Portfolio object implements mean-variance portfolio optimization. Every
property and function of the
Portfolio object is public, although
some properties and functions are hidden. See
Portfolio for the properties and
functions of the
Portfolio object. The
Portfolio object is a value object where every instance of
the object is a distinct version of the object. Since the
Portfolio object is also a MATLAB® object, it inherits the default functions associated with MATLAB objects.
Portfolio object and its functions are an interface for mean-variance
portfolio optimization. So, almost everything you do with the
Portfolio object can be done using the associated functions.
The basic workflow is:
Design your portfolio problem.
Portfolio to create the
Portfolio object or use the various
set functions to set up your portfolio
Use estimate functions to solve your portfolio problem.
In addition, functions are available to help you view intermediate
results and to diagnose your computations. Since MATLAB features are part of a
Portfolio object, you can
save and load objects from your workspace and create and manipulate arrays of
objects. After settling on a problem, which, in the case of mean-variance portfolio
optimization, means that you have either data or moments for asset returns and a
collection of constraints on your portfolios, use
Portfolio to set the properties for
Portfolio lets you create an object
from scratch or update an existing object. Since the
object is a value object, it is easy to create a basic object, then use functions to
build upon the basic object to create new versions of the basic object. This is
useful to compare a basic problem with alternatives derived from the basic problem.
For details, see Creating the Portfolio Object.
You can set properties of a
Portfolio object using either
Portfolio or various
Although you can also set properties directly, it is not recommended since error-checking is not performed when you set a property directly.
Portfolio object supports setting
properties with name-value pair arguments such that each argument name is a property
and each value is the value to assign to that property. For example, to set the
AssetCovar properties in an
p with the values
C, use the
p = Portfolio(p, 'AssetMean', m, 'AssetCovar', C);
In addition to
Portfolio, which lets you set
individual properties one at a time, groups of properties are set in a
Portfolio object with various “set” and
“add” functions. For example, to set up an average turnover
constraint, use the
setTurnover function to specify the
bound on portfolio average turnover and the initial portfolio. To get individual
properties from a Portfolio object, obtain properties directly or use an assortment
of “get” functions that obtain groups of properties from a
Portfolio object. The
Portfolio object and the
set functions have several useful features:
Portfolio and the
set functions try to determine the dimensions of
your problem with either explicit or implicit inputs.
Portfolio and the
set functions try to resolve ambiguities with
Portfolio and the
set functions perform scalar expansion on arrays
Portfolio object functions try to
diagnose and warn about problems.
Portfolio object uses the default display functions provided by
a Portfolio object and its properties with or without the object variable
Save and load
Portfolio objects using the MATLAB
Estimating efficient portfolios and efficient frontiers is the primary purpose of the portfolio optimization tools. Anefficient portfolio is the portfolios that satisfy the criteria of minimum risk for a given level of return and maximum return for a given level of risk. A collection of “estimate” and “plot” functions provide ways to explore the efficient frontier. The “estimate” functions obtain either efficient portfolios or risk and return proxies to form efficient frontiers. At the portfolio level, a collection of functions estimates efficient portfolios on the efficient frontier with functions to obtain efficient portfolios:
At the endpoints of the efficient frontier
That attains targeted values for return proxies
That attains targeted values for risk proxies
Along the entire efficient frontier
These functions also provide purchases and sales needed to shift from an initial or current portfolio to each efficient portfolio. At the efficient frontier level, a collection of functions plot the efficient frontier and estimate either risk or return proxies for efficient portfolios on the efficient frontier. You can use the resultant efficient portfolios or risk and return proxies in subsequent analyses.
Although all functions associated with a
Portfolio object are designed to
work on a scalar
Portfolio object, the array capabilities of
MATLAB enable you to set up and work with arrays of
Portfolio objects. The easiest way to do this is with the
repmat function. For example, to
create a 3-by-2 array of
p = repmat(Portfolio, 3, 2); disp(p)
Portfolioobjects, you can work on individual
Portfolioobjects in the array by indexing. For example:
p(i,j) = Portfolio(p(i,j), ... );
Portfoliofor the (
j) element of a matrix of
Portfolioobjects in the variable
If you set up an array of
Portfolio objects, you can access properties of a
Portfolio object in the array by indexing so that you
can set the lower and upper bounds
for the (
k) element of
a 3-D array of
p(i,j,k) = setBounds(p(i,j,k),lb, ub);
[lb, ub] = getBounds(p(i,j,k));
Portfolioobject functions work on only one
Portfolioobject at a time.
You can subclass the
Portfolio object to override existing functions or to
add new properties or functions. To do so, create a derived class from the
Portfolio class. This gives you all
the properties and functions of the
Portfolio class along with
any new features that you choose to add to your subclassed object. The
Portfolio class is derived from an abstract class called
AbstractPortfolio. Because of this, you can also create a
derived class from
AbstractPortfolio that implements an entirely
different form of portfolio optimization using properties and functions of the
The portfolio optimization tools follow these conventions regarding the representation of different quantities associated with portfolio optimization:
Asset returns or prices are in matrix form with samples for a given asset going down the rows and assets going across the columns. In the case of prices, the earliest dates must be at the top of the matrix, with increasing dates going down.
The mean and covariance of asset returns are stored in a vector and a matrix and the tools have no requirement that the mean must be either a column or row vector.
Portfolios are in vector or matrix form with weights for a given portfolio going down the rows and distinct portfolios going across the columns.
Constraints on portfolios are formed in such a way that a portfolio is a column vector.
Portfolio risks and returns are either scalars or column vectors (for multiple portfolio risks and returns).