Browsing by Subject "Market reaction"

Now showing 1 - 2 of 2
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    Dividend announcement and the value of sentiment analysis
    (Taylor & Francis Group, 2024-02-26) Álvarez Díez, Susana; Baixauli Soler, Juan Samuel; Kondratenko, Anna; Lozano Reina, Gabriel; Organización de Empresas y Finanzas; Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Organización de Empresas y Finanzas
    Payout policy constitutes one of the most important corporate financial decisions since dividends are essential factors in determining a firm’s value. A dividend announcement generates a market signal which translates into changes in stock returns, impacting short-term price fluctuations and producing abnormal returns. The sample consists of 394 companies listed on the S&P500 index, from which 1574 dividend announcements and 7222 news items are derived during the years 2022–2023. News pieces are obtained from 58 specialized sources, and ChatGPT is used to automate the sentiment extracted from them. Using sentiment analysis, this paper shows the key role played by sentiments derived from financial news posted just after dividend announcements in predicting market reaction and helping investors to select optimal investment strategies. This paper contributes to the current literature, highlighting the influence that sentiments have on determining stock market returns.
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    Intraday stock prediction using sentiment analysis: evidence from dividend announcements
    (Taylor and Francis Group, Routledge, 2025-07-31) Álvarez-Diez, Susana; Baixauli Soler, J. Samuel; Kondratenko, Anna; Lozano Reina, Gabriel; Organización de Empresas y Finanzas
    This study explores whether sentiment extracted from financial news using large language models (LLMs) can predict abnormal intraday stock returns following dividend announcements. Drawing on 4,682 news items linked to 1,258 announcements from 394 S&P 500 companies (January 2023–January 2024), we use ChatGPT to extract sentiment polarity scores and we apply different models to forecast cumulative abnormal returns (CARs) in 30-minute intervals. Our findings reveal that sentiment – especially when captured immediately after news releases – has significant predictive power over intraday price movements. Strategies based on ChatGPT-derived sentiment consistently outperform benchmark models, particularly within the first two hours of trading. These results remain robust across alternative specifications and placebo tests, highlighting the value of LLMs for real-time market prediction. This research advances the literature on sentiment analysis and behavioral finance by linking emotion-driven news interpretation to high-frequency trading performance.