TY - JOUR
T1 - Applied mean-ETL optimization in using earnings forecasts
AU - Shao, Barret Pengyuan
AU - Rachev, Svetlozar T.
AU - Mu, Yu
N1 - Publisher Copyright:
© 2014 International Institute of Forecasters.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - 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.
AB - 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.
KW - Consensus temporary earnings forecasting
KW - Mean-expected tail loss optimization
KW - Multivariate normal tempered stable distribution
UR - http://www.scopus.com/inward/record.url?scp=84940001561&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2014.10.005
DO - 10.1016/j.ijforecast.2014.10.005
M3 - Article
AN - SCOPUS:84940001561
SN - 0169-2070
VL - 31
SP - 561
EP - 567
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 2
ER -