Jay Rajamony – Beyond Factors: Reimagining Quant Equity for the Modern Era (S7E23)

Hey, everyone. Corey here. Thanks for tuning into another episode of flirting with models. If you're enjoying the show, I'd greatly appreciate it if you take a moment to rate, review, and most importantly, with a friend. Word-of-mouth is how this podcast grows. And if you'd like to learn more about Newfound's platform of return stacked mutual funds, ETFs, and model portfolios, head over to returnstacks.com. Now on with the show. Alright, Jay. Are you ready? Yes. Alright. Three, two, 1. Let's jam. Hello, and welcome, everyone. I'm Corey Hofstein, and this is flirting with models, the podcast th

Corey Hofstein is the cofounder and chief investment officer of Newfound Research. Due to industry regulations, he will not discuss any of Newfound Research's funds on this podcast. All opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of Newfound Research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Newfound Research may maintain positions positions and securities discussed in this podcast. For more information, visit thinknewfound.com.

In this episode, I speak with Jay Rajamani, director of alternatives at Man Numeric. Jay has been with the firm since 02/2004, giving him a front row seat to the evolution of quant equity from simple factor models and broad signals to today's world of alternative data, model ensembles, the human machine collaboration. We start with the history. What's changed in quant over the last two decades? Why the two thousand seven quake still matters? And how the definition of alpha has shifted alongside new tools and data. From there, we explore the interplay between factors and macro regimes, how spar

Thank you so much for having me. Looking forward to chatting about the evolution of the space and so on.

Now, you've been with Man Numeric going back a little over twenty years now since 02/2004, which really gives you a front row seat as a practitioner to several of the major eras of quant equity. Can you walk us through how the quant equity landscape has changed over, say, the last two decades from back in the early 2000s with simpler factor portfolios to a lot of the modern complexities that we see and face in portfolios today?

Absolutely. In fact, if you go back to around the time I started in the industry or even just prior to that in the nineties and so on, it was a very different industry back then. Compared to today, it might seem like simpler times. One of the most difficult things back in those days was simply gathering data in a timely manner, getting that into a systematic process and so on. In retrospect, if any idea today, for example, tests exceptionally well back in those days, that's simply because the way we look at these data, the way we understand these data, that would not have been possible in real

I wanna talk about the last two points you brought up there. One about this idea of crowding and second about the way in which compute and data has changed over time. But let's start with the former. That early two thousands period seemed to be a time when the risk of crowding was low and a lot of people ended up unintentionally walking into the same trades. At least maybe perhaps the perceived risk of crowding was low and a lot of people walked into the same trades and that resulted in that quant quake of two thousand and seven, which I don't think unless you're a quant, you're necessarily aw

Some of this, of course, goes simply back to how we as human beings behave and what incen