Creating the Portfolio Object

To create a fully specified mean-variance portfolio optimization problem, instantiate the Portfolio object using Portfolio. For information on the workflow when using Portfolio objects, see Portfolio Object Workflow.

Syntax

Use Portfolio to create an instance of an object of the Portfolio class. You can use Portfolio in several ways. To set up a portfolio optimization problem in a Portfolio object, the simplest syntax is:

p = Portfolio;

This syntax creates a Portfolio object, p, such that all object properties are empty.

The Portfolio object also accepts collections of argument name-value pair arguments for properties and their values. The Portfolio object accepts inputs for public properties with the general syntax:

	p = Portfolio('property1', value1, 'property2', value2, ... );

If a Portfolio object already exists, the syntax permits the first (and only the first argument) of Portfolio to be an existing object with subsequent argument name-value pair arguments for properties to be added or modified. For example, given an existing Portfolio object in p, the general syntax is:

p = Portfolio(p, 'property1', value1, 'property2', value2, ... );

Input argument names are not case-sensitive, but must be completely specified. In addition, several properties can be specified with alternative argument names (see Shortcuts for Property Names). The Portfolio object detects problem dimensions from the inputs and, once set, subsequent inputs can undergo various scalar or matrix expansion operations that simplify the overall process to formulate a problem. In addition, a Portfolio object is a value object so that, given portfolio p, the following code creates two objects, p and q, that are distinct:

q = Portfolio(p, ...)

Portfolio Problem Sufficiency

A mean-variance portfolio optimization is completely specified with the Portfolio object if these two conditions are met:

The moments of asset returns must be specified such that the property AssetMean contains a valid finite mean vector of asset returns and the property AssetCovar contains a valid symmetric positive-semidefinite matrix for the covariance of asset returns.
The first condition is satisfied by setting the properties associated with the moments of asset returns.
The set of feasible portfolios must be a nonempty compact set, where a compact set is closed and bounded.
The second condition is satisfied by an extensive collection of properties that define different types of constraints to form a set of feasible portfolios. Since such sets must be bounded, either explicit or implicit constraints can be imposed, and several functions, such as estimateBounds, provide ways to ensure that your problem is properly formulated.

Although the general sufficiency conditions for mean-variance portfolio optimization go beyond these two conditions, the Portfolio object implemented in Financial Toolbox™ implicitly handles all these additional conditions. For more information on the Markowitz model for mean-variance portfolio optimization, see Portfolio Optimization.

Portfolio Function Examples

If you create a Portfolio object, p, with no input arguments, you can display it using disp:

p = Portfolio;
disp(p)

    Portfolio with properties:

                       BuyCost: []
                      SellCost: []
                  RiskFreeRate: []
                     AssetMean: []
                    AssetCovar: []
                 TrackingError: []
                  TrackingPort: []
                      Turnover: []
                   BuyTurnover: []
                  SellTurnover: []
                          Name: []
                     NumAssets: []
                     AssetList: []
                      InitPort: []
                   AInequality: []
                   bInequality: []
                     AEquality: []
                     bEquality: []
                    LowerBound: []
                    UpperBound: []
                   LowerBudget: []
                   UpperBudget: []
                   GroupMatrix: []
                    LowerGroup: []
                    UpperGroup: []
                        GroupA: []
                        GroupB: []
                    LowerRatio: []
                    UpperRatio: []
                  MinNumAssets: []
                  MaxNumAssets: []
    ConditionalBudgetThreshold: []
        ConditionalUpperBudget: []
                     BoundType: []

The approaches listed provide a way to set up a portfolio optimization problem with the Portfolio object. The set functions offer additional ways to set and modify collections of properties in the Portfolio object.

