Summary of Innovative Sentiment Analysis and Prediction Of Stock Price Using Finbert, Gpt-4 and Logistic Regression: a Data-driven Approach, by Olamilekan Shobayo et al.
Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach
by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistical Finance (q-fin.ST); Applications (stat.AP); Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study compares the performance of three AI models – Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generative 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. The aim is to classify market sentiment, generate sentiment scores, and predict market price movements. The models are assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results show that Logistic Regression outperformed FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The study highlights the strengths and limitations of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares special AI models to see which one works best at predicting how people feel about the stock market based on news articles. They used three different models: FinBERT, GPT-4, and Logistic Regression. The goal is to figure out what’s going to happen with the stock market in the future. The models were tested using special metrics like accuracy and precision. The results showed that one model, called Logistic Regression, was the best at making predictions. This study helps us understand how AI can be used to analyze complex financial data. |
Keywords
» Artificial intelligence » Auc » Encoder » F1 score » Gpt » Logistic regression » Precision » Recall » Transformer
