Create Portfolio object for mean-variance portfolio optimization and analysis

Use the `Portfolio`

function to create a
`Portfolio`

object for mean-variance portfolio
optimization.

The main workflow for portfolio optimization is to create an instance of a Portfolio object that completely specifies a portfolio optimization problem and to operate on the Portfolio object using supported functions to obtain and analyze efficient portfolios. For details on this workflow, see Portfolio Object Workflow.

You can use the `Portfolio`

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

p = Portfolio;

`p`

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

object also accepts collections of name-value pair
arguments for properties and their values. The `Portfolio`

object
accepts inputs for properties with the general
syntax:

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

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

object to be an existing object with
subsequent 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 = PortfolioCVaR(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 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
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, ...)

After creating a `Portfolio`

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

For more detailed information on the theoretical basis for mean-variance optimization, see Portfolio Optimization Theory.

`p = Portfolio`

`p = Portfolio(Name,Value)`

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

creates an empty
Portfolio object for mean-variance portfolio optimization and analysis. You
can then add elements to the Portfolio object using the supported "add" and
"set" functions. For more information, see Creating the Portfolio Object.`p`

= Portfolio

creates a Portfolio object (`p`

= Portfolio(`Name,Value`

)`p`

) and sets Properties using name-value
pairs. For example, ```
p =
Portfolio('AssetList',Assets(1:12))
```

. You can specify multiple
name-value pairs.

creates a Portfolio object (`p`

= Portfolio(`p`

,`Name,Value`

)`p`

) using a previously created
Portfolio object `p`

and sets Properties using name-value
pairs. You can specify multiple name-value pairs.

`setAssetList` | Set up list of identifiers for assets |

`setInitPort` | Set up initial or current portfolio |

`setDefaultConstraints` | Set up portfolio constraints with nonnegative weights that sum to 1 |

`getAssetMoments` | Obtain mean and covariance of asset returns from Portfolio object |

`setAssetMoments` | Set moments (mean and covariance) of asset returns for Portfolio object |

`estimateAssetMoments` | Estimate mean and covariance of asset returns from data |

`setCosts` | Set up proportional transaction costs |

`addEquality` | Add linear equality constraints for portfolio weights to existing constraints |

`addGroupRatio` | Add group ratio constraints for portfolio weights to existing group ratio constraints |

`addGroups` | Add group constraints for portfolio weights to existing group constraints |

`addInequality` | Add linear inequality constraints for portfolio weights to existing constraints |

`getBounds` | Obtain bounds for portfolio weights from portfolio object |

`getBudget` | Obtain budget constraint bounds from portfolio object |

`getCosts` | Obtain buy and sell transaction costs from portfolio object |

`getEquality` | Obtain equality constraint arrays from portfolio object |

`getGroupRatio` | Obtain group ratio constraint arrays from portfolio object |

`getGroups` | Obtain group constraint arrays from portfolio object |

`getInequality` | Obtain inequality constraint arrays from portfolio object |

`getOneWayTurnover` | Obtain one-way turnover constraints from portfolio object |

`setGroups` | Set up group constraints for portfolio weights |

`setInequality` | Set up linear inequality constraints for portfolio weights |

`setBounds` | Set up bounds for portfolio weights for a portfolio object |

`setBudget` | Set up budget constraints |

`setCosts` | Set up proportional transaction costs |

`setDefaultConstraints` | Set up portfolio constraints with nonnegative weights that sum to 1 |

`setEquality` | Set up linear equality constraints for portfolio weights |

`setGroupRatio` | Set up group ratio constraints for portfolio weights |

`setInitPort` | Set up initial or current portfolio |

`setOneWayTurnover` | Set up one-way portfolio turnover constraints |

`setTurnover` | Set up maximum portfolio turnover constraint |

`setTrackingPort` | Set up benchmark portfolio for tracking error constraint |

`setTrackingError` | Set up maximum portfolio tracking error constraint |

`setMinMaxNumAssets` | Set cardinality constraints on the number of assets invested in a portfolio object |

`checkFeasibility` | Check feasibility of input portfolios against portfolio object |

`estimateBounds` | Estimate global lower and upper bounds for set of portfolios |

`estimateFrontier` | Estimate specified number of optimal portfolios on the efficient frontier |

`estimateFrontierByReturn` | Estimate optimal portfolios with targeted portfolio returns |

`estimateFrontierByRisk` | Estimate optimal portfolios with targeted portfolio risks |

`estimateFrontierLimits` | Estimate optimal portfolios at endpoints of efficient frontier |

`plotFrontier` | Plot efficient frontier |

`estimateMaxSharpeRatio` | Estimate efficient portfolio to maximize Sharpe ratio for Portfolio object |

`estimatePortMoments` | Estimate moments of portfolio returns for Portfolio object |

`estimatePortReturn` | Estimate mean of portfolio returns |

`estimatePortRisk` | Estimate portfolio risk according to risk proxy associated with corresponding object |

`setSolver` | Choose main solver and specify associated solver options for portfolio optimization |

`setSolverMINLP` | Choose mixed integer nonlinear programming (MINLP) solver for portfolio optimization |

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

`PortfolioCVaR`

| `PortfolioMAD`

| `estimateFrontier`

| `plotFrontier`

- Creating the Portfolio Object
- Working with Portfolio Constraints Using Defaults
- Estimate Efficient Portfolios for Entire Efficient Frontier for Portfolio Object
- Estimate Efficient Frontiers for Portfolio Object
- Asset Allocation Case Study
- Portfolio Optimization Examples
- Portfolio Optimization with Semicontinuous and Cardinality Constraints
- Black-Litterman Portfolio Optimization
- Portfolio Optimization Theory
- Portfolio Object Workflow
- Portfolio Object Properties and Functions
- Working with Portfolio Objects
- Setting and Getting Properties
- Displaying Portfolio Objects
- Saving and Loading Portfolio Objects
- Estimating Efficient Portfolios and Frontiers
- Arrays of Portfolio Objects
- Subclassing Portfolio Objects
- Conventions for Representation of Data