Portfolio managers must respond quickly to market changes and communicate portfolio metrics to their clients. Portfolio research teams use MATLAB to analyze and measure portfolios and to prototype and backtest strategies faster than with traditional programming languages like C++. Once strategies have been validated, researchers and developers deploy their analysis, strategies, and models into applications for investment managers and clients.
MATLAB enables you to access information instantly, compare portfolios and benchmarks, visualize performance history, and indicate recent transactions. You utilize prebuilt portfolio analysis and optimization functions to quantify risk and return. With MATLAB and associated toolboxes, portfolio research teams can:
Mean-Variance Efficient Frontier (Example)
To test and enhance portfolio management strategies, you perform backtests and undertake sensitivity analyses, such as examining the impact of interest rate changes on bond portfolios. MATLAB enables you to rapidly build backtest engines that can:
Developing Portfolio Optimization Models (Article)
Solve computationally intensive optimization problems in a fraction of the time it takes with a single computing processor by using MATLAB parallel computing tools. You can define your portfolio objectives and backtesting strategies to distribute tasks across multiple computing nodes with little-to-no modification of your MATLAB code.
"Our complex calculations depend on numerous iterations and a large amount of data. This is not something we can do with a spreadsheet. With MATLAB, we have a computational platform for easily performing these calculations, developing models, testing strategies, and deploying quantitative tools to our portfolio managers and risk managers."Read the story