Portfolio Optimization and Asset Allocation

Create portfolios, evaluate composition of assets, perform mean-variance, CVaR, or mean absolute-deviation portfolio optimization, backtest investment strategies

Quantitative investment managers and risk managers use portfolio optimization to choose the proportions of various assets to be held in a portfolio. The goal of portfolio optimization is to maximize a measure or proxy for a portfolio's return contingent on a measure or proxy for a portfolio’s risk. This toolbox provides a comprehensive suite of portfolio optimization and analysis tools for performing capital allocation, asset allocation, and risk assessment. This toolbox provides a comprehensive suite of portfolio optimization and analysis tools for performing capital allocation, asset allocation, and risk assessment as well as a backtesting framework to backtest portfolio allocation strategies.

Frequently Viewed Topics

Portfolio Optimization Theory
Background theory for Portfolio optimization problems
Mean-Variance Portfolio Optimization
Create Portfolio object, evaluate composition of assets, perform mean-variance portfolio optimization
Conditional Value-at-Risk Portfolio Optimization
Create portfolios, evaluate composition of assets, perform CVaR portfolio optimization
Mean-Absolute Deviation Portfolio Optimization
Create portfolios, evaluate composition of assets, perform MAD portfolio optimization
Portfolio Analysis
Analyze portfolio for returns variance and covariance, simulate correlation of assets, calculate portfolio value at risk (VaR)
Backtest Framework
Define investment strategies, run backtests, analyze strategy performance

Featured Examples

Portfolio Optimization Examples

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.

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Portfolio Optimization Against a Benchmark

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

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Backtest Investment Strategies

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.

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Backtest Investment Strategies with Trading Signals

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:

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