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Speaker 1: Hello, and welcome to The Credit Edge, a weekly markets podcast.

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Speaker 1: My name is James Crumbie, I'm a senior editor at Bloomberg.

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Speaker 2: And I'm Sam Geyer, a credit strategist with Bloomberg Intelligence.

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Speaker 2: This week, we're very pleased to welcome Scott Richardson, director

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Speaker 2: of systematic credit at Acadian Asset Management. How are you

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Speaker 2: doing today, Scott gro.

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Speaker 3: Well, Thanks Sam, good to looking to you again, and

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Speaker 3: thanks James for happing me on the.

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Speaker 2: Short looking forward to it. So, for those of you

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Speaker 2: who will not be familiar with Acadian, it's a global

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Speaker 2: investment management firm with the one hundred and seventy eight

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Speaker 2: billion in assets under management, and it specializes in systematic

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Speaker 2: investing strategies across credit, equities and alternatives. To leading Acadian

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Speaker 2: systematic credit strategy, Scott was a principal at AQR Capital

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Speaker 2: Management and head of credit research at BGI.

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Speaker 3: Which I came part of Blackbred. He also teaches at

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Speaker 3: London Business School.

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Speaker 2: He's published numerous petipers and authored the book Systematic Fixed

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Speaker 2: Income An Investor's Guide, which I would definitely recommend picking

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Speaker 2: up if you're interested in getting a really deep dive

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Speaker 2: in the.

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Speaker 3: World of systematic credit.

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Speaker 2: All that is really to say Scott's been at the

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Speaker 2: forefront of systematic credit for years and has helped drive

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Speaker 2: the field forward in both practice and research. Scott really

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Speaker 2: appreciate you taking the time and looking forward to the conversation.

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Speaker 3: Yeah.

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Speaker 1: Great to have you on the show. Scott, thanks a

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Speaker 1: lot to kick it off. Though, in general terms, you

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Speaker 1: use big data and research to spot market opportunities and

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Speaker 1: manage risk, setting rules for the machines to follow. How

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Speaker 1: is that better though, than human judgment and good old

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Speaker 1: fashioned voice trading? And what evidence is there to suggest

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Speaker 1: that systematic investing does an better than index tracking, ETFs,

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Speaker 1: portfolio management that is active or anything else that you

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Speaker 1: could do when you're trading credit.

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Speaker 3: Okay, that's a multi facetted question to goodbye, so I'll

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Speaker 3: trying to break it up into pieces. So yeah, so

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Speaker 3: my focus for the last twenty five years is being

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Speaker 3: taking a systematic approach to active risk, which means security

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Speaker 3: selection in the space of what I call credit sensitive assets.

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Speaker 3: But let's be specific. I'm not to talk about corporate bonds.

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Speaker 3: A systematic approach does use AI or mL tools. But

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Speaker 3: you made a phrase human judgment. It's not devoid of

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Speaker 3: human judgment. So always like to sort of step back

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Speaker 3: and remind folks systematic credit you've got to be find

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Speaker 3: these changes very precisely. So systematic would mean data driven,

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Speaker 3: So be ruthless in your search for data that can

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Speaker 3: help you do a couple of things. One is forecast

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Speaker 3: returns out of sample. Two would be forecast risks associated

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Speaker 3: with said returns, and three would be walk out to

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Speaker 3: your ability to get the stuff you want without giving

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Speaker 3: a liver and a kidney to gold and sacks along

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Speaker 3: the way. So you may data will do it, and

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Speaker 3: machines can help you measure those things better. But the

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Speaker 3: sis the way process takes all those data inputs and

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Speaker 3: optimally blends it together to build a portfolio. If that's

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Speaker 3: done well, you end up with something that's highly differentiated,

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Speaker 3: so it looks and feels different to your traditional SAT

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Speaker 3: voice driven. I'm going to use the term discretionary portfolio manager.

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Speaker 3: I love that approach. I learned a lot from talking

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Speaker 3: to people like that over the last twenty five years.

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Speaker 3: MIAMI is always to distill the economic intuition, get data,

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Speaker 3: and ruthlessly capture diversified set of the idiosyncratic credit access

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Speaker 3: return opportunities.

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Speaker 2: So Scott, then, given that, where are the inefficiencies coming

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Speaker 2: from in the market? You know, is it really driven

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Speaker 2: by biases that discretionary investors have. Is it coming from

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Speaker 2: potentially passive ETFs? Where are you seeing those in afficiencies

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Speaker 2: arrived in the market that you ultimately take advantage of that?

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Speaker 3: It could be both. I mean so at the end

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Speaker 3: of the day, anyone who is taking active risk in

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Speaker 3: the market needs to question who is on the other

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Speaker 3: side of my trade? Am I facing adverse selection? I

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Speaker 3: might think that's a great opportunity. The fact that someone's

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Speaker 3: willing to sell me something at a good price got

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Speaker 3: to worry they may know more than you. So I think

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Speaker 3: this question should be asked for anyone who's entertaining and

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Speaker 3: taking active risk. But where do I think at a

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Speaker 3: high level of these opportunities come from. Part of it

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Speaker 3: will be liquidity takers in the market, and then that

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Speaker 3: ETF process would be but part of that. So there

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Speaker 3: are investors who are looking to change their portfolios for

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Speaker 3: a variety of reasons, and their footprint can give rise

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Speaker 3: to dislocations temporary or otherwise that you will see with

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Speaker 3: yield price and sport dynamics. Other parts of the olph

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Speaker 3: opportunity are getting better data to forecast things that are

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Speaker 3: relevant for where spread should be. So you can sort

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Speaker 3: of think about valuation frame. So you look to buy

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Speaker 3: a corporate bond, I'm going to abstract away from rate risk,

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Speaker 3: but I think if you have a view rates, corporate

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Speaker 3: bond is not the most efficient hasset to trade. So

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Speaker 3: we want this spread retone potential from the corporate bond,

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Speaker 3: and the primary drive revert is risk mutual expected loss

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Speaker 3: given default. So if you can build a better forecast

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Speaker 3: of that the company transition to a crappiest state and

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Speaker 3: there's a lot of data or features that could be

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Speaker 3: used for that. You're then expressly challenging how the market's

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Speaker 3: going to implyde you on that credit migration.

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Speaker 2: Yeah, I think the data side is another important piece

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Speaker 2: here for you, Like how important is the data? I

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Speaker 2: would think, you know, just doing the work that I do,

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Speaker 2: It's it's crucial, you know, because ultimately, if you're feeding

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Speaker 2: bad data into a model, you're going to get bad

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Speaker 2: data as the output. And then secondly with that, are

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Speaker 2: you kind of utilizing some alternative data sets? Because I

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Speaker 2: know we hear a lot about that on the equity side,

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Speaker 2: and wondering, you know, for credit, are there ways that

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Speaker 2: you're I don't know that, maybe like geospatial data or

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Speaker 2: something along those lines, but I'm just wondering alternatives wise,

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Speaker 2: are you using anything to kind of build out your strategies.

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Speaker 3: It's a very I'll unpack that into a couple of pieces.

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Speaker 3: So first getting the data and or it gets alterately

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Speaker 3: data later, but you're going to make sure you're using

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Speaker 3: the right data your credit. I mean, so a legal

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Speaker 3: entity will issue a bond, but that legal entity will

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Speaker 3: sit inside a corporate hierarchy. Now, some of those corporate

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Speaker 3: er arpies are simple and some are god awfully conflicts. Okay,

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Speaker 3: So if you're getting financial statement data or equity market

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Speaker 3: data or analyst data or option market data or security

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Speaker 3: lending market data or whatever from which entities that data

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Speaker 3: contained through, and what's the economic link for the entity

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Speaker 3: that issued the bonde. Even before we start talking about

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Speaker 3: the types of data, you first got to convince yourself

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Speaker 3: that you're using the data appropriately. So I think that's

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Speaker 3: something that's not fully understood by a lot of people

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Speaker 3: taking active riskin of the systematic labor in credit markets,

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Speaker 3: then we can pivot to alternative data. Well, yes, I

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Speaker 3: mean as long as my compliance officer isn't breathing one

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Speaker 3: long neck on, hey, you muther art to use this data?

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Speaker 3: Will impertain any data set? And then you look to

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Speaker 3: see is it your footings? Is it conceptually additive to

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Speaker 3: what you're already doing. Have to articulate an economic hypothesis.

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Speaker 3: How have I got this data and I did the following?

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Speaker 3: Why might it improve our investment process? Then you get

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Speaker 3: a look, does it improve your investment process? So called

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Speaker 3: that an empirical additivity to have to overcome those two hurdles,

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Speaker 3: and then if something works, of course we would we

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Speaker 3: would look at that or entertain looking at that in

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Speaker 3: the portfolio construction process.

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Speaker 1: So in theory, that sounds great and it definitely has

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Speaker 1: been taken on in the exit market. But in credit,

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Speaker 1: you know the difference between the theorest will return from

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Speaker 1: an ideal portfolio in the actual return after you've incorporated

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Speaker 1: trading cost price moves inliquidity that seems to be more

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Speaker 1: of a challenging credit. Is that what you meant when

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Speaker 1: you said giving your liver and kidney to Coldman Sachs.

