Descriptive statistics often use percentiles to analyze large amounts of financial data. This is done by ranking data on a 0-100 percentile scale. The smallest value receives the value 0 percentile and the largest value is the 100th percentile. All other values receive proportional percentiles in between. The most important percentiles are the 25th percentile (first quartile), the 50th percentile (median), and the 75th percentile (third quartile). The graphical tool to show these values is a box plot graph (below).

Percentiles have the advantage of being resistant to outliers in the data set, which occur frequently in finance research. For this reason, most financial analysis concducted by the research company Obermatt is based on percentile calculations, while simple averages are rarely used.

In particular, Obermatt Indexed Operating Performance is generally expressed in this way: as a percentile rank relative value within the selected Peer Universe index. The percentile rank is also one of a variety of ways of calculating Operating Alpha, with any rank above the median representing that relative amount of positive Operating Alpha, and any rank below the 50th percentile representing that relative amount of negative Operating Alpha. This is called Operating Alpha Rank. It is also possible to measure Absolute Operating Alpha as an absolute value amount above or below the absolute value amount of the index median.

Click here to view the many Obermatt tools, or graphs, that show relative operating performance as a percentile rank.

Sample Box Plot