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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Master's in

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Speaker 1: Business with Barry rid Holds on Bloomberg Radio. This week

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Speaker 1: on the podcast, I have a fascinating and extra special guest.

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Speaker 1: David Sneinermann has put together an incredible career in fixed income,

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Speaker 1: alternative credit, and really just an amazing way of looking

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Speaker 1: at risk and trade structure and how to figure out

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Speaker 1: probabilistic potential outcomes rather than playing the usual forecasting and

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Speaker 1: macro tourist game. He is global head of all credit

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Speaker 1: and fixed income and managing partner at Magnetar. They have

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Speaker 1: an incredible track record. They've put together a string of

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Speaker 1: j huge returns. They are not like any other funds

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Speaker 1: that you'll hear me talk about. Their pre unique and

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Speaker 1: specific in the world. I found this conversation to be fascinating,

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Speaker 1: and even though we kind of wander off into the

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Speaker 1: weeds of private credit, it's so informative and so interesting.

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Speaker 1: I think you'll you'll really enjoy it. With no further

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Speaker 1: ado my discussion with Magnetars David Snyderman.

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Speaker 2: Thank you very much for having me, Barry. I really

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Speaker 2: appreciate it and I'm looking forward to our conversation.

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Speaker 1: I am. Also, I'm very familiar with Magnetar and it's history.

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Speaker 1: It's really a fascinating firm in so many ways. Let's start, though,

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Speaker 1: talking a little bit about your background. You grow up

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Speaker 1: in suburban New Jersey and then you head to Saint

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Speaker 1: Louis for college. Tell us a little bit about where

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Speaker 1: you went, what you studied.

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Speaker 2: Sure, I grew up in Freehold, New Jersey, so most

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Speaker 2: people a know home of Bruce Springsteen. You know, my

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Speaker 2: focus coming out of high school was playing football. I

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Speaker 2: want to play football at the highest level I could.

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Speaker 1: You are not much bigger than me. What made you

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Speaker 1: think you could play on the gridiron?

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Speaker 2: I don't know. I thought I could, but I definitely

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Speaker 2: thought I could at the time, and so I wanted

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Speaker 2: to play at the highest level possible. My parents were

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Speaker 2: much more focused on academic institution and so wash you

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Speaker 2: sort of met both criteria.

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Speaker 1: Did you play born college?

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Speaker 2: I did all four years. It was a lot of fun.

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Speaker 1: What position did you play?

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Speaker 2: I played strong safety, and yeah Division three is the

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Speaker 2: highest level I could play up, but I loved it.

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Speaker 1: Right, So safety you have to be pretty fast, and.

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Speaker 2: That was the issue.

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Speaker 1: So but for that you would have gone pro there

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Speaker 1: you go. What did you study at Washoe.

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Speaker 2: Washoo back then? Was it was a great They had

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Speaker 2: a great medical school and they still do today. And

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Speaker 2: in my family, being a doctor was the highest level

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Speaker 2: of achievement. So I had an older sister starting medical school,

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Speaker 2: and I had a relative that is actually a dean

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Speaker 2: of Duke Medical School. So I had this nice glide

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Speaker 2: path to be a doctor.

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Speaker 1: Right.

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Speaker 2: So I started off pre med, but I didn't end

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Speaker 2: pre med. I found out quickly that's not what I

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Speaker 2: wanted to do. The hardest part is telling my parents

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Speaker 2: and especially my grandparents, you know, no more pre med.

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Speaker 2: So I switched to be an economics major. I graduated

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Speaker 2: economics with a lot of courseworking accounting and finance.

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Speaker 1: Huh. Interesting. So you come out of college, you go

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Speaker 1: to Price Waterhouse Cooper and then Coke Industries, where you're

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Speaker 1: focusing on convertible securities, merger arm and special situations. How

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Speaker 1: do you get from medical school to that? What was

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Speaker 1: the career plan?

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Speaker 2: Yeah? My path was certainly non traditional. I didn't go

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Speaker 2: to one of the East Coast Ivy League schools, knowing

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Speaker 2: I wanted to go to Wall Street. I didn't even

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Speaker 2: know what Wall Street. Working on Wall Street meant at

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Speaker 2: the time, So for me, it was much more around,

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Speaker 2: you know, being around fantastic people and really taking advantage

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Speaker 2: of opportunities. It's like you said, I started a price

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Speaker 2: waterhouse and I went through a one year rotation there.

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Speaker 2: So it started with audit, so I saw many companies,

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Speaker 2: then tax and financial services, so it's a great training

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Speaker 2: ground to understand how, you know, theoretics went into the

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Speaker 2: practical business. From there, I went to Coke Industries and

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Speaker 2: I had a great experience of Coke. I was there

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Speaker 2: five years. I worked in three different places for them.

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Speaker 2: So I started in Houston, Texas, and I worked on

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Speaker 2: their natural gas business. Then this opportunity came up in Switzerland.

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Speaker 2: So it's a thirteen thousand person company and there were

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Speaker 2: going to be five people in Switzerland to manage about

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Speaker 2: several hundred million dollars more in cash optimization. So I

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Speaker 2: had the opportunity to be a junior person there. I'd

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Speaker 2: never left the US before, so I was sat in

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Speaker 2: the middle of Switzerland and sat there for two years

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Speaker 2: and worked in that business, and then went to Wichita, Kansas,

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Speaker 2: which tak Kansas was the home office and there were

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Speaker 2: sort of a dozen of us, very simply situated, you know,

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Speaker 2: all young and hungry. But they had great management at Coke.

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Speaker 2: They really encouraged us to start businesses. So I remember

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Speaker 2: writing the merger our business plan there and then implementing

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Speaker 2: the business. So a quick fun fact about Coke at

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Speaker 2: Magnetar today we have three of my prior bosses that

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Speaker 2: you know from Coke, so it's pretty neat. But to

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Speaker 2: answer your question, like, I had a lot of broad

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Speaker 2: experiences by the time I was in my mid twenties,

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Speaker 2: but no real direction on what my career was going

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Speaker 2: to be.

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Speaker 1: Where in Switzerland was a Geneva somewhere.

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Speaker 2: Else it was Frebourg, So a town twenty minutes from

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Speaker 2: Burnho was a tax free canton.

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Speaker 1: Uh huh.

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Speaker 2: So I was in a town that spoke, you know,

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Speaker 2: half French and half German and I spoke English.

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Speaker 1: So there you go. But no taxes, no income.

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Speaker 2: Taxes, no income taxes for the company.

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Speaker 1: And then Coke Industries. I don't think a lot of

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Speaker 1: people realized one of the largest private companies in the

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Speaker 1: United States and maybe even the largest. They're giant energy powerhouse.

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Speaker 1: What else does Coke do?

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Speaker 2: Yeah, so when I was there, that had thirteen thousand people,

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Speaker 2: and that was before they bought Georgia Pacific. I think

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Speaker 2: now it's probably thirty five thousand people. Immens It's immense,

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Speaker 2: and so they have many, many different business lines there.

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Speaker 2: For me, I sat mostly in their internal really an

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Speaker 2: internal hedge fund, so it was their excess cash. They

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Speaker 2: borrowed money at live bid at the time, so they

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Speaker 2: borrowed money very cheaply, and our job was to make

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Speaker 2: money on that money.

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Speaker 1: So you end up as head of global credit and

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Speaker 1: senior managing director at Citadel Investment Group. Was that right

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Speaker 1: from Coke Industries? That was seven years at Citadel. That's

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Speaker 1: supposed to be a tough shop.

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Speaker 2: To work at.

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Speaker 1: What was your experience like there?

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Speaker 2: It was a perfect job for me at the time,

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Speaker 2: So I always thought I worked at a high level

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Speaker 2: of intensity right right. But when I got there, I

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Speaker 2: realized I was one of many. But I had the

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Speaker 2: opportunity to work for gentleman Dave Bunning. He was one

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Speaker 2: of the original few handful of people that started at Citadel.

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Speaker 2: And Dave was fantastic in so many different ways. A

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Speaker 2: great leader, a great investor, but really a great person

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Speaker 2: and he took me under his wing there. It was

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Speaker 2: a lot of work, but a lot of formidable lessons

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Speaker 2: came out of my time there. Right. So the first

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Speaker 2: one that I think about is the investing business itself

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Speaker 2: is an operating business. So we really have to understand

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Speaker 2: what we're going to invest in. Value everything in the universe, rank,

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Speaker 2: order them, and then only can we put together portfolios.

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Speaker 2: And the second, and this is very credit specific, was

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Speaker 2: when you own a credit portfolio, your short volatility. So

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Speaker 2: what that simply means is if you have a dislocation,

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Speaker 2: you're gonna lose a lot of money, uh huh. And

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Speaker 2: so to put together credit portfolios, we have to find

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Speaker 2: hedges that offset that short volatility. So really learning the

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Speaker 2: value of options right was probably the biggest lesson coming

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Speaker 2: out of Citadel.

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Speaker 1: So I want to rephrase that for some of the

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Speaker 1: less option involve savvy members of the audience. When we

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Speaker 1: buy fixed income, we just wanted to be steady and

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Speaker 1: pay a divinend and not swing up and down. And

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Speaker 1: if it does swing up and down, the odds are

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Speaker 1: it's not in your favor. That volatility you can look

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Speaker 1: at as an insurance product. If the volatility goes up, Hey,

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Speaker 1: we can make a bet that will offset the draw

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Speaker 1: down in the bonds.

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Speaker 2: That's exactly right, all right.

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Speaker 1: And you you at Citadel, you were running a convertible

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Speaker 1: bond and credit trading desk. Is that what you eventually

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Speaker 1: ended up as head of Global Credit?

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Speaker 2: That's correct. I started there on the convertible bond arbitrage desk,

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Speaker 2: and then we started capital structure arbitrage, which meant we were,

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Speaker 2: you know, buying or selling credit and against that buying

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Speaker 2: and selling equities. And finally we consolidated that together and

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Speaker 2: I ran that business for Ken and Citadel.