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Speaker 3: In part yes, so it will if I put my

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Speaker 3: academic hat on. So an academic might write a paper

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Speaker 3: that says, I've got a characteristic X. If I sought

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Speaker 3: bonds high low, build a called a farm of French

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Speaker 3: alconymic in portfolio. Then correlated with returns of a Bloomberg

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Speaker 3: or ice, you might see something that looks really good. Okay,

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Speaker 3: that might not even be a necessary condition to do

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Speaker 3: it in the real world. You have to get that position.

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Speaker 3: Oftentimes it's impossible to get the position, so you may

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Speaker 3: one quantity or one side by or sell. It's just

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Speaker 3: not available. There's no price at which you could transact.

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Speaker 3: Then other times, if you could transact, you've got to

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Speaker 3: cross a spread, and the magnitude of that's spread is huge,

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Speaker 3: like a high yield. If you're a passive liquidity taker

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Speaker 3: seventy eighty basis points, there might be an average get

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Speaker 3: up with spread ig is probably thirty to forty. So

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Speaker 3: if you're turning your portfolio over once a year, that's

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Speaker 3: the cost your pain, So your return might only be

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Speaker 3: that amount, which means unless you're very efficient with respective trading,

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Speaker 3: don't bother. We spent a lot of time thinking about

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Speaker 3: you know, build an audibook so updated continually. Where could

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Speaker 3: we see the other side of the trade and then

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Speaker 3: can we trade in a cost efficient way? Will we

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Speaker 3: minimize that adverse selection?

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Speaker 1: Does that push you to certain issuers and certain parts

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Speaker 1: of the market and say, you know, you have to

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Speaker 1: say in higher quality, much more developed capital structures because

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Speaker 1: of that, or can you really roam around and have

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Speaker 1: a divers much more diversified portfolio.

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Speaker 3: You can roam around a lot that the roaming is

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Speaker 3: not limitless. So the primary I mentioned by which you're

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Speaker 3: limited is late liquidity. So if there's a bond out

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Speaker 3: there that hasn't traded and sits in like an insurance

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Speaker 3: company's desk draw, you're never going to find, so that

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Speaker 3: there will be a subset of the market that's just

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Speaker 3: really liquid to waste the time. But you've still then

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Speaker 3: got way more enough bonds to choose from produced secure refluction.

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Speaker 3: You won't be forcing into certain ratings or sectors of

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Speaker 3: industries or pants in the curves you get, you'll get

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Speaker 3: a diversified basket to play.

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Speaker 2: So how do you go about then balancing that turnover

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Speaker 2: reduction piece along with kind of reducing you know, holding

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Speaker 2: maybe some stale names in the portfolio itself.

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Speaker 3: Yeah, So the stale is of a name in and

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Speaker 3: of itself, isn't the problem? First and foremost, You're you're

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Speaker 3: looking to trade because of credit access, return potential, so

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Speaker 3: you'll have a view on every bond and that views

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Speaker 3: updating continually and you're always looking to have your portfolio

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Speaker 3: holding stuff that gives you the highest the cone a

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Speaker 3: sort of key input get returns. But then they're going

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Speaker 3: to do it in a liquidity and risk aware way.

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Speaker 3: So all of the bill portfolios that are very diversified

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Speaker 3: with respect for macroeconomic exposure, so things like country or region, sector,

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Speaker 3: industry creating buckets, beta buckets, liquidity buckets, so they're not

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Speaker 3: looking to take risk on those dimensions and then really

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Speaker 3: focus on taking this within those dimensions. So Jans, that

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Speaker 3: goes back to your initial question, how why is systematic

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Speaker 3: of interest to people? In part because of that, it's

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Speaker 3: the portfolio construction process. You were looking to dial down

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Speaker 3: risk on what we think are less well compensated dimensions

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Speaker 3: the country, regions, set the industry, et cetera, and really

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Speaker 3: get your tracking error through areas where you are well compensated.

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Speaker 3: Idiosyncratic alcohol opportunities, a lot of discretionary the traditional discretionary

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Speaker 3: active credit managers have house top down views. Like you've

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Speaker 3: had hosts on in the past, they will have a

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Speaker 3: view on the their you. I have a personal view

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Speaker 3: in the but that's speak in the portfolio. It's a

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Speaker 3: part of the difference in the portfolio is the extent

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Speaker 3: which you have these macro expersus and we're looking to

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Speaker 3: immunize that portfolio with respect to those things.

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Speaker 2: So then how about just in terms of across asset classes,

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Speaker 2: because you talked about it in your book, how you

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Speaker 2: kind of have this dislike of the distinction between investment

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Speaker 2: grate and high yield. And I think it's been well

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Speaker 2: documented the benefit of following angels versus rising stars. So

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Speaker 2: what sort of other benefits come from, you know, when

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Speaker 2: you're ultimately going through that portfolio construction process removing that distinction.

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Speaker 3: Yeah, So the writing distinction is an interesting one, and

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Speaker 3: that's an art effective history. So, for better or for worse,

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Speaker 3: most asset owners still refer to ratings in the investigator

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Speaker 3: investment guidle given im a it'll say what we can

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Speaker 3: account do with respect the ratings. There's a small number

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Speaker 3: of rating agencies. There's like a little duop oligopli in

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Speaker 3: play that's just amator of the beast, So you'll be

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Speaker 3: limited as to what you can do. If you took

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Speaker 3: that out, then blending IRG and HYEL together would strictly

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Speaker 3: make more sense. But most tacinaires have a policy benchmark

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Speaker 3: where you'll be a valuated relative to say the ag

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Speaker 3: broken up to a corporate component that's just investment. Great,

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Speaker 3: then your riskier stuff you'll have a higher than gosts,

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Speaker 3: I mean, theme and whatever it is. So you you

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Speaker 3: were you're sort of forced by institutional constraints to think

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Speaker 3: of the world in that rating biffecated way.

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Speaker 1: In terms of adoption of this strategy systematic credit, we

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Speaker 1: estimate that it accounts for just one or two percent

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Speaker 1: of actively managed credit funds, compared with about twenty percent inequities.

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Speaker 1: Barclay's had an estimate of assets at around ninety billion

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Speaker 1: two hundred and forty billion in twenty twenty four, but

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Speaker 1: we haven't seen anything from them since on that you

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Speaker 1: were on this show or a related show i'd say

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Speaker 1: two years ago talking about this. How has it grown

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Speaker 1: over that to year period, And you know, what's your

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Speaker 1: expectation in terms of increased adoption Let's say over the

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Speaker 1: next few years.

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Speaker 3: Super exciting. Increased adoption potential is enormous. So let's trup

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Speaker 3: with some numbers on the table. So we're going to

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Speaker 3: talk about public credit for now. When to come back

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Speaker 3: to private credit leader. So public credit here would be

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Speaker 3: corporate bonds issued by companies, primarily DM domicile, but you

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Speaker 3: can have some em stuff in there, IG and high yields.

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Speaker 3: It's probably seventeen eighteen trillion depending on publish index you

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Speaker 3: look at. But that's your big sandbox. Most that's IG.

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Speaker 3: You maybe a couple of trillions in the high yield.

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Speaker 3: So you want to look at that space and say

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Speaker 3: who's taking active risk. So we can touch on active

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Speaker 3: and passive. Actually, most people allocate to credit do so

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Speaker 3: in an active way. Even buy and maintain is active.

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Speaker 3: It's not strictly tracking the index. So I like equity,

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Speaker 3: you've got a lot of the assets that's allocated to

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Speaker 3: credit managing an active way, in part because passive solutions

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Speaker 3: doesn't use the words suck but great. So there are

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Speaker 3: non deficiencies with respect to getting passive exposure to credit,

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Speaker 3: whether that be an etf there's no any contract equivalent

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Speaker 3: which you could you could trade it in microsecond latency

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Speaker 3: essentially for free. These things are costs then have tracking

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Speaker 3: error that most asset owners know that, so they're looking

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Speaker 3: for an active allocation gives them the beta with some

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Speaker 3: alpha potential. Now, so that once you get into active,

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Speaker 3: as you said, the vast majority, probably more than ninety

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Speaker 3: five percent of that I'm going to call traditional discretionary.

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Speaker 3: You guys said one or two percent. Now I think

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Speaker 3: it's maybe two three percent. That you're still talking a

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Speaker 3: couple one hundred billion out of seventeen trillion to dropture group.

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Speaker 3: So it's a small but growing slice of that actively

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Speaker 3: in manage pie and credit. Has it grown a lot

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Speaker 3: in the last two years. It's grown a little bit.

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Speaker 3: So you might hear some people say it's triple, although

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Speaker 3: it'll be speaking with respect to their local book. If

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Speaker 3: you start with five million and you go to fifteen millium,

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Speaker 3: that's a tripling, but that's not their systematic group pretty large.

