Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach
This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and...
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MDPI AG
2024-10-01
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| author | Olamilekan Shobayo Sidikat Adeyemi-Longe Olusogo Popoola Bayode Ogunleye |
| author_facet | Olamilekan Shobayo Sidikat Adeyemi-Longe Olusogo Popoola Bayode Ogunleye |
| author_sort | Olamilekan Shobayo |
| collection | DOAJ |
| description | This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The 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 resource-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 the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics. |
| format | Article |
| id | doaj-art-b4502fd7a00f45e389992b3223fb54c7 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-b4502fd7a00f45e389992b3223fb54c72024-11-26T17:51:09ZengMDPI AGBig Data and Cognitive Computing2504-22892024-10-0181114310.3390/bdcc8110143Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven ApproachOlamilekan Shobayo0Sidikat Adeyemi-Longe1Olusogo Popoola2Bayode Ogunleye3School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UKSchool of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UKSchool of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UKDepartment of Computing & Mathematics, University of Brighton, Brighton BN2 4GJ, UKThis study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The 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 resource-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 the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics.https://www.mdpi.com/2504-2289/8/11/143FinBERT modellogistic regressionFinBERTOptunatime series cross-validation |
| spellingShingle | Olamilekan Shobayo Sidikat Adeyemi-Longe Olusogo Popoola Bayode Ogunleye Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach Big Data and Cognitive Computing FinBERT model logistic regression FinBERT Optuna time series cross-validation |
| title | Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach |
| title_full | Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach |
| title_fullStr | Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach |
| title_full_unstemmed | Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach |
| title_short | Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach |
| title_sort | innovative sentiment analysis and prediction of stock price using finbert gpt 4 and logistic regression a data driven approach |
| topic | FinBERT model logistic regression FinBERT Optuna time series cross-validation |
| url | https://www.mdpi.com/2504-2289/8/11/143 |
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