Braden Warwick

I wanted to quantify that impact on the financial advice, but I also wanted to look at their model using the DMS data.

That's one thing we didn't really talk about too much with John, but they were using monthly index data with 12-month rolling time periods.

The index data only went back to 2003 for all of the asset classes that we were looking at.

So it was a relatively short time period.

The first thing I want to look at was what happens when we use the annualized DMS data that goes back to over 100 years of data.

How will that change, if at all, the outcomes of his analysis?

I'll say up front, ideally, I'd like to answer this question of how does this new model impact the financial advice?

I'd like to answer that directly in Conquest, but at the moment, we can't.

We can't send those 4 million data points that I talked about earlier to Conquest directly via API so I can make real-time changes.

Instead of doing that, I built a pretty simple lifecycle-style model

where I was able to capture the basics of the life cycle.

So I modeled everything in terms of after-tax cash flows, modeled accumulation and decumulation.

I modeled different ages, so different time horizons.

So I modeled investors that were 20 years old all the way up to 80 years old in 10-year increments.

I fixed retirement age at age 65, so that way we can model different lengths of the accumulation and decumulation.

and also decumulation only so all that was captured and we looked at different asset mixes as well so the 60 40 portfolio 80 20 and 100 equities and then in each case i fixed the spending at the sustainable spending amount so i solved for the spending amount that would lead to an 80 success rate using our previous gaussian distribution

That way, we have a benchmark that we can go back to, and it's also a realistic benchmark.

It's representative of an actual plan that we would work with our clients on and not something that is someone spending way below their means or way above their means.

It's sort of representative of a realistic client scenario.

That way, we can benchmark the difference between this new model and our old Gaussian approach.