estimateMaxSharpeRatio - Class: Portfolio

Estimate efficient portfolio to maximize Sharpe ratio

Syntax

[pwgt,pbuy,psell] = estimateMaxSharpeRatio(obj)

Description

[pwgt,pbuy,psell] = estimateMaxSharpeRatio(obj) estimates an efficient portfolio that maximizes the Sharpe ratio.

Tips

Use dot notation to estimate an efficient portfolio that maximizes the Sharpe ratio:

[pwgt,pbuy,psell] = obj.estimateMaxSharpeRatio;

Input Arguments

obj

Portfolio object [Portfolio].

Note The risk-free rate is obtained from the property RiskFreeRate in the Portfolio object. If you leave the RiskFreeRate unset, it is assumed to be 0.

Output Arguments

`pwgt`	A portfolio on the efficient frontier with a maximum Sharpe ratio `[NumAssets` vector`]`.
`pbuy`	Purchases relative to an initial portfolio for a portfolio on the efficient frontier with a maximum Sharpe ratio `[NumAssets` vector`]`.
`psell`	Sales relative to an initial portfolio for a portfolio on the efficient frontier with maximum Sharpe ratio `[NumAssets` vector`]`.

Definitions

Sharpe Ratio

The Sharpe ratio is the ratio of the difference between the mean of portfolio returns and the risk-free rate divided by the standard deviation of portfolio returns. This method maximizes the Sharpe ratio among portfolios on the efficient frontier.

Attributes

`Access`	`public`
`Static`	`false`
`Hidden`	`false`

To learn about attributes of methods, see Method Attributes in the MATLAB Object-Oriented Programming documentation.

Examples

Estimate the efficient portfolio that maximizes the Sharpe ratio:

p = Portfolio('AssetMean',[0.3, 0.1, 0.5], 'AssetCovar',...
[0.01, -0.010,  0.004; -0.010,  0.040, -0.002;  0.004, -0.002,  0.023]);
p = p.setDefaultConstraints;
p.plotFrontier(20);
weights = p.estimateMaxSharpeRatio;
[risk, ret] = p.estimatePortMoments(weights);
hold on
plot(risk,ret,'*r');

Algorithms

The maximization of the Sharpe ratio is accomplished by a one-dimensional optimization using fminbnd to find the portfolio that minimizes the negative of the Sharpe ratio. The method takes only a fully qualified portfolio object as its input and uses all information in the object to solve the problem.