Benjamin Felix

So John Yang is a financial engineering student at Columbia engineering with experience in quantitative modeling markets and applied analytics.

The class has now ended, but his project with professor Micah Robbins

was on a synthetic data generation project for PWL Capital, which was focused on building more realistic market scenarios for financial planning and risk analysis.

John's work on this project examines how advanced simulation methods can better capture asset behavior, tail risk, and periods of market stress than traditional models.

All right, let's go to our conversation with John.

John Yang, welcome to the Rational Reminder Podcast.

Great to be here.

We're very excited to be talking to you.

So real quick for listeners, I'll give a little bit of background.

Professor Michael Robbins, who's at Columbia University, reached out to me.

He runs an industry project with his class every semester.

And the industry project basically takes a group of students and assigns them to someone in an industry or a company in industry.

And they're given a problem from that person or company or whatever it may be to solve a data-based project.

So Professor Robbins reached out, asked if I thought I had anything interesting for students to work on.

And I was like, actually, yeah, there's something that we have been trying to solve and we just haven't dedicated the time to do it.

And so we landed on that as a project.

But Brayden, maybe you can real quick introduce the problem that we asked John and his team to solve.

So it's basically like we know returns aren't random.

Empirically, you can see that by looking at real data.

We had been using randomly generated returns to do retirement modeling and stress testing.