IDEAS home Printed from https://ideas.repec.org/a/wly/coacre/v6y1990i2p501-517.html
   My bibliography  Save this article

Forecasts of earnings per share: Possible sources of analyst superiority and bias

Author

Listed:
  • JOHN AFFLECK†GRAVES
  • LARRY R. DAVIS
  • RICHARD R. MENDENHALL

Abstract

. Previous research has shown that analysts' forecasts of quarterly earnings per share (EPS) are more accurate than those of accepted time†series models. In addition, some previous research suggests that, on average, analysts' forecasts tend to be optimistic (i.e., biased). Two explanations for analysts' superiority have been proposed: (1) analysts use more recent information than can time†series models and (2) analysts use forecast†relevant information not included in the time†series of past earnings. This paper provides evidence on a third potential source of analyst superiority: the possibility that humans can use past earnings data to predict future earnings more accurately than can mechanical time†series models. We find that human judges do no worse than accepted time†series models when both use the same information set: namely, the series of past EPS figures. To date, little or no research has attempted to determine why analyst bias might exist. Still, some possible reasons have been forwarded. First, pessimistic forecasts or reports may hinder future efforts of the analyst or the analyst's employer to obtain information from the company being analyzed. Second, forecast data bases may suffer a selection bias if analysts tend to stop following those firms that they perceive as performing poorly. This study proposes, and provides evidence regarding, a third possible explanation for analyst bias: the use of judgmental heuristics by analysts. Many studies have shown that human predictions are often biased because of the use of such heuristics. We present evidence that suggests this may be the case for analysts' forecasts of earnings per share. Résumé. De précédents travaux de recherche ont démontré que les prévisions des analystes relatives au bénéfice par action (BPA) trimestriel sont plus exactes que celles que permettent d'obtenir les modèles reconnus basés sur les séries chronologiques. De plus, les résultats de certains travaux de recherche laissent croire qu'en moyenne, les prévisions des analystes tendent à être optimistes (c'est†à †dire biaisées). Deux explications à cette supériorité ont été proposées: 1) l'information que les analystes utilisent est plus récente que celles utilisées dans les modèles fondés sur les séries chronologiques et 2) les analystes utilisent de l'information pertinente aux prévisions qui ne figure pas dans les séries chronologiques relatives aux bénéfices passes. Les auteurs attribuent à un troisième facteur potentiel cette supériorité: la possibilité pour les humains d'utiliser les données relatives aux bénéfices passés pour prédire les bénéfices futurs de façon plus précise que ne le peuvent les modèles fondés sur les séries chronologiques. Ils en viennent à la conclusion que les humains obtiennent des résultats tout aussi efficaces que les modèles chronologiques reconnus lorsqu'ils utilisent un jeu de renseignements identique, soit les données historiques relatives au BPA. Jusqu'à maintenant, peu de chercheurs, sinon aucun, ont tenté de déterminer à quoi tiendrait l'existence d'un biais chez l'analyste. Malgré tout, certaines explications possibles ont été proposées. Premièrement, les prévisions ou les rapports pessimistes peuvent faire obstacle aux efforts futurs de l'analyste ou de son employeur pour obtenir de l'information de la société faisant l'objet de l'analyse. Deuxièmement, les bases de données servant à la prévision peuvent être entachées d'un biais de sélection si les analystes ont tendance à cesser de suivre les entreprises qui leur semblent afficher une piètre performance. Les auteurs proposent et attestent une troisième explication possible du biais de l'analyste: l'utilisation de méthodes heuristiques fondées sur le jugement. De nombreuses études ont démontré que les prédictions humaines sont souvent biaisées par suite de l'utilisation de ces méthodes heuristiques. Les auteurs apportent des arguments qui permettent de croire que ce pourrait être le cas des prévisions des analystes du bénéfice par action.

