Braden Warwick

So the results.

First, I'll talk about the differences between the DMS data and the index data.

What we found were pretty similar results, but we found the Gaussian represented the DMS data better than it did the index data.

This wasn't too surprising for me to see.

It was kind of what I was expecting to see because the DMS data goes back a lot further.

It's a lot more independent samples.

The 12-month rolling average

time periods, monthly data has data points like the March 2020 drawdown in it, which can lead to these very extreme left tail events.

And that was one of the struggles that we worked with over the course of the project was trying to account for this lack of data and the index data and the over-representation of the March 2020 drawdown, especially when it didn't really

impact our long-term financial planning projections.

Our clients obviously had to be able to withstand that drawdown, but beyond the behavioral side of it, it didn't really impact our financial planning projections necessarily.

But importantly, the new model improved on both.

So even though the Gaussian was a better representation for the DMS data, this new model performs better regardless and both the DMS data and with the index data.

So I think it's a step forward regardless of how you look at it.

We can see the same thing, too, when we're looking at the tails.

And maybe Matt can overlay some of these graphics on the screen for the viewers to see.

But ultimately, we can see the same thing that I just talked about, where the tails of the index data were fatter than the tails of the DMS data, specifically looking at fixed income and U.S.

equities, where the DMS data was closer to the Gaussian.

But like I said, it's still a better representation of the data than the Gaussian.

So it's a step forward nonetheless.