Review of Quantitative Finance and Accounting

, Volume 37, Issue 4, pp 477–507 | Cite as

Relative accuracy of analysts’ earnings forecasts over time: a Markov chain analysis

Original Research

Abstract

The main purpose of this paper is to analyze the time patterns of individual analysts’ relative accuracy ranking in earnings forecasts using a Markov chain model. Two levels of stochastic persistence are found in analysts’ relative accuracy over time. Factors underlying analysts’ performance persistence are identified and they include analyst’s length of experience, workload, and the size and growth rate of firms followed by the analyst. The strength and the composition of these factors are found to vary markedly in different industries. The findings support the general notion that analysts are heterogeneous in their accuracy in earnings forecasts and that their superior/inferior performance tends to persist over time. An analysis based on a refined measure of analysts’ forecast accuracy ranking that strips off firm-specific factors further enhances the empirical validity of the findings. These findings provide a concrete basis for researchers to further explore why and how analysts perform differently in the competitive market of investment information services.

Keywords

Earnings forecasts Persistence of performance Information efficiency Markov chain models 

JEL Classification

G14 C21 C22 C53 D84 

References

  1. Abarbanell JS, Bernard VL (1992) Tests of analysts’ overreaction/underreaction to earnings information as an explanation for anomalous stock price behavior. J Finance 47:1181–1207CrossRefGoogle Scholar
  2. Abarbanell JS, Lehavy R (2003) Biased forecasts or biased earnings? The role of reported earnings in explaining apparent bias and over/underreaction in analysts’ earnings forecasts. J Acc Econ 36:105–146CrossRefGoogle Scholar
  3. Bao D, Chien C, Lee C (1997) Characteristics of earnings-leading versus price-leading firms. Rev Quant Financ Acc 8:229–244CrossRefGoogle Scholar
  4. Beneish M, Harvey C (1998) Measurement error and nonlinearity in the earnings-returns relation. Rev Quant Financ Acc 11:219–247CrossRefGoogle Scholar
  5. Breeden DT, Gibbons MR, Litzenberger RH (1989) Empirical tests of the consumption-oriented CAPM. J Finance 44:231–262CrossRefGoogle Scholar
  6. Brown LD (1997) Analyst forecasting errors: additional evidence. Financ Anal J 53:81–88CrossRefGoogle Scholar
  7. Brown LD, Rozeff MS (1980) Analysts can forecast accurately! J Portfolio Manag 6(3):31–34CrossRefGoogle Scholar
  8. Butler KC, Lang LHP (1991) The forecast accuracy of individual analysts: evidence of systematic optimism and pessimism. J Acc Res 29:150–156CrossRefGoogle Scholar
  9. Carhart M (1997) On persistence in mutual fund performance. J Finance 52:57–82CrossRefGoogle Scholar
  10. Clarke J, Subramanian A (2006) Dynamic forecasting behavior by analysts: theory and evidence. J Financ Econ 80:81–113CrossRefGoogle Scholar
  11. Clement MB (1999) Analyst forecast accuracy: do ability, resources, and portfolio complexity matter? J Acc Econ 27:285–303CrossRefGoogle Scholar
  12. Cooper RA, Day TE, Lewis CM (2001) Following the leader: a study of individual analysts’ earnings forecasts. J Financ Econ 61:383–416CrossRefGoogle Scholar
  13. Copeland T, Dolgoff A, Moel A (2004) The role of expectations in explaining the cross-section of stock returns. Rev Acc Stud 9:149–188CrossRefGoogle Scholar
  14. Diether KB, Malloy CJ, Scherbina A (2002) Differences of opinion and the cross section of stock returns. J Finance 57:2113–2141CrossRefGoogle Scholar
  15. Ghosh D, Whitecotton SM (1997) Some determinants of analysts’ forecast accuracy. Behav Res Acc 9:50–68Google Scholar
  16. Grimmet GR, Stirzaker DR (2001) Probability and random processes, 3rd edn. Oxford University Press, OxfordGoogle Scholar
  17. Hanke JE, Wichern DW (2009) Business forecasting, 9th edn. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  18. Hilary G, Menzly L (2006) Does past success lead analysts to become overconfident? Manag Sci 52:489–500CrossRefGoogle Scholar
  19. Ho L (2004) Analysts’ forecasts of Taiwanese firms’ earnings: some empirical evidence. Rev Pacific Basin Financ Mark Policies (RPBFMP) 7:571–597CrossRefGoogle Scholar
  20. Jacob J, Lys TZ, Neale MA (1999) Expertise in forecasting performance of security analysts. J Acc Econ 28:51–82CrossRefGoogle Scholar
  21. Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis, 5th edn. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  22. Lim T (2001) Rationality and analysts’ forecast bias. J Finance 56:369–385CrossRefGoogle Scholar
  23. Loh R, Mian G (2006) Do accurate earnings forecasts facilitate superior investment recommendations? J Financ Econ 80:455–483CrossRefGoogle Scholar
  24. Lou Z, Wahba G (1997) Hybrid adaptive splines. J Am Stat Asso 92:107–116CrossRefGoogle Scholar
  25. Mikhail MB, Walther BR, Willis RH (1997) Do security analysts improve their performance with experience? J Acc Res 35:131–166CrossRefGoogle Scholar
  26. Mikhail MB, Walther BR, Willis RH (2003) The effect of experience on security analyst underreaction. J Acc Econ 35:101–116CrossRefGoogle Scholar
  27. O’Brien P (1987) Individual forecasting ability. Manag Finance 13:3–9Google Scholar
  28. O’Brien P (1990) Forecast accuracy of individual analysts in nine industries. J Acc Res 28:286–304CrossRefGoogle Scholar
  29. Ramnath S, Rock S, Shane P (2008) The financial analyst forecasting literature: a taxonomy with suggestions for further research. Int J Forecast 24(1):34–75CrossRefGoogle Scholar
  30. Richards RM (1976) Analysts’ performance and the accuracy of corporate earnings forecasts. J Bus 49:350–357CrossRefGoogle Scholar
  31. Sinha P, Brown LD, Das S (1997) A re-examination of financial analysts’ differential earnings forecast accuracy. Contemp Acc Res 14:1–42CrossRefGoogle Scholar
  32. Stickel SE (1992) Reputation and performance among security analysts. J Finance 47:1811–1836CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Sheldon B. Lubar School of BusinessUniversity of Wisconsin, MilwaukeeMilwaukeeUSA
  2. 2.Department of BusinessMissouri Western State UniversitySt. JosephUSA

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