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Multiple Discriminant Analysis – Definition

Multiple Discriminant Analysis (MDA) Definition

Multiple discriminant analysis (MDA) is a statistical measure that financial planners use to ascertain the prospective investments when a lot of variables need to be considered. It minimizes the dissimilarity between many variables, and organize them into large groups, where they can be compared with some other variable. Financial analysts use this technique for compressing the variance among securities while screening for many variables. MDA is associated with discriminant analysis that enables to differentiate a data set by assigning it a value that offers the most significant separation.

A Little More on What is  Multiple Discriminant Analysis

A financial analyst evaluating various stocks may use multiple discriminant analysis technique so as to emphasize on the data points that influence the decision in picture. It makes it easier to understand the variations and differences among the stocks, and not neglecting them. For instance, a person who chooses volatility and historical consistency as factors while choosing securities, he/she may prefer using MDA approach to rule out other factors including price.

In statistical language, multiple discriminant analysis is also known as canonical variates analysis or canonical discriminant analysis. Researchers generally use this approach for analyzing information in several areas.

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