Suggested Citation

  • John Affleck†Graves & Larry R. Davis & Richard R. Mendenhall, 1990. "Forecasts of earnings per share: Possible sources of analyst superiority and bias," Contemporary Accounting Research, John Wiley & Sons, vol. 6(2), pages 501-517, March.
  • Handle: RePEc:wly:coacre:v:6:y:1990:i:2:p:501-517
    DOI: 10.1111/j.1911-3846.1990.tb00771.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1911-3846.1990.tb00771.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1911-3846.1990.tb00771.x?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. Ashton, Rh & Kramer, Ss, 1980. "Students As Surrogates In Behavioral Accounting Research - Some Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 1-15.
    2. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    3. Brown, Lawrence D. & Hagerman, Robert L. & Griffin, Paul A. & Zmijewski, Mark E., 1987. "Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings," Journal of Accounting and Economics, Elsevier, vol. 9(1), pages 61-87, April.
    4. Lipe, Rc, 1986. "The Information Contained In The Components Of Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 24, pages 37-64.
    5. Beaver, Wh & Clarke, R & Wright, Wf, 1979. "Association Between Unsystematic Security Returns And The Magnitude Of Earnings Forecast Errors," Journal of Accounting Research, Wiley Blackwell, vol. 17(2), pages 316-340.
    6. Griffin, Pa, 1977. "Time-Series Behavior Of Quarterly Earnings - Preliminary Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 15(1), pages 71-83.
    7. Collins, Wa & Hopwood, Ws & Mckeown, Jc, 1984. "The Predictability Of Interim Earnings Over Alternative Quarters," Journal of Accounting Research, Wiley Blackwell, vol. 22(2), pages 467-479.
    8. Brown, Lawrence D. & Hagerman, Robert L. & Griffin, Paul A. & Zmijewski, Mark E., 1987. "An evaluation of alternative proxies for the market's assessment of unexpected earnings," Journal of Accounting and Economics, Elsevier, vol. 9(2), pages 159-193, July.
    9. Joyce, Ej & Biddle, Gc, 1981. "Are Auditors Judgments Sufficiently Regressive," Journal of Accounting Research, Wiley Blackwell, vol. 19(2), pages 323-349.
    10. Barefield, Russell M. & Comiskey, Eugene E., 1975. "The accuracy of analysts' forecasts of earnings per share," Journal of Business Research, Elsevier, vol. 3(3), pages 241-252, July.
    11. Fried, Dov & Givoly, Dan, 1982. "Financial analysts' forecasts of earnings : A better surrogate for market expectations," Journal of Accounting and Economics, Elsevier, vol. 4(2), pages 85-107, October.
    12. Brown, Ld & Rozeff, Ms, 1979. "Univariate Time-Series Models Of Quarterly Accounting Earnings Per Share - Proposed Model," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 179-189.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Phillip J. McKnight & Steven K. Todd, 2013. "Forecast Bias and Analyst Independence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 3-32.
    2. Mehran, Hamid & Stulz, Rene M., 2007. "The economics of conflicts of interest in financial institutions," Journal of Financial Economics, Elsevier, vol. 85(2), pages 267-296, August.
    3. Robert Libby & James E. Hunton & Hun‐Tong Tan & Nicholas Seybert, 2008. "Retracted: Relationship Incentives and the Optimistic/Pessimistic Pattern in Analysts' Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 46(1), pages 173-198, March.
    4. 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.
    5. Douglas Stevens & Arlington Williams, 2004. "Inefficiency in Earnings Forecasts: Experimental Evidence of Reactions to Positive vs. Negative Information," Experimental Economics, Springer;Economic Science Association, vol. 7(1), pages 75-92, February.
    6. Horton, Joanne & Serafeim, George & Wu, Shan, 2017. "Career concerns of banking analysts," Journal of Accounting and Economics, Elsevier, vol. 63(2), pages 231-252.
    7. Amir, Eli & Ganzach, Yoav, 1998. "Overreaction and underreaction in analysts' forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 37(3), pages 333-347, November.
    8. Brown, Philip & Clarke, Alex & How, Janice C. Y. & Lim, Kadir J. P., 2002. "Analysts' dividend forecasts," Pacific-Basin Finance Journal, Elsevier, vol. 10(4), pages 371-391, September.
    9. 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.
    10. Selima Mansour & Elyès Jouini & Clotilde Napp, 2006. "Is There a “Pessimisticâ€\x9D Bias in Individual Beliefs? Evidence from a Simple Survey," Theory and Decision, Springer, vol. 61(4), pages 345-362, December.
    11. Mintchik, Natalia, 2009. "The impact of SFAS No. 141 on earnings predictability of merging firms: Evidence from the initial year of implementation," Research in Accounting Regulation, Elsevier, vol. 21(2), pages 89-99.
    12. Mehtab Arshad Butt & Haroon Shafi & Kashif-Ur-Rehman & Rana Rashid Rehman & Hafiz Muhammad Shoaib, 2011. "Investor’s Dilemma: Fundamentals or Biasness in Investment Decision," Journal of Economics and Behavioral Studies, AMH International, vol. 3(2), pages 122-127.
    13. Anna M. Cianci & Satoris S. Culbertson, 2010. "The Impact of Motivational and Cognitive Factors on Optimistic Earnings Forecasts," Chapters, in: Brian Bruce (ed.), Handbook of Behavioral Finance, chapter 11, Edward Elgar Publishing.
    14. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    15. Lucy F. Ackert & Bryan K. Church & Mohamed Shehata, 1996. "What Affects Individuals' Decisions to Acquire Forecasted Information?," Contemporary Accounting Research, John Wiley & Sons, vol. 13(2), pages 379-399, September.
    16. Michael Calegari & Neil L. Fargher, 1997. "Evidence that Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings: An Experimental Markets Approach," Contemporary Accounting Research, John Wiley & Sons, vol. 14(3), pages 397-433, September.
    17. Chen, Yuan & Han, Dongmei & Zhou, Xiaofeng, 2023. "Mining the emotional information in the audio of earnings conference calls : A deep learning approach for sentiment analysis of securities analysts' follow-up behavior," International Review of Financial Analysis, Elsevier, vol. 88(C).

