The financial portfolio model often referred to as the Black-Litterman model is analyzed using two approaches; a mathematical and a behavioral finance approach. After a detailed description of its framework, the Black-Litterman model is derived mathematically using a sampling theoretical approach. This approach generates a new interpretation of the model and gives an interpretable formula for the mystical parameter ? , the weight-on-views. Secondly, implications are drawn from research results within behavioral finance. One of the most interesting features of the Black-Litterman model is that the benchmark portfolio, against which the performance of the portfolio manager is evaluated, functions as the point of reference. According to behavioral finance, the actual utility function of the investor is reference-based and investors estimate losses and gains in relation to this benchmark. Implications drawn from research results within behavioral finance indicate and explain why the portfolio output given by the Black-Litterman model appears more intuitive to fund managers than portfolios generated by the Markowitz model.
Another feature of the Black-Litterman model is that the user assigns levels of confidence to each asset view in the form of confidence intervals. Research results within behavioral finance have, however, shown that people tend to be badly calibrated when estimating their levels of confidence. Research has shown that people are overconfident in financial decision-making, particularly when stating confidence intervals. This is problematic. For a deeper understanding of the use of the Black-Litterman model it seems that we should turn to those financial fields in which social and organizational context and issues are taken into consideration, to generate better knowledge of the use of the Black-Litterman model.
In 1952 Markowitz published the article Portfolio Selection, which can be seen as the genesis of modern portfolio theory. Portfolio models are tools intended to help portfolio managers of investors decide the weights of the assets within a fund or a portfolio. The ideas of Markowitz have had a great impact on portfolio theory and have, theoretically, withstood the test of time. However, in practical portfolio management the use of Markowitz’ model has not had the same impact as it has had in academia. Many fund and portfolio managers consider the composition of the portfolio given by the Markowitz model as unintuitive (Michaud, 1989; Black & Litterman, 1992). The practical problems in using the Markowitz model motivated Fisher Black and Robert Litterman (1992) to develop a new model in the early 1990s. The model, often referred to as the Black-Litterman model (hereafter the B-L model), builds on Markowitz’ model and aims at handling some of its practical problems. While optimization in the Markowitz model begins from the null portfolio, the optimization in the B-L model begins from, what Black and Litterman refer to as, the equilibrium portfolio (often assessed as the benchmark weights of the assets in the portfolio). “Bets” or deviations from the equilibrium portfolio are then taken on assets to which the investor has assigned views. To each view, the manager assigns a level of confidence, indicating how sure he/she is of that particular view. The level of confidence affects how much the weight of that particular asset in the B-L portfolio differs from the weights of the equilibrium portfolio.
The studies presented in this thesis, are intended to investigate, develop and test the B-L model in an applied perspective. This is done by (1) carefully and methodologically describing and mathematically deriving the model, (2) searching for and locating relevant research results within the field of behavioral finance and discussing their implications in relation to the use of the B-L model and, in conclusion, (3) reflecting on and discussing the research results and also presenting and discussing some theoretical starting points for future research.
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The Black-Litterman Model
