Predicting cryptocurrency price bubbles using social media data and epidemic modelling
| Autor: | Ross C. Phillips, Denise Gorse |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Cryptocurrency
Buy and hold 050208 finance Computer science 05 social sciences 02 engineering and technology Data modeling Identification (information) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing Trading strategy Social media Time series Hidden Markov model |
| Zdroj: | SSCI |
| DOI: | 10.1109/ssci.2017.8280809 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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