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Title: Risk reduction and portfolio optimization using clustering methods Authors:  Anna-Katharina Thoes - TU Kaiserslautern (Germany) [presenting]
Joern Sass - University of Kaiserslautern (Germany)
Abstract: Diversification is one of the main pillars of investment strategies. The prominent $1/N$-portfolio, which puts equal weight on each asset, is apart from its simplicity a method which is hard to outperform in realistic settings. But depending on the number of considered assets this method can lead to very large portfolios. We investigate how the number of assets can be reduced and which advantages and disadvantages arise. The idea is to reduce the number of chosen assets by clustering. Using clustering techniques the possible assets are separated into non-overlapping clusters and the assets within a cluster are ordered by their Sharpe ratio. Then the best asset of each portfolio is chosen to be a member of the new portfolio with equal weights, the cluster portfolio. We show that this portfolio inherits the advantages of the $1/N$-portfolio and can even outperform it empirically. To this end different clustering methods and performance measures are used to compare the portfolios on simulated and real data. To explain the observations on real data, we finally derive corresponding results in different model settings.