Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis

• URL: http://arxiv.org/abs/2507.03350v1
• Date: Fri, 04 Jul 2025 07:32:59 GMT
• Title: Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis
• Authors: Elvys Linhares Pontes, Carlos-Emiliano González-Gallardo, Georgeta Bordea, José G. Moreno, Mohamed Ben Jannet, Yuxuan Zhao, Antoine Doucet,
• Abstract summary: This study bridges gap by evaluating sentiment-based trading strategies for generating positive alpha.<n>We use sentiment predictions from three models (two classification and one regression) applied to news articles on Dow Jones 30 stocks, comparing them to the benchmark Buy&Hold strategy.<n>Results show all models produced positive returns, with the regression model achieving the highest return of 50.63% over 28 months.
• Score: 12.070866618696483
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Sentiment analysis, widely used in product reviews, also impacts financial markets by influencing asset prices through microblogs and news articles. Despite research in sentiment-driven finance, many studies focus on sentence-level classification, overlooking its practical application in trading. This study bridges that gap by evaluating sentiment-based trading strategies for generating positive alpha. We conduct a backtesting analysis using sentiment predictions from three models (two classification and one regression) applied to news articles on Dow Jones 30 stocks, comparing them to the benchmark Buy&Hold strategy. Results show all models produced positive returns, with the regression model achieving the highest return of 50.63% over 28 months, outperforming the benchmark Buy&Hold strategy. This highlights the potential of sentiment in enhancing investment strategies and financial decision-making.

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