Using the Portfolio Function for a Single-Step Setup

You can use the Portfolio object to directly set up a “standard” portfolio optimization problem, given a mean and covariance of asset returns in the variables m and C:

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

p = Portfolio('assetmean', m, 'assetcovar', C, ...
'lowerbudget', 1, 'upperbudget', 1, 'lowerbound', 0)

p =

Portfolio with properties:

                       BuyCost: []
                      SellCost: []
                  RiskFreeRate: []
                     AssetMean: [4×1 double]
                    AssetCovar: [4×4 double]
                 TrackingError: []
                  TrackingPort: []
                      Turnover: []
                   BuyTurnover: []
                  SellTurnover: []
                          Name: []
                     NumAssets: 4
                     AssetList: []
                      InitPort: []
                   AInequality: []
                   bInequality: []
                     AEquality: []
                     bEquality: []
                    LowerBound: [4×1 double]
                    UpperBound: []
                   LowerBudget: 1
                   UpperBudget: 1
                   GroupMatrix: []
                    LowerGroup: []
                    UpperGroup: []
                        GroupA: []
                        GroupB: []
                    LowerRatio: []
                    UpperRatio: []
                  MinNumAssets: []
                  MaxNumAssets: []
    ConditionalBudgetThreshold: []
        ConditionalUpperBudget: []
                     BoundType: []

The LowerBound property value undergoes scalar expansion since AssetMean and AssetCovar provide the dimensions of the problem.

You can use dot notation with the function plotFrontier.

p.plotFrontier

Using the Portfolio Function with a Sequence of Steps

An alternative way to accomplish the same task of setting up a “standard” portfolio optimization problem, given a mean and covariance of asset returns in the variables m and C (which also illustrates that argument names are not case-sensitive):

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

p = Portfolio;
p = Portfolio(p, 'assetmean', m, 'assetcovar', C);
p = Portfolio(p, 'lowerbudget', 1, 'upperbudget', 1);
p = Portfolio(p, 'lowerbound', 0);
 
plotFrontier(p)

This way works because the calls to Portfolio are in this particular order. In this case, the call to initialize AssetMean and AssetCovar provides the dimensions for the problem. If you were to do this step last, you would have to explicitly dimension the LowerBound property as follows:

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

p = Portfolio;
p = Portfolio(p, 'LowerBound', zeros(size(m)));
p = Portfolio(p, 'LowerBudget', 1, 'UpperBudget', 1);
p = Portfolio(p, 'AssetMean', m, 'AssetCovar', C);
 
plotFrontier(p)

If you did not specify the size of LowerBound but, instead, input a scalar argument, the Portfolio object assumes that you are defining a single-asset problem and produces an error at the call to set asset moments with four assets.

Shortcuts for Property Names

The Portfolio object has shorter argument names that replace longer argument names associated with specific properties of the Portfolio object. For example, rather than enter 'assetcovar', the Portfolio object accepts the case-insensitive name 'covar' to set the AssetCovar property in a Portfolio object. Every shorter argument name corresponds with a single property in the Portfolio object. The one exception is the alternative argument name 'budget', which signifies both the LowerBudget and UpperBudget properties. When 'budget' is used, then the LowerBudget and UpperBudget properties are set to the same value to form an equality budget constraint.

Shortcuts for Property Names

Shortcut Argument Name	Equivalent Argument / Property Name
`ae`	`AEquality`
`ai`	`AInequality`
`covar`	`AssetCovar`
`assetnames` or `assets`	`AssetList`
`mean`	`AssetMean`
`be`	`bEquality`
`bi`	`bInequality`
`group`	`GroupMatrix`
`lb`	`LowerBound`
`n` or `num`	`NumAssets`
`rfr`	`RiskFreeRate`
`ub`	`UpperBound`
`budget`	`UpperBudget` and `LowerBudget`

For example, this call Portfolio uses these shortcuts for properties and is equivalent to the previous examples:

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

p = Portfolio('mean', m, 'covar', C, 'budget', 1, 'lb', 0);
plotFrontier(p)

Direct Setting of Portfolio Object Properties

Although not recommended, you can set properties directly, however no error-checking is done on your inputs:

m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0; 
    0.00408 0.0289 0.0204 0.0119;
    0.00192 0.0204 0.0576 0.0336;
    0 0.0119 0.0336 0.1225 ];

p = Portfolio;
p.NumAssets = numel(m);
p.AssetMean = m;
p.AssetCovar = C;
p.LowerBudget = 1;
p.UpperBudget = 1;
p.LowerBound = zeros(size(m));

plotFrontier(p)