Earticle

In this paper, a model combined with explanatory artificial intelligence (xAI) models was presented to secure the reliability of machine learning-based sentiment analysis and prediction. The applicability of the proposed model was tested and described using the IMDB dataset. This approach has an advantage in that it can explain how the data affects the prediction results of the model from various perspectives. In various applications of sentiment analysis such as recommendation system, emotion analysis through facial expression recognition, and opinion analysis, it is possible to gain trust from users of the system by presenting more specific and evidence-based analysis results to users.

In this study we explore the integration of Large Language Models (LLMs), specifically GPT-4o-mini, with traditional stock market technical indicators—MACD, RSI, and Bollinger Bands—to enhance stock market prediction and decision-making frameworks. By combining quantitative analysis with qualitative insights generated by LLMs, this research demonstrates how artificial intelligence can improve the interpretability and predictive accuracy of technical trading strategies. Historical data for Tesla and Palantir, spanning six months, was analyzed with indicators calculated to identify market trends and anomalies. LLM integration provided contextual narratives that complemented technical signals, enhancing interpretability for investors. The performance evaluation revealed significant improvements in risk-adjusted returns, alpha generation, and prediction accuracy when LLM insights were incorporated into trading strategies. Key contributions include a novel methodology for structuring indicator outputs for LLM analysis, scalability across diverse stocks, and the potential for democratizing access to advanced financial analytics. Challenges such as computational complexity, data sensitivity, and the dynamic nature of financial markets are discussed, alongside opportunities for real-time adaptive models and expanded indicator integration. This research highlights the transformative potential of LLM-augmented technical analysis and offers a foundation for future innovations in AI-driven financial decision-making.