Combining Trading, Sentiment, Search Interest, and Hash Rate to Forecast Bitcoin Volatility

Summary

This study examines whether information beyond price history can help forecast Bitcoin–dollar volatility. Its proposed CoMForE system combines historical trading data with sentiment from related tweets, search-volume measures of public interest, and blockchain hash-rate data. It uses an AdaBoost–LSTM ensemble and also aims to predict changes in the distribution of cryptocurrency values, with investment decisions as a potential use.

The document reports experiments in which combining these data sources performed better than relying on trading data alone, and claims a 19.29% improvement over existing forecasting approaches. It does not specify the evaluation period, benchmark details, forecast horizon, or whether the reported improvement is out of sample across different market regimes. Those omissions make it difficult to assess how broadly the result applies or whether the model would remain useful after accounting for data availability and trading costs.

Key ideas

• The model combines market history with tweet sentiment, search interest, and blockchain hash rate.
• CoMForE uses an AdaBoost–LSTM ensemble for cryptocurrency volatility forecasting.
• The system also targets fluctuations in the distribution of cryptocurrency values.
• Reported experiments favor multimodal inputs over trading data alone.
• The brief description omits evaluation details needed to judge robustness and practical trading value.

Tags

# Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility


# Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility









Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.