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From Man vs. Machine to Man + Machine: The art and AI of stock analyses

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  • Cao, Sean
  • Jiang, Wei
  • Wang, Junbo
  • Yang, Baozhong

Abstract

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

Suggested Citation

  • Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:jfinec:v:160:y:2024:i:c:s0304405x24001338
    DOI: 10.1016/j.jfineco.2024.103910
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    1. Sean Cao Robert H. Smith & Wei Jiang & Baozhong Yang J. Mack Robinson & Alan L Zhang & Tarun Ramadorai, 2023. "How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI," The Review of Financial Studies, Society for Financial Studies, vol. 36(9), pages 3603-3642.
    2. Hirshleifer, David & Levi, Yaron & Lourie, Ben & Teoh, Siew Hong, 2019. "Decision fatigue and heuristic analyst forecasts," Journal of Financial Economics, Elsevier, vol. 133(1), pages 83-98.
    3. Pollet, Joshua M. & Wilson, Mungo, 2010. "Average correlation and stock market returns," Journal of Financial Economics, Elsevier, vol. 96(3), pages 364-380, June.
    4. Doyle, Jeffrey T. & Jennings, Jared N. & Soliman, Mark T., 2013. "Do managers define non-GAAP earnings to meet or beat analyst forecasts?," Journal of Accounting and Economics, Elsevier, vol. 56(1), pages 40-56.
    5. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022. "Artificial Intelligence and Jobs: Evidence from Online Vacancies," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
    6. Basu, Sanjoy, 1983. "The relationship between earnings' yield, market value and return for NYSE common stocks : Further evidence," Journal of Financial Economics, Elsevier, vol. 12(1), pages 129-156, June.
    7. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    8. Amit Goyal & Ivo Welch & Athanasse Zafirov, 2024. "A Comprehensive 2022 Look at the Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 37(11), pages 3490-3557.
    9. Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
    10. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    11. Richardson, Scott A. & Sloan, Richard G. & Soliman, Mark T. & Tuna, Irem, 2005. "Accrual reliability, earnings persistence and stock prices," Journal of Accounting and Economics, Elsevier, vol. 39(3), pages 437-485, September.
    12. Li, Xi, 2005. "The persistence of relative performance in stock recommendations of sell-side financial analysts," Journal of Accounting and Economics, Elsevier, vol. 40(1-3), pages 129-152, December.
    13. De Bondt, Werner F M & Thaler, Richard H, 1990. "Do Security Analysts Overreact?," American Economic Review, American Economic Association, vol. 80(2), pages 52-57, May.
    14. Leonid Kogan & Dimitris Papanikolaou & Amit Seru & Noah Stoffman, 2017. "Technological Innovation, Resource Allocation, and Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 665-712.
    15. Abarbanell, Jeffery S., 1991. "Do analysts' earnings forecasts incorporate information in prior stock price changes?," Journal of Accounting and Economics, Elsevier, vol. 14(2), pages 147-165, June.
    16. Chen, Qi & Francis, Jennifer & Jiang, Wei, 2005. "Investor learning about analyst predictive ability," Journal of Accounting and Economics, Elsevier, vol. 39(1), pages 3-24, February.
    17. Lamont, Owen & Polk, Christopher & Saa-Requejo, Jesus, 2001. "Financial Constraints and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 529-554.
    18. Dilip Mookherjee & Debraj Ray, 2022. "Growth, Automation and the Long-Run Share of Labor," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 46, pages 1-26, October.
    19. Chen, Yong & Eaton, Gregory W. & Paye, Bradley S., 2018. "Micro(structure) before macro? The predictive power of aggregate illiquidity for stock returns and economic activity," Journal of Financial Economics, Elsevier, vol. 130(1), pages 48-73.
    20. Kathleen Weiss Hanley & Gerard Hoberg, 2019. "Dynamic Interpretation of Emerging Risks in the Financial Sector," The Review of Financial Studies, Society for Financial Studies, vol. 32(12), pages 4543-4603.
    21. Asquith, Paul & Mikhail, Michael B. & Au, Andrea S., 2005. "Information content of equity analyst reports," Journal of Financial Economics, Elsevier, vol. 75(2), pages 245-282, February.
    22. Mark A Chen & Qinxi Wu & Baozhong Yang, 2019. "How Valuable Is FinTech Innovation?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
    23. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    24. repec:bla:jfinan:v:43:y:1988:i:2:p:507-28 is not listed on IDEAS
