How News Headlines Affect Cryptocurrency Prices: A Case Study on Bitcoin and Ethereum

Abstract

This thesis examines whether curated news headlines are systematically associated with Bitcoin and Ethereum returns in the sample. A mixed-source corpus compiled from CoinDesk and the historical CryptoPanic API is scored with two domain models, FinBERT and CryptoBERT, applied to the same headlines to isolate model effects from data effects. Daily sentiment indices are constructed after cleaning, deduplication, and UTC alignment with prices from Investing.com. Associations are evaluated at three fixed horizons, t = 0, 7 D, and 30 D, with a limited t + 1 check for forecast evaluation only. Estimation uses ordinary least squares with Newey West heteroskedasticity and autocorrelation consistent standard errors. For weekly and monthly windows, overlap is acknowledged; confirmatory non-overlapping specifications with a simple market factor and realized-volatility control are reported in the appendix. Results show clear horizon and asset differences. For Bitcoin, FinBERT sentiment is positively associated with same-day returns, while effects fade at 7 D and 30 D; CryptoBERT produces a smaller same-day association. For Ethereum, both models yield positive and statistically significant associations at 7 D and 30 D, with weak or insignificant same-day links. No specification produces a reliable t + 1 effect. The evidence is strictly confirmatory and does not claim causality or tradable prediction. The contribution is to document, on a single curated headline corpus and under harmonized methods, that model choice and asset differences matter for the timing of sentiment–return associations. The findings inform monitoring and risk governance by identifying when sentiment shocks are most likely to appear in prices.