Angana Jacob - Data as the True Competitive Moat (S7E26)

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, Aga. Are you ready to go?

Yes. I'm ready.

Alright. Three, two, one. Let's jam. Hello, and welcome, everyone. I'm Corey Hofstein, and this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

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 and securities discussed in this podcast. For more information, visit thinknewfound.com.

Today, I am speaking with Angana Jacob, head of the research data group within the enterprise data business at Bloomberg. We talk about Angana's career path through quantitative research and data platforms and how the industry has evolved from a world dominated by bespoke models and back tests to one where many models have become increasingly commoditized. A central theme of our conversation is the idea that while models are easier than ever to replicate data and how it's sourced, cleaned, standardized, linked and delivered has become the true competitive moat. We discuss what it means to quot

Thanks, Corey. Love your podcast and very happy to be here.

So let's start with your story. You've had a really fascinating career arc from Deutsche Bank's early QIS Research Desk. That's a mouthful to say. Through S and P, State Street, SigTech, and you are now leading the research data group within the enterprise data business at Bloomberg. Can you walk us through that journey and maybe talk about the common threads that have led you to where you are today?

So let me start with Bloomberg. I've been here for three years now, and my group's focus on building machine readable cross asset data products needed in fundamental and systematic strategies. How I got here? I'm flattered you call it fascinating, that part. Not sure I would say that, but I do think I was pretty lucky to be right at the start of many different trends. Early QIS in Deutsche from 2008 and then at S and P around the very early factor boom where massive inflows into factor ETFs and factor mandates. I also worked on ESG at S and P, the first implementations of integrating ESG in po

Well, I'm really excited to dive into all that with you. And we could probably spend a whole podcast just going through the different parts of your career. I'm maybe going to barbell this because I want to contrast the beginning of your career to where things are today. And you started with Deutsche back in 2008. You and I were talking our pre call. You said that back then quant research was still very model driven. There were a lot of hand built factors. There was a lot of back testing. You went so far as to say, quote you here, everything back then was overfit, which is maybe in hindsight tr