Optimization

Michaud optimization refers to the patented process used to find the Michaud Resampled Efficient Frontier™. Before you optimize, two steps are required. First, you need to choose how optimization will take place by reviewing and adjusting your options.  (The Info Worksheet provides a convenient review of your current selections.) Second, you must set up an investment problem.

After preparing an investment problem, initiate the optimization process by clicking the Optimize Button on the NFA ribbon.  The Optimizer begins to run the simulations necessary for resampling; a progress meter appears. This progress meter displays the progress of all steps that occur when you click the Optimize Button: running the simulations, averaging the simulations, etc. If you have multiple cores, progress made on alternate cores is displayed in a lighter blue.

When the simulations begin, a progress meter for the entire process appears at the bottom left. When the efficient frontier simulations begin, the NFA Optimizer Running Window appears with an optimization progress meter, a portfolio convergence measurement, and a Stop Button. When the More Button has been toggled, the optimization progress bar exhibits the convergence to the final Michaud Resampled Efficient Frontier on two simplified charts: an Efficient Frontier Chart (showing the Classical Markowitz Mean Variance Frontier, the converging Michaud Resampled Efficient Frontier, and a few of the simulated efficient frontiers) and a converging Portfolio Composition Map. At the end of optimization, the NFA Optimizer Running Window disappears, but the progress meter in the lower left indicates the remaining functions that the application must perform before you can see the results.  

• Stopping the Optimizer during optimization populates the worksheets with results from the simulations completed at the time the button was clicked.

• The optimization progress meter in the NFA Optimizer Running Window displays the simulation progress. If you set 100 simulations and stop the Optimizer when this progress meter has hit 30%, the results consist of the average after 30 simulations. When a target portfolio convergence is set, the target number of simulations increases in discrete jumps until the target number is achieved. The progress bar shows progress toward the current target number of simulations. When the progress gets to 100%, the target simulations are revised upward until the target portfolio convergence (maximum standard error on any portfolio weight) is attained.   

• The portfolio convergence measures the maximum standard error of any asset's portfolio weight.  

For large and complicated optimization cases it can be beneficial to test the optimization on a small number of simulations. It is worth examining results to check that they make intuitive sense, i.e. assets with high returns and/or lower standard deviations or lower correlations should be receiving more portfolio weight. If this is not the case, check the constraints to make sure the weights are not being unintentionally coerced to unreasonable values. Constraint Analysis I is useful for this purpose. Remember that a limited number of simulations limits the usefulness of the results for anything other than a cursory review.

See Interpreting the Results for tools in understanding the optimal portfolios.  

For more information about Michaud optimization, visit New Frontier's website.