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Speaker 1: And some of the folks Ken being Ken Griffin. When

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Speaker 1: people say Citadel is a lot of work, you don't

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Speaker 1: realize there's a whole other gear you have to move

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Speaker 1: into and its next level was that your experience.

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Speaker 2: It was and for me, I actually loved that part

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Speaker 2: of Citadel. It was sixteen hour days and it was

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Speaker 2: six or seven days a week, but you really got

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Speaker 2: to learn the financial markets there.

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Speaker 1: Huh. Interesting. So Magnetar launches in two thousand and five

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Speaker 1: with some capital and you joined. You you weren't one

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Speaker 1: of the original founders, but you joined not long afterwards.

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Speaker 2: That's correct. So Alec Litowitz and Ross Lazar founded the firm,

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Speaker 2: and you know I did join the day we launched

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Speaker 2: our main fund. Now, for me, Alec was a known quantity.

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Speaker 2: He ran equities at Citadel with Dave Bunning, my prior

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Speaker 2: boss there, and then when I moved up into Dave's spot,

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Speaker 2: Alec moved out and they started and he spent I

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Speaker 2: think two years in a noncompete and then started Magnetar.

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Speaker 2: Him and Ross Lazar co founded the firm, and they

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Speaker 2: had a vision to co found the firm, and I

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Speaker 2: bought into the vision immediately, and Alec always did a

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Speaker 2: great job of laying it out right. And first was

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Speaker 2: we're going to have a culture of collaboration. So back then,

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Speaker 2: you probably remember in two thousand and five, you know,

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Speaker 2: there were a lot of what they call pod shops,

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Speaker 2: so they give individual asset allocation to people and they'd

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Speaker 2: go invest their money. This was going to be a

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Speaker 2: multi strategy vehicle, so we'd have credit, we'd have equities,

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Speaker 2: we'd have hedge fund strategies, but with no silos. So

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Speaker 2: we're going to work together and put best opportunities into

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Speaker 2: the portfolio.

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Speaker 1: So you have people from Coke Industries with you. You

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Speaker 1: have people from Citadel. Did those prior employees have a

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Speaker 1: piece of you guys? Did they seed you, did they

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Speaker 1: invest you? Or was it just a clean break and

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Speaker 1: we're off on our own.

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Speaker 2: It was a clean break. And Ross Lazar came from

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Speaker 2: the fund of Funds World and he was the primary

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Speaker 2: money raiser and business builder there and so he did

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Speaker 2: a fantastic job. I think were the largest launch of

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Speaker 2: two thousand and five with about two point three billion dollars.

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Speaker 1: How long did it take you to get up and running?

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Speaker 1: Were you felt, oh, this is really all the pieces

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Speaker 1: are in place.

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Speaker 2: Yeah, it's a good question and funny funny you asked

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Speaker 2: that question because we talk about it often around Magnetar.

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Speaker 2: You know, I started and I hired three or four

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Speaker 2: people that I started with, and Ross Lazar, right, and

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Speaker 2: again he's a he's my partner, my close friend, and

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Speaker 2: a great business builder. Two weeks into it, he came

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Speaker 2: to me and said, what's the first investment, Like, when

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Speaker 2: are you going to start investing? And I said to Ross, look,

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Speaker 2: we were gonna build the systems in infrastructure to prepare

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Speaker 2: to invest first, and.

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Speaker 1: I need a computer and an internet netline and maybe

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Speaker 1: a trader to help us out.

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Speaker 2: That's exactly what what Ross was saying. And he very

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Speaker 2: politely said to me, you know, you're here to invest,

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Speaker 2: not to build software. And so he I think he

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Speaker 2: stopped by my desk for the next nine months every

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Speaker 2: single day and asked the same question. But it truly

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Speaker 2: took us nine months to build the systems in infrastructure

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Speaker 2: just to be investment ready.

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Speaker 1: Wow, that's amazing nine months. And I have to ask

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Speaker 1: why Evanston in Illinois? I mean, I like Lumel, Natty's

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Speaker 1: and super Dog as much as the next guy, but

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Speaker 1: why the middle of the Illinois suburbs the Chicago suburbs.

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Speaker 2: Yeah, so it was just north of the city and

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Speaker 2: it's across the street from Northwestern, so that would be

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Speaker 2: the draw. You know, the train lines in there, so

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Speaker 2: you can recruit people from the city. But it was

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Speaker 2: probably a little more selfish, like we all lived on

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Speaker 2: the north shore of Chicago and so it was an

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Speaker 2: easy commute for us to work, and so that that's

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Speaker 2: where we started the firm.

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Speaker 1: And that is really a lovely part of the world

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Speaker 1: on the lake. It's such a manageable, easy city to

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Speaker 1: operate within. I mean, the winters are a little cold,

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Speaker 1: but still it's a lovely place.

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Speaker 2: It's a great quality of life in Chicago and outside

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Speaker 2: of Chicago.

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Speaker 1: So only a few years later, we're right in the

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Speaker 1: teeth of the Great Financial Crisis. How did you guys

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Speaker 1: navigate that?

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Speaker 2: We were very fortunate and we performed quite well in

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Speaker 2: our credit strategies, which which certainly we can talk about.

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Speaker 2: We had both long and short credit products, and we

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Speaker 2: had a long voltary position, meaning meaning we protected the

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Speaker 2: balance sheet very well if there was a dislocation. And

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Speaker 2: I think that went back to some of the prior

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Speaker 2: lessons from prior firms, like we really need to have

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Speaker 2: portfolios that we protect the balance sheet and make sure

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Speaker 2: that we're able to stand up in difficult environments.

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Speaker 1: I have noticed that a lot of firms that describe

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Speaker 1: themselves as hedge funds really aren't very hedged. You guys

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Speaker 1: operated pretty fully hedged it most of the time, right.

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Speaker 2: We really did. And the systems in infrastructure we built

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Speaker 2: were not only to measure risk, but to manage that risk,

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Speaker 2: and so we'd find good investments both on the long

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Speaker 2: and short side.

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Speaker 1: So even if you have a position that that's long

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Speaker 1: you have an offsetting or matching position, or do you

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Speaker 1: just hedge out that long position with a short bet.

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Speaker 2: So there's a quality of earning's question embedded, and I

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Speaker 2: think what you said, and that's we're trying not to

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Speaker 2: take macro level bets. Those for us are low quality bets.

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Speaker 2: And so what we're trying to take is idiosyncratic bets,

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Speaker 2: meaning we're focused on one factor or're betting on that factor.

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Speaker 2: Then we're going to hedge out all of the macro

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Speaker 2: risks around the portfolio.

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Speaker 1: Huh. Really interesting. So we were talking about you guys

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Speaker 1: launched a few years right before the financial crisis. I

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Speaker 1: wanted to talk about a couple of trades from that era,

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Speaker 1: perhaps most famously, you guys put on a CDO bet,

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Speaker 1: a collateralized debt obligation bet that was designed to do

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Speaker 1: well if housing made some extreme moves and it was nondirectional.

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Speaker 1: It was hedge. Tell us a little bit about the

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Speaker 1: magnetar CDO bet from the financial crisis.

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Speaker 2: I talked about setting up the infrastructure to prepare to invest,

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Speaker 2: and we looked at every asset class. So we looked

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Speaker 2: at corporates, we looked at mortgages, we looked at credit cards,

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Speaker 2: and what we found in the mortgage market is something

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Speaker 2: you don't read about in textbooks. We found that we

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Speaker 2: could invest on the longside in what they called the

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Speaker 2: equity piece or the most risky piece of a CDO,

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Speaker 2: and we could short the next level up, so the

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Speaker 2: mezzanine piece, and we could short two or three times

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Speaker 2: the amount. But what was super interesting was we were

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Speaker 2: getting paid to hold an option that never happened.

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Speaker 1: Right, Options cost you money, and that's the old joke.

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Speaker 1: Option traders never die, they just expire worthless.

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Speaker 2: That's exactly right. In this case, we were going to

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Speaker 2: hold an option that we were going to get paid

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Speaker 2: fifteen to twenty percent a year.

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Speaker 1: Though, oh really, that's real money.

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Speaker 2: So you never see that and you never read about that.

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Speaker 2: But that's the way the markets set up. It was

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Speaker 2: just too fragmented. You had people that were willing to

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Speaker 2: buy pieces of these structured products because of the ratings,

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Speaker 2: and on things that weren't rated, no one was willing

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Speaker 2: to buy. So we took the other side of that trade.

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Speaker 1: So you bought the unrated portions and you shorted the

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Speaker 1: rated portions. That's correct, that's very contrary that's very interesting.

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Speaker 1: How did you identify that opportunity? That's such a talk

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Speaker 1: about idiosyncretic niche trades? How did you figure that out?

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Speaker 2: The firm was built on finding white spaces, and so

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Speaker 2: I remember back back in two thousand and five when

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Speaker 2: we first started, you know, we'd think about the banks.

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Speaker 2: The banks would have an equity trading desk and they'd

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Speaker 2: have a debt desk, and they'd both value the same companies,

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Speaker 2: and both sides of the firm with vlume completely differently.

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Speaker 2: And so for us, those are exactly the opportunities we

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Speaker 2: were looking for. But we didn't find it in the

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Speaker 2: corporate markets. We found it in the mortgage market. It

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Speaker 2: was so fragmented that the machine that sold rated products

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Speaker 2: hit all the right buyers, but no one could sell

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Speaker 2: the unrated piece. The unrated piece yielded twenty twenty five percent,

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Speaker 2: where the rated piece would yield three to five percent,

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Speaker 2: And so that difference was the arbitrage that we saw.

