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Applied mean-ETL optimization in using earnings forecasts

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  • Shao, Barret Pengyuan
  • Rachev, Svetlozar T.
  • Mu, Yu

Abstract

In this article, we apply the mean-expected tail loss (ETL) portfolio optimization to the consensus temporary earnings forecasting (CTEF) data from global equities. The time series model with multivariate normal tempered stable (MNTS) innovations is used to generate the out-of-sample scenarios for the portfolio optimization. We find that (1) the CTEF variable continues to be of value in portfolio construction, (2) the mean-ETL portfolio optimization produces statistically significant active returns, and (3) the active returns generated in the mean-ETL portfolio with CTEF indicate a statistically significant stock selection.

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  • 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.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:2:p:561-567
    DOI: 10.1016/j.ijforecast.2014.10.005
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    Cited by:

    1. 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.
    2. Kurosaki, Tetsuo & Kim, Young Shin, 2022. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Finance Research Letters, Elsevier, vol. 45(C).
    3. Tetsuo Kurosaki & Young Shin Kim, 2020. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Papers 2010.08900, arXiv.org.
    4. 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.
    5. Bianchi, Michele Leonardo & De Luca, Giovanni & Rivieccio, Giorgia, 2023. "Non-Gaussian models for CoVaR estimation," International Journal of Forecasting, Elsevier, vol. 39(1), pages 391-404.

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