## What is ‘Platykurtic’

Platykurtic describes a particular statistical distribution with thinner tails than a normal distribution. Because this distribution has thin tails, it has fewer outliers (e.g., extreme values three or more standard deviations from the mean) than do mesokurtic and leptokurtic distributions. The prefix of the term, ‘platy’, means broad, which is actually an historical error, since platykurtic distributions are not necessarily broad. Distributions are deemed platykurtic when the excess kurtosis value is negative, owing to the fact that the distribution has less outlier, or extreme value, character than does the normal distribution.

It is difficult to distinguish platykurtic, leptokurtic, and mesokurtic distributions using density or histogram graphs, because the tail behavior is not easy to see. The following figure shows density plots of example data having these three types of distributions, all with the same standard deviation, and the comparison of the tail behavior is not clear in the density plots. On the other hand, the three types are easily distinguished by using normal quantile-quantile plots, as the figure also shows.

## BREAKING DOWN ‘Platykurtic’

Platykurtic distributions produce less extreme outliers compared to outliers found in a normal distribution. Investors often consider asset returns’ kurtosis because the distribution of values can offer an estimation of asset risk on potential investments.

Platykurtic distributions generally have a fairly basic and structured data layout; returns that follow this kind of distribution tend to have less major fluctuations than do asset with leptokurtic or normal distributions. For this reason, the investment comes with less risk.

Market, or equity, returns are regarded as being closer to leptokurtic distributions, as opposed to platykurtic and normal. Random and unpredictable events – otherwise known as black swans – are less likely to occur when market conditions are more platykurtic; black swan deviations don’t typically fall inside the short tails of platykurtic distributions. Cautious and traditional investors find investments with platykurtic return distributions best suited to their wants and needs.

Platykurtic distributions produce less extreme outliers compared to outliers found in a normal distribution. Investors often consider asset returns’ kurtosis because the distribution of values can offer an estimation of asset risk on potential investments.

Platykurtic distributions generally have a fairly basic and structured data layout; returns that follow this kind of distribution tend to have less extreme fluctuations than do asset with leptokurtic or normal distributions. For this reason, the investment comes with less risk.

Market, or equity, returns are regarded as being closer to leptokurtic distributions, as opposed to platykurtic and normal. Random and unpredictable events – otherwise known as black swans – are less likely to occur when market conditions are more platykurtic; black swan deviations don’t typically fall inside the short tails of platykurtic distributions. Cautious and traditional investors find investments with platykurtic return distributions best suited to their wants and needs.

## Leptokurtic, Mesokurtic and Platykurtic

There are three basic data set distributions: leptokurtic, mesokurtic and platykurtic; see the figure above for examples.

Leptokurtic distributions have outlier data points, or occasional values exceeding (in terms of standard deviations from the mean) what is predicted by the normal distribution. Leptokurtic distributions are directly opposite of platykurtic distributions, which have fewer extreme values than predicted by the normal distribution.

The kurtosis is a measurement of the tails (outliers or extremes) a distribution. The coefficient for a normal distribution is three. Thus, data distributions with a kurtosis higher than three – leptokurtic distributions – have fat tails and data distributions with a kurtosis of less than three – platykurtic distributions – have thin tails.