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Robust approach to earnings forecast: A comparison

Author

Listed:
  • Xiaojian Yu
  • Xiaoqian Zhang
  • Donald Lien

Abstract

This paper applies three robust approaches, namely, the MM estimation, the Theil–Sen estimation, and the quantile regression, to generate earnings forecasts in Chinese financial market and evaluates the forecast accuracy of these three methods based on three forecasting criteria. We examine six forecasting models where the predicted variables include earnings per share, net income, and three profitability measures. We show that the three robust methods significantly outperform the OLS method. Moreover, the MM estimation and the quantile regression have better forecast accuracy than the Theil–Sen approach.

Suggested Citation

  • Xiaojian Yu & Xiaoqian Zhang & Donald Lien, 2024. "Robust approach to earnings forecast: A comparison," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1530-1558, August.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:5:p:1530-1558
    DOI: 10.1002/for.3085
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    References listed on IDEAS

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    1. Brown, Lawrence D., 1993. "Earnings forecasting research: its implications for capital markets research," International Journal of Forecasting, Elsevier, vol. 9(3), pages 295-320, November.
    2. Patricia M. Fairfield & Sundaresh Ramnath & Teri Lombardi Yohn, 2009. "Do Industry‐Level Analyses Improve Forecasts of Financial Performance?," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 47(1), pages 147-178, March.
    3. 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.
    4. Harrison Hong & Jeffrey D. Kubik & Amit Solomon, 2000. "Security Analysts' Career Concerns and Herding of Earnings Forecasts," RAND Journal of Economics, The RAND Corporation, vol. 31(1), pages 121-144, Spring.
    5. Harrison Hong & Jeffrey D. Kubik, 2003. "Analyzing the Analysts: Career Concerns and Biased Earnings Forecasts," Journal of Finance, American Finance Association, vol. 58(1), pages 313-351, February.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Hou, Kewei & van Dijk, Mathijs A. & Zhang, Yinglei, 2012. "The implied cost of capital: A new approach," Journal of Accounting and Economics, Elsevier, vol. 53(3), pages 504-526.
    8. Brown, Lawrence D., 1993. "Reply to commentaries on "Earnings forecasting research: its implications for capital markets research"," International Journal of Forecasting, Elsevier, vol. 9(3), pages 343-344, November.
    9. Xia, Hui & Min, Xinyu & Deng, Shijie, 2015. "Effectiveness of earnings forecasts in efficient global portfolio construction," International Journal of Forecasting, Elsevier, vol. 31(2), pages 568-574.
    10. Wu, Joanna Shuang & Zang, Amy Y., 2009. "What determine financial analysts' career outcomes during mergers?," Journal of Accounting and Economics, Elsevier, vol. 47(1-2), pages 59-86, March.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Loh, Roger K. & Mian, G. Mujtaba, 2006. "Do accurate earnings forecasts facilitate superior investment recommendations?," Journal of Financial Economics, Elsevier, vol. 80(2), pages 455-483, May.
    13. Qu, Li, 2021. "A new approach to estimating earnings forecasting models: Robust regression MM-estimation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1011-1030.
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