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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
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    Citations

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    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. Sun, Chuanping, 2024. "Factor correlation and the cross section of asset returns: A correlation-robust machine learning approach," Journal of Empirical Finance, Elsevier, vol. 77(C).
    3. Zhang, Junsheng & Peng, Zezhi & Zeng, Yamin & Yang, Haisheng, 2023. "Do big data mutual funds outperform?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    4. 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.
    5. Itay Goldstein, 2023. "Information in Financial Markets and Its Real Effects," Review of Finance, European Finance Association, vol. 27(1), pages 1-32.
    6. Murray Z. Frank & Jing Gao & Keer Yang, 2023. "Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact," Papers 2303.16158, arXiv.org.
    7. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    8. Cao, Sean Shun & Jiang, Wei & Lei, Lijun (Gillian) & Zhou, Qing (Clara), 2024. "Applied AI for finance and accounting: Alternative data and opportunities," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    9. 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

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