezbradica, M. and Crane, M. (2018). Bitcoin Currency Fluctuation. In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-297-4; ISSN 2184-5034, SciTePress, pages 31-41. DOI: 10.5220/0006794000310041

@conference{complexis18,
author={Marius Kinderis. and Marija Bezbradica. and Martin Crane.},
title={Bitcoin Currency Fluctuation},
booktitle={Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2018},
pages={31-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006794000310041},
isbn={978-989-758-297-4},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - Bitcoin Currency Fluctuation
SN - 978-989-758-297-4
IS - 2184-5034
AU - Kinderis, M.
AU - Bezbradica, M.
AU - Crane, M.
PY - 2018
SP - 31
EP - 41
DO - 10.5220/0006794000310041
PB - SciTePress

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