IDEAS home Printed from https://ideas.repec.org/a/bla/abacus/v60y2024i1p172-204.html
   My bibliography  Save this article

Analysts’ Earnings per Share Forecasts: The Effects of Forecast Uncertainty and Forecast Precision on Investor Judgements

Author

Listed:
  • Clarence Goh

Abstract

This study uses controlled experiments to investigate the joint effects of forecast uncertainty and forecast precision on investor judgements. It finds that forecast precision moderates the effects of forecast uncertainty on investors’ forecast reliability judgements such that the effects of forecast uncertainty on investors’ judgements of forecast reliability are more negative when an analyst's point earnings per share (EPS) forecast is rounded than when it is precise. In addition, the relationship between forecast precision and investors’ judgements of forecast reliability is mediated by investors’ perceptions of forecast attributes. The evidence also suggests that while forecast uncertainty exerts a negative effect on investment judgements, forecast precision does not play a role in mitigating these negative effects. Using a supplementary within‐participants experiment, the study further finds that investors may not be consciously aware of how forecast precision influences their judgements of forecast reliability.

Suggested Citation

  • Clarence Goh, 2024. "Analysts’ Earnings per Share Forecasts: The Effects of Forecast Uncertainty and Forecast Precision on Investor Judgements," Abacus, Accounting Foundation, University of Sydney, vol. 60(1), pages 172-204, March.
    • Handle: RePEc:bla:abacus:v:60:y:2024:i:1:p:172-204
    DOI: 10.1111/abac.12302
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/abac.12302
    Download Restriction: no
    File URL: https://libkey.io/10.1111/abac.12302?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kathy Rupar, 2017. "Significance of Forecast Precision: The Importance of Investors’ Expectations," Contemporary Accounting Research, John Wiley & Sons, vol. 34(2), pages 849-870, June.
    2. Parkash, Mohinder & Dhaliwal, Dan S. & Salatka, William K., 1995. "How certain firm-specific characteristics affect the accuracy and dispersion of analysts' forecasts : A latent variables approach," Journal of Business Research, Elsevier, vol. 34(3), pages 161-169, November.
    3. Loibl, Cäzilia & Hira, Tahira K., 2009. "Investor information search," Journal of Economic Psychology, Elsevier, vol. 30(1), pages 24-41, February.
    4. P. Eric Yeung, 2009. "Uncertainty and Expectation Revisions after Earnings Announcements," Contemporary Accounting Research, John Wiley & Sons, vol. 26(1), pages 273-301, March.
    5. X. Frank Zhang, 2006. "Information Uncertainty and Stock Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 105-137, February.
    6. Amitabh Dugar & Siva Nathan, 1995. "The Effect of Investment Banking Relationships on Financial Analysts' Earnings Forecasts and Investment Recommendations," Contemporary Accounting Research, John Wiley & Sons, vol. 12(1), pages 131-160, September.
    7. So, Eric C., 2013. "A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?," Journal of Financial Economics, Elsevier, vol. 108(3), pages 615-640.
    8. Charness, Gary & Gneezy, Uri & Kuhn, Michael A., 2012. "Experimental methods: Between-subject and within-subject design," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 1-8.
    9. Hirst, De & Koonce, L & Simko, Pj, 1995. "Investor Reactions To Financial Analysts Research Reports," Journal of Accounting Research, Wiley Blackwell, vol. 33(2), pages 335-351.
    10. Kristina Rennekamp, 2012. "Processing Fluency and Investors’ Reactions to Disclosure Readability," Journal of Accounting Research, Wiley Blackwell, vol. 50(5), pages 1319-1354, December.
    11. Lisa Koonce & Jeffrey Miller & Jennifer Winchel, 2015. "The Effects of Norms on Investor Reactions to Derivative Use," Contemporary Accounting Research, John Wiley & Sons, vol. 32(4), pages 1529-1554, December.
    12. 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.
    13. Sheng, Xuguang & Thevenot, Maya, 2012. "A new measure of earnings forecast uncertainty," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 21-33.
    14. Michael Clement & Richard Frankel & Jeffrey Miller, 2003. "Confirming Management Earnings Forecasts, Earnings Uncertainty, and Stock Returns," Journal of Accounting Research, Wiley Blackwell, vol. 41(4), pages 653-679, September.
    15. Timothy C. Johnson, 2004. "Forecast Dispersion and the Cross Section of Expected Returns," Journal of Finance, American Finance Association, vol. 59(5), pages 1957-1978, October.
    16. Sarah E. Bonner & Artur Hugon & Beverly R. Walther, 2007. "Investor Reaction to Celebrity Analysts: The Case of Earnings Forecast Revisions," Journal of Accounting Research, Wiley Blackwell, vol. 45(3), pages 481-513, June.
    17. Michael Tang & Li Zhang, 2018. "Range has it: decoding the information content of forecast ranges," Review of Accounting Studies, Springer, vol. 23(2), pages 589-621, June.
    18. Tang, Michael & Zarowin, Paul & Zhang, Li, 2015. "How do analysts interpret management range forecasts?," Accounting, Organizations and Society, Elsevier, vol. 42(C), pages 48-66.
