Your Sentiment Matters: A Machine Learning Approach for Predicting Regime Changes in the Cryptocurrency Market
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2023-01-03
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920
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Research suggests that a significant number of those investing in cryptocurrencies do not follow what we might call rational, profit-maximizing behavior. We also know that with the progressive lowering of entry barriers to online trading platforms, an increasing number of inexperienced investors are investing in cryptocurrencies. Increasingly, the behavior of investors contradicts the predictions made by traditional financial models and challenges the assumptions on which such models have previously relied when anticipating returns on cryptocurrency investments. To overcome this issue we develop a random forest model which we train with features stemming from a sentiment analysis performed on data generated by cryptocurrency enthusiasts using Twitter, Google Trends, and Reddit. Our findings show that such features have an important role to play in capturing the behavior of cryptocurrency investors and increase our model’s ability to anticipate regime changes in the cryptocurrency market. Our model outperforms the predictive ability of the Log-Periodic Power Law model—currently, the model most widely-used to predict regime changes in financial markets. These results imply that scholars and practitioners aiming to understand and predict the development of cryptocurrency markets stand to benefit from analyzing social media data generated by cryptocurrency enthusiasts.
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Data, Text, and Web Mining for Business Analytics, bitcoin, cyptocurrencies, lppl model, machine learning, sentiment analysis
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10
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Proceedings of the 56th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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