Version 1
: Received: 13 September 2024 / Approved: 13 September 2024 / Online: 13 September 2024 (16:56:12 CEST)
How to cite:
Shobayo, O.; Adeyemi-longe, S.; Popoola, O.; Ogunleye, B. Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4, and Logistic Regression: A Data-Driven Approach. Preprints2024, 2024091089. https://doi.org/10.20944/preprints202409.1089.v1
Shobayo, O.; Adeyemi-longe, S.; Popoola, O.; Ogunleye, B. Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4, and Logistic Regression: A Data-Driven Approach. Preprints 2024, 2024091089. https://doi.org/10.20944/preprints202409.1089.v1
Shobayo, O.; Adeyemi-longe, S.; Popoola, O.; Ogunleye, B. Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4, and Logistic Regression: A Data-Driven Approach. Preprints2024, 2024091089. https://doi.org/10.20944/preprints202409.1089.v1
APA Style
Shobayo, O., Adeyemi-longe, S., Popoola, O., & Ogunleye, B. (2024). Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4, and Logistic Regression: A Data-Driven Approach. Preprints. https://doi.org/10.20944/preprints202409.1089.v1
Chicago/Turabian Style
Shobayo, O., Olusogo Popoola and Bayode Ogunleye. 2024 "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4, and Logistic Regression: A Data-Driven Approach" Preprints. https://doi.org/10.20944/preprints202409.1089.v1
Abstract
This study explores the comparative performance of FinBERT, GPT-4, and Logistic Regression for sentiment analysis and stock index prediction using the NGX All-Share Index dataset. By leveraging advanced language models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment score, and predict market price movements. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed both FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was computationally demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of these approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 showing promise for future exploration.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
