Geometric Brownian Motion Calculation
When making stock projections by running a Monte Carlo with Geometric Brownian Motion, what is the best way to calculate drift? Laugh all you like at how simple this question is to answer, but I'm a new analyst at a securities consulting firm and this is the first time we've had to do this.
If you have a view, Black Litterman. If not, an equilibrium model. Google it and you will find a ton of results. Start off by defining your 'market portfolio' and go from there. I don't think it's a simple question to answer at all.
The Black Litterman framework allows the user to input things like "I think x asset will outperform y asset by 2% per year with 65% confidence" or any other input you would like (absolute or relative performance). Based on that input, it will recalibrate the other expected returns so that they 'make sense' in the context of that view. For instance, if my only view is that I believe FB will underperform the market by 5% per year with a 75% confidence, the model will overweight and underweight each component that has higher or lower correlation to FB.
The point is your portfolio weights and expected returns are linked (you buy more of things you think will appreciate). The market portfolio is a known thing, so you are better off starting off with that and deriving expected returns and then taking those expected returns to construct your portfolio.
Drift should be your expected return/growth. Been a while since I've taken any stochastic calc, so double check.
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