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Speaker 1: What really drives the expansion, do you think? Is it

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Speaker 1: electronic trading? Is it the increased use of AI? Is

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Speaker 1: it the fact that people just need more efficient trading systems.

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Speaker 3: That's an excellent question. So I've been asked that for

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Speaker 3: the last twenty five years, so I would have liked

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Speaker 3: the slices this fire to be larger today than what

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Speaker 3: it actually is. Part of the friction is cultural slash institutional.

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Speaker 3: A lot of the incumbents who manage or take risks

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Speaker 3: do so in I want to use the word nonsystematic.

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Speaker 3: I think traditional discussionary. So if a lot of your

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Speaker 3: incumbents do things that way or think that way, and

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Speaker 3: that is also reflected in asset allocators consultants in your life,

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Speaker 3: it's like an accepted wisdom. This is the way things

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Speaker 3: get done. You're trying to introduce the way for things

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Speaker 3: to be done, just as we get people over a

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Speaker 3: hum get them comfortable that it's feasible to do so.

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Speaker 3: So maybe that's easy, but that oftentimes means repeated dialogue

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Speaker 3: with people to make it clear what we're doing. Let

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Speaker 3: me just pivot a little bit. Some people under the

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Speaker 3: systematic label don't help the label because if you're a

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Speaker 3: black box, that opacity makes it very difficult for someone

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Speaker 3: to truly understand what we're doing, and that will create distance.

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Speaker 3: So that they're less willing to engage. So I always

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Speaker 3: say there should be a huge premium for putting transparency.

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Speaker 3: So opening it up not to the world, but to

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Speaker 3: people that you would like to enguest with, you make

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Speaker 3: it really clear, really intuitive. Why do you like the

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Speaker 3: bonds you did? What data do you use to generate

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Speaker 3: a forecast? And you can show it's a choice. You

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Speaker 3: can show four posts on twenty five thousand bonds updty

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Speaker 3: every day and in that level of transparency sort of

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Speaker 3: eye opening the same people. So I see that that

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Speaker 3: transparency being a key thing as we go forward. Part

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Speaker 3: of that is driven by data and we ease with

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Speaker 3: which you can visualize data. So maybe sixteen twenty years

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Speaker 3: ago this would have been a real pain in the

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Speaker 3: butt to set systems up to allow that little bit

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Speaker 3: of transparency. So systems desire, I think, are making it

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Speaker 3: easier in a way that an increase adoption. Are you

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Speaker 3: touched on trading now that's a slow and never ending

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Speaker 3: story or progress as well that the direction of travel

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Speaker 3: is good. So you're seeing improved liquidity sort of across

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Speaker 3: the board for investment breed in how you part of

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Speaker 3: that's the ETF create your being process, portfolio trainings increased,

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Speaker 3: you get neptune. There's different ways bublished love of information

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Speaker 3: can be captured and collectful, and then from our perspective,

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Speaker 3: systems are alaid to ingest that data real time, essentially

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Speaker 3: build and water.

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Speaker 2: So I want to circle back real quick to kind

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Speaker 2: of the tail end of one of James's questions, just

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Speaker 2: in terms of the future for systematic do you do

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Speaker 2: you see like it expanding? That's some other markets, other

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Speaker 2: asset classes of the fixed income world, you know, like

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Speaker 2: munis or structured products, or does that then add a

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Speaker 2: lot more added complexity, whether that's due to lack of

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Speaker 2: liquidity in certain parts of the market or just complexity

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Speaker 2: of the structure itself.

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Speaker 3: Yeah, I think corporate credit in and of itself is

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Speaker 3: sufficiently complex. So if you nail that, the system is

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Speaker 3: then potentially well suited to extend elsewhere. But for data

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Speaker 3: and liquidity to the ease of which you could trade

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Speaker 3: the thing in that asset class. So where do I

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Speaker 3: see systematic credit approaches being not readily but extended down

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Speaker 3: the road? Loans would be one place. I mean, the

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Speaker 3: liquidity and lons is worse than the bonds, but there's

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Speaker 3: still enough secondary market liquidity, so you could take an

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Speaker 3: usher approach to do security selection in the leverage loan market.

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Speaker 3: That could be extended to structured things. That's sit on

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Speaker 3: top of that and the like im corporate. That's fair.

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Speaker 3: I mean that they're just companies domicile outside developed markets.

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Speaker 3: But the saying the data during the process that gives

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Speaker 3: rise to you writing risk and credit risk. Hence why

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Speaker 3: spreads change hance quite as a return. That framework is

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Speaker 3: the same. Communis is an interesting one. If you think

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Speaker 3: corporate bonds are a liquid, communions are really pliquid. You

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Speaker 3: know they're liquid on the day of issue, and these

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Speaker 3: things disappear. I've looked at this several times over the

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Speaker 3: last twenty five years. Could never convince myself that you

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Speaker 3: could really do true bottom up security selection in UNIS.

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Speaker 3: You might be able to do something that's like stratified sampling,

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Speaker 3: have a view on different types of MEATI bonds in

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Speaker 3: different geographies. The YUP could see something like that potentially

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Speaker 3: working with The opportunity there, I think is small relative

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Speaker 3: to corporate private that's I don't think a systematic approach

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Speaker 3: is anytime soon because the complete lack of data on

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Speaker 3: the quality of the issuer and then there's no price,

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Speaker 3: so these things don't really trade in secondary markets.

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Speaker 2: Yeah, that's where I was going to go next, because

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Speaker 2: I just wanted to get your thoughts just on privates

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Speaker 2: in general, like how that plays a role in the portfolio.

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Speaker 2: And I know, you know there's metrics published around private markets,

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Speaker 2: and I feel like you potentially have brought it up

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Speaker 2: in the past, just you know, maybe there's some risk

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Speaker 2: that's under the surface that that might not be fully realized,

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Speaker 2: and just you know how you feel about private markets

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Speaker 2: in general.

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Speaker 3: Okay, I like the last pitch I had, do I

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Speaker 3: feel about So let me start with the friendly comment

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Speaker 3: and then it would become less less famous. So the

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Speaker 3: friendly comment is, yeah, private credit deserves a place in

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Speaker 3: an aspetoon a portfolio. Why different companies raised it through

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Speaker 3: the private channel versus the traditional public channel. So you're

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Speaker 3: getting exposure to credit rest premium from a different set

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Speaker 3: of phones. Okay, So having some of your electation go

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Speaker 3: into private makes complete sense. Going all into private, I

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Speaker 3: think is crazy. And so let me sort of try

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Speaker 3: and lay out why I think this is. Enthusiasm is overblown.

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Speaker 3: People will make comments like, oh, the returns in private

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Speaker 3: credit are better than public the yields are high, there's

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Speaker 3: a liquidity cringing. Maybe there's comments are true. So we've

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Speaker 3: gone back and I say we my team have gone

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Speaker 3: back and actually looked at this data at the extent

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Speaker 3: we can to assess the veracity of these statements. And

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Speaker 3: there is no evidence to support that private credit generates

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Speaker 3: a higher risk adjusted return than public none. So so

403
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Speaker 3: there's going to be highlight of some of the data.

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Speaker 3: So one thing in public credit is if I look

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Speaker 3: at hazerls it that's the ICE index diversified US higher

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Speaker 3: than index. Inside that you have private companies that there

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Speaker 3: will be bonds of companies that sitting aside at corporate structure,

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Speaker 3: it's too removed from an equity parent or just doesn't

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Speaker 3: have an equity parent. You probably go a quarter or

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Speaker 3: more back in time, maybe more of that index are

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Speaker 3: of these private issuers. See said, look, we have data

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Speaker 3: on this for twenty plus years. What do these private

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Speaker 3: firms look like compared to the public firms. They have

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Speaker 3: high yields, they have lower spread duration. The riskier bonds

415
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Speaker 3: are shorter dated, higher spreads, higher beta, lower rating. These

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Speaker 3: are riskier securities hands down, so when private so out

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Speaker 3: the yield is higher, it's like yeah, because they're riskier.

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Speaker 3: Private zims tend to be smaller firms that call middle

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Speaker 3: market terms for a reason, fifty million ebita. They're small,

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Speaker 3: they're geographically concentrated. They tend to have a coast to

421
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Speaker 3: a pure play business and a certain geography, and that

422
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Speaker 3: has get multiple operating segment lines, you get less business diversification,

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Speaker 3: less geographic diversification. Smaller firms sipically is more leverage. That's

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Speaker 3: a riskier firm. So the xanity they should be priced

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Speaker 3: to the higher yielding spread. But then if you go

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Speaker 3: go back to that public market broke it up in

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Speaker 3: a Q pieces, the private piece actually has over twenty

428
00:24:08,520 --> 00:24:13,159
Speaker 3: years a slightly lower risk adjusted return. And so with

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Speaker 3: true marked market leta no evidence that private ferns out

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Speaker 3: put from public. Not to say you shouldn't have private

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Speaker 3: in there, it's going all in private makes new sense.

