Braden Warwick

But having Ben and I and other people to think through this stuff and to take care of it in conquest, I think is pretty powerful.

It comes back to our expected return assumptions model.

We still are using a Gaussian distribution to sample our expected returns for each asset class.

And it's not a perfect approach, but it was simple and it wasn't extremely obvious to me how to improve it or what needed to be done to improve the model.

We know that returns don't follow a normal distribution in reality, but we knew that it was also going to be a big undertaking to improve that.

When this opportunity came up to engage an academic partner, it seemed obvious that this was a great opportunity to improve our model.

So John, why don't you tell us a bit about how you tried to tackle that problem?

It's even interesting, too, how it differs from asset class to asset class.

Like if we look at the fixed income, there's a dramatic difference between the Gaussian data and the sample data.

Same thing with U.S.

equities, but Canadian equities and international equities seem to be a little bit closer.

So I see like a dramatic improvement in the shape and the tail score, but the co-movement score is relatively similar to what we had before.

Is that right?

Yeah, that's correct.

For sure.

It was great.

Really enjoyed working with them over the course of the past few months and really cool to see what they come up with.

The main question left in my mind after hearing his analysis is, great, this is real improvements on our modeling approach, but how is this going to impact the financial advice?

How may this impact the recommendations that we're providing to clients or the financial decisions that we're helping our clients with?

That's sort of the genesis of this post-analysis.