The Black Litterman Asset Allocation Model : An empirical comparison of approaches for estimating the subjective view vector and implications for risk-return characteristics

Background In the early 90’s, Black and Litterman extended the pioneering work of Markowitz by developing a model combining qualitative and quantitative research in a delicate optimization process. It allows for a subjective view parameter in a quantitative model and with absent views, the investor will have no reason to deviate from the market equilibrium portfolio. As one can imagine, the investors’ views incorporated in the Black-Litterman model is crucial and is the unique advantage or problem of the model, depending on the user’s ability to properly forecast expected return. However, it has yet to be covered thoroughly in the academic literature how different approaches for estimating subjective views actually yield a more attractive risk-return profile. Purpose In this study we intend to use the Black-Litterman model with subjective views generated from analysts’ forecasts and a statistical valuation multiple in order to compare and analyze how portfolios differentiate regarding asset allocation and risk-return characteristics. Methodology Two different valuation approaches are compared and analyzed in the BlackLitterman Asset Allocation Model by running historical simulations on risk adjusted performance. To generate elements for the subjective view vector we use analysts’ forecasts and a statistical valuation multiple approach from a fixed effect panel regression. The empirical study has a Swedish perspective with simulations based on data from the OMXS30, with a analyzed period stretching from March of 2008 to March 2018. Conclusions Even though analysts’ forecasts proved to be the most accurate approach estimating the direction of the stock price and outright return for all given time horizons, the statistical counterpart was the superior when applied in a risk adjusted context in the Black-Litterman model. This holds true for the larger portion of occasions when modifying key input variables such as transaction costs, risk aversion, certainty level and time horizon. Our empirical findings show that the BlackLitterman model is suitable for investment managers committing to the CAPM approach to estimate expected return in the long turn, but who still is managing an alpha driven portfolio in the short term, capitalizing on mispricing