IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2602.00196.html

Generative AI for Stock Selection

Author

Listed:
  • Keywan Christian Rasekhschaffe

Abstract

We study whether generative AI can automate feature discovery in U.S. equities. Using large language models with retrieval-augmented generation and structured/programmatic prompting, we synthesize economically motivated features from analyst, options, and price-volume data. These features are then used as inputs to a tabular machine-learning model to forecast short-horizon returns. Across multiple datasets, AI-generated features are consistently competitive with baselines, with Sharpe improvements ranging from 14% to 91% depending on dataset and configuration. Retrieval quality is pivotal: better knowledge bases materially improve outcomes. The AI-generated signals are weakly correlated with traditional features, supporting combination. Overall, generative AI can meaningfully augment feature discovery when retrieval quality is controlled, producing interpretable signals while reducing manual engineering effort.

Suggested Citation

  • Keywan Christian Rasekhschaffe, 2026. "Generative AI for Stock Selection," Papers 2602.00196, arXiv.org.
    • Handle: RePEc:arx:papers:2602.00196
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2602.00196
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    2. Keywan Christian Rasekhschaffe & Robert C. Jones, 2019. "Machine Learning for Stock Selection," Financial Analysts Journal, Taylor & Francis Journals, vol. 75(3), pages 70-88, July.
    3. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    4. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    5. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    6. Jeremiah Green & John R. M. Hand & X. Frank Zhang, 2017. "The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4389-4436.
    7. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    8. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    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. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    2. Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    3. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
    4. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    5. Li, Bin & Rossi, Alberto G. & Yan, Xuemin (Sterling) & Zheng, Lingling, 2025. "Machine learning from a “Universe” of signals: The role of feature engineering," Journal of Financial Economics, Elsevier, vol. 172(C).
    6. repec:cam:camjip:2506 is not listed on IDEAS
    7. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    8. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    9. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    10. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble Learning for Sector Rotation: A Gradient-Free Framework," Papers 2304.09947, arXiv.org, revised Nov 2025.
    11. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    12. Allen Yikuan Huang & Zheqi Fan, 2026. "Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI," Papers 2603.14288, arXiv.org, revised Apr 2026.
    13. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    14. Cakici, Nusret & Zaremba, Adam, 2021. "Liquidity and the cross-section of international stock returns," Journal of Banking & Finance, Elsevier, vol. 127(C).
    15. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    16. Karim, Sitara & Shafiullah, Muhammad & Naeem, Muhammad Abubakr, 2024. "When one domino falls, others follow: A machine learning analysis of extreme risk spillovers in developed stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    17. Baba-Yara, Fahiz & Boons, Martijn & Tamoni, Andrea, 2024. "Persistent and transitory components of firm characteristics: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 154(C).
    18. Rizwan Ullah & Muhammad Naveed Jan & Muhammad Tahir, 2025. "Unveiling the optimal factor model in Pakistan: a machine learning approach using support vector regression and extreme gradient boosting algorithms," Future Business Journal, Springer, vol. 11(1), pages 1-20, December.
    19. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
    20. Blanco, Ivan & De Jesus, Miguel & Remesal, Alvaro, 2023. "Overlapping momentum portfolios," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 1-22.
    21. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.

    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:2602.00196. 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.