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Speaker 1: So heading into five six, we saw real estate peek

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Speaker 1: in I want to say, in volume in five and

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Speaker 1: price and six. So if you're getting paid fifteen twenty

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Speaker 1: percent to hold the unrated piece, isn't there a lot

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Speaker 1: of downside risk that hey, if some of these mortgages

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Speaker 1: go south, you could see, you know, you get cut

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Speaker 1: in half or worse.

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Speaker 2: That's exactly right, and so our what the modeling actually said, though,

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Speaker 2: is if nothing happens in the world, we make this

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Speaker 2: twenty percent return. But if anything happened, not only would

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Speaker 2: our equity piece suffer, but the short side or our

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Speaker 2: mezzanine pieces would make the money back. And that's the raceo,

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Speaker 2: that's the ratio we had to be on. So what

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Speaker 2: they call that is delta neutral in the options world.

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Speaker 2: So we had a we were hedging an option, and

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Speaker 2: that hedge made us a lot of money in downside scenarios,

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Speaker 2: but that was never the focus. We didn't know the

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Speaker 2: housing market would crash. We had no idea. What we

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Speaker 2: had was a trade or an investment that we'd make

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Speaker 2: twenty percent a year on and if anything happened in

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Speaker 2: the world, we've really protected the balance sheet. It just

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Speaker 2: happened quite quickly.

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Speaker 1: So let's talk a little bit about what's going on today,

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Speaker 1: especially in some of the private alternative spaces you've talked about.

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Speaker 1: Pensions are now facing ill equids. The issues because private

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Speaker 1: equity and venture capital have gates up a lot of

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Speaker 1: long term tie ups. How has this affected your business?

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Speaker 2: That's been the most challenging part of the business. Really,

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Speaker 2: it really hasn't. And pension funds they're on hold today,

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Speaker 2: they're not investing, and it's been not just a headwind

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Speaker 2: for us, but for the entire industry. So I'll step

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Speaker 2: back and I'll give you my view on it. So

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Speaker 2: pensions have this mandate, they have a diversified portfolio they

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Speaker 2: invest in, they receive cash flow from the portfolio, and

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Speaker 2: that supports their retiree benefits. So they're always making this

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Speaker 2: judgment while I produce enough cash to manage those liabilities.

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Speaker 2: What happened over the last year and a half or

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Speaker 2: so is rates went up and valuations went down. Now,

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Speaker 2: the handshake agreement with the venture firms and the private

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Speaker 2: equity firms was give them a dollar today and in

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Speaker 2: five years they'll give you back two or three dollars, right,

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Speaker 2: depending on how the fund did. They've stopped giving back

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Speaker 2: that capital today. Oh really, And so the pension funds

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Speaker 2: are faced with this illiquidity problem, and so they're borrowing

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Speaker 2: money against their portfolios. They're selling positions in their portfolios.

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Speaker 2: But what they're not doing is taking on new investments. Now,

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Speaker 2: there's a flip side to this. Whenever we have trouble

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Speaker 2: raising capital, the investment opportunities are usually very good. So

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Speaker 2: our pipeline is extremely robust today.

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Speaker 1: Huh, that's really intriguing. Do you see this across the

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Speaker 1: board or is it really just more generalized that when

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Speaker 1: you have the dislocation of five hundred plus basis points

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Speaker 1: in eighteen months, what does that do to the landscape.

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Speaker 2: It always changes the landscape, and so no one's ever

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Speaker 2: prepared for moves of that size, even though everyone says

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Speaker 2: they are. And so it's opportunities that have come out

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Speaker 2: of this mainly are around the banks today, right, and

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Speaker 2: so we can talk a little bit more about that.

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Speaker 1: Well, let's talk a bit about Magnetar has more of

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Speaker 1: a specialty finance focus than other credit managers. Tell us

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Speaker 1: about that and how has the shift and rates impacted

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Speaker 1: specialty financed.

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Speaker 2: Yeah, so after the GFC, these private credit markets really

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Speaker 2: developed and they went in two different directions. They went

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Speaker 2: in direct lending, right, and so ninety percent of the

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Speaker 2: market went direct lending. So that's going to middle market

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Speaker 2: companies and disintermating the banks and lending directly to them.

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Speaker 2: For us, we went in a different direction. We went

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Speaker 2: in specialty finance and especially finances is a bit smaller,

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Speaker 2: but it's been around for ages and it touches our

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Speaker 2: lives every day.

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Speaker 1: Define it if you would, Yeah, so's it's.

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Speaker 2: The cars we drive, so auto loans, it's the houses

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Speaker 2: we buyer ranso, it's mortgages. It's the podcasts that we stream, right,

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Speaker 2: so it's all the music royalties and streaming royalties. So

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Speaker 2: it's it's assets like that. And the interesting part about

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Speaker 2: these assets is there's a very strong investment thesis around

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Speaker 2: them because they have three attributes when combined together, that

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Speaker 2: most other asset classes don't have, and certainly I don't

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Speaker 2: think direct lending has. So the first is you can

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Speaker 2: find very stable payoff profiles. Second, you can find assets

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Speaker 2: or these payoff profiles that don't correlate to the overall market,

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Speaker 2: so you're not worried about them moving with the SMP

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Speaker 2: or the highyield index. And third, and most importantly, they

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Speaker 2: don't correlate to one another. And so I'll give you

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Speaker 2: an example of a three asset portfolio. So In our

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Speaker 2: music royalty portfolio, returns could be driven by an artist's

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Speaker 2: song downloads like Taylor Swift downloads. And in our solar

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Speaker 2: finance portfolio, it's by how much sunlight there is in

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Speaker 2: a particular region. Or lately we've been lending a lot

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Speaker 2: against in video GPUs for cloud usage and that's driven

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Speaker 2: by aim machine learning growth. If I think about just

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Speaker 2: those three assets, they shouldn't correlate to the SMP, but

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Speaker 2: they certainly shouldn't correlate to one another. That's how we

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Speaker 2: can really produce a high quality of earnings for our investors.

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Speaker 1: Really interesting you mentioned in banks earlier. I know that

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Speaker 1: Magnetar has had opportunities to partner with banks via what

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Speaker 1: some people call rig cap transactions. Tell us a little

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Speaker 1: bit about those.

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Speaker 2: So rag cap or some people call them significant risk

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Speaker 2: transfer transactions. That is a massive opportunity for credit funds today.

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Speaker 2: And so a lot of people would think that the

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Speaker 2: banks are selling assets, right, but in our experience, we're

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Speaker 2: seeing them efficiently transfer the credit risk of assets but

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Speaker 2: keeping the customer relationship. It's a very important distinction.

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Speaker 1: How do you do that either you have the asset

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Speaker 1: and the credit risk. I would imagine or if you don't,

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Speaker 1: if it's a mortgage, you sell the mortgage and you're out,

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Speaker 1: how do you have How are you a little bit pregnant?

436
00:22:43,520 --> 00:22:47,919
Speaker 2: Exactly? So the solution to that are these reglatory capital solutions.

437
00:22:48,480 --> 00:22:52,080
Speaker 2: And so you're taking a portfolio of credit risk and

438
00:22:52,400 --> 00:22:55,520
Speaker 2: you're transferring that credit risk to a private credit fund

439
00:22:55,600 --> 00:22:59,399
Speaker 2: like us, but maintaining the customer relationship. And what banks

440
00:22:59,600 --> 00:23:04,199
Speaker 2: I think eminently realizes the customer relationship is how they

441
00:23:04,280 --> 00:23:09,199
Speaker 2: drive revenues. So traditional banking FX advisory services, you know,

442
00:23:09,720 --> 00:23:12,520
Speaker 2: high net worth and so without that they start to

443
00:23:12,560 --> 00:23:16,360
Speaker 2: lose their franchise. This is the product that allows them

444
00:23:16,359 --> 00:23:19,960
Speaker 2: to transfer credit risk. And for private credit firms, we

445
00:23:20,000 --> 00:23:22,199
Speaker 2: all of a sudden have access to some of their

446
00:23:22,280 --> 00:23:26,080
Speaker 2: highest quality lending. Right It's it's been the fastest growing

447
00:23:26,080 --> 00:23:27,080
Speaker 2: part of our portfolio.

448
00:23:27,359 --> 00:23:30,840
Speaker 1: So I'm trying to figure out if they're transferring the

449
00:23:30,880 --> 00:23:35,679
Speaker 1: credit risk to you, I'm assuming you're taking some sort

450
00:23:35,720 --> 00:23:39,000
Speaker 1: of contract with the bank that you're going to assume

451
00:23:39,000 --> 00:23:43,200
Speaker 1: the liability if X happens, and then you, with your expertise,

452
00:23:43,600 --> 00:23:48,879
Speaker 1: are hedging out that risk through your options or credit desk.

453
00:23:49,040 --> 00:23:52,479
Speaker 2: Yeah, and that's exactly right. But importantly, the first thing

454
00:23:52,520 --> 00:23:55,679
Speaker 2: we're doing is we're using data to really understand what

455
00:23:55,720 --> 00:23:58,520
Speaker 2: the credit risk is. And with that data, then we

456
00:23:58,600 --> 00:24:01,520
Speaker 2: can start thinking about what what the likely hedges are

457
00:24:01,560 --> 00:24:03,560
Speaker 2: for the macro risk of the portfolio.

458
00:24:03,720 --> 00:24:06,480
Speaker 1: So let's talk about that. What is your approach to data,

459
00:24:06,560 --> 00:24:11,359
Speaker 1: How do you institutionalize data management and how do you

460
00:24:11,720 --> 00:24:14,880
Speaker 1: leverage the idea of hey, we know a lot about this,

461
00:24:15,320 --> 00:24:16,680
Speaker 1: here's how we monetize it.