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Speaker 3: Other things to look at. You've got BDC's business development companies,

433
00:24:27,600 --> 00:24:31,240
Speaker 3: so cut close and infguitable funds. Pick the five or

434
00:24:31,280 --> 00:24:33,640
Speaker 3: ten largest of those. I can't mention names on the call,

435
00:24:33,720 --> 00:24:38,080
Speaker 3: but you know exactly because they are you want take

436
00:24:38,119 --> 00:24:40,639
Speaker 3: the price changes of those Like, the sharp rature over

437
00:24:40,640 --> 00:24:43,280
Speaker 3: the last ten years for BDCs is about point six.

438
00:24:44,480 --> 00:24:47,880
Speaker 3: The sharp rature of multiple leaning disies is about point six.

439
00:24:48,400 --> 00:24:50,880
Speaker 3: The sharp breachure of US higher market is also about

440
00:24:50,880 --> 00:24:53,840
Speaker 3: point six. So it's telling you you're getting a similar

441
00:24:53,880 --> 00:24:58,040
Speaker 3: amount of risk adjusted returns across across these things. So

442
00:24:58,080 --> 00:24:59,919
Speaker 3: I think there's a lot of private get a lot

443
00:25:00,040 --> 00:25:03,000
Speaker 3: of these myths get depotuated. That creates I think too

444
00:25:03,080 --> 00:25:05,080
Speaker 3: much gibrits for that allocation.

445
00:25:06,640 --> 00:25:08,880
Speaker 1: Going back to the public stuff that you mentioned, Scott

446
00:25:09,040 --> 00:25:12,200
Speaker 1: on the absolutely emerging markets side, I've covered emerging markets

447
00:25:12,240 --> 00:25:14,640
Speaker 1: for a long time and the em corporate traders will

448
00:25:14,640 --> 00:25:16,320
Speaker 1: tell you that only they can do this. I mean,

449
00:25:16,359 --> 00:25:18,439
Speaker 1: obviously this is an institutional thing that they need, they

450
00:25:18,520 --> 00:25:21,320
Speaker 1: need to keep their jobs. But you know, it's pretty

451
00:25:21,320 --> 00:25:24,920
Speaker 1: tricky stuff. And you could say that that some of

452
00:25:25,440 --> 00:25:28,560
Speaker 1: corporate America is trading in you know, similar terms in

453
00:25:28,720 --> 00:25:32,600
Speaker 1: terms of volatility. You know, we see the loans drop

454
00:25:33,160 --> 00:25:38,159
Speaker 1: very very suddenly, very sharply, in priced on earnings, on

455
00:25:39,040 --> 00:25:41,199
Speaker 1: stroke of the pen, risk from a certain president who

456
00:25:41,320 --> 00:25:43,960
Speaker 1: likes to change the rules all the time. You know,

457
00:25:44,080 --> 00:25:48,360
Speaker 1: all those things are making markets I think much more volatile.

458
00:25:49,440 --> 00:25:51,960
Speaker 1: How do you get a machine to work around those

459
00:25:52,000 --> 00:25:55,080
Speaker 1: sorts of things? They just seem, you know, very tricky

460
00:25:55,160 --> 00:25:55,720
Speaker 1: to navigate.

461
00:25:57,160 --> 00:25:59,600
Speaker 3: Yeah, make sure I understand the question. So there will

462
00:25:59,640 --> 00:26:05,720
Speaker 3: be shocks that happen to markets, and you might say

463
00:26:07,640 --> 00:26:10,199
Speaker 3: big change in tariff policy April last year. Let's call

464
00:26:10,280 --> 00:26:15,000
Speaker 3: that a shock. Okay, distinguish forecasts and said shock from

465
00:26:15,480 --> 00:26:17,560
Speaker 3: shock happens, then what do you do from the portfolio?

466
00:26:18,600 --> 00:26:22,280
Speaker 3: People tend to conflate those two. So actually try to

467
00:26:22,320 --> 00:26:26,119
Speaker 3: forecast true shocks out a sample I think is almost impossible.

468
00:26:27,160 --> 00:26:29,399
Speaker 3: What you then want to have is a portfolio that

469
00:26:29,600 --> 00:26:33,800
Speaker 3: understands these things may happen and be humbled with respect

470
00:26:33,840 --> 00:26:36,760
Speaker 3: your ability to forecast them ahead of time. So then

471
00:26:36,960 --> 00:26:40,760
Speaker 3: build your portfolio that's not going to be trashed too

472
00:26:40,840 --> 00:26:44,720
Speaker 3: badly if said thing happened, and there's been multiple of these,

473
00:26:44,800 --> 00:26:48,760
Speaker 3: like call a macro risks. You could tarot policy, could

474
00:26:48,760 --> 00:26:51,760
Speaker 3: be one AI hyper scalers, what if you want to

475
00:26:51,800 --> 00:26:53,359
Speaker 3: call it could be that's better than user lot the

476
00:26:53,480 --> 00:26:57,720
Speaker 3: last six plus months. If you build a portfolio that's

477
00:26:57,760 --> 00:27:01,600
Speaker 3: got those macro risks sort of so you're not leaning

478
00:27:01,680 --> 00:27:04,560
Speaker 3: into sectors and industries very heavily. You're not leaning into

479
00:27:04,600 --> 00:27:06,920
Speaker 3: counts of the curve, spread, battle failer buckets, et cetera.

480
00:27:07,480 --> 00:27:09,679
Speaker 3: You actually end up with not having much of these

481
00:27:09,800 --> 00:27:14,119
Speaker 3: unintended or unwanted exposures. So I prefer to think be humble,

482
00:27:15,280 --> 00:27:18,080
Speaker 3: build guard rails in the portfolio, so as and when

483
00:27:18,160 --> 00:27:20,920
Speaker 3: some true shock happens, you're not going to be put

484
00:27:21,240 --> 00:27:21,760
Speaker 3: too badly.

485
00:27:22,520 --> 00:27:25,680
Speaker 1: Do the models naturally become forced sellers because they just

486
00:27:25,800 --> 00:27:28,639
Speaker 1: see extreme moves that they have to react to and

487
00:27:28,720 --> 00:27:30,800
Speaker 1: then dump the bonds and then that sort of fees

488
00:27:30,840 --> 00:27:31,280
Speaker 1: on itself.

489
00:27:32,560 --> 00:27:34,600
Speaker 3: Typically not, because a lot of what we're doing is

490
00:27:34,680 --> 00:27:37,840
Speaker 3: in the cross section, so it's all relative. So you

491
00:27:37,920 --> 00:27:41,879
Speaker 3: are saying like these events will tend to affect certain

492
00:27:42,000 --> 00:27:44,520
Speaker 3: issues and issues more than others. But yes, there could

493
00:27:44,520 --> 00:27:46,720
Speaker 3: be a common component if you're really looking at the

494
00:27:46,800 --> 00:27:50,840
Speaker 3: dislocations in the cross section. So not always, but oftentimes

495
00:27:51,000 --> 00:27:55,080
Speaker 3: these shocks to macrocum environments creates more opportunities.

496
00:27:54,800 --> 00:27:56,040
Speaker 1: Right, I mean, I know you don't don't like to

497
00:27:56,080 --> 00:27:58,919
Speaker 1: talk sectors, but right now in the software area, we're

498
00:27:58,920 --> 00:28:03,120
Speaker 1: seeing everything just getting dumped, regardless of you know, fundamentals

499
00:28:03,280 --> 00:28:08,000
Speaker 1: or whatever. How would a systematic approach deal with that

500
00:28:08,160 --> 00:28:10,600
Speaker 1: kind of thing where you've got everything just being dumped

501
00:28:11,680 --> 00:28:12,920
Speaker 1: and there's probably some value in that.

502
00:28:14,800 --> 00:28:16,800
Speaker 3: Yeah, So I would take issue with the comment everything

503
00:28:16,880 --> 00:28:20,800
Speaker 3: they do now, that's not how I how I sud said.

504
00:28:20,800 --> 00:28:23,800
Speaker 3: The software space, whether it's ID or high yield, and.

505
00:28:24,160 --> 00:28:27,400
Speaker 1: Most of software, you know, the lower quality stuff which

506
00:28:27,640 --> 00:28:30,080
Speaker 1: kind of is trading. I mean not everything obviously, but

507
00:28:30,240 --> 00:28:32,200
Speaker 1: there is a lot of bath and baby.

508
00:28:34,840 --> 00:28:37,760
Speaker 3: Yeah, right, So that group in high yood has underperformed recently.

509
00:28:37,840 --> 00:28:40,320
Speaker 3: And then but within what we're interested in is more

510
00:28:40,520 --> 00:28:45,360
Speaker 3: variation within me and you you step back the first principles,

511
00:28:45,760 --> 00:28:50,320
Speaker 3: what what determines non payment of your principle? It will

512
00:28:50,360 --> 00:28:52,640
Speaker 3: be that entity not being able to jerrant free cash

513
00:28:52,640 --> 00:28:56,640
Speaker 3: fload between now and said Judy to satisfied their debt.

