Bitcoin Currency Fluctuation

Marius Kinderis, Marija Bezbradica, Martin Crane

2018

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.

Paper Citation

in Harvard Style

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 - Volume 1: COMPLEXIS, ISBN 978-989-758-297-4, pages 31-41. DOI: 10.5220/0006794000310041

in Bibtex Style

@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 - Volume 1: COMPLEXIS,},
year={2018},
pages={31-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006794000310041},
isbn={978-989-758-297-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - Bitcoin Currency Fluctuation
SN - 978-989-758-297-4
AU - Kinderis M.
AU - Bezbradica M.
AU - Crane M.
PY - 2018
SP - 31
EP - 41
DO - 10.5220/0006794000310041