    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. Brown, Lawrence D., 1996. "Influential accounting articles, individuals, Ph.D. granting institutions and faculties: A citational analysis," Accounting, Organizations and Society, Elsevier, vol. 21(7-8), pages 723-754.
    2. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    3. S. P. Kothari & Charles Wasley, 2019. "Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years," Journal of Accounting Research, Wiley Blackwell, vol. 57(5), pages 1117-1159, December.
    4. Lawrence D. Brown & Mark E. Zmijewski, 1987. "The effect of labor strikes on security analysts' forecast superiority and on the association between risk†adjusted stock returns and unexpected earnings," Contemporary Accounting Research, John Wiley & Sons, vol. 4(1), pages 61-75, September.
    5. Byung T. Ro, 1989. "Earnings news and the firm size effect," Contemporary Accounting Research, John Wiley & Sons, vol. 6(1), pages 177-195, September.
    6. Zana Grigaliuniene, 2013. "Time-Series Models Forecasting Performance In The Baltic Stock Market," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 4(1).
    7. Higgins, Huong, 2013. "Can securities analysts forecast intangible firms’ earnings?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 155-174.
    8. Sen, Kaustav, 2009. "Earnings surprise and sophisticated investor preferences in India," Journal of Contemporary Accounting and Economics, Elsevier, vol. 5(1), pages 1-19.
    9. JS Armstrong, 2004. "Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings," General Economics and Teaching 0412007, University Library of Munich, Germany.
    10. Carabias, Jose M., 2018. "The real-time information content of macroeconomic news: implications for firm-level earnings expectations," LSE Research Online Documents on Economics 86399, London School of Economics and Political Science, LSE Library.
    11. Sean Shun Cao & Ganapathi S. Narayanamoorthy, 2012. "Earnings Volatility, Post–Earnings Announcement Drift, and Trading Frictions," Journal of Accounting Research, Wiley Blackwell, vol. 50(1), pages 41-74, March.
    12. Wilkie-Thomson, Mary E. & Onkal-Atay, Dilek & Pollock, Andrew C., 1997. "Currency forecasting: an investigation of extrapolative judgement," International Journal of Forecasting, Elsevier, vol. 13(4), pages 509-526, December.
    13. Rä‚Zvan Popa, 2020. "Improving Earnings Predictions With Neural Network Models," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 26, pages 77-96, December.
    14. Pagach, Donald P. & Warr, Richard S., 2020. "Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited," Advances in accounting, Elsevier, vol. 51(C).
    15. Lorek, Kenneth S., 2014. "A critical assessment of the time-series literature in accounting pertaining to quarterly accounting numbers," Advances in accounting, Elsevier, vol. 30(2), pages 315-321.
    16. Jose M. Carabias, 2018. "The real-time information content of macroeconomic news: implications for firm-level earnings expectations," Review of Accounting Studies, Springer, vol. 23(1), pages 136-166, March.
    17. Allen, Arthur & Cho, Jang Youn & Jung, Kooyul, 1997. "Earnings forecast errors: Comparative evidence from the Pacific-Basin capital markets," Pacific-Basin Finance Journal, Elsevier, vol. 5(1), pages 115-129, February.
    18. A. Rashad Abdel†Khalik, 1990. "Specification problems with information content of earnings: revisions and rationality of expectations and self†selection bias," Contemporary Accounting Research, John Wiley & Sons, vol. 7(1), pages 142-172, September.
    19. Dennis Fan & Raymond So & Jason Yeh, 2006. "Analyst Earnings Forecasts for Publicly Traded Insurance Companies," Review of Quantitative Finance and Accounting, Springer, vol. 26(2), pages 105-136, March.
    20. WaQar Ghani & Samuel Szewczyk & Tayyeb Shabbir, 2007. "Financial Analysts’ Forecasts and Unprecedented Events: The Case of German Reunification," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(2), pages 123-138, May.

    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:wly:coacre:v:6:y:1990:i:2:p:501-517. 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: https://doi.org/10.1111/(ISSN)1911-3846 .

    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.