TY - GEN
T1 - Mean-variance-skewness-kurtosis-based portfolio optimization
AU - Lai, Kin Keung
AU - Yu, Lean
AU - Wang, Shouyang
PY - 2006
Y1 - 2006
N2 - In the mean-variance-skewness-kurtosis framework, this study solve multiple conflicting and competing portfolio objectives such as maximizing expected return and skewness and minimizing risk and kurtosis simultaneously, by construction of a polynomial goal programming (PGP) model into which investor preferences over higher return moments are incorporated. To examine its practicality, the approach is tested on four major stock indices. Empirical results indicate that, for all examined investor preferences and stock indices, the PGP approach is significantly efficient way to solve multiple conflicting portfolio objectives in the mean-variance-skewness-kurtosis framework. In the meantime, we find that the different investors' preferences not only affect asset allocations of portfolio, but also affect the four moment statistics of return. © 2006 IEEE.
AB - In the mean-variance-skewness-kurtosis framework, this study solve multiple conflicting and competing portfolio objectives such as maximizing expected return and skewness and minimizing risk and kurtosis simultaneously, by construction of a polynomial goal programming (PGP) model into which investor preferences over higher return moments are incorporated. To examine its practicality, the approach is tested on four major stock indices. Empirical results indicate that, for all examined investor preferences and stock indices, the PGP approach is significantly efficient way to solve multiple conflicting portfolio objectives in the mean-variance-skewness-kurtosis framework. In the meantime, we find that the different investors' preferences not only affect asset allocations of portfolio, but also affect the four moment statistics of return. © 2006 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=33845592337&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33845592337&origin=recordpage
U2 - 10.1109/IMSCCS.2006.252
DO - 10.1109/IMSCCS.2006.252
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769525814
SN - 9780769525815
VL - 2
SP - 292
EP - 297
BT - First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06
T2 - First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06
Y2 - 20 April 2006 through 24 April 2006
ER -