IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.00415.html
   My bibliography  Save this paper

MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents

Author

Listed:
  • George Fatouros
  • Kostas Metaxas
  • John Soldatos
  • Manos Karathanassis

Abstract

MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies.

Suggested Citation

  • George Fatouros & Kostas Metaxas & John Soldatos & Manos Karathanassis, 2025. "MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents," Papers 2502.00415, arXiv.org.
    • Handle: RePEc:arx:papers:2502.00415
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.00415
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    2. Frankel, R & Johnson, M & Skinner, DJ, 1999. "An empirical examination of conference calls as a voluntary disclosure medium," Journal of Accounting Research, Wiley Blackwell, vol. 37(1), pages 133-150.
    3. Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
    4. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    5. Spurthi Setty & Harsh Thakkar & Alyssa Lee & Eden Chung & Natan Vidra, 2024. "Improving Retrieval for RAG based Question Answering Models on Financial Documents," Papers 2404.07221, arXiv.org, revised Jul 2024.
    6. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    7. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    8. Jingwei Ni & Julia Bingler & Chiara Colesanti Senni & Mathias Kraus & Glen Gostlow & Tobias Schimanski & Dominik Stammbach & Saeid Vaghefi & Qian Wang & Nicolas Webersinke & Tobias Wekhof & Tingyu Yu , 2023. "chatReport: Democratizing Sustainability Disclosure Analysis through LLM-based Tools," Swiss Finance Institute Research Paper Series 23-111, Swiss Finance Institute.
    9. Thomas R. Cook & Sophia Kazinnik & Anne Lundgaard Hansen & Peter McAdam, 2023. "Evaluating Local Language Models: An Application to Bank Earnings Calls," Research Working Paper RWP 23-12, Federal Reserve Bank of Kansas City.
    10. Yang Li & Yangyang Yu & Haohang Li & Zhi Chen & Khaldoun Khashanah, 2023. "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance," Papers 2309.03736, arXiv.org.
    11. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2024. "Financial Statement Analysis with Large Language Models," Papers 2407.17866, arXiv.org, revised Feb 2025.
    12. Gaurang Sonkavde & Deepak Sudhakar Dharrao & Anupkumar M. Bongale & Sarika T. Deokate & Deepak Doreswamy & Subraya Krishna Bhat, 2023. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications," IJFS, MDPI, vol. 11(3), pages 1-22, July.
    13. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    14. William J. Mayew & Mohan Venkatachalam, 2012. "The Power of Voice: Managerial Affective States and Future Firm Performance," Journal of Finance, American Finance Association, vol. 67(1), pages 1-44, February.
    15. Byeungchun Kwon & Taejin Park & Fernando Perez-Cruz & Phurichai Rungcharoenkitkul, 2024. "Large language models: a primer for economists," BIS Quarterly Review, Bank for International Settlements, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michał Dzieliński & Alexander F. Wagner & Richard J. Zeckhauser, 2017. "Straight Talkers and Vague Talkers: The Effects of Managerial Style in Earnings Conference Calls," NBER Working Papers 23425, National Bureau of Economic Research, Inc.
    2. Kristian D. Allee & Matthew D. Deangelis, 2015. "The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion," Journal of Accounting Research, Wiley Blackwell, vol. 53(2), pages 241-274, May.
    3. S. McKay Price & Michael J. Seiler & Jiancheng Shen, 2017. "Do Investors Infer Vocal Cues from CEOs During Quarterly REIT Conference Calls?," The Journal of Real Estate Finance and Economics, Springer, vol. 54(4), pages 515-557, May.
    4. Devrimi Kaya & Christian Maier & Tobias Böhmer, 2020. "Empirische Kapitalmarktforschung zu Conference Calls: Eine Literaturanalyse [Empirical Capital Market Research on Conference Calls: A Literature Review]," Schmalenbach Journal of Business Research, Springer, vol. 72(2), pages 183-212, June.
    5. Bhagwat, Vineet & Shirley, Sara E. & Stark, Jeffrey R., 2024. "Task-oriented speech and information processing," Journal of Banking & Finance, Elsevier, vol. 161(C).
    6. Borochin, Paul A. & Cicon, James E. & DeLisle, R. Jared & Price, S. McKay, 2018. "The effects of conference call tones on market perceptions of value uncertainty," Journal of Financial Markets, Elsevier, vol. 40(C), pages 75-91.
    7. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    8. Perico Ortiz, Daniel & Schnaubelt, Matthias & Seifert, Oleg, 2023. "A topic modeling perspective on investor uncertainty," FAU Discussion Papers in Economics 04/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Andrew Todd & James Bowden & Yashar Moshfeghi, 2024. "Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
    10. Rennekamp, Kristina M. & Sethuraman, Mani & Steenhoven, Blake A., 2022. "Engagement in earnings conference calls," Journal of Accounting and Economics, Elsevier, vol. 74(1).
    11. Blau, Benjamin M. & DeLisle, Jared R. & Price, S. McKay, 2015. "Do sophisticated investors interpret earnings conference call tone differently than investors at large? Evidence from short sales," Journal of Corporate Finance, Elsevier, vol. 31(C), pages 203-219.
    12. Baochen Yang & Yifang Liu & Yunpeng Su, 2023. "Earnings communication conferences and post‐earnings‐announcement drift: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(2), pages 2145-2185, June.
    13. Kim, Jongkyum & Lim, Jee-Hae & Yoon, Kyunghee, 2022. "How do the content, format, and tone of Twitter-based corporate disclosure vary depending on earnings performance?," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    14. Huang, Xin & Huang, Hao & Yuan, Liang, 2024. "Do firms incur financial restatements? A recognition study based on textual features of key audit matters reports," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    15. Beattie, Vivien, 2014. "Accounting narratives and the narrative turn in accounting research: Issues, theory, methodology, methods and a research framework," The British Accounting Review, Elsevier, vol. 46(2), pages 111-134.
    16. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.
    17. An, Suwei, 2023. "Essays on incentive contracts, M&As, and firm risk," Other publications TiSEM dd97d2f5-1c9d-47c5-ba62-f, Tilburg University, School of Economics and Management.
    18. Renato Camodeca & Alex Almici & Umberto Sagliaschi, 2018. "Sustainability Disclosure in Integrated Reporting: Does It Matter to Investors? A Cheap Talk Approach," Sustainability, MDPI, vol. 10(12), pages 1-34, November.
    19. Shuangyan Li & Guangrui Wang & Yongli Luo, 2022. "Tone of language, financial disclosure, and earnings management: a textual analysis of form 20-F," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    20. José María Liberti & Mitchell A. Petersen, 2018. "Information: Hard and Soft," NBER Working Papers 25075, National Bureau of Economic Research, Inc.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2502.00415. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.