514
00:28:56,800 --> 00:29:00,360
Speaker 3: So too much debt and crappy profitability or free cashal

515
00:29:00,440 --> 00:29:03,720
Speaker 3: journally and capability. All of it you can measure with

516
00:29:04,120 --> 00:29:07,640
Speaker 3: data that's available today and then also data that's forward looking.

517
00:29:07,720 --> 00:29:09,760
Speaker 3: So what are you expecting to see happening tomorrow?

518
00:29:10,840 --> 00:29:13,240
Speaker 2: So I want to stay on just kind of current

519
00:29:13,360 --> 00:29:16,280
Speaker 2: market environment, like with investment grade and high yield. I

520
00:29:16,320 --> 00:29:19,280
Speaker 2: think if you look at where the spread for both

521
00:29:19,440 --> 00:29:22,760
Speaker 2: indices is at right now, you know, pretty tight range

522
00:29:22,840 --> 00:29:26,680
Speaker 2: over the past five six months. So how how does

523
00:29:26,720 --> 00:29:30,440
Speaker 2: your model stay disciplined and handle those types of situations

524
00:29:30,520 --> 00:29:34,320
Speaker 2: where you know it maybe becomes less about bond selection

525
00:29:34,600 --> 00:29:37,320
Speaker 2: and more about kind of the portfolio as a whole,

526
00:29:38,200 --> 00:29:40,120
Speaker 2: or do you kind of see it a different way?

527
00:29:40,160 --> 00:29:42,920
Speaker 2: Do you see it really regardless of what part of

528
00:29:42,960 --> 00:29:46,400
Speaker 2: the cycle we're in, as all about selecting the right

529
00:29:46,480 --> 00:29:48,640
Speaker 2: bonds and following the signals that they're providing.

530
00:29:50,160 --> 00:29:52,840
Speaker 3: Yes, all to do with your latter statement completely, but

531
00:29:53,560 --> 00:29:58,000
Speaker 3: it's good that had some context of this. So index level,

532
00:29:58,160 --> 00:30:00,560
Speaker 3: so whether it's investment grade or high yield, computer and

533
00:30:00,600 --> 00:30:03,400
Speaker 3: option agust and sped up the index level. You can

534
00:30:03,440 --> 00:30:05,960
Speaker 3: then do that every quantum time back in history and

535
00:30:06,080 --> 00:30:10,880
Speaker 3: you say, oh, hey, spreads are low today related to history, Okay,

536
00:30:12,320 --> 00:30:15,280
Speaker 3: what does that matter? And it could be two reasons

537
00:30:15,440 --> 00:30:18,160
Speaker 3: why that might matter. One could be the asset allocation

538
00:30:19,480 --> 00:30:22,360
Speaker 3: and two would be your ability to take active wolk

539
00:30:22,440 --> 00:30:24,920
Speaker 3: in the cross section as well compensated, so that which

540
00:30:24,920 --> 00:30:27,680
Speaker 3: wry I answer those two things separately. So on the

541
00:30:27,800 --> 00:30:31,360
Speaker 3: asset allocation side, while it's true that spreads a low

542
00:30:31,960 --> 00:30:34,520
Speaker 3: not the lowest today, but they're still relative to out

543
00:30:34,520 --> 00:30:37,480
Speaker 3: of history. That doesn't roan you should be out of

544
00:30:37,520 --> 00:30:40,959
Speaker 3: the market. So we wrote a cheeky paper last year

545
00:30:40,960 --> 00:30:45,080
Speaker 3: because this topic comes up all the time, like, okay,

546
00:30:45,120 --> 00:30:48,120
Speaker 3: well we're data driven people. Let's look at the data

547
00:30:48,120 --> 00:30:50,719
Speaker 3: that we have and let's data mine the hell out

548
00:30:50,760 --> 00:30:54,240
Speaker 3: of it. Okay, So kick as many credit sensitive assets

549
00:30:54,280 --> 00:30:57,800
Speaker 3: that we could find, us high yields, IG Europe, em

550
00:30:58,680 --> 00:31:02,000
Speaker 3: a whole bunch of indices, and then pick as many

551
00:31:02,640 --> 00:31:05,200
Speaker 3: exitmentary rules as we could. So is the spread today

552
00:31:05,240 --> 00:31:09,680
Speaker 3: above the fifty percent? Fifty second? So think of the

553
00:31:09,760 --> 00:31:12,960
Speaker 3: number of combinations, actually get up to around four million combinations,

554
00:31:13,760 --> 00:31:17,640
Speaker 3: and then test them all. And your test is do

555
00:31:17,800 --> 00:31:21,280
Speaker 3: you beat buy and hold? And roughly seventy percent of

556
00:31:21,360 --> 00:31:24,080
Speaker 3: the time you do not beat buy and hold, which

557
00:31:24,160 --> 00:31:26,760
Speaker 3: means the credit excess return from staying in the market

558
00:31:27,440 --> 00:31:31,120
Speaker 3: the entire time is better than only getting the credit

559
00:31:31,240 --> 00:31:33,320
Speaker 3: excess returns when you deem the spread to be on

560
00:31:33,480 --> 00:31:37,400
Speaker 3: the next So the vast majority. So, in terms of

561
00:31:37,440 --> 00:31:40,880
Speaker 3: the statistical test that strongly says don't time your market,

562
00:31:41,160 --> 00:31:46,760
Speaker 3: seventy percent doesn't work. Might ask why spreads can stay

563
00:31:46,840 --> 00:31:49,520
Speaker 3: low for longer than you think, and there's still a

564
00:31:49,600 --> 00:31:51,640
Speaker 3: steepness to the curve, so you're picking up a roll

565
00:31:52,280 --> 00:31:55,440
Speaker 3: along the way. That's surprising the people. But when we

566
00:31:55,720 --> 00:31:58,880
Speaker 3: show people to day too like, actually, you're right, but

567
00:31:59,440 --> 00:32:02,280
Speaker 3: I still think this time is different. Well, okay, can

568
00:32:02,360 --> 00:32:05,480
Speaker 3: I think I can't have that dialogue. But what you

569
00:32:05,560 --> 00:32:07,600
Speaker 3: want to them try and do is get people to pivot.

570
00:32:07,640 --> 00:32:09,360
Speaker 3: You know, I think youngs you had a John on

571
00:32:09,440 --> 00:32:11,360
Speaker 3: a week or two ago. We add some of this dialogue,

572
00:32:11,600 --> 00:32:15,600
Speaker 3: which I liked a lot. Looked through the index. Okay,

573
00:32:15,680 --> 00:32:17,720
Speaker 3: so we talked about publics and privates and the index.

574
00:32:17,840 --> 00:32:20,160
Speaker 3: So for the high yield as an example, and maybe

575
00:32:20,200 --> 00:32:22,000
Speaker 3: twenty five percent of the companies, it's really hard to

576
00:32:22,080 --> 00:32:24,800
Speaker 3: get data. But on the seventy five percent you can.

577
00:32:25,400 --> 00:32:29,600
Speaker 3: You can measure book leverage, market leverage, profitability, leavered, unleaded

578
00:32:29,680 --> 00:32:33,440
Speaker 3: coverage ratios, volatility distance. The default metric just goes through

579
00:32:33,440 --> 00:32:37,800
Speaker 3: a laundry list of Open up a financial statement analysis textbook,

580
00:32:37,880 --> 00:32:40,520
Speaker 3: go to chapter eight and take all the credit risk measures,

581
00:32:41,320 --> 00:32:43,640
Speaker 3: roll that up to the index level, and what we

582
00:32:43,760 --> 00:32:48,200
Speaker 3: see is the credit risk today underlying the index is

583
00:32:48,600 --> 00:32:52,080
Speaker 3: actually pretty good. It's certainly no worse than the last

584
00:32:52,120 --> 00:32:55,200
Speaker 3: five or ten years, and in particular in high yield

585
00:32:55,240 --> 00:32:59,160
Speaker 3: you've got shorter data debt like the maturity shortened as

586
00:32:59,160 --> 00:33:01,880
Speaker 3: a credit care are upward sloping, so you actually should

587
00:33:01,920 --> 00:33:04,800
Speaker 3: be seeing spreads lower. It's always trying to get people

588
00:33:04,840 --> 00:33:08,479
Speaker 3: to think, if you look at it spreads, look at

589
00:33:08,520 --> 00:33:11,880
Speaker 3: something else in addition to the spread, to say, oh,

590
00:33:12,240 --> 00:33:14,480
Speaker 3: spread is true low given the risk outs people eg

591
00:33:15,360 --> 00:33:18,000
Speaker 3: that might give you a different answer on the ass

592
00:33:18,080 --> 00:33:23,440
Speaker 3: allocation security selections the same. You sort of into that

593
00:33:23,840 --> 00:33:27,560
Speaker 3: the spreads are low. At the corner case, say every

594
00:33:27,680 --> 00:33:30,240
Speaker 3: bond had a spread that went to one hundred. It

595
00:33:30,400 --> 00:33:33,560
Speaker 3: never happened, never will, but if that did happen, then

596
00:33:33,680 --> 00:33:39,760
Speaker 3: the sandbox will expect the terms basically identical. Yeah, unless

597
00:33:39,800 --> 00:33:44,560
Speaker 3: your forecast is explicit change in spread, you've still got dispersion.

