Articles
| Open Access |
DOI: Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions
Md Tarake Siddique , Master of Science in Information Technology, Washington University of Science and Technology, USA Sakib Salam Jamee , Department of Management Information Systems, University of Pittsburgh, PA, USA Ashadujjaman Sajal , Department of Management Science and Quantitative Methods, Gannon University, USA Sanjida Nowshin Mou , Department of Management Science and Quantitative Methods, Gannon University, USA Md Rayhan Hassan Mahin , Department of Computer Science, Monroe University, New Rochelle, USA Md Omar Obaid , Department of Business Analytics, California State Polytechnic University Pomona, CA, USA Md Refat Hossain , Master of Business Administration, Westcliff University, USA Mahabub Hasan , Master’s In Information Systems, Touro University, New York, USA MD Sajedul Karim Chy , Department of Business Administration, Washington University of Science and Technology, USAAbstract
This study explores the use of Large Language Models (LLMs) for automating investment strategies through sentiment analysis of financial news, social media, and market data. By fine-tuning models like GPT-3 on financial datasets, sentiment indicators are extracted and integrated with traditional machine learning algorithms to predict stock price movements. A comparative analysis of various models, including LLM-based, traditional machine learning models, and hybrid approaches, was conducted. The results reveal that the hybrid model, combining LLM-generated sentiment with machine learning algorithms, outperforms other models in terms of both prediction accuracy and financial performance. The hybrid approach achieved an accuracy of 77.4%, cumulative returns of 17.2%, and a Sharpe ratio of 1.20, demonstrating its potential for real-world trading applications. These findings highlight the importance of sentiment data in enhancing market predictions and provide a promising framework for automating investment strategies. However, challenges such as ambiguity in sentiment classification and the need for model adaptation to changing market conditions remain. Future research should focus on improving sentiment analysis accuracy and incorporating reinforcement learning for real-time trading.
Keywords
Large Language Models (LLMs), sentiment analysis, financial markets, automated investment strategies, hybrid models
References
Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTIC ACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.
Rahman, M. M., Akhi, S. S., Hossain, S., Ayub, M. I., Siddique, M. T., Nath, A., ... & Hassan, M. M. (2024). EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY. The American Journal of Engineering and Technology, 6(12), 68-83.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.
Das, P., Pervin, T., Bhattacharjee, B., Karim, M. R., Sultana, N., Khan, M. S., ... & Kamruzzaman, F. N. U. (2024). OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(12), 163-177.
Hossain, M. N., Hossain, S., Nath, A., Nath, P. C., Ayub, M. I., Hassan, M. M., ... & Rasel, M. (2024). ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS. American Research Index Library, 23-35.
Rishad, S. S. I., Shakil, F., Tisha, S. A., Afrin, S., Hassan, M. M., Choudhury, M. Z. M. E., & Rahman, N. (2025). LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS. American Research Index Library, 6-25.
Uddin, A., Pabel, M. A. H., Alam, M. I., KAMRUZZAMAN, F., Haque, M. S. U., Hosen, M. M., ... & Ghosh, S. K. (2025). Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques. The American Journal of Management and Economics Innovations, 7(01), 5-20.
Ahmed, M. P., Das, A. C., Akter, P., Mou, S. N., Tisha, S. A., Shakil, F., ... & Ahmed, A. (2024). HARNESSING MACHINE LEARNING MODELS FOR ACCURATE CUSTOMER LIFETIME VALUE PREDICTION: A COMPARATIVE STUDY IN MODERN BUSINESS ANALYTICS. American Research Index Library, 06-22.
Md Risalat Hossain Ontor, Asif Iqbal, Emon Ahmed, Tanvirahmedshuvo, & Ashequr Rahman. (2024). LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS’ PERFORMANCE: A MACHINE LEARNING APPROACH. International Journal of Computer Science & Information System, 9(11), 45–56. https://doi.org/10.55640/ijcsis/Volume09Issue11-05
Rahman, A., Iqbal, A., Ahmed, E., & Ontor, M. R. H. (2024). PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT. International journal of business and management sciences, 4(12), 18-32.
Iqbal, A., Ahmed, E., Rahman, A., & Ontor, M. R. H. (2024). ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(11), 78-91.
Nguyen, Q. G., Nguyen, L. H., Hosen, M. M., Rasel, M., Shorna, J. F., Mia, M. S., & Khan, S. I. (2025). Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction. The American Journal of Applied sciences, 7(01), 21-30.
Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing,Management and Economics Journal, 4(12), 66-83.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Nath, F., Chowdhury, M. O. S., & Rhaman, M. M. (2023). Navigating produced water sustainability in the oil and gas sector: A Critical review of reuse challenges, treatment technologies, and prospects ahead. Water, 15(23), 4088.
Chowdhury, O. S., & Baksh, A. A. (2017). IMPACT OF OIL SPILLAGE ON AGRICULTURAL PRODUCTION. Journal of Nature Science & Sustainable Technology, 11(2).
Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shinn, N., & Wu, J. (2020). Language models are few-shot learners. Proceedings of NeurIPS 2020.
Chen, H. (2013). Financial sentiment analysis: A review. Journal of Computational Finance, 17(2), 35-67. https://doi.org/10.21314/JCF.2013.235
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. https://doi.org/10.1111/j.1540-6261.1970.tb00518.x
Feng, X. (2019). Predicting stock market movements using deep learning. Journal of Financial Data Science, 2(3), 7-29.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jiang, H., Zhang, Z., & Li, X. (2017). Hybrid sentiment-based stock prediction using machine learning algorithms. Journal of Finance and Data Science, 3(4), 56-71.
Li, J., & Tetreault, J. (2021). Sentiment analysis of financial news with large language models. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, 2021, 343-352.
Mnih, V., Silver, D., Kavukcuoglu, K., & others. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1-2), 1-135. https://doi.org/10.1561/1500000011
Ruder, S. (2019). A survey of sentiment analysis and its applications. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 6-15.
Shiller, R. J. (2000). Irrational exuberance. Princeton University Press.
Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.
Shakil, F., Afrin, S., Al Mamun, A., Alam, M. K., Hasan, M. T., Vansiya, J., & Chandi, A. (2025). HYBRID MULTI-MODAL DETECTION FRAMEWORK FOR ADVANCED PERSISTENT THREATS IN CORPORATE NETWORKS USING MACHINE LEARNING AND DEEP LEARNING. American Research Index Library, 6-20.
Rishad, S. S. I., Shakil, F., Tisha, S. A., Afrin, S., Hassan, M. M., Choudhury, M. Z. M. E., & Rahman, N. (2025). LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS. American Research Index Library, 6-25.
Das, A. C., Rishad, S. S. I., Akter, P., Tisha, S. A., Afrin, S., Shakil, F., ... & Rahman, M. M. (2024). ENHANCING BLOCKCHAIN SECURITY WITH MACHINE LEARNING: A COMPREHENSIVE STUDY OF ALGORITHMS AND APPLICATIONS. The American Journal of Engineering and Technology, 6(12), 150-162.
Al-Imran, M., Ayon, E. H., Islam, M. R., Mahmud, F., Akter, S., Alam, M. K., ... & Aziz, M. M. (2024). TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS. The American Journal of Engineering and Technology, 6(11), 20-32.
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Copyright (c) 2025 Md Tarake Siddique, Sakib Salam Jamee, Ashadujjaman Sajal, Sanjida Nowshin Mou, Md Rayhan Hassan Mahin, Md Omar Obaid, Md Refat Hossain, Mahabub Hasan
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