One major criticism about the traditional mean-variance portfolio optimization is that it tends to magnify the estimation error. A little estimation error can cause the distortion of the whole portfolio. Two popular ways to solve this problem are to use a resampling method or the Black-Litterman method (Bayesian method). The clustering method is a newer way to solve the problem. Clustering means we group the highly correlated stocks first and treat the group as a single stock. After we group the stocks, we will have some clusters of stocks, then we run the traditional mean-variance portfolio optimization for these clusters. The clustering method can improve the stability of the portfolio and reduce the impact of estimation error. In this project, we will explain why it works and we will perform tests to determine if clustering methods do improve the stabilities and performance of the portfolio.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-1312 |
| Date | 26 April 2005 |
| Creators | Ren, Zhiwei |
| Contributors | Arthur C. Heinricher, Advisor, , |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
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