This master thesis investigates factor premiums on the European stock market and how to combine themin order to achieve the best investment strategy based on different performance and risk objectives. Additionally, the thesis examines whether macroeconomic indicators can be used to predict factor returnsas well as contribute to optimization of returns in a multifactor portfolio.The analyzed factors are the market, size, value, momentum, betting-against-beta and quality-minusjunk. First, the individual factors are evaluated in an aggregated portfolio including 17 European markets.Second, an equally weighted portfolio of the six factors is constructed to examine whether it is possibleto utilize the potential diversification benefits between the factors. Third, dynamic mean-variance optimized portfolios are constructed to examine whether it is possible to optimize the static multifactor portfolios performance and risk objectives by re-balancing the factor exposures continually on the basis ofthe minimum variance and tangency portfolio, created using shrinkage of the covariance matrices.Fourth, factor timing portfolios are constructed based on three methods, all of which have different approaches to how the weights are tilted towards the different factors in the given macroeconomic regime.The analyzed factors have proven to behave relatively as the theory portrays after testing over a nearly25-year period. The thesis finds attractive factor premiums for the momentum, betting-against-beta andquality-minus-junk factors, whereas the market-, size- and value factors have delivered insignificant excess returns and unattractive factor premiums. However, a combination of the six factors seems to be afavorable investment strategy. The minimum-variance portfolios outperform the majority of the tangencyportfolios and the static benchmark portfolio from a risk adjusted perspective. The analysis show that thetangency portfolio may produce higher total returns, nevertheless, can be a very risky method that leadsto large losses beyond the portfolio’s initial value. The factors show different performances across themacroeconomic regimes, which is consistent with the hypothesis. Although the factor timing portfoliosare not uniquely advantageous compared to the benchmark portfolio, they have succeeded in deliveringsatisfactory risk-adjusted returns using a simplified Black-Litterman method. The problem with the models is the use of back testing, where the models in some periods acts on the basis of outdated signalsabout the regime.
|Educations||MSc in Finance and Accounting, (Graduate Programme) Final Thesis|
|Number of pages||132|