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Earnings forecasting in a global stock selection model and efficient portfolio construction and management

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  • Guerard, John B.
  • Markowitz, Harry
  • Xu, GanLin

Abstract

Stock selection models often use analysts’ expectations, momentum, and fundamental data. We find support for composite modeling using these sources of data for global stocks during the period 1997–2011. We also find evidence to support the use of SunGard APT and Axioma multi-factor models for portfolio construction and risk control. Three levels of testing for stock selection and portfolio construction models are developed and estimated. We create portfolios for January 1997–December 2011. We report three conclusions: (1) analysts’ forecast information was rewarded by the global market between January 1997 and December 2011; (2) analysts’ forecasts can be combined with reported fundamental data, such as earnings, book value, cash flow and sales, and also with momentum, in a stock selection model for identifying mispriced securities; and (3) the portfolio returns of the multi-factor risk-controlled portfolios allow us to reject the null hypothesis for the data mining corrections test. The earnings forecasting variable dominates our composite model in terms of its impact on stock selection.

Suggested Citation

  • Guerard, John B. & Markowitz, Harry & Xu, GanLin, 2015. "Earnings forecasting in a global stock selection model and efficient portfolio construction and management," International Journal of Forecasting, Elsevier, vol. 31(2), pages 550-560.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:2:p:550-560
    DOI: 10.1016/j.ijforecast.2014.10.003
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    Cited by:

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    2. John B. Guerard, Jr. & Robert A. Gillam & Harry Markowitz & Ganlin Xu & Shijie Deng & Ziwei (Elaine) Wang, 2018. "Data Mining Corrections Testing in Chinese Stocks," Interfaces, INFORMS, vol. 48(2), pages 108-120, April.
    3. Beheshti, Bijan, 2015. "A note on the integration of the alpha alignment factor and earnings forecasting models in producing more efficient Markowitz Frontiers," International Journal of Forecasting, Elsevier, vol. 31(2), pages 582-584.
    4. Moh’d, Shamis Said & Ozgur, Ceyhun & Mohd, Mohd Yaziz & Khalfan, Mohamed Hafidh, 2021. "The Combined Effects of Managerial and Operational Performance of Various Fundamental Components on Stock Selection," OSF Preprints mqh46, Center for Open Science.
    5. Masoud Rahiminezhad Galankashi & Farimah Mokhatab Rafiei & Maryam Ghezelbash, 2020. "Portfolio selection: a fuzzy-ANP approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-34, December.
    6. Xiaoyu Ji & Hua Ke, 2017. "No-arbitrage theorem for multi-factor uncertain stock model with floating interest rate," Fuzzy Optimization and Decision Making, Springer, vol. 16(2), pages 221-234, June.
    7. 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.
    8. John B. Guerard & Ganlin Xu & Harry Markowitz, 2021. "A further analysis of robust regression modeling and data mining corrections testing in global stocks," Annals of Operations Research, Springer, vol. 303(1), pages 175-195, August.
    9. Shao, Barret Pengyuan & Rachev, Svetlozar T. & Mu, Yu, 2015. "Applied mean-ETL optimization in using earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 561-567.
    10. John B. Guerard & Harry Markowitz & Ganlin Xu & Ziwei Wang, 2018. "Global portfolio construction with emphasis on conflicting corporate strategies to maximize stockholder wealth," Annals of Operations Research, Springer, vol. 267(1), pages 203-219, August.
    11. Buncic, Daniel & Stern, Cord, 2019. "Forecast ranked tailored equity portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    12. Gillam, Robert A. & Guerard, John B. & Cahan, Rochester, 2015. "News volume information: Beyond earnings forecasting in a global stock selection model," International Journal of Forecasting, Elsevier, vol. 31(2), pages 575-581.

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