IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/28800.html
   My bibliography  Save this paper

From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses

Author

Listed:
  • Sean Cao
  • Wei Jiang
  • Junbo L. Wang
  • Baozhong Yang

Abstract

An AI analyst we build to digest corporate financial information, qualitative disclosure and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analyst. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.

Suggested Citation

  • Sean Cao & Wei Jiang & Junbo L. Wang & Baozhong Yang, 2021. "From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses," NBER Working Papers 28800, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28800
    Note: AP CF
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w28800.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zarifhonarvar, Ali, 2023. "Economics of ChatGPT: A Labor Market View on the Occupational Impact of Artificial Intelligence," EconStor Preprints 268826, ZBW - Leibniz Information Centre for Economics.
    2. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
    3. Itay Goldstein, 2023. "Information in Financial Markets and Its Real Effects," Review of Finance, European Finance Association, vol. 27(1), pages 1-32.
    4. Murray Z. Frank & Jing Gao & Keer Yang, 2023. "Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact," Papers 2303.16158, arXiv.org.
    5. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

    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:nbr:nberwo:28800. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.