462
00:24:16,840 --> 00:24:19,280
Speaker 2: People talk a lot about the importance of data, but

463
00:24:19,359 --> 00:24:21,640
Speaker 2: it's usually in a different context. It's usually for these

464
00:24:21,720 --> 00:24:25,639
Speaker 2: quantitative strategies or quantitative hedge funds. For US, data is

465
00:24:25,680 --> 00:24:30,400
Speaker 2: the lifeblood of specialty finance. So for US, we use

466
00:24:30,520 --> 00:24:33,760
Speaker 2: data to solidify our assumptions. What we do with the

467
00:24:33,840 --> 00:24:38,360
Speaker 2: data is we forecast a performance of assets by matching

468
00:24:38,400 --> 00:24:44,360
Speaker 2: statistically significant characteristics. So back to the redcap examples, we've

469
00:24:44,359 --> 00:24:48,480
Speaker 2: looked at hundreds and hundreds of these types of investments

470
00:24:48,960 --> 00:24:52,560
Speaker 2: and we've taken all the data from those transactions. Now

471
00:24:52,640 --> 00:24:54,879
Speaker 2: we look at a new transaction. A bank comes to us

472
00:24:54,920 --> 00:24:58,520
Speaker 2: and says, I need to produce more regulatory capital on

473
00:24:58,960 --> 00:25:01,960
Speaker 2: this one hundred to ten thousand loans. We can take

474
00:25:02,000 --> 00:25:05,119
Speaker 2: the character risks of their polio today and out of

475
00:25:05,200 --> 00:25:08,359
Speaker 2: sample price them through history. That helps us price the

476
00:25:08,440 --> 00:25:11,320
Speaker 2: credit right and understand what risk we're taking on.

477
00:25:11,920 --> 00:25:16,720
Speaker 1: So this is really fairly sophisticated financial engineering. That is,

478
00:25:17,240 --> 00:25:20,199
Speaker 1: it sounds like it's a way for the banks to

479
00:25:20,320 --> 00:25:25,520
Speaker 1: meet the SEC requirements, the increased post financial crisis financial

480
00:25:25,560 --> 00:25:29,320
Speaker 1: reserves that they're required to have, but not have to

481
00:25:29,359 --> 00:25:31,320
Speaker 1: sell off big parts of the business and not have

482
00:25:31,400 --> 00:25:33,920
Speaker 1: to sell off the relationships you described.

483
00:25:34,040 --> 00:25:36,360
Speaker 2: I think that's exactly right. And even when you get

484
00:25:36,359 --> 00:25:39,920
Speaker 2: to what happened earlier in twenty twenty three with Credit Swiss,

485
00:25:40,240 --> 00:25:43,800
Speaker 2: that again put pressure on the banks to really think

486
00:25:43,840 --> 00:25:46,560
Speaker 2: about how they're going to hedge their credit risk. This

487
00:25:46,760 --> 00:25:48,080
Speaker 2: is their hedge to credit risk.

488
00:25:48,320 --> 00:25:51,040
Speaker 1: And then related to the way you guys work with

489
00:25:51,160 --> 00:25:54,440
Speaker 1: data management, tell us a little bit about Magnetar Labs.

490
00:25:54,680 --> 00:25:57,160
Speaker 2: Yeah, Magnetar Labs has been a great initiative for us.

491
00:25:57,359 --> 00:26:01,119
Speaker 2: It's really the institutionalization of our data. So we're trying

492
00:26:01,119 --> 00:26:05,600
Speaker 2: to produce infrastructure where we can ingest large data sets

493
00:26:05,680 --> 00:26:09,760
Speaker 2: very quickly and not only use them in specific business lines,

494
00:26:09,920 --> 00:26:13,240
Speaker 2: but use it across business lines. So I'll give you

495
00:26:13,280 --> 00:26:17,640
Speaker 2: a few examples In our merger arbitrage business, we've tracked

496
00:26:17,960 --> 00:26:21,679
Speaker 2: every detail and every characteristic of every merger and acquisition

497
00:26:22,080 --> 00:26:25,600
Speaker 2: for the last twenty plus years, and even our recent

498
00:26:25,640 --> 00:26:29,639
Speaker 2: restaurant finance business, we have itemized bills of every customer.

499
00:26:30,040 --> 00:26:33,359
Speaker 2: This is really useful data. So here's an example from

500
00:26:33,480 --> 00:26:35,560
Speaker 2: just a couple of months ago. We were looking at

501
00:26:35,720 --> 00:26:40,160
Speaker 2: an auto loan transaction and the servicer tried to overload information,

502
00:26:40,240 --> 00:26:43,440
Speaker 2: so they gave us eighty million line items of information

503
00:26:43,760 --> 00:26:46,159
Speaker 2: on purpose or I don't know if it's on purpose

504
00:26:46,240 --> 00:26:49,600
Speaker 2: or not, but eighty million line items, one hundred different files,

505
00:26:50,160 --> 00:26:54,680
Speaker 2: forty gigabytes of memory. So that's far too much for

506
00:26:55,320 --> 00:26:58,920
Speaker 2: Excel to handle or any local Python right or overload

507
00:26:58,960 --> 00:27:02,280
Speaker 2: to any one machine. But our Magtar Labs team was

508
00:27:02,320 --> 00:27:05,000
Speaker 2: able to take that in in just minutes. Right now,

509
00:27:05,040 --> 00:27:07,800
Speaker 2: we can analyze the data and then look at look

510
00:27:07,840 --> 00:27:10,879
Speaker 2: at the attributes to that investment and see if it

511
00:27:10,920 --> 00:27:14,160
Speaker 2: fits in our portfolio. We actually made the made the investment.

512
00:27:14,240 --> 00:27:16,360
Speaker 1: So so what sort of hardware using is this? Old?

513
00:27:16,400 --> 00:27:19,879
Speaker 1: Cloud based? Is this I think of like, oh, sounds

514
00:27:19,920 --> 00:27:22,040
Speaker 1: like a mainframe. I don't even know if mainframes still

515
00:27:22,080 --> 00:27:22,880
Speaker 1: exist anymore.

516
00:27:22,960 --> 00:27:26,320
Speaker 2: Everything's gone to the cloud now, right, it is pretty amazing.

517
00:27:26,520 --> 00:27:30,840
Speaker 1: And that sort of distributed computer has no ceiling, essentially

518
00:27:30,880 --> 00:27:35,879
Speaker 1: no capacity correct, infinite capacity correct. Really really interesting. So

519
00:27:36,040 --> 00:27:39,359
Speaker 1: let's talk a little bit about the status quo. I

520
00:27:39,480 --> 00:27:43,040
Speaker 1: read something where you said it was important to not

521
00:27:43,280 --> 00:27:46,520
Speaker 1: maintain the status quo. Explain what that means.

522
00:27:46,880 --> 00:27:50,520
Speaker 2: We're not efficient market theorists, but we certainly believe that

523
00:27:50,560 --> 00:27:53,720
Speaker 2: in the medium for long term the markets are efficient.

524
00:27:53,680 --> 00:27:55,800
Speaker 1: Kind of mostly eventually.

525
00:27:55,359 --> 00:27:59,119
Speaker 2: Efficient, eventually efficient. Right, So we know that what works

526
00:27:59,119 --> 00:28:03,520
Speaker 2: today at work several years forward, right, And so I'll

527
00:28:03,560 --> 00:28:06,639
Speaker 2: give you the converts example. Like you mentioned, I've been

528
00:28:06,640 --> 00:28:09,960
Speaker 2: in the convert market for thirty years now, and sometimes

529
00:28:10,000 --> 00:28:13,320
Speaker 2: converts are very cheap, you know, commal bonn arbitrage, and

530
00:28:13,359 --> 00:28:15,160
Speaker 2: when they are, we have a lot of our portfolio

531
00:28:15,240 --> 00:28:17,960
Speaker 2: in it. But today we have less than one percent

532
00:28:17,960 --> 00:28:21,320
Speaker 2: of our portfolio in the asset class. And it's just

533
00:28:21,400 --> 00:28:24,679
Speaker 2: because it's not cheap or not cheap enough versus what

534
00:28:24,720 --> 00:28:25,600
Speaker 2: we can invest in.

535
00:28:25,680 --> 00:28:29,480
Speaker 1: And is the expectation is that whatever inefficiencies were there,

536
00:28:29,960 --> 00:28:33,280
Speaker 1: markets figured it out. It's arbitraged way, and the odds

537
00:28:33,280 --> 00:28:36,520
Speaker 1: are against that ever becoming really cheap or might it

538
00:28:37,080 --> 00:28:38,800
Speaker 1: you know, become a trade again.

539
00:28:38,920 --> 00:28:42,040
Speaker 2: Yeah, some of it's supplied demand, right, and you're driven.

540
00:28:42,320 --> 00:28:44,480
Speaker 2: But I think the most important part is we're not

541
00:28:45,080 --> 00:28:48,360
Speaker 2: hiring desks of people to stay in an asset class.

542
00:28:48,760 --> 00:28:51,200
Speaker 2: That's the status quo. That's not what we're looking for.

543
00:28:51,520 --> 00:28:55,000
Speaker 2: We're looking to aggressively rotate our capital to get to

544
00:28:55,040 --> 00:28:58,040
Speaker 2: the optimal portfolio, to get to the best risk adjusted return.

545
00:28:58,400 --> 00:29:01,200
Speaker 1: So does this mean you're exploring new business areas and

546
00:29:01,240 --> 00:29:04,880
Speaker 1: strategies or is it just that you're rolling through the

547
00:29:05,000 --> 00:29:09,240
Speaker 1: various other opportunities that you've fished in before.

548
00:29:09,440 --> 00:29:12,920
Speaker 2: Yeah, it's a good question. We maintain our diligence on

549
00:29:12,960 --> 00:29:15,720
Speaker 2: other strategies, but we always have a strong research and

550
00:29:15,760 --> 00:29:17,240
Speaker 2: development pipeline. Huh.

