What Is Multiple Discriminant Analysis (MDA)?

The term multiple discriminant analysis (MDA) refers to a statistical technique used by financial planners, investment advisors, and analysts to evaluate potential investments when many variables are at stake. MDA allows financial professionals the viability of investing in various market securities by studying different factors or variables, such as volatility. This is a branch of discriminant analysis, which is used by researchers and statisticians who make classifications of individuals and data based on different variables.

Key Takeaways

  • Multiple discriminant analysis is used by financial planners to evaluate potential investments when a number of variables must be taken into account.
  • MDA is a branch of discriminant analysis, which is commonly used by statisticians and other researchers.
  • This technique is used to compress the variance between securities while screening for several variables.
  • An analyst who is considering a number of stocks may use multiple discriminant analysis to focus on the data points that are most important to the decision in question.
  • Financial professionals often use MDA as a way to develop Markowitz efficient sets, a type of portfolio that maximizes returns based on certain levels of risk.

Understanding Multiple Discriminant Analysis (MDA)

Multiple discriminant analysis is a technique that distinguishes datasets from each other based on the characteristics observed by the professional. It is, therefore, used in finance to compress the variance between securities while screening for several variables.

By using the MDA technique, financial professionals reduce the differences between certain variables so they can be classified into a number of larger groups and then be compared to another variable. In most cases, professionals who use MDA often try to group data into at least three, if not more, different groups.

An analyst who is considering a number of stocks may use multiple discriminant analysis as a tool to focus on the data points that are the most important. This simplifies the other differences among the stocks without totally dismissing them. For instance, an analyst who wants to select securities based on values that measure volatility and historical consistency may use MDA in order to factor out other variables such as price.

Other variables that analysts can use when employing multiple discriminant analyses include different financial ratios.

The main reason that professionals use this technique is to develop Markowitz efficient sets. These investment portfolios are developed based on returns that are maximized for a certain level of risk. It was named after economist Harry Markowitz, who is also considered to be the father of modern portfolio theory.

Special Considerations

As noted above, multiple discriminant analysis is related to discriminant analysis, which is commonly used by statisticians and other researchers. MDA 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. Discriminant analysis helps researchers and statisticians classify different data sets by setting a rule or selecting a value that will provide the most meaningful separation.