Earticle

초록

영어

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.

목차

Abstract
1. Introduction
2. Methodology
2.1 Data Collection
2.2 Technical Indicator Calculations
2.3 LLM Integration
2.4 Pipeline Design
3. Experiments
3.1 Indicator-Specific Analysis
3.2 LLM-Generated Insights
3.3 Performance Metrics
4. Discussion
4.1 Interpretability
4.2 Limitations
4.3 Implications
5. Conclusion
5.1 Summary of Findings
5.2 Contributions
5.3 Future Directions
References

키워드

저자

• ByungJoo Kim [ Professor, Department of Electrical and Electronics Engineering, Youngsan University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380