551
00:29:17,720 --> 00:29:20,400
Speaker 1: Really interesting. So let's talk about some of the things

552
00:29:20,400 --> 00:29:25,680
Speaker 1: that are going on today. Artificial intelligence AI dominated the

553
00:29:26,120 --> 00:29:30,160
Speaker 1: twenty twenty three narrative. You made investments in core Weave,

554
00:29:30,240 --> 00:29:34,000
Speaker 1: a specialized cloud provider. Tell us a little bit about

555
00:29:34,000 --> 00:29:37,400
Speaker 1: what you're doing in that space. Is that related it

556
00:29:37,440 --> 00:29:40,520
Speaker 1: all to what we talked about earlier with Magnetar Labs.

557
00:29:40,960 --> 00:29:44,320
Speaker 2: Yeah. Core Weave is such an exciting story for Magnetar.

558
00:29:44,480 --> 00:29:47,240
Speaker 2: I can't say enough good things about it. Sometimes the

559
00:29:47,280 --> 00:29:50,040
Speaker 2: stars just align. You have the right time, the right product,

560
00:29:50,120 --> 00:29:54,000
Speaker 2: the right team. And for the listeners that don't know

561
00:29:54,040 --> 00:29:57,360
Speaker 2: who core Weave is, corev is the largest owner of

562
00:29:57,560 --> 00:30:02,440
Speaker 2: GPUs outside of the hyperscalers, Google or Amazon Web services.

563
00:30:02,920 --> 00:30:05,680
Speaker 2: They sell is high performance compute, which is sort of

564
00:30:05,720 --> 00:30:09,200
Speaker 2: the picks and shovels to enable AI. So if you

565
00:30:09,280 --> 00:30:13,840
Speaker 2: are a new AI lab, you need somebody like core

566
00:30:13,920 --> 00:30:17,080
Speaker 2: Weave to host that specialized cloud for you. Now, we

567
00:30:17,080 --> 00:30:20,120
Speaker 2: were the first institutional investor, so all the way back

568
00:30:20,480 --> 00:30:23,800
Speaker 2: in twenty twenty and at that point, core We've had

569
00:30:23,920 --> 00:30:26,840
Speaker 2: just twenty six million dollars of top line revenue, and

570
00:30:26,880 --> 00:30:29,400
Speaker 2: I think we're the first firm to really get comfortable

571
00:30:29,880 --> 00:30:34,160
Speaker 2: lending against that asset called high performance compute. Right, So

572
00:30:34,560 --> 00:30:37,640
Speaker 2: they've had explosive growth, But what we haven't been is

573
00:30:37,680 --> 00:30:40,560
Speaker 2: just a capital provider. We've really been a partner to

574
00:30:40,600 --> 00:30:43,280
Speaker 2: them within the business. Are you guys also a customer

575
00:30:43,320 --> 00:30:46,280
Speaker 2: of their We're a customer there is in Magnetar Labs,

576
00:30:45,920 --> 00:30:49,800
Speaker 2: just like you intimated before, and so we use them

577
00:30:49,800 --> 00:30:53,680
Speaker 2: for Magnetar Labs. But we have Ernie Rodgers, our COO,

578
00:30:53,840 --> 00:30:57,479
Speaker 2: sits on their board. We have daily interaction between our

579
00:30:57,520 --> 00:31:01,640
Speaker 2: management teams. This company is growing so quickly they need

580
00:31:01,960 --> 00:31:03,880
Speaker 2: all the help they can get around them, and what

581
00:31:03,960 --> 00:31:06,440
Speaker 2: we try to help with is mostly balance sheet management.

582
00:31:06,840 --> 00:31:11,040
Speaker 1: So for a firm that specializes in credit, this almost

583
00:31:11,080 --> 00:31:12,560
Speaker 1: sounds like a venture investment.

584
00:31:12,960 --> 00:31:15,360
Speaker 2: There are parts of this that are venture ish, but

585
00:31:15,440 --> 00:31:19,120
Speaker 2: what's interesting is the underlying asset, this high performance compute

586
00:31:19,640 --> 00:31:22,520
Speaker 2: is something that we can really scale with and so

587
00:31:22,960 --> 00:31:26,080
Speaker 2: I think that's been the innovation in the marketplace. So

588
00:31:26,120 --> 00:31:28,800
Speaker 2: you mentioned in twenty twenty three on the venture side,

589
00:31:29,080 --> 00:31:31,240
Speaker 2: we actually let around for them a four hundred million

590
00:31:31,240 --> 00:31:34,440
Speaker 2: dollars Series B round, but we also let a two

591
00:31:34,520 --> 00:31:38,800
Speaker 2: point three billion dollar financing on their high performance compute assets.

592
00:31:38,880 --> 00:31:41,600
Speaker 1: So it's capital and credit, it's equity and credit.

593
00:31:41,760 --> 00:31:45,440
Speaker 2: It's equity and credit, and it's a true partnership between

594
00:31:45,440 --> 00:31:49,320
Speaker 2: the firms. Towards the end of last year, in December,

595
00:31:49,720 --> 00:31:52,880
Speaker 2: the firm got valued at seven billion dollars. And to me,

596
00:31:52,920 --> 00:31:56,560
Speaker 2: it's just a start. This company just the you're just

597
00:31:56,560 --> 00:31:58,480
Speaker 2: going to see it continue to grow over time.

598
00:31:58,640 --> 00:32:00,640
Speaker 1: Well, let me know about the C round when that comes.

599
00:32:01,920 --> 00:32:04,480
Speaker 1: What do you guys, in all seriousness, what are you

600
00:32:04,520 --> 00:32:08,040
Speaker 1: guys looking for? What sort of characteristics are you looking

601
00:32:08,080 --> 00:32:11,000
Speaker 1: for when a company like this comes along. You mentioned

602
00:32:11,040 --> 00:32:16,400
Speaker 1: idiosyncratic types of investment. This sounds very specific and not

603
00:32:16,480 --> 00:32:17,240
Speaker 1: all that usual.

604
00:32:17,440 --> 00:32:19,920
Speaker 2: It is. It's very specific, but we always start with

605
00:32:19,960 --> 00:32:23,840
Speaker 2: the assets. So it's assets, it's data, and its structure. Right,

606
00:32:23,920 --> 00:32:27,280
Speaker 2: So first on the assets, we're usually focused on specialty

607
00:32:27,280 --> 00:32:31,360
Speaker 2: finance because the assets drive the performance of the company. Right.

608
00:32:31,480 --> 00:32:33,680
Speaker 2: The next thing we need is data. We can't predict

609
00:32:33,720 --> 00:32:37,160
Speaker 2: the future, so we're trying to do is use historical

610
00:32:37,280 --> 00:32:40,680
Speaker 2: data to predict how an asset reacts in different states

611
00:32:40,680 --> 00:32:44,200
Speaker 2: of the economy, and finally we use structure around that

612
00:32:44,560 --> 00:32:46,800
Speaker 2: to protect the downside of the investment itself.

613
00:32:47,440 --> 00:32:51,040
Speaker 1: Huh. Sounds really intriguing. So as long as we're talking

614
00:32:51,080 --> 00:32:53,720
Speaker 1: about twenty twenty three, we saw a lot of bank

615
00:32:53,760 --> 00:32:58,160
Speaker 1: failures last year, we saw the response to a rapid

616
00:32:58,200 --> 00:33:01,400
Speaker 1: increase in rates. You had a front row seat to

617
00:33:01,480 --> 00:33:05,120
Speaker 1: what transpired. Share what that was like, and what did

618
00:33:05,160 --> 00:33:07,960
Speaker 1: you guys see in the space. Tell us about the

619
00:33:08,000 --> 00:33:09,960
Speaker 1: opportunities that came up from those events.

620
00:33:10,400 --> 00:33:13,959
Speaker 2: Those were stressful events for the entire community, you know,

621
00:33:14,200 --> 00:33:17,120
Speaker 2: for Silicon Valley Bank in particular. I remember it was

622
00:33:17,200 --> 00:33:22,080
Speaker 2: Friday night, and the question of moral hazard appeared immediately.

623
00:33:22,640 --> 00:33:25,520
Speaker 2: So it's California based, right. It was a lot of

624
00:33:25,640 --> 00:33:29,560
Speaker 2: venture funds that had accounts there, and the questions started

625
00:33:29,560 --> 00:33:32,360
Speaker 2: coming out, is their cash safe, will they be able

626
00:33:32,360 --> 00:33:34,880
Speaker 2: to access it? If so, when will they be able

627
00:33:34,920 --> 00:33:37,200
Speaker 2: to make payroll? A lot of these smaller companies were

628
00:33:37,280 --> 00:33:41,000
Speaker 2: very worried about payroll, and in California specifically will the

629
00:33:41,200 --> 00:33:44,240
Speaker 2: border directors be liable if they couldn't make payroll? And

630
00:33:44,240 --> 00:33:46,000
Speaker 2: then they started rolling it out to what about all

631
00:33:46,000 --> 00:33:49,640
Speaker 2: the similar situated banks. So we all know that by

632
00:33:49,680 --> 00:33:52,640
Speaker 2: Monday morning, the contagion risk was too high and the

633
00:33:52,680 --> 00:33:56,400
Speaker 2: government did step in. But the opportunities really arose from that.

634
00:33:56,600 --> 00:33:59,160
Speaker 2: And so the first opportunity, which is very similar to

635
00:33:59,360 --> 00:34:04,120
Speaker 2: doing regulatory capital investments with large banks, is being a

636
00:34:04,240 --> 00:34:07,560
Speaker 2: risk capital provider to the small and regional banks. And

637
00:34:07,600 --> 00:34:09,200
Speaker 2: I think we're going to see more and more of

638
00:34:09,200 --> 00:34:13,440
Speaker 2: this overtime. It's credit firms partnering with banks where we

639
00:34:13,480 --> 00:34:17,200
Speaker 2: have access to all the diligence around their customers and

640
00:34:17,280 --> 00:34:20,280
Speaker 2: together we can jointly underwrite and make loans.

