Decoding the 'Trump Trade': A FinBERT-Based Sentiment Analysis of Cryptocurrency Market Reactions
Decoding the 'Trump Trade': A FinBERT-Based Sentiment Analysis of Cryptocurrency Market Reactions

Summary/Abstract: This study decodes the "Trump Trade" phenomenon by investigating the intersection of political sentiment and cryptocurrency market dynamics during the 2024 US presidential election. Focusing on major digital assets, (Bitcoin, Ethereum, and Dogecoin) we employ a dual-methodology approach that integrates Natural Language Processing (NLP) via the FinBERT model with traditional event study analysis and GARCH (1,1) volatility modelling. The analyse of financial news sentiment and price volatility, results show a rapid increase in volatility and positive abnormal returns following the election, correlated with Trump's pro-crypto rhetoric. A comparative analysis with the 2016 and 2020 elections reveals an intensification of the market's sensitivity to political signals, indicating that cryptocurrencies are maturing into assets responsive to policy communication. The study contributes to the growing literature on the intersection of politics and digital finance.

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