598
00:33:44,840 --> 00:33:48,160
Speaker 3: This is key. So spreads are low, there's still dispersing

599
00:33:49,040 --> 00:33:51,400
Speaker 3: so that you can still take active risk and be

600
00:33:51,520 --> 00:33:55,960
Speaker 3: well compensated for that active risk, but you take less

601
00:33:56,040 --> 00:33:59,560
Speaker 3: active risks. If you're familiar with the term tracking error,

602
00:33:59,600 --> 00:34:03,720
Speaker 3: it's actual hard to get one. Tracking are in the

603
00:34:03,800 --> 00:34:07,040
Speaker 3: higher markets of late why because spreads is are just

604
00:34:07,120 --> 00:34:09,880
Speaker 3: sort of low. So you get like the the sandbox

605
00:34:09,960 --> 00:34:13,160
Speaker 3: is a little smaller, but there's still lots of opportunity.

606
00:34:13,200 --> 00:34:17,160
Speaker 2: Bel So then I want to turn to you modeling

607
00:34:17,239 --> 00:34:21,480
Speaker 2: out kind of default risk for bonds. When you're doing

608
00:34:21,560 --> 00:34:26,200
Speaker 2: that that process, let's say the default cycle ultimately signals

609
00:34:26,280 --> 00:34:30,000
Speaker 2: something that's different than you than you model. Maybe you're

610
00:34:30,080 --> 00:34:32,920
Speaker 2: modeling defaults at a at a lower level than what's

611
00:34:33,440 --> 00:34:36,719
Speaker 2: you know, actually being realized. How do you kind of

612
00:34:36,760 --> 00:34:41,719
Speaker 2: hold yourself back from trying to you know, change your

613
00:34:41,880 --> 00:34:45,040
Speaker 2: your underlying model and and you know, I guess not

614
00:34:46,440 --> 00:34:50,399
Speaker 2: bend to your internal biases. Uh when you're when you're

615
00:34:50,440 --> 00:34:51,200
Speaker 2: building those out.

616
00:34:52,520 --> 00:34:55,520
Speaker 3: Okay, so the last book I agree is completely that's

617
00:34:55,520 --> 00:34:59,600
Speaker 3: the easiest one to answer that you step away from biases.

618
00:35:00,000 --> 00:35:02,560
Speaker 3: I've been disciplined and sort of writing down why do

619
00:35:02,719 --> 00:35:05,479
Speaker 3: I like some data attribute to help you forecast something?

620
00:35:06,080 --> 00:35:10,040
Speaker 3: And so the context of forecasts, who was default is

621
00:35:10,120 --> 00:35:13,080
Speaker 3: that's a measurable thing. You know, don't get your money back,

622
00:35:13,280 --> 00:35:16,480
Speaker 3: or you can define default in different ways. But bad event.

623
00:35:17,520 --> 00:35:19,520
Speaker 3: But what we would typically do is go back at

624
00:35:19,560 --> 00:35:22,120
Speaker 3: the time, get as much data as we can to

625
00:35:22,440 --> 00:35:25,680
Speaker 3: calibrate different types of models that can forecast out of

626
00:35:25,880 --> 00:35:29,000
Speaker 3: sample that bad event. Happening okay, and you can use

627
00:35:29,280 --> 00:35:31,279
Speaker 3: written papers on this. You can use linear models, you

628
00:35:31,280 --> 00:35:34,480
Speaker 3: can use use four models. Structural models are very common

629
00:35:34,719 --> 00:35:37,000
Speaker 3: and make a lot of sense theoretically that you can

630
00:35:37,080 --> 00:35:39,799
Speaker 3: use the machine nuning models. This is one area where

631
00:35:40,920 --> 00:35:44,839
Speaker 3: large not l lms specifically, but the ability to lean

632
00:35:44,920 --> 00:35:49,600
Speaker 3: into large data sets can be very helpful because adding

633
00:35:49,640 --> 00:35:52,759
Speaker 3: your measure leverage. I can go to the textbook and say,

634
00:35:52,800 --> 00:35:55,280
Speaker 3: there's a book leverage a trick. Well do I shoot

635
00:35:55,280 --> 00:35:58,000
Speaker 3: all of the same maybe maybe not on balance sheet?

636
00:35:58,000 --> 00:36:01,680
Speaker 3: Off balance sheet? What about market leverage? How do I

637
00:36:01,719 --> 00:36:05,200
Speaker 3: measure profitability on leveed? Again, different choices? How do I

638
00:36:05,239 --> 00:36:07,680
Speaker 3: what a measure volatility? Lots of ways where I could

639
00:36:07,680 --> 00:36:10,920
Speaker 3: get better on volatility. There could be other metrics, and

640
00:36:11,360 --> 00:36:13,000
Speaker 3: now we get too close to the weed, so it

641
00:36:13,080 --> 00:36:15,279
Speaker 3: could be other things you want to look at. But

642
00:36:15,320 --> 00:36:17,360
Speaker 3: then you can end up with a couple of hundred features.

643
00:36:18,440 --> 00:36:20,320
Speaker 3: My brain's not big enough to work out what's the

644
00:36:20,360 --> 00:36:22,719
Speaker 3: optimal way to blend those things together. So you can

645
00:36:23,000 --> 00:36:25,200
Speaker 3: lean into when we should them to help you with this,

646
00:36:26,840 --> 00:36:29,319
Speaker 3: So back to defaults. You've got a lot of rich

647
00:36:29,440 --> 00:36:35,680
Speaker 3: data to forecast as defaults. Those models work and the

648
00:36:35,800 --> 00:36:37,960
Speaker 3: musket on the point costs. Okay, so that they're working

649
00:36:38,040 --> 00:36:42,239
Speaker 3: at a sample to your question, do I think they're

650
00:36:42,280 --> 00:36:45,160
Speaker 3: working less well today than what they work five or

651
00:36:45,200 --> 00:36:48,040
Speaker 3: ten years ago. I don't see evidence of that. I mean,

652
00:36:48,440 --> 00:36:51,640
Speaker 3: what gives rise to default? You can't generate free cash

653
00:36:51,680 --> 00:36:54,920
Speaker 3: flows to pay the contractual commitment you vode. That doesn't change.

654
00:36:56,360 --> 00:36:59,239
Speaker 3: What's changed is how easy or how difficult it might

655
00:36:59,280 --> 00:37:01,360
Speaker 3: be to measures featurs.

656
00:37:02,800 --> 00:37:04,960
Speaker 2: I want to touch real quick on something you just

657
00:37:05,080 --> 00:37:09,279
Speaker 2: mentioned around you know, fundamental data being available, especially more

658
00:37:09,360 --> 00:37:11,960
Speaker 2: so on the high yield side. Do you ever get

659
00:37:12,040 --> 00:37:15,200
Speaker 2: worried where maybe you're in a situation where you know

660
00:37:15,480 --> 00:37:18,640
Speaker 2: twenty percent of the index, you can't get any fundamentals

661
00:37:18,760 --> 00:37:23,520
Speaker 2: just data available availability or okay it's a private company,

662
00:37:24,440 --> 00:37:26,880
Speaker 2: maybe their bonds are going to be a little less liquid.

663
00:37:27,040 --> 00:37:30,120
Speaker 2: So I'm not even worried that I can't get any

664
00:37:30,200 --> 00:37:32,400
Speaker 2: information on that particular part of the market.

665
00:37:33,640 --> 00:37:36,200
Speaker 3: Yeah, that's that's an excellent question. And there's so many

666
00:37:36,640 --> 00:37:40,680
Speaker 3: avenues approach we could go. So you're correct in that.

667
00:37:41,000 --> 00:37:43,279
Speaker 3: In there we talked about Zilier. So the to US

668
00:37:43,360 --> 00:37:47,600
Speaker 3: high index will have this private subset. Okay, these are

669
00:37:47,640 --> 00:37:49,919
Speaker 3: companies where it's a pain in the proverbial to source

670
00:37:50,000 --> 00:37:53,919
Speaker 3: the financial statements. It's not impossible, just the pain. Yeah,

671
00:37:54,600 --> 00:37:57,080
Speaker 3: so you can get the data. Let's say you couldn't

672
00:37:57,080 --> 00:38:00,680
Speaker 3: get the data, then you would be structurally uninformed. Okay,

673
00:38:01,360 --> 00:38:03,800
Speaker 3: so you might A prudent response might be because I

674
00:38:03,880 --> 00:38:06,200
Speaker 3: can't get financial statement data for these companies, I'm not

675
00:38:06,239 --> 00:38:09,600
Speaker 3: really in a position to, Oh, they're free, casual generating capability,

676
00:38:09,640 --> 00:38:12,840
Speaker 3: what their contractual commitments are. You're sort of at a disadvantage,

677
00:38:12,920 --> 00:38:14,920
Speaker 3: and no, will I get my money back? So you

678
00:38:15,040 --> 00:38:17,720
Speaker 3: might say rational and not going to touch this stuff. Okay,

679
00:38:18,600 --> 00:38:20,640
Speaker 3: go back to what I said earlier, say twenty percent

680
00:38:20,680 --> 00:38:23,520
Speaker 3: of the indexes of that Hi, If it's the case

681
00:38:23,600 --> 00:38:27,400
Speaker 3: these are riskier bonds that do not generate a commensarate

682
00:38:27,480 --> 00:38:32,000
Speaker 3: risk addstion return. By throwing them out, you're not handicapping yourself.

