Portfolio optimization with factor views

Abstract

In this thesis, I have implemented the Black-Litterman model with factor views as forward-looking forecasts constructed according to the average value and momentum, which can be measured in sector portfolios of the European equity market index STOXX 600. The goal was to investigate whether it is possible to use factor views, constructed from the past pricing information, in combination with the BL model, and if this approach results in improved risk-return properties of the optimized portfolios. Additionally, it was investigated if such optimized portfolios, and in which parameter setting, deliver risk-adjusted returns in excess of the STOXX 600 Index as a benchmark. The portfolio optimization was conducted on 10 sector portfolios defined by the STOXX Europe 600 Index universe in the period from 1999 to 2019. The factor portfolios were constructed using best 2 and worst 2 performing sectors according to the 12-week momentum and the book-to-market ratio respectively. First 5 years of data have been used for estimating the first sector covariance matrix and for computation of the factor views. The historical simulations have been performed from 2004 to 2019 using 4-week rebalancing period. My empirical findings show that, over the investigated period, the Momentum factor has shown higher premia relative to the Value factor. This fact has also been reflected in the resulting Black-Litterman optimized portfolios. The BL approach with the Momentum factor has resulted in superior risk-return portfolios relative to the benchmark. The optimization with the Value factor shows close to no positive effect on the portfolio characteristics. Surprisingly, using both factors in combination, yields no benefits over the Black-Litterman optimization with the Momentum factor. Contrary to the efficient market theory developed in the 1970s, by using the approach described in this research, under no transaction-costs condition and by using only publicly available data, I was able to outperform the European equity market in risk-adjusted terms by using a wide range of Black-Litterman framework parameter settings. However, the performance inevitably comes with additional factor risk, which must be regarded in further analysis. The presented BL factor approach is suitable for tilting diversified portfolios towards factors that are known to be performance relevant (Fama and French, 1992).