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Forecasting Brazilian Stock Market Using Sentiment Indices from Textual Data, Chat-GPT-Based and Technical Indicators

Author

Listed:
  • Diego Pitta Jesus

    (Rural Federal University of Pernambuco - UFRPE)

  • Elvira Helena Oliveira Medeiros

    (Federal University of Juiz de Fora - UFJF)

  • Lucas Lúcio Godeiro

    (Federal University Rural Semi-Arid - UFERSA)

  • Andressa Lemes Proque

    (Federal University of São João del-Rei - UFSJ)

Abstract

The rapid advancement of artificial intelligence, exemplified by tools such as Chat-GPT, has significantly transformed the landscape of stock market analysis. This paper aims to leverage these technological developments to predict the daily returns of the Ibovespa by utilizing predictors derived from technical indicators and sentiment indices extracted from textual data and Chat-GPT-generated sentiment indices. Our findings reveal that the Chat-GPT-based sentiment index does not enhance the out-of-sample prediction of Ibovespa returns. Conversely, the sentiment index derived from financial news data, utilizing a time-varying dictionary, demonstrates improved out-of-sample predictive accuracy for the Ibovespa. Notably, the predictor based on the technical indicator Accumulation–Distribution (AD) outperforms the historical average benchmark, establishing itself as the superior forecasting model. This study contributes to the ongoing discourse on the integration of artificial intelligence and traditional financial analysis, offering insights into the efficacy of sentiment indices and technical indicators for forecasting stock market returns in the Brazilian context.

Suggested Citation

  • Diego Pitta Jesus & Elvira Helena Oliveira Medeiros & Lucas Lúcio Godeiro & Andressa Lemes Proque, 2025. "Forecasting Brazilian Stock Market Using Sentiment Indices from Textual Data, Chat-GPT-Based and Technical Indicators," Computational Economics, Springer;Society for Computational Economics, vol. 66(5), pages 3735-3780, November.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:5:d:10.1007_s10614-024-10835-7
    DOI: 10.1007/s10614-024-10835-7
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    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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