Benjamin Felix

So since hearing from Scott and learning about his research and reading his paper, we've been trying to figure out, basically me and you Braden have been trying to figure out how we can improve our expected returns modeling when we're running financial planning projections.

And so that's what we're going to talk about in this episode.

Braden, you're going to talk about some of the modeling considerations that go into expected returns modeling and what we're doing now.

And then, and this part's pretty cool.

We're going to hear from John Yang, who's a financial engineering student at Columbia Engineering, the engineering school at Columbia University.

PWL engaged with John and some of his classmates for their industry project where the students, supported by their professor, Professor Michael Robbins,

aim to solve a real problem for a firm.

So it's pretty cool.

There are a bunch of industry projects that are available to students and they get to choose which one they want to do.

And so I talked to Professor Michael Robbins, explained this expected returns modeling challenge that we had.

And he was like, cool, I'll propose that as a project.

And John and his group of students for the class chose our project and worked through it for a semester.

So it was pretty cool experience.

Yeah, I thought it was cool.

Our financial planning software that PWL uses right now is called Conquest Planning.

They're just Conquest.

The way that its returns analysis works, when you want to look at how sustainable is this financial planning projection over a projected range of future outcomes,

we upload a thousand runs of simulated data.

Doing it that way is really cool because some other financial planning softwares will run a Monte Carlo inside the software, but you don't get to change any of the parameters or the shape of the distribution.

But because Conquest lets us, or the only way that you can do it is by uploading your thousand runs of data,