I want to apply a Black-Litterman approach for portfolio optimization. My question is how to select investor views? I need to base the choice on a model. I would be thankful if you could give me some references or suggestions.
Any potential source of "alpha" would suffice, in fact. And your research would be research of how this "alpha" source is able to produce alpha. On my mind, candidates could be (1) some well-documented predictions of somebody (like Prechter or Dow) - in that case you'll have 2-3 views for period, and the rest of assets or classes remain in equilibrium - see for example this paper by Batyrbekova - http://cyberleninka.ru/article/n/using-elliott-wave-theory-predictions-as-inputs-in-equilibrium-portfolio-models-with-views; (2) or broker recommendations (ANR in Bloomberg); (3) or some machine learning algorithm, like random forest - something similar to that http://cyberleninka.ru/article/n/application-of-ensemble-learning-for-views-generation-in-meucci-portfolio-optimization-framework; (4) or even traditional technical analysis, some method which would give you target prices; (5) see also this: http://cyberleninka.ru/article/n/cycle-adjusted-capital-market-expectations-under-black-litterman-framework-in-global-tactical-asset-allocation ; the author uses FED business cycle indicator.
Remember that in Black-Litterman view is normally distributed random variable, so you would have to make certain assumptions.