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Recent advancements in large language models and multiagent large language model based systems show that these technologies can be applied to a large number of problems. They can automate complex tasks and perform advanced analyses that would take an expert a significant amount of time. This article describes a multiagent large language model (LLM) based platform for investment advisory in the energy natural resources sector. The system integrates multiple types of investment analyses e.g. technical analysis, fundamental analysis, sentiment analysis and stock price prediction. The approach of integrating multiple types of analyses in one system allows the investor to save significant amount of time on analyzing potential investments.
Rocznik
Tom
Strony
11--18
Opis fizyczny
Bibliogr. 33 poz., tab., rys.
Twórcy
autor
- Warsaw University of Technology
autor
- Warsaw University of Technology
Bibliografia
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- [13] M. Wawer, J. A. Chudziak, and E. Niewiadomska-Szynkiewicz, “Large language models and the elliott wave principle: A multi-agent deep learning approach to big data analysis in financial markets,” Applied Sciences, vol. 14, no. 24, 2024.
- [14] J. J. Murphy, Technical Analysis of the Financial Markets: A Compre-hensive Guide to Trading Methods and Applications. New York Institute of Finance, 1999.
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- [20] D. Araci, “Finbert: Financial sentiment analysis with pre-trained language models,” 2019. [Online]. Available: https://arxiv.org/abs/1908.10063
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- [28] X. Li, S. Wang, S. Zeng, Y. Wu, and Y. Yang, “A survey on llm-based multi-agent systems: workflow, infrastructure, and challenges,” Vicinagearth, vol. 1, no. 1, 2024.
- [29] J. A. Chudziak and M. Wawer, “Elliottagents: A natural language-driven multi-agent system for stock market analysis and prediction,” in Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation, Tokyo, Japan, 2024.
- [30] V. Ekambaram, A. Jati, P. Dayama, S. Mukherjee, N. H. Nguyen, W. M. Gifford, C. Reddy, and J. Kalagnanam, “Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series,” 2024. [Online]. Available: https://arxiv.org/abs/2401.03955
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-814e65a8-7b6d-4059-97d2-7c7d97f8963e
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