Abstract -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. Index Terms -cryptocurrency price bubbles, social media data mining, hidden Markov model, trading strategy, epidemic detection.
A cryptocurrency price prediction using news and social media sentiment was first introduced in [69] , followed shortly by the predicting of cryptocurrency price bubbles using social media data and epidemic modeling in REF .
Predicting cryptocurrency price bubbles using social media data and epidemic modelling
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Computer Science