What Is Multiple Discriminant Analysis (MDA)?

Multiple discriminant analysis (MDA) is a statistician's technique used by financial planners to evaluate potential investments when a number of variables must be taken into account. This technique reduces the differences between some variables so that they can be classified in a set number of broad groups, which can then be compared to another variable.

In finance, this technique is used to compress the variance between securities while screening for several variables.

Multiple discriminant analysis is related to discriminant analysis, which helps classify a data set by setting a rule or selecting a value that will provide the most meaningful separation.

How Multiple Discriminant Analysis Is Used

An analyst who is considering a number of stocks might use multiple discriminant analysis to focus on the data points that are most important to the decision that is under consideration. This simplifies the other differences among the stocks without totally dismissing them.

Key Takeaways

  • MDA is used by financial planners to evaluate potential investments when a number of variables must be taken into account.
  • This technique is used to compress the variance between securities while screening for several variables.
  • An analyst who is considering a number of stocks might use multiple discriminant analysis to focus on the data points that are most important to the decision that is under consideration.

For example, an analyst who wants to select securities based on values that measure volatility and historical consistency might use multiple discriminant analysis to factor out other variables such as price.

Multiple discriminant analysis is also known, at least to statisticians, as canonical variates analysis or canonical discriminant analysis. It is a type of discriminant analysis, which is widely used by researchers analyzing data in many fields.