641
00:34:20,760 --> 00:34:24,000
Speaker 1: You mentioned moral hazard. Where was the moral hazard with

642
00:34:24,040 --> 00:34:27,560
Speaker 1: Silicon Valley Bank? Was it the equity investors in the

643
00:34:27,600 --> 00:34:32,399
Speaker 1: bank or was it the customers with way over the

644
00:34:32,480 --> 00:34:36,000
Speaker 1: FDIC limits? And if there isn't a quarter million or

645
00:34:36,040 --> 00:34:41,200
Speaker 1: half a million dollar ceiling, did the Federal Reserve essentially say, okay,

646
00:34:41,640 --> 00:34:44,840
Speaker 1: FDIC insurance is now unlimited? Is that the moral hazard?

647
00:34:45,200 --> 00:34:47,560
Speaker 2: We found that to be the moral hazard. Who's the

648
00:34:47,600 --> 00:34:49,680
Speaker 2: governor of how much risk a bank can take? So

649
00:34:49,719 --> 00:34:52,120
Speaker 2: the federal government came out and they said, you have

650
00:34:52,120 --> 00:34:54,800
Speaker 2: a two hundred and fifty thousand dollars limit, but people

651
00:34:54,840 --> 00:34:57,160
Speaker 2: were putting in one hundred million dollars into the account

652
00:34:57,320 --> 00:35:00,399
Speaker 2: because they got twenty five basis points more of interest, right,

653
00:35:00,440 --> 00:35:03,880
Speaker 2: So how do you actually control that? That's the moral

654
00:35:03,920 --> 00:35:05,920
Speaker 2: hazard we saw now. I think at the end of

655
00:35:05,960 --> 00:35:07,799
Speaker 2: the day, it was just too big of a risk

656
00:35:07,840 --> 00:35:08,600
Speaker 2: to the economy.

657
00:35:08,719 --> 00:35:12,680
Speaker 1: The contagion risk was Hey, there's a moral hazard question

658
00:35:12,960 --> 00:35:16,920
Speaker 1: to the depositors. But rather than stand on ceremony, let's

659
00:35:16,960 --> 00:35:18,640
Speaker 1: stop this before it spreads.

660
00:35:18,880 --> 00:35:19,799
Speaker 2: That's exactly right.

661
00:35:20,000 --> 00:35:23,359
Speaker 1: Huh, that's really that's really kind of intriguing. What else

662
00:35:23,400 --> 00:35:26,959
Speaker 1: has been the result of this rapid spike in interest rates?

663
00:35:26,960 --> 00:35:30,560
Speaker 1: What do you see in the private credit world that hey,

664
00:35:30,640 --> 00:35:34,640
Speaker 1: blame the fed. But here's what's gone off the rails.

665
00:35:34,880 --> 00:35:38,680
Speaker 2: For credit investors. Everyone thinks about fixed rate risk, right,

666
00:35:38,680 --> 00:35:41,600
Speaker 2: but that's easily hedgable, and that's a choice that that

667
00:35:41,680 --> 00:35:45,080
Speaker 2: credit investors make. So for people like magnets, are we

668
00:35:45,120 --> 00:35:47,600
Speaker 2: swap everything back to floating rate, we don't have any

669
00:35:47,719 --> 00:35:50,720
Speaker 2: edge on a macro risk like that. But the second

670
00:35:50,800 --> 00:35:53,719
Speaker 2: order effect is much much more difficult, and that's the

671
00:35:53,760 --> 00:35:58,400
Speaker 2: business impact of rates changing. So when we think about businesses,

672
00:35:58,440 --> 00:36:02,440
Speaker 2: we think about that margins change as rates go up

673
00:36:02,520 --> 00:36:06,600
Speaker 2: or down, to originations change, What about the refinancing of

674
00:36:06,640 --> 00:36:08,680
Speaker 2: their debt? I think those are the things that are

675
00:36:08,680 --> 00:36:12,160
Speaker 2: going to keep lawyers and restructuring advisors very busy for

676
00:36:12,200 --> 00:36:13,280
Speaker 2: the foreseeable future.

677
00:36:13,480 --> 00:36:17,640
Speaker 1: So given this current environment where first rates went up

678
00:36:17,760 --> 00:36:22,480
Speaker 1: further and faster than it seemed like, the consensus amongst

679
00:36:22,560 --> 00:36:28,200
Speaker 1: analysts was they stayed higher longer than people expected. There's

680
00:36:28,239 --> 00:36:31,000
Speaker 1: no recession. People have been talking about that for two years,

681
00:36:31,560 --> 00:36:35,480
Speaker 1: and the expected rate cuts I guess tied to that

682
00:36:35,520 --> 00:36:38,640
Speaker 1: recession haven't showed up yet. We were talking about March,

683
00:36:38,719 --> 00:36:42,160
Speaker 1: now we're talking about May, even June of twenty twenty four.

684
00:36:43,120 --> 00:36:47,280
Speaker 1: How does this affect how you think about putting portfolios together,

685
00:36:47,320 --> 00:36:50,600
Speaker 1: constructing portfolios. And I am very aware that you guys

686
00:36:50,640 --> 00:36:54,360
Speaker 1: aren't macro tourists. You don't play that game. But given

687
00:36:54,520 --> 00:36:58,759
Speaker 1: the volatility and the various probabilistic outcomes, how does that

688
00:36:58,800 --> 00:36:59,640
Speaker 1: impact your thinking.

689
00:37:00,200 --> 00:37:02,719
Speaker 2: It's a very good question, and for us, we think

690
00:37:02,760 --> 00:37:06,200
Speaker 2: a lot about the affordability factor. So I'll give you

691
00:37:06,200 --> 00:37:10,120
Speaker 2: two examples at both extremes. So we have a partial

692
00:37:10,160 --> 00:37:13,640
Speaker 2: ownership in an auto loan business in Ireland, and so

693
00:37:13,719 --> 00:37:16,759
Speaker 2: when rates are at zero, we're loaning to consumers. It's

694
00:37:16,760 --> 00:37:19,160
Speaker 2: somewhere between five and a half and six percent, and

695
00:37:19,200 --> 00:37:22,520
Speaker 2: we're gaining market share rapidly. All of a sudden, risk

696
00:37:22,560 --> 00:37:25,920
Speaker 2: free rate goes to five percent, that equivalent loan we're

697
00:37:25,920 --> 00:37:28,960
Speaker 2: gonna have to charge consumers eleven percent. It's just it's

698
00:37:28,960 --> 00:37:30,440
Speaker 2: simply unaffordable.

699
00:37:29,960 --> 00:37:31,759
Speaker 1: Right, different calculus, different.

700
00:37:31,440 --> 00:37:34,440
Speaker 2: Calculus, And so we have a decision to make. We

701
00:37:34,480 --> 00:37:37,560
Speaker 2: can stay at eleven percent, keep the same margin but

702
00:37:37,719 --> 00:37:41,920
Speaker 2: reduce origination, or we can take our margin down and

703
00:37:41,960 --> 00:37:44,360
Speaker 2: try to keep market share. Either way, the business is

704
00:37:44,400 --> 00:37:47,600
Speaker 2: worth a lot less. That has a lot of affordability

705
00:37:47,640 --> 00:37:50,200
Speaker 2: factor effect to it. On the other end of the

706
00:37:50,239 --> 00:37:53,680
Speaker 2: stream is our music royalties business. So in music royalties,

707
00:37:54,280 --> 00:37:57,640
Speaker 2: you know, the simplification is you get some small part

708
00:37:57,680 --> 00:38:02,200
Speaker 2: of worldwide streaming revenue. So it takes Spotify. Spotify raise

709
00:38:02,320 --> 00:38:05,920
Speaker 2: rates recently and they had no customer churn, so some

710
00:38:06,000 --> 00:38:09,080
Speaker 2: percentage of that rate went directly to the royalty holder.

711
00:38:09,480 --> 00:38:13,680
Speaker 2: There was very little affordability factor. So we're veering away

712
00:38:13,680 --> 00:38:17,640
Speaker 2: from things that the business impact on affordability is high,

713
00:38:18,080 --> 00:38:20,160
Speaker 2: and we're investing in things where it's lower.

714
00:38:20,480 --> 00:38:22,680
Speaker 1: Private credit seems to be getting a lot of attention

715
00:38:22,800 --> 00:38:24,279
Speaker 1: these days. Why is that.

716
00:38:24,480 --> 00:38:26,560
Speaker 2: If you would have asked me going into the global

717
00:38:26,560 --> 00:38:29,359
Speaker 2: financial crisis, I know we keep going back fifteen years now,

718
00:38:29,480 --> 00:38:31,400
Speaker 2: I would have said the banks had it all right.

719
00:38:31,440 --> 00:38:34,520
Speaker 2: They controlled origination of all of the different asset classes,

720
00:38:34,600 --> 00:38:38,000
Speaker 2: especially finance and lending, so whether its credit cards or

721
00:38:38,040 --> 00:38:43,040
Speaker 2: mortgages or loans to their customers. But after as the

722
00:38:43,040 --> 00:38:46,839
Speaker 2: financial crisis happened, there was a spotlight flashed on their

723
00:38:46,880 --> 00:38:49,640
Speaker 2: balance sheet. They just had too much risk and so

724
00:38:49,680 --> 00:38:51,960
Speaker 2: the regulators came in to reduce that risk. So the

725
00:38:52,040 --> 00:38:56,240
Speaker 2: simple question is that private credit came in and stepped

726
00:38:56,239 --> 00:39:00,719
Speaker 2: in the shoes of banks and really took markets. But

727
00:39:01,080 --> 00:39:04,280
Speaker 2: this scale was much larger than anyone could have anticipated.

728
00:39:04,520 --> 00:39:07,239
Speaker 2: But for me, what I think about a lot is

729
00:39:07,280 --> 00:39:11,960
Speaker 2: the more profound effect is the talent transfer. The talent

730
00:39:12,040 --> 00:39:15,239
Speaker 2: transfer from the banks that went to the credit providers,

731
00:39:15,320 --> 00:39:18,759
Speaker 2: the private credit providers that set the stage for this

732
00:39:18,960 --> 00:39:21,320
Speaker 2: mass growth in private credit.

