用AI分析财经新闻预测股市，传统逻辑回归反胜复杂大模型
Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach
- 结合FinBERT、GPT-4与逻辑回归，从新闻文本提取情绪并预测指数走势
- 逻辑回归准确率达81.83%，ROC AUC达89.76%，优于更复杂的模型
- 揭示大模型在金融预测中的潜力与成本代价，适合追求效率的从业者
本研究对比了前沿AI模型——金融领域双向编码器（FinBERT）、生成式预训练模型GPT-4与传统机器学习模型逻辑回归，在金融新闻数据和尼日利亚证券交易所全指（NGX All-Share Index）标签上的情感分析与股价预测表现。通过自然语言处理技术提取市场情绪，生成情绪评分并预测价格变动。模型评估采用准确率、精确率、召回率、F1分数及ROC AUC。结果表明，逻辑回归以81.83%准确率和89.76% ROC AUC领先于计算开销更大的FinBERT与预设提示的GPT-4；后者准确率仅为54.19%，但展现处理复杂数据的潜力；而FinBERT虽分析更深入，却因资源消耗高导致性能中等。通过Optuna超参数优化与交叉验证提升模型稳健性。研究揭示了不同AI方法在股市预测中的优劣，强调逻辑回归在实际应用中的高效性，同时指出FinBERT与GPT-4作为未来探索工具的价值。
原文摘要 · Abstract (English)
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
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