Aggregation of Forecasts and Recommendations of Financial Analysts in the Framework of Evidence Theory

  • Ekaterina Kutynina
  • Alexander LepskiyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 642)


The article is dedicated to the method of aggregation of financial analysts’ recommendations in the framework of the evidence theory. This method considered on the example of Russian stock market and the quality of the obtained results was compared with the classical consensus forecast. It is shown that the combination rules, which are widely developed in the theory of evidence, allow aggregating the recommendations of analysts taking into account the historical reliability of information sources, the nature of the taken decisions (pessimism-optimism), the conflict between forecasts and recommendations, etc. In most cases it turned out that, obtained aggregated forecasts are more accurate than consensus forecast.


Evidence theory Combining rule Recommendations of financial analysts Consensus forecast Discounting of evidence 



The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project “5-100”.


  1. 1.
    Autchariyapanitkul, K., Chanaim, S., Sriboonchitta, S., Denoeux, T.: Predicting stock returns in the capital asset pricing model using quantile regression and belief functions. In: Cuzzolin, F. (ed.) BELIEF 2014. LNCS, vol. 8764, pp. 219–226. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Barber, B., Lehavy, R., McNichols, M., Trueman, B.: Buys, holds, and sells: the distribution of investment banks’ stock ratings and the implications for the profitability of analysts’ recommendations. J. Account. Econ. 41, 87–117 (2006)CrossRefGoogle Scholar
  3. 3.
    Berkman, H., Yang, W.: Analyst Recommendations and International Stock Market Returns, 1 July 2016. Available at SSRN:
  4. 4.
    Bradley, D., Clarke, J., Cooney, J.: The impact of reputation on analysts’ conflicts of interest: hot versus cold markets. J. Bank. Fin. 36, 2190–2202 (2012)CrossRefGoogle Scholar
  5. 5.
    Bronevich, A., Lepskiy, A., Penikas, H.: The application of conflict measure to estimating incoherence of analyst’s forecasts about the cost of shares of Russian companies. Proc. Comp. Sc. 55, 1113–1122 (2015)CrossRefGoogle Scholar
  6. 6.
    Bronevich, A., Lepskiy, A., Penikas, H.: An analysis of coherence of financial analysts’ recommendations in the framework of evidence theory. In: CEUR-Workshop, vol. 1687, pp. 12–23 (2016)Google Scholar
  7. 7.
    Brown, L.D., Kim, K.: Timely aggregate analyst forecasts as better proxies for market earnings expectations. J. Account. Res. 29, 382–385 (1991)CrossRefGoogle Scholar
  8. 8.
    Clement, M.B.: Analyst forecast accuracy: do ability, resources, and portfolio complexity matter? J. Account. Econ. 27(3), 285–303 (1999)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Dempster, A.P.: Upper and lower probabilities induced by a multi-valued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)CrossRefzbMATHGoogle Scholar
  10. 10.
    Ertimur, Y., Sunder, J., Sunder, S.V.: Measure for measure: the relation between forecast accuracy and recommendation profitability of analysts. J. Account. Res. 45(3), 567–606 (2007)CrossRefGoogle Scholar
  11. 11.
    Farooq, O.: Information content of analyst recommendations: evidence from the danish bio-technology sector. J. Appl. Bus. Res. 32(2), 379–386 (2016)CrossRefGoogle Scholar
  12. 12.
    Faias, J.: Predicting Influential Recommendation Revisions, 6 January 2015. Available at SSRN:
  13. 13.
    Howe, J.S., Unlu, E., Yan, X.: The predictive content of aggregate analyst recommendations. J. Account. Res. 47(3), 799–821 (2009)CrossRefGoogle Scholar
  14. 14.
    Hsieh, J., Ng, L.K., Wang, Q.: How Informative are Analyst Recommendations and Insider Trades? 12 April 2005. AFA 2006 Boston Meetings Paper. Available at SSRN:
  15. 15.
    Huang, J., Mian, G.M., Sankaraguruswamy, S.: The value of combining the information content of analyst recommendations and target prices. J. Financ. Mark. 12, 754–777 (2009)CrossRefGoogle Scholar
  16. 16.
    Kanjanatarakul, O., Sriboonchitta, S., Denoeux, T.: Forecasting using belief functions: an application to marketing econometrics. Int. J. Approx. Reas. 55(5), 1113–1128 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Kanne, S., Klobucnik, J., Kreutzmann, D., Sievers, S.: To buy or not to buy? The value of contradictory analyst signals. Fin. Mark. Portf. Manag. 26, 405–428 (2012)CrossRefGoogle Scholar
  18. 18.
    Kim, O., Lim, S.C., Shaw, K.W.: The inefficiency of the mean forecast as a summary forecast of earnings. J. Account. Res. 39, 329–336 (2001)CrossRefGoogle Scholar
  19. 19.
    Marinelli, C., Weissensteiner, A.: On the relation between forecast precision and trading profitability of financial analysts. J. Financ. Mark. 20, 39–60 (2014)CrossRefGoogle Scholar
  20. 20.
    Pacelli, J.: Integrity Culture and Analyst Forecast Quality. Kelley School of Business Research Paper, No. 15–57, May 2016. Available at SSRN:
  21. 21.
    Ramnath, S., Rock, S., Shane, P.: Financial analysts’ forecasts and stock recommendations: a review of the research. Found. Trends Fin. 2(4), 311–421 (2008)CrossRefGoogle Scholar
  22. 22.
    Sentz, K., Ferson, S.: Combination of evidence in Dempster-Shafer theory. Report SAND 2002–0835, Sandia National Laboratories (2002)Google Scholar
  23. 23.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  24. 24.
    Utkin, L.V.: Cautious analysis of project risks by interval-valued initial data. Int. J. Uncert. Fuz. Knowl. Based Syst. 14(6), 663–685 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Xu, Y., Wu, L., Wu, X., Xu, Z.: Belief fusion of predictions of industries in China’s stock market. In: Cuzzolin, F. (ed.) BELIEF 2014. LNAI, vol. 8764, pp. 348–355. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.National Research University – Higher School of EconomicsMoscowRussia

Personalised recommendations