733
00:39:21,520 --> 00:39:24,279
Speaker 1: So let's talk about talent a little bit. One of

734
00:39:24,320 --> 00:39:27,680
Speaker 1: the things I know your firm is proud of is

735
00:39:28,160 --> 00:39:30,600
Speaker 1: more than half of your workforce has been with the

736
00:39:30,600 --> 00:39:34,480
Speaker 1: firm for five years or longer. So first, I'm assuming

737
00:39:34,520 --> 00:39:37,880
Speaker 1: that's not typical in your space, and second I have

738
00:39:37,960 --> 00:39:41,440
Speaker 1: to ask what contributed to that sort of retention.

739
00:39:42,080 --> 00:39:44,680
Speaker 2: I'm very proud. I think we're very proud of that fact,

740
00:39:44,680 --> 00:39:47,880
Speaker 2: and I think it is very atypical. But the credit

741
00:39:47,960 --> 00:39:51,600
Speaker 2: really goes to so many people at Magnetar. You know,

742
00:39:51,680 --> 00:39:55,719
Speaker 2: we're a global firm, but I think with a Midwestern ethos,

743
00:39:56,120 --> 00:39:59,680
Speaker 2: so it's work hard, stay humble, be a good teammate,

744
00:40:00,080 --> 00:40:04,560
Speaker 2: good person, And I think if we can consistently demonstrate

745
00:40:04,600 --> 00:40:07,799
Speaker 2: those qualities will attract people who value them. And it's

746
00:40:08,120 --> 00:40:11,040
Speaker 2: a virtuous circle. And what's incredible about the firm is

747
00:40:11,080 --> 00:40:13,279
Speaker 2: when we get when we're focused, how much we can

748
00:40:13,320 --> 00:40:15,839
Speaker 2: get done. So I'll give you a simple example. We

749
00:40:15,920 --> 00:40:19,960
Speaker 2: started a summer internship program several years ago, and we

750
00:40:20,000 --> 00:40:22,960
Speaker 2: started with two interns and we built the program around them,

751
00:40:23,239 --> 00:40:26,759
Speaker 2: and this last summer we had sixty interns for a

752
00:40:26,760 --> 00:40:30,399
Speaker 2: two hundred person organization. You know, it's pretty humbling when

753
00:40:30,440 --> 00:40:33,440
Speaker 2: you think about all the exceptional people around Magnetar and

754
00:40:33,440 --> 00:40:34,600
Speaker 2: how much we can get done.

755
00:40:34,760 --> 00:40:37,200
Speaker 1: So one of the things we've been hearing a lot

756
00:40:37,239 --> 00:40:41,760
Speaker 1: about as big companies try and get their staff back

757
00:40:41,800 --> 00:40:45,840
Speaker 1: in the office five days a week is corporate culture.

758
00:40:46,480 --> 00:40:50,800
Speaker 1: Tell us a little bit about what is differentiating Magnetar

759
00:40:51,360 --> 00:40:57,040
Speaker 1: from a cultural perspective. You know, starting with Evanston, Illinois,

760
00:40:57,080 --> 00:40:59,960
Speaker 1: not a lot of private credit shops in the neighborho

761
00:41:01,000 --> 00:41:01,720
Speaker 1: that's true.

762
00:41:01,880 --> 00:41:04,640
Speaker 2: You know, first principles, it's always about integrity. But I

763
00:41:04,680 --> 00:41:09,759
Speaker 2: think for most tenured firms integrity is high. But for us,

764
00:41:09,800 --> 00:41:13,160
Speaker 2: the north star is always creating the best portfolios to

765
00:41:13,160 --> 00:41:17,319
Speaker 2: deliver to our clients. And we really have two foundational

766
00:41:17,360 --> 00:41:20,600
Speaker 2: points there. One is we run a very flat organization

767
00:41:21,120 --> 00:41:23,480
Speaker 2: and secondly, we've thought a lot about alignment. So on

768
00:41:23,520 --> 00:41:27,000
Speaker 2: the flat organization, it doesn't matter who has the right answer.

769
00:41:27,360 --> 00:41:29,960
Speaker 2: We know we're trying to reach the right answer. So

770
00:41:30,120 --> 00:41:33,360
Speaker 2: I'll take our investment committees as an example. We have

771
00:41:33,440 --> 00:41:37,120
Speaker 2: bi weekly investment committees. And it's not the top two

772
00:41:37,239 --> 00:41:39,600
Speaker 2: or three people that sit on the investment committee. We

773
00:41:39,640 --> 00:41:42,759
Speaker 2: have one hundred and twenty people in that meeting, you know,

774
00:41:43,160 --> 00:41:45,840
Speaker 2: every two weeks. Wow, And we really want people to

775
00:41:45,920 --> 00:41:48,560
Speaker 2: voice opinions, right, and that's how we're going to get

776
00:41:48,600 --> 00:41:50,560
Speaker 2: to the best answer. You know, we talk about it

777
00:41:50,600 --> 00:41:53,879
Speaker 2: internally a lot. We're trying to manage investments by consensus,

778
00:41:54,239 --> 00:41:58,200
Speaker 2: and so especially in private credit, if someone doesn't like something,

779
00:41:58,200 --> 00:42:00,360
Speaker 2: we can change it. We can change you know, what

780
00:42:00,400 --> 00:42:03,440
Speaker 2: a structure looks like. And so we'll get to something

781
00:42:03,760 --> 00:42:07,600
Speaker 2: where we actually get consensus, you know. On the alignment point.

782
00:42:08,120 --> 00:42:12,240
Speaker 2: It really goes back to not giving individual capital allocations,

783
00:42:12,480 --> 00:42:16,040
Speaker 2: but incentivizing people to create the best portfolio. So you

784
00:42:16,040 --> 00:42:19,120
Speaker 2: asked about pretention before. I think the reason why people

785
00:42:19,440 --> 00:42:22,840
Speaker 2: stay at Magnetar long term is because they believe in

786
00:42:22,880 --> 00:42:25,040
Speaker 2: these philosophies and they believe if we get to the

787
00:42:25,080 --> 00:42:28,240
Speaker 2: right portfolio that everyone wins in the long term.

788
00:42:28,440 --> 00:42:31,480
Speaker 1: Huh. Really very interesting, So we only have you for

789
00:42:31,920 --> 00:42:33,799
Speaker 1: a limited amount of time. Let me jump to my

790
00:42:33,960 --> 00:42:37,680
Speaker 1: favorite questions that I ask all of my guests starting

791
00:42:37,719 --> 00:42:40,640
Speaker 1: with tell us what you've been streaming these days? What's

792
00:42:40,640 --> 00:42:45,800
Speaker 1: been keeping you entertained? Either video or audio, Netflix or podcast?

793
00:42:45,840 --> 00:42:47,280
Speaker 1: What's keeping you entertained?

794
00:42:47,480 --> 00:42:49,320
Speaker 2: Yeah, I think this will be different than most of

795
00:42:49,320 --> 00:42:51,200
Speaker 2: the people have stated on this show. But for me,

796
00:42:51,280 --> 00:42:51,840
Speaker 2: it's been.

797
00:42:51,840 --> 00:42:55,120
Speaker 1: Flow sports, flow sports.

798
00:42:54,680 --> 00:42:57,359
Speaker 2: Flow sports. So I have my older son is in

799
00:42:57,400 --> 00:43:00,680
Speaker 2: between high school and college right now, and he's playing

800
00:43:00,719 --> 00:43:03,640
Speaker 2: hockey and juniors for a year, and so all of

801
00:43:03,680 --> 00:43:07,000
Speaker 2: his games are on flow Sports. So Christy and my

802
00:43:07,080 --> 00:43:09,960
Speaker 2: son Jake and I sit around and watch every game together.

803
00:43:10,120 --> 00:43:11,959
Speaker 1: What does he what positions does he play?

804
00:43:12,160 --> 00:43:15,600
Speaker 2: He plays defense. It's been a lot of fun flow sports.

805
00:43:15,680 --> 00:43:18,440
Speaker 1: Is that like a YouTube channel on internet channel? How

806
00:43:18,440 --> 00:43:19,000
Speaker 1: do you find that?

807
00:43:19,680 --> 00:43:21,920
Speaker 2: We pull it up on Apple TV or on our phone,

808
00:43:22,000 --> 00:43:25,800
Speaker 2: and yeah, it's been great for a lot of youth sports.

809
00:43:26,040 --> 00:43:26,880
Speaker 1: Huh. Interesting.

810
00:43:26,920 --> 00:43:30,000
Speaker 2: And then on the podcast side, this podcast aside on.

811
00:43:29,960 --> 00:43:31,879
Speaker 1: Here you never have to bring this podcast up.

812
00:43:31,760 --> 00:43:35,120
Speaker 2: Of course. So I listened to one by Larry Burnsteed

813
00:43:35,200 --> 00:43:39,240
Speaker 2: what Happens Next, and he's been doing it since COVID,

814
00:43:39,320 --> 00:43:43,440
Speaker 2: and it's sort of six minutes of really relevant topics

815
00:43:43,440 --> 00:43:44,520
Speaker 2: that come out every weekend.

816
00:43:45,239 --> 00:43:47,560
Speaker 1: What happens next. I'm going to check that out. I

817
00:43:47,600 --> 00:43:50,799
Speaker 1: love the idea of these having done long form for

818
00:43:50,880 --> 00:43:54,000
Speaker 1: a decade, I love the idea of five, ten, twelve

819
00:43:54,040 --> 00:43:57,359
Speaker 1: minutes and you're done, And there's something very appealing about that.

820
00:43:58,480 --> 00:44:02,080
Speaker 1: Let's talk about your mentors who helped to shape your career.

