Performance testing theblack-litterman model on OMXS30

An investor wants to maximize return at the cost of as little risk as possible and theBlack-Litterman model can help see that this condition is met. This thesis willinvestigate whether a portfolio created by using modern portfolio theory can beat thebenchmark index in terms of risk-adjusted return during a five year backtest period(2013-2017). Harry Markowitz provides the mean variance optimization frameworkwhile the practical Black-Litterman model adds the opportunity to tweak performancewith views on stock returns. The method for producing views for the Black-Littermanmodel can vary a lot and is what makes this thesis, for all that we know, unique. Theviews in our model stem from regression on the summed up earnings per share forthe last four quarters multiplied by the corresponding historical price earnings ratioand the historical stock price. The regressions provide data on how over orundervalued the stocks are. Backtesting our modified Black-Litterman model yieldsimpressive results in terms of risk-adjusted return and we encourage other studentsof the financial market to further investigate the performance of our modified portfolio.However most of the results are not statistically significant on a 5% significance leveldue to the need for more data points. This method is purely quantitative and can befully replicated to yield the same results for any interested investor.