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Create PortfolioMAD object for mean-absolute deviation portfolio optimization

Use the `PortfolioMAD`

function to
create a `PortfolioMAD`

object for mean-absolute
deviation portfolio optimization. For more information, see `PortfolioMAD`

.

You can use the `PortfolioMAD`

function in
several ways. To set up a portfolio optimization problem in a PortfolioMAD
object, the simplest syntax is:

p = PortfolioMAD;

`p`

, such that
all object properties are empty.
The `PortfolioMAD`

function also accepts collections
of argument name-value pair arguments for properties and their values.
The `PortfolioMAD`

function accepts inputs for properties
with the general syntax:

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

If a PortfolioMAD object exists, the syntax permits the first
(and only the first argument) of the `PortfolioMAD`

function
to be an existing object with subsequent argument name-value pair
arguments for properties to be added or modified. For example, given
an existing PortfolioMAD object in `p`

, the general
syntax is:

p = PortfolioMAD(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 `PortfolioMAD`

function
tries to detect 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 PortfolioMAD object is a value object so that, given portfolio `p`

,
the following code creates two objects, `p`

and `q`

,
that are distinct:

q = PortfolioMAD(p, ...)

After creating a `PortfolioMAD`

object, you
can use the associated object functions to set portfolio constraints,
analyze the efficient frontier, and validate the portfolio model.

For details on this workflow, seePortfolioMAD Object Workflow and for more detailed information on the theoretical basis for conditional value-at-risk portfolio optimization, see Portfolio Optimization Theory.

`p = PortfolioMAD`

`p = PortfolioMAD(Name,Value)`

`p = PortfolioMAD(p,Name,Value)`

constructs
an empty PortfolioMAD object for mean-absolute deviation portfolio
optimization and analysis. You can then add elements to the PortfolioMAD
object using the supported add and set functions. For more information,
see Creating the PortfolioMAD Object. `p`

= PortfolioMAD

constructs
a PortfolioMAD object for mean-absolute deviation portfolio optimization
and analysis with additional options specified by one or more `p`

= PortfolioMAD(`Name,Value`

)`Name,Value`

arguments.

constructs
a PortfolioMAD object for mean-absolute deviation portfolio optimization
and analysis using a previously constructed PortfolioMAD object `p`

= PortfolioMAD(`p`

,`Name,Value`

)`p`

with
additional options specified by one or more `Name,Value`

arguments.

[1] For a complete list of references for the PortfolioMAD object, see Portfolio Optimization.

`estimateFrontier`

| `plotFrontier`

| `setScenarios`

- Creating the PortfolioMAD Object
- Common Operations on the PortfolioMAD Object
- Working with MAD Portfolio Constraints Using Defaults
- Asset Returns and Scenarios Using PortfolioMAD Object
- Validate the MAD Portfolio Problem
- Estimate Efficient Portfolios Along the Entire Frontier for PortfolioMAD Object
- Estimate Efficient Frontiers for PortfolioMAD Object
- Postprocessing Results to Set Up Tradable Portfolios
- Portfolio Optimization Theory
- PortfolioMAD Object Workflow

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