821
00:44:02,320 --> 00:44:04,160
Speaker 2: You know, it always starts with their parents, and then

822
00:44:04,520 --> 00:44:07,920
Speaker 2: you know football coaches like like Larry Kimbaum. But I

823
00:44:08,000 --> 00:44:10,680
Speaker 2: mentioned Dave Bunning before. I think most people would say,

824
00:44:10,920 --> 00:44:13,479
Speaker 2: you know, I'm a product of his teachings over time.

825
00:44:13,840 --> 00:44:16,800
Speaker 1: Huh. Interesting. How about books? What are some of your favorites?

826
00:44:16,880 --> 00:44:18,040
Speaker 1: What are you reading right now?

827
00:44:18,239 --> 00:44:20,359
Speaker 2: You know I always like Michael Lewis books. We had

828
00:44:20,440 --> 00:44:23,600
Speaker 2: him at one of our off sites a few years ago.

829
00:44:24,000 --> 00:44:26,120
Speaker 2: You ever remember this book is one of my favorites.

830
00:44:26,160 --> 00:44:28,960
Speaker 2: You know, Memos from the Chairman by Alan Greenberg. Sure,

831
00:44:29,360 --> 00:44:29,759
Speaker 2: that was a.

832
00:44:29,680 --> 00:44:32,880
Speaker 1: Great bay Greenberg Greenberg correct.

833
00:44:32,880 --> 00:44:34,759
Speaker 2: And what was so interesting about his book is, you

834
00:44:34,760 --> 00:44:37,160
Speaker 2: know he's running the firm, but he's really in the

835
00:44:37,280 --> 00:44:38,960
Speaker 2: nuche of every detail.

836
00:44:39,560 --> 00:44:43,920
Speaker 1: It was very interesting, including the paper clips recycling.

837
00:44:43,440 --> 00:44:45,040
Speaker 2: Between every expense.

838
00:44:45,080 --> 00:44:48,200
Speaker 1: So let me interrupt you one second. Say I was

839
00:44:48,320 --> 00:44:51,799
Speaker 1: at a lunch just with you know, three people at

840
00:44:51,800 --> 00:44:54,880
Speaker 1: a table and he came in and sat like a

841
00:44:54,960 --> 00:44:58,319
Speaker 1: table or two over and the whole meal, I mean

842
00:44:58,360 --> 00:45:00,560
Speaker 1: this was later in his life. The whole meal was

843
00:45:00,600 --> 00:45:04,439
Speaker 1: a parade of people coming in to genuflect in front

844
00:45:04,440 --> 00:45:08,040
Speaker 1: of him and just pay their respects. It was like

845
00:45:08,120 --> 00:45:12,040
Speaker 1: the Pope was having lunch. I don't know how well

846
00:45:12,040 --> 00:45:15,399
Speaker 1: you know of him, and the book certainly is kind

847
00:45:15,440 --> 00:45:18,040
Speaker 1: of you know, you don't get a sense of how

848
00:45:18,080 --> 00:45:21,239
Speaker 1: other people perceived him, but fascinating guy.

849
00:45:21,440 --> 00:45:23,319
Speaker 2: I met him when he was at bear Stearns and

850
00:45:23,640 --> 00:45:27,200
Speaker 2: I felt the same way. He was a special person.

851
00:45:27,719 --> 00:45:29,640
Speaker 1: What other books are you reading? Anything else you want

852
00:45:29,680 --> 00:45:30,000
Speaker 1: to mention?

853
00:45:30,600 --> 00:45:32,879
Speaker 2: So my colleague and the head of our London ops,

854
00:45:32,880 --> 00:45:36,840
Speaker 2: Alan Schafferan, recommended the book The Missing Billionaires and the

855
00:45:36,880 --> 00:45:39,239
Speaker 2: reason that I just started. But the reason it's interesting

856
00:45:39,320 --> 00:45:44,160
Speaker 2: is it's very focused on asset allocation and mistakes and

857
00:45:44,239 --> 00:45:47,799
Speaker 2: asset allocation and how much that can cost a portfolio

858
00:45:47,800 --> 00:45:50,160
Speaker 2: over time. So it has a lot of parallels to

859
00:45:50,200 --> 00:45:52,720
Speaker 2: the way we think about asset allocation and magnets are.

860
00:45:52,719 --> 00:45:56,640
Speaker 1: Huh really interesting. Our final two questions, what sort of

861
00:45:56,680 --> 00:45:59,680
Speaker 1: advice would you give a recent college grad interest in

862
00:45:59,680 --> 00:46:04,680
Speaker 1: the career in either private creditor alts, fixed income, any

863
00:46:04,680 --> 00:46:06,120
Speaker 1: of the areas you specialized in.

864
00:46:06,800 --> 00:46:08,520
Speaker 2: It's what we think about for the firm. I know

865
00:46:08,840 --> 00:46:12,840
Speaker 2: what I tell my kids would be, it's people on platform.

866
00:46:13,160 --> 00:46:16,719
Speaker 2: You need to be around good integrist people that are

867
00:46:16,880 --> 00:46:20,760
Speaker 2: great mentors, and the platform needs to be growing over time,

868
00:46:21,000 --> 00:46:23,160
Speaker 2: so each seat should be more more than the person

869
00:46:23,200 --> 00:46:23,400
Speaker 2: in it.

870
00:46:23,840 --> 00:46:26,920
Speaker 1: Huh. Interesting. And our final question, what do you know

871
00:46:26,960 --> 00:46:30,640
Speaker 1: about the world of investing, of credit of risk management

872
00:46:31,480 --> 00:46:33,479
Speaker 1: today that you wish you knew when you were first

873
00:46:33,800 --> 00:46:35,640
Speaker 1: getting started thirty years or so ago.

874
00:46:35,960 --> 00:46:38,359
Speaker 2: Yeah, this may be an atypical answer, but I think

875
00:46:38,400 --> 00:46:40,719
Speaker 2: about luck versus skill a lot more than I ever

876
00:46:40,760 --> 00:46:43,680
Speaker 2: did before. If you make a decision today and don't

877
00:46:43,680 --> 00:46:46,080
Speaker 2: have an outcome for ten years, you don't really know

878
00:46:46,160 --> 00:46:48,359
Speaker 2: if you were good at it or not right, whether

879
00:46:48,400 --> 00:46:50,680
Speaker 2: you won or lost. If you're able to have a

880
00:46:50,800 --> 00:46:54,640
Speaker 2: much faster feedback loop now, you can really hone your

881
00:46:54,680 --> 00:46:59,840
Speaker 2: skills and understand whether you're making good decisions or bad decisions.

882
00:47:00,360 --> 00:47:02,640
Speaker 2: And so I think for me, and as we look

883
00:47:02,640 --> 00:47:05,560
Speaker 2: at people's track records, we really try to think about

884
00:47:05,840 --> 00:47:08,960
Speaker 2: how often do they get to make the same decision

885
00:47:09,480 --> 00:47:12,440
Speaker 2: and what's the process around that decision and how different

886
00:47:12,480 --> 00:47:13,160
Speaker 2: is it over time?

887
00:47:13,800 --> 00:47:16,279
Speaker 1: Very interesting. I have a book for you, but I'm

888
00:47:16,280 --> 00:47:19,799
Speaker 1: gonna bet you've already read it, Michael Mobison's book I

889
00:47:19,840 --> 00:47:24,239
Speaker 1: have not Please, Separating skill from luck in investing, business

890
00:47:24,239 --> 00:47:27,080
Speaker 1: and sports like that is right up your al that

891
00:47:27,239 --> 00:47:31,080
Speaker 1: is thank you, and he's a fascinating author and really

892
00:47:31,120 --> 00:47:33,839
Speaker 1: a fascinating book. I would bet you you would appreciate it.

893
00:47:34,080 --> 00:47:34,440
Speaker 2: Excellent.

894
00:47:34,800 --> 00:47:37,200
Speaker 1: Thank you David for being so generous with your time.

895
00:47:37,440 --> 00:47:41,040
Speaker 1: We have been speaking with David Snyderman. He is the

896
00:47:41,080 --> 00:47:44,560
Speaker 1: global head of Alternative credit and fixed income and managing

897
00:47:44,600 --> 00:47:48,960
Speaker 1: partner at Magnetar, a fifteen billion dollar multi strategy, multi

898
00:47:49,040 --> 00:47:53,920
Speaker 1: product alternative investment management firm. If you enjoy this conversation,

899
00:47:54,080 --> 00:47:56,520
Speaker 1: well check out any of the previous five hundred or

900
00:47:56,560 --> 00:48:02,120
Speaker 1: so we've had. You can find those ato Spotify, YouTube, Bloomberg,

901
00:48:02,280 --> 00:48:06,399
Speaker 1: wherever you find your favorite podcast. Be sure and check

902
00:48:06,400 --> 00:48:10,200
Speaker 1: out my new podcast at the Money, ten minutes each

903
00:48:10,239 --> 00:48:14,480
Speaker 1: week with an expert discussing a topic that's relevant to

904
00:48:14,719 --> 00:48:17,360
Speaker 1: you and your money. I would be remiss if I

905
00:48:17,400 --> 00:48:19,600
Speaker 1: did not thank the crack team that helps me put

906
00:48:19,640 --> 00:48:24,719
Speaker 1: these conversations together each week. Sarah Livesey is my audio engineer.

907
00:48:25,000 --> 00:48:28,440
Speaker 1: Attika val Brun is my project manager. Anna Luke is

908
00:48:28,440 --> 00:48:32,200
Speaker 1: my producer. Sean Russo is my head of research. Sage

909
00:48:32,200 --> 00:48:36,680
Speaker 1: Bauman is our head of podcasts. I'm Barry Ritolfs. You've

910
00:48:36,719 --> 00:48:40,720
Speaker 1: been listening to Master's in Business on Bloomberg Radio