Carter Lyons ’97 on AI and Finance

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In this episode of Lehigh University’s College of Business ilLUminate podcast, host Stephanie Veto talks with Carter Lyons, Co-CEO of Two Sigma Investments– a financial sciences company that combines technology and data science with human inquiry to solve finance challenges. Mr. Lyons is a 1997 graduate of the College of Business and this year's Donald M. Gruhn Distinguished Finance Speakers Series guest. 

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Below is an edited excerpt from the conversation. Read the complete podcast transcript [PDF]. 

Veto: What did you major in? And how did Lehigh help you prepare for your career? 

Lyons: I like to joke that I feel like I'm one of the only people of my age group that studied what I've done professionally. I majored in finance and was in the business school from day one. I wasn't really sure what I wanted to do. And the curriculum forces you to take marketing classes, economics classes, management classes. The one thing I was very sure I didn't want to do was accounting. 

Ironically, my first job out of school was as an accountant. So I guess I took a step back, but I've been working in the financial services field now for almost 30 years, and I still, almost every day, have some situation where I think, "Oh, I learned this in school. I learned this in school." Some of those situations, I say, "Wow, I should have paid better attention in that class." But it's been a fascinating multi-decade run with the Lehigh finance degree as a foundation. 

Veto: Tell me about Two Sigma Investments. What is it? And what's its vision? 

Lyons: Two Sigma Investments, we actually call ourselves a financial sciences company. So while I do use my finance background, much of what we do is based more on the scientific method. What I mean by that is we take a scientific approach heavily influenced by technology and computer engineering to predict markets. 

So what we do looks very similar to any financial services firm, but we take a different approach. And the vision that the founders of the company used 25 years ago has largely played out to be very accurate and is very relevant for not only today, but for the future. 

And that is the world will continue to be automated, will continue to generate data. And in order to make successful investment predictions, you have to utilize technology and data science to be successful. 

Terms like data science didn't even exist when they founded the company. Big data as a term didn't exist. Cloud computing didn't exist. Artificial intelligence did, but certainly not like it does today. So it's been fascinating to see how the world has moved our way, and it's exciting for the future as well. 

Veto: What first drew you to the company and how has it evolved? We just talked a little bit about it, but let's go a little deeper. 

Lyons: I left Lehigh, moved back home to the Bay Area and worked for a firm that was based in San Francisco for about 15 years. It was my first introduction into systematic or quantitative investing. And when you spend a few days inside a quant firm, you start to think that way. We really believe that that's the best way to invest. 

Two Sigma came calling and I thought, "Well, this company was a smaller company looking to capitalize on a very successful first 10 years, but try to really scale itself up." And I thought my personal experience, my belief in where the world was going, aligned very much with what Two Sigma had already achieved, but was really positioned well to achieve going forward. 

I went and had a conversation with my family who was very happy to live in Northern California. And I said, "Do you want to go on an adventure?" I dragged my wife and three very young kids from the suburbs of San Francisco to Manhattan. So we went from suburban living to urban living, all on this idea that Two Sigma could be a great place to be. 

And what a great trade. It's been a fantastic life experience for us and just a tremendous professional experience as the company has thrived, created a lot of opportunity for me personally and my colleagues, and provided a lot of fun along the way as well. 

Veto: How does the company balance human judgment and data? 

Lyons: I think that everything starts with the data, but there's going to be that subjectivity at the end. And that's where experience matters. So while the vast majority of our trading every day is done by machines-- I mean, we trade thousands and thousands of securities 24 hours a day, about five and a half days a week. But there's a human overseeing that process and making sure, "Does the computer know everything that we know?" 

There are times in the markets where you might not have any historical data. Right now, the federal government is shut down. We have some of those experiences in the past, but maybe not enough of those to really have a strong data-driven perspective of, "What does that mean?" So we might turn things down that rely on government data that's not available. Those are the easier ones because you can see those coming. 

An analogy we often use is that it's like a speed bump. If you're driving along in your car and you see a speed bump, what do you do? You slow down, you go over it, and then you speed back up. So you see a government shutdown coming. You look at any exposure you might have. You slow down. Right now, we're going over it. And as soon as our friends in Washington figure their stuff out, then we'll speed back up. 

Sometimes things happen that you weren't expecting. And COVID is a great example of that. Sure, there were other pandemics in the past, but there were hundreds of years before. You don't have any data. The computers don't know what's going on. That's where you need the humans to step in and say, "You know what? We don't know what's going on. So for us, let's take our risk down. Let's figure out what's going on." 

If you don't have conviction the computers don't have the data to make that decision, you still need that experience. And I think that's one of the keys to what's happening with AI and all the innovation that's occurring across the world. You still do need to have those humans who've gone through things before, who have the confidence and the experience to make the necessary judgment calls.