@inproceedings{linhares-pontes-etal-2025-backtesting,
    title = "Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis",
    author = "Linhares Pontes, Elvys  and
      Gonz{\'a}lez-Gallardo, Carlos-Emiliano  and
      Bordea, Georgeta  and
      G Moreno, Jose  and
      Ben Jannet, Mohamed  and
      Zhao, Yuxuan  and
      Doucet, Antoine",
    editor = "Bechet, Fr{\'e}d{\'e}ric  and
      Chifu, Adrian-Gabriel  and
      Pinel-sauvagnat, Karen  and
      Favre, Benoit  and
      Maes, Eliot  and
      Nurbakova, Diana",
    booktitle = "Actes de la session industrielle de CORIA-TALN 2025",
    month = "6",
    year = "2025",
    address = "Marseille, France",
    publisher = "ATALA {\textbackslash}{\textbackslash}{\&} ARIA",
    url = "https://preview.aclanthology.org/bulk-corrections-2025-11-25/2025.jeptalnrecital-industrielle.2/",
    pages = "17--32",
    language = "fra",
    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."
}