Tid: 27 juni 2017 kl 11.00-12.00.Seminarierummet 3721, Institutionen för matematik, KTH, Lindstedtsvägen 25, plan 7. Karta!
Föredragshållare: Trotte Boman och Samuel Jangenstål
Title: Beating the MSCI USA Index by Using Other Weighting Techniques
Abstract: In this thesis various portfolio weighting strategies are tested. Their performance is determined by their average annual return, Sharpe ratio, tracking error, information ratio and annual standard deviation. The data used is provided by Öhman from Bloomberg and consists of monthly data between 1996-2016 of all stocks that were in the MSCI USA Index at any time between 2002-2016. For any given month we use the last five years of data as a basis for the analysis. Each time the MSCI USA Index changes portfolio constituents we update which constituents are in our portfolio. The traditional weighting strategies used in this thesis are market capitalization, equal, risk-adjusted alpha, fundamental and minimum variance weighting. On top of that, the weighting strategies are used in a cluster framework where the clusters are constructed by using K-means clustering on the stocks each month. The clusters are assigned equal weight and then the traditional weighting strategies are applied within each cluster. Additionally, a GARCH-estimated covariance matrix of the clusters is used to determine the minimum variance optimized weights of the clusters where the constituents within each cluster are equally weighted. We conclude in this thesis that the market capitalization weighting strategy is the one that earns the least of all traditional strategies. From the results we can conclude that there are weighting strategies with higher Sharpe ratio and lower standard deviation. The risk-adjusted alpha in a traditional framework performed best out of all strategies. All cluster weighting strategies with the exception of risk-adjusted alpha outperform their traditional counterpart in terms of return.
|Sidansvarig: Jimmy Olsson