683
00:38:33,200 --> 00:38:35,560
Speaker 3: That's something you have people have to look at very carefully.

684
00:38:36,400 --> 00:38:40,840
Speaker 3: So it's a super important topic. We're just seeing increasing

685
00:38:41,400 --> 00:38:46,719
Speaker 3: frequency with which private companies within public markets look like

686
00:38:46,800 --> 00:38:47,640
Speaker 3: what you just debscribe.

687
00:38:48,000 --> 00:38:51,759
Speaker 1: Okay, that's your spread comment. You spreads are very tight.

688
00:38:51,840 --> 00:38:53,319
Speaker 1: We talk about that all the time on the show,

689
00:38:53,360 --> 00:38:56,439
Speaker 1: and it's hard to find relative value. The assumption from

690
00:38:56,480 --> 00:38:58,840
Speaker 1: a lot of investors in credit p sickly on the

691
00:38:58,920 --> 00:39:02,359
Speaker 1: IG side is that the FED will save you, as

692
00:39:02,440 --> 00:39:06,000
Speaker 1: it did during the pandemic. I'm wondering, you know how

693
00:39:06,080 --> 00:39:08,759
Speaker 1: much of that is in your model that you know,

694
00:39:08,840 --> 00:39:11,040
Speaker 1: if there is, let's say twenty percent downside that you

695
00:39:11,120 --> 00:39:13,040
Speaker 1: know the Fed's going to come in with a trillion

696
00:39:13,040 --> 00:39:14,760
Speaker 1: dollars and buy everything and then you'll be a winner.

697
00:39:16,480 --> 00:39:20,200
Speaker 3: Yeah, again, distinct this thin is beta from alpha. If

698
00:39:20,280 --> 00:39:23,279
Speaker 3: I was timing my allocation to the credit market, that

699
00:39:23,560 --> 00:39:26,400
Speaker 3: might be something that would fapor into the model. If

700
00:39:26,440 --> 00:39:29,120
Speaker 3: you're taking appid risk within the market, that's like a

701
00:39:29,200 --> 00:39:31,279
Speaker 3: second or thurt order effect, so that that wouldn't really

702
00:39:31,360 --> 00:39:36,000
Speaker 3: drive which issue or issue what I think is more attractive. Yeah,

703
00:39:36,080 --> 00:39:39,120
Speaker 3: that's some complicated This happens. This happened. This happened, So

704
00:39:39,600 --> 00:39:41,759
Speaker 3: I don't worry about that for security selection. But if

705
00:39:41,840 --> 00:39:44,840
Speaker 3: we were timing the market, then you might be detained

706
00:39:44,880 --> 00:39:49,000
Speaker 3: things like this. All those questions are what else are

707
00:39:49,000 --> 00:39:52,400
Speaker 3: you comparing it to? As it might be like Europe

708
00:39:52,480 --> 00:39:55,720
Speaker 3: versus US? If you'd say timing US credit versus European credit,

709
00:39:56,120 --> 00:39:58,000
Speaker 3: you might have a view on which central bank or

710
00:39:58,040 --> 00:40:00,840
Speaker 3: government's more likely to help. You know, you might shade

711
00:40:00,840 --> 00:40:02,680
Speaker 3: your view not off on the back of them.

712
00:40:03,440 --> 00:40:06,759
Speaker 2: What I wanted to hit on obviously hot topic. I

713
00:40:06,840 --> 00:40:09,640
Speaker 2: feel like we see news articles every day on how

714
00:40:09,840 --> 00:40:12,919
Speaker 2: lll m's are are being built out and how they're

715
00:40:12,960 --> 00:40:17,919
Speaker 2: increasing productivity for development, especially with you know, your world.

716
00:40:17,960 --> 00:40:20,520
Speaker 2: I'm sure the development side is a really really significant

717
00:40:20,600 --> 00:40:23,799
Speaker 2: part of the investment process. So how are you all

718
00:40:24,000 --> 00:40:28,320
Speaker 2: using large language models? And you know, are there areas

719
00:40:28,360 --> 00:40:30,960
Speaker 2: where you've seen maybe it fall a little bit short

720
00:40:31,160 --> 00:40:33,239
Speaker 2: for you know, helping the team out.

721
00:40:34,560 --> 00:40:37,719
Speaker 3: Yeah so yeah, again, huge question to not going to

722
00:40:37,760 --> 00:40:40,319
Speaker 3: do with justice in a couple of minutes. But bottom line,

723
00:40:40,360 --> 00:40:43,840
Speaker 3: do we use lll M broadly defined? Yes, you should

724
00:40:43,840 --> 00:40:47,719
Speaker 3: also define what we mean by ll M, So it's yeah,

725
00:40:48,200 --> 00:40:52,719
Speaker 3: a way together utilize text in some way. You could

726
00:40:52,719 --> 00:40:54,920
Speaker 3: be looking at pre existing texts, we could be using

727
00:40:55,000 --> 00:40:58,560
Speaker 3: to generate new text. Okay, so there are different use cases.

728
00:41:00,120 --> 00:41:03,279
Speaker 3: Your productivity gains here are potentially very large. That the

729
00:41:03,360 --> 00:41:05,840
Speaker 3: productivity gains come in different parts of the organization, so

730
00:41:05,920 --> 00:41:09,719
Speaker 3: you could have upstream benefits with just simple things like

731
00:41:10,040 --> 00:41:12,600
Speaker 3: your ingesting data into one environment want to skip it

732
00:41:12,640 --> 00:41:17,080
Speaker 3: to another environment. It could traditionally take weeks of testing

733
00:41:17,200 --> 00:41:20,200
Speaker 3: to say if I moved from an environment one environment two,

734
00:41:21,120 --> 00:41:25,680
Speaker 3: are we happy with some agenic stuff. The turnaround time

735
00:41:25,760 --> 00:41:27,840
Speaker 3: on this is a lot, a lot bigger, and so

736
00:41:27,880 --> 00:41:29,920
Speaker 3: there's a lot of productivity gains in terms of the

737
00:41:30,000 --> 00:41:33,360
Speaker 3: warehousing of beta. There are productivity gains in how you

738
00:41:33,480 --> 00:41:38,120
Speaker 3: generate forecasts. It will use LMS as part of utilizing

739
00:41:38,160 --> 00:41:42,040
Speaker 3: text data to generate re return forecasts. Then on the

740
00:41:42,200 --> 00:41:45,160
Speaker 3: sort of client side, there's huge gains here. So you're

741
00:41:45,200 --> 00:41:51,080
Speaker 3: writing regular reports, commentary, summarizing positions, a lot of that

742
00:41:51,400 --> 00:41:54,239
Speaker 3: can be not fully automated, but close to fully automated.

743
00:41:54,480 --> 00:41:58,239
Speaker 3: But by leaning on these types of tools, you still

744
00:41:58,320 --> 00:42:02,359
Speaker 3: need be human, and that's sort of paramit thou sort

745
00:42:02,360 --> 00:42:05,120
Speaker 3: of internal polity, thou shalt not blindly use these tools.

746
00:42:05,400 --> 00:42:08,120
Speaker 3: You need to have someone vetting, so you do need

747
00:42:08,200 --> 00:42:09,759
Speaker 3: the expert in the room just to make sure that

748
00:42:09,840 --> 00:42:14,000
Speaker 3: what you get out makes sense. Maybe ten years or

749
00:42:14,040 --> 00:42:17,239
Speaker 3: down the road that the need for that human touch

750
00:42:18,200 --> 00:42:20,400
Speaker 3: I think will become less, Will it becomes zero that

751
00:42:20,520 --> 00:42:22,919
Speaker 3: I don't know. I'm based on what I've saying today,

752
00:42:23,080 --> 00:42:27,840
Speaker 3: not zero. Do you still need giving touch ye, prompting

753
00:42:28,080 --> 00:42:31,919
Speaker 3: becomes super important. So how do you sort of set

754
00:42:32,000 --> 00:42:35,160
Speaker 3: the stage for what you want the LM to do? Wow,

755
00:42:35,920 --> 00:42:38,759
Speaker 3: And this is so fluid because there's new versions coming up,

756
00:42:38,840 --> 00:42:41,200
Speaker 3: so hallucinations of risk fuel versions have less of that,

757
00:42:41,360 --> 00:42:44,280
Speaker 3: So it's it's a very very dynamic sperence.

