Two Researchers Explain How Quants Are Going To Revolutionize Long-Term Investing

Joe Weisenthal (0:08)

Hello, and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

Tracy Alloway (0:14)

And I'm Tracy Alloway.

Joe Weisenthal (0:15)

Tracy, I really liked last week's episode with Andrew Lowe talking about quant stuff and his sort of evolution of the efficient market hypothesis and where that might go.

Tracy Alloway (0:27)

Yeah, I did too. I really like the ecosystem analogy, the idea that you have all these different players with different motivations and they're constantly evolving and adapting to the market.

Joe Weisenthal (0:39)

That point about adapting, and of course that's the name of his book, Adaptive Markets, it's the name of his hypothesis, is really key because one of the main points that he made that I loved was this idea of hedge funds as sort of the R&D laboratory for all of the financial industry.

Tracy Alloway (0:59)

Right, the hedge funds are where innovative new techniques get to be sort of hashed out without doing usually too much damage, I guess, to the wider ecosystem.

Joe Weisenthal (1:09)

Hopefully.

Right, not always. There's certainly examples of hedge funds actually having done major damage from time to time. But ideally, what the evolution seems to be that some new idea sort of starts in the hedge fund world and eventually makes its way to the broader world. And I think the most obvious example of that that we could cite these days is a lot of the sort of popular smart beta strategies, ETFs that are built on things like momentum or value or other factors, sort of quantitative ideas that for many years were only available to researchers at hedge funds.

Tracy Alloway (1:48)

Right, so these sorts of quantitative investment or trading methods were usually the purview of sophisticated hedge funds who had the time and resources to develop them.

And then you had a bunch of ETFs who kind of caught on and managed to replicate them. And now we can all trade like hedge funds for 0% fees, right?

Joe Weisenthal (2:07)

Exactly right.

And of course, once everyone can do it for very few fees, I think it's safe to say those strategies aren't going to produce the same returns. And hence the market is forced to adapt again.

Tracy Alloway (2:22)

Right, and presumably the hedge funds are always trying to stay one step ahead as well, right?

Joe Weisenthal (2:27)

Exactly, which raises the idea of what will be the next thing. If anyone can invest in a crude momentum strategy for virtually no fees, then that requires the people on the cutting edge, the people doing the R&D of this industry to figure out what the next big thing is going to be.

Tracy Alloway (2:46)

Do you know what the next big thing is going to be, Joe? Can you share it with your fellow partner at Odd Lots LLC?

Joe Weisenthal (2:53)

Sadly and unfortunately to all of the Odd Lots fan, I myself do not know what the next big thing in quantitative strategy or sort of advanced investing is going to be, but I'm hoping that our guests on today's episode might be able to shed some light.

Tracy Alloway (3:11)

Oh, who are they?

Joe Weisenthal (3:13)

Okay, so today we are going to be talking to John Alberg. He is the founder of Euclidean Technologies, a quant firm, as well as Zach Lipton, a professor at Carnegie Mellon University in the business school and an expert on machine learning.

They recently published a paper titled Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals. So what I think that means, and we'll talk to them, is we talk all this stuff about price and computers and algorithms figuring out what signal we can get from price, but maybe the next generation can actually tell us something about the fundamental workings of the company itself, and maybe this could be sort of the next wave of where quant investing goes.

Tracy Alloway (3:58)

And this sounds absolutely fascinating, Joe. Let's bring them on.

Joe Weisenthal (4:12)

John and Zach, thank you very much for joining us. Was that a reasonable characterization of sort of where your paper and where your research is taking things?

John Alberg (4:23)

Yeah, I think it is. So first of all, machine learning has been kind of on a rocket ship of innovation for the last 10 years or so. And with the advent of deep learning, you know, computers and machine learning have been able to do things that, you know, historically have been very challenging, like image captioning and language translation.

So we, Zach and I, you know, a couple of years back, thought of the idea of collaborating to apply deep learning to the problem of long-term investing.