John Yang

Thank you.

So first of all, it's great to be working with you guys for the past two months.

And Professor Michael Robbins actually helped us a lot in the process.

And how this is going to work is I'm just going to share my screen and I will talk about the approach we took in improving the model.

So like Braden have talked about before, we were essentially trying to simulate test cases for wealth planning using synthetic data generation.

And in the past, it has been done through Gaussian or normal distribution-based models.

What we are trying to do is to improve that.

And I think a good way for the audience to grasp what's going on here is we're essentially translating the forward-looking expected returns and volatilities into market scenarios that can be used as test cases.

Expected returns and volatilities are very useful in looking at how your portfolio is doing or how the market is going to perform, but they're one-dimensional.

They're still summary statistics.

They don't actually tell us the sequence of returns that you might live through.

And when planning, let's say, for your retirement, that sequence matters a lot.

You're unlikely to experience, say, 6% return year over year, like in a straight line.

You experience some good years, some bad years, some recovery years, some drawdowns.

And during crisis, assets that crash together when they're expected to diversify each other

The question for us is once we have the four looking assumptions, how do we turn them into realistic return path that can be used to test the resilience of a wealth plan?

This is why we are generating synthetic data.

I think one thing that I need to clarify before I start to talk about approach is that synthetic data is not randomly generated data.

It's very mathematically grounded estimation that is designed to represent historical scenarios.

In our case, each synthetic data point is a part of possible return path that a portfolio could experience.