758
00:42:44,960 --> 00:42:47,400
Speaker 1: And in terms of you mentioned that it will always

759
00:42:47,440 --> 00:42:49,880
Speaker 1: need a human But what does it take for this

760
00:42:50,480 --> 00:42:55,239
Speaker 1: systematic strategy to gain critical mass? I mean we're still

761
00:42:55,280 --> 00:42:59,560
Speaker 1: at the single percentage points and it's really scratching the surface.

762
00:42:59,600 --> 00:43:01,160
Speaker 1: What it take to break through.

763
00:43:03,080 --> 00:43:05,480
Speaker 3: Frou from the building. So just demonstrate year after you

764
00:43:05,840 --> 00:43:08,399
Speaker 3: your ability to beat the benchmark. So one like big

765
00:43:08,440 --> 00:43:12,399
Speaker 3: the benchmark. Two I think lead on this transparency point,

766
00:43:12,520 --> 00:43:15,400
Speaker 3: so making it easy for people to understand what you do.

767
00:43:15,800 --> 00:43:20,120
Speaker 3: So systematic is often equated with black box. Sometimes that's

768
00:43:20,160 --> 00:43:23,120
Speaker 3: appropriate that I wish we would smash that and say, look,

769
00:43:23,360 --> 00:43:26,920
Speaker 3: systematic can be very transparent. We pride ourselves on that.

770
00:43:27,040 --> 00:43:30,400
Speaker 3: I think that will help a lot. And then this

771
00:43:30,880 --> 00:43:33,160
Speaker 3: notion of liquidity provision, we're talking about how difficult it

772
00:43:33,239 --> 00:43:37,800
Speaker 3: is to trade. Systematic processes are uniquely positioned to harvest

773
00:43:37,880 --> 00:43:41,480
Speaker 3: alpha from this wine. We have a view on every bond,

774
00:43:42,160 --> 00:43:45,880
Speaker 3: every bond, twenty five thousand bond blurbally, that view updates

775
00:43:45,920 --> 00:43:49,200
Speaker 3: every day, it updates intra date. You marry that with

776
00:43:49,320 --> 00:43:53,080
Speaker 3: a system that's ingesting all the data across different platforms, protocols,

777
00:43:53,160 --> 00:43:56,240
Speaker 3: venues as to where you could trade. You build an audible.

778
00:43:57,000 --> 00:44:01,320
Speaker 3: You've made the utility curve for every bond sings together continually.

779
00:44:02,400 --> 00:44:04,600
Speaker 3: So when you trade, trade as a liquidity provide and

780
00:44:04,640 --> 00:44:08,160
Speaker 3: what a liquidity taker. That's something that a traditional discretion

781
00:44:08,320 --> 00:44:10,560
Speaker 3: shop can't do by definition if only get a view

782
00:44:10,600 --> 00:44:13,800
Speaker 3: on a small number of rituals. But you put a

783
00:44:13,840 --> 00:44:17,359
Speaker 3: lot of stuff together. Transparency, full model, make it dear

784
00:44:17,440 --> 00:44:21,000
Speaker 3: to people and harves this implementation off and we've got

785
00:44:21,760 --> 00:44:23,880
Speaker 3: a very good chance of being the largest siene of

786
00:44:23,920 --> 00:44:25,040
Speaker 3: the high down the road.

787
00:44:25,480 --> 00:44:27,839
Speaker 1: We have talked about transaction costs in credit. So how

788
00:44:27,960 --> 00:44:32,320
Speaker 1: often do you actually have to rebalance reject the portfolio?

789
00:44:34,120 --> 00:44:37,040
Speaker 3: Uh, that's a choice so that it comes out of

790
00:44:37,080 --> 00:44:40,040
Speaker 3: the speed of the return forecast. So the faster you

791
00:44:40,120 --> 00:44:42,280
Speaker 3: return forecast, the more you need to turn over the portfolio.

792
00:44:42,440 --> 00:44:46,719
Speaker 3: To think, aratisant dodguments component to portfolio construction. It gives

793
00:44:46,760 --> 00:44:49,520
Speaker 3: in an horizon of which you are forecasting your turns.

794
00:44:50,440 --> 00:44:53,680
Speaker 3: The traditional way to rebalance a systematic approach would be

795
00:44:54,080 --> 00:44:56,440
Speaker 3: a third of those the other months. And the prank

796
00:44:56,840 --> 00:44:59,600
Speaker 3: throw some buyers themselves into the trade. Trade does go

797
00:44:59,719 --> 00:45:03,520
Speaker 3: at to and that's what equity systematic equity approaches would

798
00:45:03,520 --> 00:45:07,120
Speaker 3: typically do. Right, you could do that in pork. I

799
00:45:07,200 --> 00:45:09,320
Speaker 3: don't think you should. You want to be in the

800
00:45:09,360 --> 00:45:14,839
Speaker 3: market continual looking for those opportunities. So you've got a view. Oh,

801
00:45:15,400 --> 00:45:17,600
Speaker 3: someone's willing to show me a side of the trade.

802
00:45:18,120 --> 00:45:22,520
Speaker 3: It's consistent with our view. Let's trade it mere and

803
00:45:24,160 --> 00:45:28,320
Speaker 3: the systems in a systematic approach lend themselves well to

804
00:45:28,520 --> 00:45:32,440
Speaker 3: not pay to trade, but be paid to trade. Okay.

805
00:45:32,680 --> 00:45:35,080
Speaker 1: And on the transparency point, I mean, you can't tell

806
00:45:35,160 --> 00:45:37,600
Speaker 1: us very much on this call. But if we are

807
00:45:37,680 --> 00:45:39,440
Speaker 1: clients of yours, then we sit down with you and

808
00:45:39,520 --> 00:45:41,000
Speaker 1: you go through all of the detail. This is what

809
00:45:41,080 --> 00:45:42,360
Speaker 1: we're doing, this is how we're doing. These are the

810
00:45:42,960 --> 00:45:45,440
Speaker 1: assumptions or these are the decisions where we're making. And

811
00:45:45,480 --> 00:45:46,719
Speaker 1: this is what the robot's going to do for you.

812
00:45:47,880 --> 00:45:50,239
Speaker 3: Well, take the word robot out. It's a human.

813
00:45:51,840 --> 00:45:54,560
Speaker 1: That's what will will the machines ever end up running

814
00:45:54,600 --> 00:45:55,000
Speaker 1: this world.

815
00:45:56,600 --> 00:45:59,000
Speaker 3: That's a good. That's something that as a parent, that's

816
00:45:59,040 --> 00:46:05,040
Speaker 3: something you worry about. Who knows, I think, But for

817
00:46:05,160 --> 00:46:08,600
Speaker 3: what I do for my career path that's left, I'm

818
00:46:08,600 --> 00:46:11,880
Speaker 3: happy that I've still got a lot to do myself.

819
00:46:12,840 --> 00:46:15,480
Speaker 3: The younger generation, I think you should be carefully about

820
00:46:15,480 --> 00:46:21,440
Speaker 3: what careers iman and ies from this risk, but protected

821
00:46:21,520 --> 00:46:22,439
Speaker 3: somewhat from that risk.

822
00:46:23,400 --> 00:46:26,239
Speaker 1: Great stuff, Scott Richardson with Acadian Asset Management. It's been

823
00:46:26,239 --> 00:46:27,520
Speaker 1: a pleasure having you on the Credit Edge Money.

824
00:46:27,560 --> 00:46:28,560
Speaker 3: Thanks, thank you.

825
00:46:29,000 --> 00:46:31,560
Speaker 1: And to Sam Guyer with Bloomberg Intelligence, thank you very

826
00:46:31,640 --> 00:46:32,560
Speaker 1: much for joining us today.

827
00:46:32,880 --> 00:46:33,800
Speaker 3: Yeah. Thanks Shaves.

828
00:46:34,440 --> 00:46:36,560
Speaker 1: For even more analysis, read all of Sam's great work

829
00:46:36,640 --> 00:46:39,200
Speaker 1: on the Bloomberg Terminal. Bloomberg Intelligence is part of our

830
00:46:39,239 --> 00:46:42,040
Speaker 1: research department, with five hundred analysts and strategists working across

831
00:46:42,080 --> 00:46:45,560
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832
00:46:45,600 --> 00:46:47,800
Speaker 1: and outlooks on more than ninety industries and one hundred

833
00:46:47,840 --> 00:46:51,680
Speaker 1: market industries, currencies and commodities. Please do subscribe to the

834
00:46:51,719 --> 00:46:54,840
Speaker 1: Credit Edge wherever you get your podcasts. We're on Apple,

835
00:46:55,040 --> 00:46:58,120
Speaker 1: Spotify and all other good podcast providers, including the Bloomberg

836
00:46:58,200 --> 00:47:01,040
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837
00:47:01,080 --> 00:47:03,600
Speaker 1: your friends, or email me directly at Jcrombie eight at

838
00:47:03,680 --> 00:47:07,160
Speaker 1: Bloomberg dot net. I'm James Crombie. It's been a pleasure

839
00:47:07,200 --> 00:47:09,760
Speaker 1: having you join us again next week on the Credit

840
00:47:09,960 --> 00:47:10,160
Speaker 1: Edge