    25. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    26. Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3213-3225, National Bureau of Economic Research, Inc.
    27. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    28. Feng Li & Russell Lundholm & Michael Minnis, 2013. "A Measure of Competition Based on 10‐K Filings," Journal of Accounting Research, Wiley Blackwell, vol. 51(2), pages 399-436, May.
    29. 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.
    30. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    31. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    32. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    33. Loughran, Tim & Wellman, Jay W., 2011. "New Evidence on the Relation between the Enterprise Multiple and Average Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(6), pages 1629-1650, December.
    34. Jacob Boudoukh & Roni Michaely & Matthew Richardson & Michael R. Roberts, 2007. "On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing," Journal of Finance, American Finance Association, vol. 62(2), pages 877-915, April.
    35. Andrew Prahl & Lyn Van Swol, 2017. "Understanding algorithm aversion: When is advice from automation discounted?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 691-702, September.
    36. Michaely, Roni & Womack, Kent L, 1999. "Conflict of Interest and the Credibility of Underwriter Analyst Recommendations," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 653-686.
    37. Andrea L. Eisfeldt & Dimitris Papanikolaou, 2013. "Organization Capital and the Cross-Section of Expected Returns," Journal of Finance, American Finance Association, vol. 68(4), pages 1365-1406, August.
    38. Simona Abis & Laura Veldkamp, 2024. "The Changing Economics of Knowledge Production," The Review of Financial Studies, Society for Financial Studies, vol. 37(1), pages 89-118.
    39. Tim de Silva & David Thesmar, 2024. "Noise in Expectations: Evidence from Analyst Forecasts," The Review of Financial Studies, Society for Financial Studies, vol. 37(5), pages 1494-1537.
    40. Abarbanell, Jeffery & Lehavy, Reuven, 2003. "Biased forecasts or biased earnings? The role of reported earnings in explaining apparent bias and over/underreaction in analysts' earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 105-146, December.
    41. Alan Crane & Kevin Crotty, 2020. "How Skilled Are Security Analysts?," Journal of Finance, American Finance Association, vol. 75(3), pages 1629-1675, June.
    42. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    43. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W, 1994. "Contrarian Investment, Extrapolation, and Risk," Journal of Finance, American Finance Association, vol. 49(5), pages 1541-1578, December.
    44. Jones, Christopher S. & Tuzel, Selale, 2013. "Inventory investment and the cost of capital," Journal of Financial Economics, Elsevier, vol. 107(3), pages 557-579.
    45. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    46. repec:cup:jfinqa:v:46:y:2011:i:06:p:1629-1650_00 is not listed on IDEAS
    47. Antonio Falato & Dalida Kadyrzhanova & Jae Sim & Roberto Steri, 2022. "Rising Intangible Capital, Shrinking Debt Capacity, and the U.S. Corporate Savings Glut," Journal of Finance, American Finance Association, vol. 77(5), pages 2799-2852, October.
    48. Novy-Marx, Robert, 2013. "The other side of value: The gross profitability premium," Journal of Financial Economics, Elsevier, vol. 108(1), pages 1-28.
    49. 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.
    50. Grennan, Jillian & Michaely, Roni, 2021. "FinTechs and the Market for Financial Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(6), pages 1877-1907, September.
    51. repec:bla:jfinan:v:58:y:2003:i:5:p:1933-1968 is not listed on IDEAS
    52. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    53. Kai Cao & Haifeng You, 2024. "Fundamental Analysis via Machine Learning," Financial Analysts Journal, Taylor & Francis Journals, vol. 80(2), pages 74-98, April.
    54. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    55. Peters, Ryan H. & Taylor, Lucian A., 2017. "Intangible capital and the investment-q relation," Journal of Financial Economics, Elsevier, vol. 123(2), pages 251-272.
    56. Andrea L. Eisfeldt & Dimitris Papanikolaou, 2014. "The Value and Ownership of Intangible Capital," American Economic Review, American Economic Association, vol. 104(5), pages 189-194, May.
    57. Yuhang Xing, 2008. "Interpreting the Value Effect Through the Q-Theory: An Empirical Investigation," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1767-1795, July.
