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I am trying to implement the Black-Litterman model using own-defined views matrix (from consensus analysts). However, I have encountered the problems of negative portfolio weights in some periods, and some extreme weightings >1 (at most 200%). All of the results end up in underperformance relative to the benchmark (which is simply the market-weighted stocks without strategy, i.e. passive index). In one data set, the cumulative returns of the assets (before implemented in the model) generates superior returns relative to the benchmark, but when after implemented in the model, it underperforms.

Not sure what I am doing wrong, as I am following the instructions. At lest the BL main formula works as intended using examples from other data set or articles. Additionally, the model is also sensitive to changes in the inputs, such as the changes in the P-matrix and Q-matrix. Slightly changing the Q-matrix from negative to positive returns changes the results of the Black-Litterman model.

Could anyone guide me in the right direction?

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  • $\begingroup$ In BL not only do you specify your "views", you also get to specify the margin of error in your views (if I recall correctly it is the matrix $\Omega$). If these estimates are too small (i.e. if you attribute unwarranted precision to your views) you would get the effect that you are seeing. $\endgroup$ – noob2 Jul 2 '18 at 16:53
  • $\begingroup$ @noob2 I have specified omega according to the classical formula that most uses., i.e. omega that is proportional to the views. $\endgroup$ – Mataunited17 Jul 4 '18 at 11:33

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