The following sequence of examples highlights features of the Portfolio object in the Financial Toolbox™. Specifically, the examples use the Portfolio object to show how to set up mean-variance portfolio optimization problems that focus on the two-fund theorem, the impact of transaction costs and turnover constraints, how to obtain portfolios that maximize the Sharpe ratio, and how to set up two popular hedge-fund strategies — dollar-neutral and 130-30 portfolios.

Demonstrates optimizing a portfolio to maximize the information ratio relative to a market benchmark.

Perform backtesting of portfolio strategies using a backtesting framework implemented in MATLAB®. Backtesting is a useful tool to compare how investment strategies perform over historical or simulated market data. This example develops five different investment strategies and then compares their performance after running over a one-year period of historical stock data. The backtesting framework is implemented in two MATLAB® classes: backtestStrategy and backtestEngine.

Perform backtesting of portfolio strategies that incorporate investment signals in their trading strategy. The term signals includes any information that a strategy author needs to make with respect to trading decisions outside of the price history of the assets. Such information can include technical indicators, the outputs of machine learning models, sentiment data, macroeconomic data, and so on. This example uses three simple investment strategies based on derivative signal data:

Use a Portfolio object for portfolio optimization that includes a social performance measure for the percentage of women on a company's board and group constraints.

Three techniques of asset diversification in a portfolio. The purpose of asset diversification is to balance the exposure of the portfolio to any given asset in order to reduce volatility over a period of time. Given the sensitivity of the minimum variance portfolio to the estimation of the covariance matrix, some practitioners have added diversification techniques to the portfolio selection with the hope of minimizing risk measures other than the variance measures such as turnover, maximum drawdown, and so on.