Decoding the 'Trump Trade': A FinBERT-Based Sentiment Analysis of Cryptocurrency Market Reactions
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Haris HAXHIMEHMETI Faculty of Contemporary Sciences and Technologies, South East, European University Tetovo, North Macedonia
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Adrian BESIMI Faculty of Contemporary Sciences and Technologies, South East, European University Tetovo, North Macedonia
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.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 25th of January, 2026; Revised 12th of February, 2026; Accepted 8th of March, 2026; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
Haxhimehmeti, H., & Besimi, A. (2026). Decoding the 'Trump Trade': A FinBERT-Based Sentiment Analysis of Cryptocurrency Market Reactions. Journal of Applied Economic Sciences, Volume XXI, Spring, 2(92), 427 – 446. https://doi.org/10.57017/jaes.v21.2(92).03
Conflict of Interest Statement: The authors declare that they have no conflicts of interest regarding the publication of this article.
Acknowledgements: The authors would like to thank the anonymous reviewers and the editorial team for their constructive comments and suggestions, which helped improve the clarity and academic quality of this manuscript.
Data Availability Statement: The data used in this study are publicly accessible via Yahoo Finance, CoinGecko, and CoinDesk. Sentiment analysis was based on news data collected from open financial news sources. Python scripts and processed datasets can be made available upon reasonable request to the corresponding author; https://coinmarketcap.com/currencies/bitcoin/historical-data/; https://coinmarketcap.com/currencies/ethereum/historical-data/; https://coinmarketcap.com/currencies/dogecoin/historical-data/; News articles crawled from: https://www.coindesk.com/election-2024-coverage-news; https://finance.yahoo.com/rss; https://finance.yahoo.com/rss/headline?s=BTC-USD; https://finance. yahoo.com/rss/headline?s=BTC.
Ethical Approval Statement: This research did not involve human participants, personal data, or confidential information. All data used in the analysis were obtained from publicly accessible sources. Therefore, ethical approval was not required for this study.
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