TY  - GEN
AV  - public
ID  - discovery10062913
KW  - Science & Technology
KW  -  Technology
KW  -  Computer Science
KW  -  Artificial Intelligence
KW  -  Engineering
KW  -  Electrical & Electronic
KW  -  Computer Science
KW  -  Engineering
KW  -  cryptocurrency price bubbles
KW  -  social media data mining
KW  -  hidden Markov model
KW  -  trading strategy
KW  -  epidemic detection
T3  - IEEE Symposium Series on Computational Intelligence (SSCI)
EP  - 400
SP  - 394
CY  - Honolulu, HI, USA
A1  - Phillips, RC
A1  - Gorse, D
N2  - Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.
PB  - IEEE
Y1  - 2018/02/05/
UR  - https://doi.org/10.1109/SSCI.2017.8280809
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Predicting cryptocurrency price bubbles using social media data and epidemic modelling
ER  -