John Yang

And the goal for us is not to say this exact path will happen, but rather we're trying to create many realistic paths so we can ask better planning questions, such as what happens if bad returns arrive early or what happens when a crisis hits.

Exactly like you said, once we think about the problem that way and think about synthetic data generation, the workflow becomes, I think, more intuitive.

So we take the expected returns and volatilities and correlation assumptions as an input to our model.

These inputs are fed into our simulation engine.

Its job is to take those assumptions and generate a portfolio return path that can be used for long-time horizon planning.

The simulation engine is not trying to create new market forecasts.

It's like grounded in historicals and try to generate something that's more realistic.

We're not the first ones that are doing this.

Like Braden said, PWA had a baseline approach before based on Gaussian distributions.

This basically utilized a multivariate normal distribution and run a Monte Carlo model on it.

And for listeners who are not familiar with what Monte Carlo simulation is, it is a common framework for generating correlated data.

You can basically take expected returns, volatilities, and correlations, and the models will generate simulated returns from that normal distribution.

There are a lot of advantages to use it.

Like Brayden mentioned, it is very easy to implement.

It is also very easy to collaborate.

Also on top of it, it's also very easy to explain.

So I think it's definitely a great baseline to start from, but it also has its issues.

At the end of the day, assuming a normal distribution, a normal distribution is smooth and it's symmetric.

We're thinking about the bell curve that look exactly the same, perfect on both sides.

And real markets don't do that at all.