The fundamental goal of portfolio theory is to optimally allocate your investments between different assets. Mean-variance optimization (MVO) is a quantitative tool that will allow you to make this allocation by considering the trade-off between risk and return.
Portfolio allocation is made based upon your expected return subject to a selected level of risk. Portfolio optimization using the mean and variance was first formulated by Markowitz. A key concept in this work was to identify the standard deviation (the square root of the variance) of a portfolio as a measure of its risk.
Limitations of the MVO approach are well documented. The overreliance on using historical returns as a means for future expected returns can result in this method being extremely sensitive to mild changes in input data. The problems with expected returns are outlined in detail in the below video:
The Black Litterman model attempts to address these concerns.
How does PrimeSolve apply the MVO approach?
PrimeSolve maintains a live co-variance table based on your approved product universe. MVO optimization can be applied across a specific asset class or your entire portfolio. When applying MVO across a total portfolio, asset class constraints based on a client/s risk profile will be applied.
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