    58. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    59. Jillian Grennan & Roni Michaely, 2020. "Artificial Intelligence and High-Skilled Work: Evidence from Analysts," Swiss Finance Institute Research Paper Series 20-84, Swiss Finance Institute.
    60. Christina Zhu, 2019. "Big Data as a Governance Mechanism," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2021-2061.
    61. Dilip Mookherjee & Debraj Ray, 2022. "Growth, Automation and the Long-Run Share of Labor," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 46, pages 1-26, October.
    62. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    63. Titman, Sheridan & Wei, K. C. John & Xie, Feixue, 2004. "Capital Investments and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 677-700, December.
    64. Cao, Sean Shun & Fang, Vivian W. & (Gillian) Lei, Lijun, 2021. "Negative peer disclosure," Journal of Financial Economics, Elsevier, vol. 140(3), pages 815-837.
    65. Evgeny Lyandres & Le Sun & Lu Zhang, 2008. "The New Issues Puzzle: Testing the Investment-Based Explanation," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2825-2855, November.
    66. repec:bla:jfinan:v:59:y:2004:i:3:p:1083-1124 is not listed on IDEAS
    67. Ortiz-Molina, Hernán & Phillips, Gordon M., 2014. "Real Asset Illiquidity and the Cost of Capital," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(1), pages 1-32, February.
    68. Gerard Hoberg & Gordon Phillips & Nagpurnanand Prabhala, 2014. "Product Market Threats, Payouts, and Financial Flexibility," Journal of Finance, American Finance Association, vol. 69(1), pages 293-324, February.
    69. Miao Liu, 2022. "Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 607-651, May.
    70. Itay Goldstein & Chester S. Spatt & Mao Ye, 2021. "Big Data in Finance," NBER Working Papers 28615, National Bureau of Economic Research, Inc.
    71. Stickel, Se, 1990. "Predicting Individual Analyst Earnings Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 28(2), pages 409-417.
    72. Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance [Institutional order handling and broker-affiliated trading venues]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3213-3225.
    73. Fama, Eugene F. & French, Kenneth R., 2006. "Profitability, investment and average returns," Journal of Financial Economics, Elsevier, vol. 82(3), pages 491-518, December.
    74. 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.
    75. Elton, Edwin J & Gruber, Martin J & Blake, Christopher R, 1996. "The Persistence of Risk-Adjusted Mutual Fund Performance," The Journal of Business, University of Chicago Press, vol. 69(2), pages 133-157, April.
    76. Lin William Cong & Tengyuan Liang & Baozhong Yang & Xiao Zhang, 2020. "Analyzing Textual Information at Scale," World Scientific Book Chapters, in: Kashi R Balachandran (ed.), Information for Efficient Decision Making Big Data, Blockchain and Relevance, chapter 10, pages 239-271, World Scientific Publishing Co. Pte. Ltd..
    77. Stickel, Scott E, 1992. "Reputation and Performance among Security Analysts," Journal of Finance, American Finance Association, vol. 47(5), pages 1811-1836, December.
    78. Jegadeesh, Narasimhan & Noh, Joonki & Pukthuanthong, Kuntara & Roll, Richard & Wang, Junbo, 2019. "Empirical tests of asset pricing models with individual assets: Resolving the errors-in-variables bias in risk premium estimation," Journal of Financial Economics, Elsevier, vol. 133(2), pages 273-298.
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    Cited by:

    1. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2024. "Financial Statement Analysis with Large Language Models," Papers 2407.17866, arXiv.org, revised Nov 2024.
    2. Caterina Giannetti & Maria Saveria Mavillonio, 2024. "Crowdfunding Success: Human Insights vs Algorithmic Textual Extraction," Discussion Papers 2024/315, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    3. Linying Lv, 2024. "The Value of Information from Sell-side Analysts," Papers 2411.13813, arXiv.org, revised Dec 2024.
    4. Shuaiyu Chen & T. Clifton Green & Huseyin Gulen & Dexin Zhou, 2024. "What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts," Papers 2409.11540, arXiv.org.

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    More about this item

    Keywords

    Artificial intelligence; Machine learning; FinTech; Stock analyst; Alternative data; Disruptive innovation;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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