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

Diego Pitta Jesus (), Elvira Helena Oliveira Medeiros, Lucas Lúcio Godeiro and Andressa Lemes Proque
Additional contact information
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

Computational Economics, 2025, vol. 66, issue 5, No 6, 3735-3780

Abstract: 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.

Keywords: Artificial intelligence; Chat-GPT; Sentiment analysis; Technical indicators; Stock market forecasting; Ibovespa (search for similar items in EconPapers)
JEL-codes: C01 C22 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10835-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:5:d:10.1007_s10614-024-10835-7

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10835-7

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().