M-V Optimizer

NOTE:  The entry-level M-V Optimizer product is no longer available for purchase, as its maintenance costs now exceed projected sales. For applications developers, website developers, or large-scale real-time traders, the MVO Library is the appropriate choice.

The Mean-Variance (M-V) Optimizer is a portfolio optimization package designed for professional money managers or individual investors, providing them with sophisticated analytics for rebalancing portfolios. The technique used by M-V Optimizer is called Mean-Variance Optimization, developed by Harry Markowitz in the 1950’s. There are two key quantities every investor seeks to control, return and risk, and we know that the key to controlling risk is diversification. But it is difficult to reduce your risk while maintaining the same expected return. M-V Optimizer was designed specifically to address this asset allocation problem. The user interface is a Microsoft Excel workbook, while a dynamic link library based on our versatile MVO Library performs the portfolio optimization. M-V Optimizer’s basic features include the ability to consider up to 250 assets (or asset classes), and functionality for generating expected returns, volatilities and correlations using user-supplied time series of returns. Available as an advanced feature is the consideration of additional linear constraints, commonly used to place bounds on a group of assets; for example, you want no more than 30% of your portfolio in energy stocks.

Below is the list of features included in M-V Optimizer:

Standard Features

  • Handles up to 250 assets/asset classes
  • User specified expected returns and volatilities
  • User specified correlation matrix
  • Provides the functionality to calculate the returns, volatilities, and
    correlation matrix from time-series data
  • Displays entire efficient frontier
  • Handles long and short positions in assets
  • User specified asset-specific minimum and maximum holding constraints
  • User specified benchmark portfolio for comparison purposes
  • Computes optimal portfolio from a user specified target return
  • Computes optimal portfolio from a user specified target volatility
  • Computes optimal Sharpe Ratio portfolio

Additional Advanced Features

  • Handles up to 125 general linear constraints