John Yang

So gang is generative adversarial networks.

Those are machine learning models that are the new cool kids on a block situation.

People are using that to generate synthetic data.

So essentially what it's trying to do is that it is generating a set of samples using a machine learning model and then using another model to judge it.

You kind of like go back and forth and find realistic scenarios.

The drawback for that is that there's still a lot of research that need to be done.

And like a lot of machine learning models,

When you're using it, you might be overfitting.

You might discover patterns that are not there at all.

When you're assimilating it, you're essentially amplifying those patterns.

That can be an issue for clients who don't want their wealth to go wrong.

Exactly.

I think to close my talk here, I think the main takeaway from this is that forward-looking assumptions are starting points.

When you're planning for wealth, to use those assumptions in planning, they had to be turned into a path.

And that's what we've been working on for this project.

The baseline Gaussian model that we start with is clean and transparent.

but it makes market looks way too smooth than it actually behaves.

And our frameworks keeps the same assumptions, but it produced paths that are better reflecting the messiness, I'll say, of the market.

We look at asymmetries, we look at the fat tails, we look at assets that are moving together during crisis.

And those are the things that matters a lot.