Paper Info
Title
Fuzzy scenarios clustering-based approach with MV model in optimizing tactical allocation
Abstract
A new interactive model for constructing a tactical global assets allocation through integrating fuzzy scenarios clustering- based approaches (FSCA) with mean-variance (MV) is proposed. This serves as an alternative forecasting rebalance quantitative model to the popular global assets allocation, in which the portfolio is first being observed in contrast with major asset and sub-assets classes which possess upward and downward positive co-movement phenomenon while considering the linkage of cross-market between different time-zones. In addition, fuzzy scenarios clustering would be induced into the MV model so as to adjust the weighting of the risk-return structural matrices. It could further enhance the efficient frontier of a portfolio as well as obtaining opportunity of excess return. By means of global major market indices as the empirical evidences, it shows that the new approach can provide a more efficient frontier for a portfolio and there would be less computational cost to solve MV model.
Year
DOI
Venue
2006
10.1007/11903697_116
SEAL
Keywords
Field
DocType
tactical allocation,global major market index,fuzzy scenarios clustering,popular global assets allocation,major asset,efficient frontier,fuzzy scenario,mv model,clustering-based approach,tactical global assets allocation,rebalance quantitative model,new interactive model,empirical evidence,asset allocation
Mathematical optimization,Weighting,Return on investment,Project portfolio management,Computer science,Fuzzy logic,Portfolio,Stock exchange,Efficient frontier,Cluster analysis
Conference
Volume
ISSN
ISBN
4247
0302-9743
3-540-47331-9
Citations 
PageRank 
References 
0
0.34
3
Authors
1
Name
Order
Citations
PageRank
Hsing-Wen Wang123.96