Year: 2026
Type: article
Abstract: This study investigates the use of financial sentiment analysis (FSA) to build trading strategies. We evaluate tweets from 25 large-cap stocks with three linguistic models (BERTweet, RoBERTa, FinBERT), aggregate daily sentiment, and transform it into algorithmic trading signals. The strategies are compared with different buy-and-hold strategies. The results show that sentiment-based portfolios outperform the benchmarks. Despite near-random directional accuracy, the signals capture large movements that improve risk-adjusted performance. Our results highlight the gap between classification metrics and economic value, demonstrating that sentiment can generate alpha under realistic conditions and motivating application-driven evaluation of natural language processing in finance.
Source: Cogent Economics & Finance
Authors: Alberto Burchi, Leonardo Regni
Institution: University of Perugia
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,165
OpenAlex ID: W7168475382