    19. Stephen Kuselias, 2020. "Follow the Crowd: How Social Information and Social Identity Influence Investing Decisions," Abacus, Accounting Foundation, University of Sydney, vol. 56(3), pages 407-435, September.
    20. Abarbanell, Jeffery S. & Lanen, William N. & Verrecchia, Robert E., 1995. "Analysts' forecasts as proxies for investor beliefs in empirical research," Journal of Accounting and Economics, Elsevier, vol. 20(1), pages 31-60, July.
    21. Beixin Lin & Rong Yang, 2012. "Does Regulation Fair Disclosure affect analysts’ forecast performance? The case of restructuring firms," Review of Quantitative Finance and Accounting, Springer, vol. 38(4), pages 495-517, May.
    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. Paul Fischer & Chongho Kim & Frank Zhou, 2022. "Disagreement about fundamentals: measurement and consequences," Review of Accounting Studies, Springer, vol. 27(4), pages 1423-1456, December.
    2. Ramnath, Sundaresh & Rock, Steve & Shane, Philip, 2008. "The financial analyst forecasting literature: A taxonomy with suggestions for further research," International Journal of Forecasting, Elsevier, vol. 24(1), pages 34-75.
    3. Sheng, Xuguang & Thevenot, Maya, 2012. "A new measure of earnings forecast uncertainty," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 21-33.
    4. Sanjay W. Bissessur & David Veenman, 2016. "Analyst information precision and small earnings surprises," Review of Accounting Studies, Springer, vol. 21(4), pages 1327-1360, December.
    5. Cornett, Marcia Millon & Tehranian, Hassan & Yalcin, Atakan, 2007. "Regulation fair disclosure and the market's reaction to analyst investment recommendation changes," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 567-588, March.
    6. Beyer, Anne & Cohen, Daniel A. & Lys, Thomas Z. & Walther, Beverly R., 2010. "The financial reporting environment: Review of the recent literature," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 296-343, December.
    7. Kryzanowski, Lawrence & Mohsni, Sana, 2015. "Earnings forecasts and idiosyncratic volatilities," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 107-123.
    8. Xuguang Sheng & Maya Thevenot, 2013. "Differential Interpretation of Public Information: Estimation and Inference," Working Papers 2013-03, American University, Department of Economics.
    9. Ormazabal, Gaizka & Barth, Mary E. & Badia, Marc & Duro, Miguel, 2017. "Firm Risk and Disclosures about Dispersion in Asset Values:," CEPR Discussion Papers 12144, C.E.P.R. Discussion Papers.
    10. Dambra, Michael & Field, Laura Casares & Gustafson, Matthew T. & Pisciotta, Kevin, 2018. "The consequences to analyst involvement in the IPO process: Evidence surrounding the JOBS Act," Journal of Accounting and Economics, Elsevier, vol. 65(2), pages 302-330.
    11. Martin, Rachel, 2019. "Examination and implications of experimental research on investor perceptions," Journal of Accounting Literature, Elsevier, vol. 43(C), pages 145-169.
    12. Christopher von Koch & Ola Nilsson & Micael Jonsson & Andreas Jansson, 2014. "An Empirical Study of the Method Effect in Analysing the Adoption of IFRS," Accounting and Finance Research, Sciedu Press, vol. 3(2), pages 153-153, May.
    13. Ackert, Lucy F. & Church, Bryan K. & Zhang, Ping, 2008. "What affects the market's ability to adjust for optimistic forecast bias? Evidence from experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 66(2), pages 358-372, May.
    14. Sunil Mohanty & Edward Aw, 2006. "Rationality of analysts' earnings forecasts: evidence from dow 30 companies," Applied Financial Economics, Taylor & Francis Journals, vol. 16(12), pages 915-929.
    15. García-Meca, Emma & Sánchez-Ballesta, Juan Pedro, 2006. "Influences on financial analyst forecast errors: A meta-analysis," International Business Review, Elsevier, vol. 15(1), pages 29-52, February.
    16. Jin, Han & Mazouz, Khelifa & Wu, Yuliang & Xu, Bin, 2023. "Can star analysts make superior coverage decisions in poor information environment?," Journal of Banking & Finance, Elsevier, vol. 146(C).
    17. Chen, Wei & Han, Jun & Tan, Hun-Tong, 2016. "Investor reactions to management earnings guidance attributions: The effects of news valence, attribution locus, and outcome controllability," Accounting, Organizations and Society, Elsevier, vol. 55(C), pages 83-95.
    18. Philip L. Baird, 2020. "Do investors recognize biases in securities analysts’ forecasts?," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 623-634, October.
    19. Schreder, Max, 2018. "Idiosyncratic information and the cost of equity capital: A meta-analytic review of the literature," Journal of Accounting Literature, Elsevier, vol. 41(C), pages 142-172.
    20. Sheng, Xuguang (Simon) & Thevenot, Maya, 2015. "Quantifying differential interpretation of public information using financial analysts’ earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 515-530.

    More about this item

    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:bla:abacus:v:60:y:2024:i:1:p:172-204. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0001-3072 .

    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.