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Troubleshooting MAD Portfolio Optimization Results

PortfolioMAD Object Destroyed When Modifying

If a PortfolioMAD object is destroyed when modifying, remember to pass an existing object into the PortfolioMAD object if you want to modify it, otherwise it creates a new object. See Creating the PortfolioMAD Object for details.

Matrix Incompatibility and "Non-Conformable" Errors

If you get matrix incompatibility or "non-conformable" errors, the representation of data in the tools follows a specific set of basic rules described in Conventions for Representation of Data.

Missing Data Estimation Fails

If asset return data has missing or NaN values, the simulateNormalScenariosByData function with the 'missingdata' flag set to true may fail with either too many iterations or a singular covariance. To correct this problem, consider this:

  • If you have asset return data with no missing or NaN values, you can compute a covariance matrix that may be singular without difficulties. If you have missing or NaN values in your data, the supported missing data feature requires that your covariance matrix must be positive-definite, that is, nonsingular.

  • simulateNormalScenariosByData uses default settings for the missing data estimation procedure that might not be appropriate for all problems.

In either case, you might want to estimate the moments of asset returns separately with either the ECM estimation functions such as ecmnmle or with your own functions.

mad_optim_transform Errors

If you obtain optimization errors such as:

Error using mad_optim_transform (line 276)
Portfolio set appears to be either empty or unbounded. Check constraints.

Error in PortfolioMAD/estimateFrontier (line 64)
	[AI, bI, AE, bE, lB, uB, f0, f, x0] = mad_optim_transform(obj);
Error using mad_optim_transform (line 281)
Cannot obtain finite lower bounds for specified portfolio set.

Error in PortfolioMAD/estimateFrontier (line 64)
	[AI, bI, AE, bE, lB, uB, f0, f, x0] = mad_optim_transform(obj);
Since the portfolio optimization tools require a bounded portfolio set, these errors (and similar errors) can occur if your portfolio set is either empty and, if nonempty, unbounded. Specifically, the portfolio optimization algorithm requires that your portfolio set have at least a finite lower bound. The best way to deal with these problems is to use the validation methods in Validate the MAD Portfolio Problem. Specifically, use estimateBounds to examine your portfolio set, and use checkFeasibility to ensure that your initial portfolio is either feasible and, if infeasible, that you have sufficient turnover to get from your initial portfolio to the portfolio set.


To correct this problem, try solving your problem with larger values for turnover and gradually reduce to the value that you want.

Efficient Portfolios Do Not Make Sense

If you obtain efficient portfolios that, do not seem to make sense, this can happen if you forget to set specific constraints or you set incorrect constraints. For example, if you allow portfolio weights to fall between 0 and 1 and do not set a budget constraint, you can get portfolios that are 100% invested in every asset. Although it may be hard to detect, the best thing to do is to review the constraints you have set with display of the PortfolioMAD object. If you get portfolios with 100% invested in each asset, you can review the display of your object and quickly see that no budget constraint is set. Also, you can use estimateBounds and checkFeasibility to determine if the bounds for your portfolio set make sense and to determine if the portfolios you obtained are feasible relative to an independent formulation of your portfolio set.

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