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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: His name is John Chisholm, and you might not have

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Speaker 1: heard of him, despite the fact that he is the

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Speaker 1: co CEO and former Chief Investment Officer of Acadian Asset Management,

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Speaker 1: which runs nearly a hundred billion dollars in institutional money

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Speaker 1: UH all around the world. Most of it is here

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Speaker 1: in the US, but a healthy UH chunk about a

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Speaker 1: third is overseas money. He has a fascinating background, UH

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Speaker 1: an aspiring rocket scientist who worked at the m I

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Speaker 1: t um Instrument Labs before taking a gig at State Street,

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Speaker 1: and then eventually him and his partners launched Acadian about

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Speaker 1: thirty two years ago. UH. They are a quantitative shop

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Speaker 1: and have a very very UH interesting approach combining essentially

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Speaker 1: fundamental factor models into a quantitative UH system and it's

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Speaker 1: really very very interesting. They've put together quite a fascinating

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Speaker 1: track record over time. If you are at all interested

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Speaker 1: in quantitative approaches, factor based UH investing, big data, artificial intelligence,

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Speaker 1: the way to approach markets UH from a data driven perspective,

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Speaker 1: then I think you're gonna find this conversation absolutely fascinating. So,

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Speaker 1: with no further ado, my interview with Acadian Asset Managements

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Speaker 1: John Chisholm. My special guest this week is John Chisholm.

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Speaker 1: He is the co CEO of Acadian Asset Management. Previously

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Speaker 1: he was Chief Investment Officer. Acadian manages a six billion

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Speaker 1: dollars in almost seventy countries around the world. UH. He

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Speaker 1: began as an analyst at State Street Bank. Previous to that,

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Speaker 1: he was a systems engineer at Draper Laboratories, which really

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Speaker 1: is a great place to start. John Chisholm, Welcome to Bloomberg.

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Speaker 1: Hi Berry, Thanks, it's great to be here. So, systems

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Speaker 1: engineer at Draper Laboratories, which became known as the M

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Speaker 1: I T Instrumentation Lab. Is that right? How did you

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Speaker 1: find your way from M I T to uh, the

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Speaker 1: instrumentation Labs. When I went to college, my passion, what

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Speaker 1: I was excited about, was really building or designing spaceships.

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Speaker 1: This was in the early eighties, So so you were

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Speaker 1: a rocket science I was a lot of the aspiring

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Speaker 1: rocket scientists exactly. And so I got my undergraduate degree,

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Speaker 1: but I found my senior year I was gotten really

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Speaker 1: interested investing, and I was spending a lot of time

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Speaker 1: mostly just reading about investing, you know whatever. It was

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Speaker 1: journal business publications, journals and uh. When I decided, okay,

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Speaker 1: what do I want to do now? I thought, well,

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Speaker 1: I probably want to go back to grad school. Do

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Speaker 1: I want to do finance investing business or do I

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Speaker 1: want to do um aerospace? And uh. I had had

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Speaker 1: a an opportunity apply to several different programs, so I

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Speaker 1: had both. I had a finance opportunity and an aerospace opportunity,

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Speaker 1: and I thought, why don't I try to test both out.

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Speaker 1: I'll get a full time job here in Boston area.

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Speaker 1: The only place there's not a lot of airspace jobs

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Speaker 1: in Boston A Draper Labs is one that works on

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Speaker 1: guidance systems. So if you've got a satellite or a missile,

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Speaker 1: you try to figure out where's it's, where's it going

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Speaker 1: to go? How does it get there? Before GPS of

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Speaker 1: headed guidance system. So uh, so I worked there full time,

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Speaker 1: and I got a part time job, like sort of

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Speaker 1: after hours job, working with a fellow named Gary Bergstrom,

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Speaker 1: who was later one of my co founders at a Kadian.

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Speaker 1: He had been a portfolio manager at Putnam and UH

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Speaker 1: in the seventies and then he left sort of off

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Speaker 1: on his own, consulting for money managers consulted at the time.

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Speaker 1: As big project when I was working with them was

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Speaker 1: for State Street later State Street Global Advisors, and we

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Speaker 1: helped building design their first international index fund and then

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Speaker 1: later on some international active strategies. So that was sort

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Speaker 1: of a part time job. UM I went back to

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Speaker 1: that made me decide that was more interesting than the

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Speaker 1: aerospace stuff I was doing at the time. That made

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Speaker 1: me decide to go back. So that's what when you

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Speaker 1: said to go back, you really started. Your first full

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Speaker 1: time job in finance was as an analyst for State Street.

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Speaker 1: Is so, so the State Street job was a was

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Speaker 1: also a part time That was also while I was

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Speaker 1: at school um UH working for them for like there

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Speaker 1: was some time off in January and then the spring semester.

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Speaker 1: I worked for them as a potential employer. But in

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Speaker 1: the end Gary's goal was to launch an asset management firm.

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Speaker 1: UH myself and we had another colleague, Churchill Franklin, and

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Speaker 1: another colleague, Ron Frasier. They all came aboard. We all

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Speaker 1: came together about the same time around when I graduated

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Speaker 1: and uh and so we launched a Kadian as an

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Speaker 1: active money manager at that point. So that's thirty two

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Speaker 1: years ago. An active manager as well as a heavily

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Speaker 1: influenced by quantitative strategies? Is that affair the work? We're

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Speaker 1: a quantitative manager. We were all you know, my background

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Speaker 1: aerospace engineering, UM all all quantitative. Gary's background, Gary gotten

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Speaker 1: a PhD from m I t uh. So we were

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Speaker 1: all very quantitative. But quant at the time was not

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Speaker 1: as sophisticated as what quant today is. Right, there wasn't

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Speaker 1: any machine learning, there wasn't any big data. There was

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Speaker 1: you know, little data. There were statistics, right, so you

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Speaker 1: know what's the average payoff to value UM and ch

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Speaker 1: how do we build a portfolio that captures that payoff?

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Speaker 1: Very simple quantitative tools that we use back in the

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Speaker 1: middle eighties. So do you consider yourselves today an active

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Speaker 1: manager or quantitatively? Like I think about firms like um

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Speaker 1: D f A or any of the FAMA French based

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Speaker 1: UM factor models, and they're somewhere between active and a

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Speaker 1: quantitative screening approach. How how would you describe a Kadian?

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Speaker 1: Describe us as active? So most of what we're doing

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Speaker 1: is highly active, potentially high tracking our against a benchmark.

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Speaker 1: We we have the flexibility so we can build low

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Speaker 1: tracking our strategies. UM. This ties into this concept of capacity.

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Speaker 1: How much money can you manage and still expect to

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Speaker 1: add the value your clients are looking for? And typically

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Speaker 1: the more money you manage, the harder is to add value.

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Speaker 1: UM So at lower levels of active risk, lower expected

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Speaker 1: value added, you can manage more money. There's some clients

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Speaker 1: who are happy hiring managers for that. They're also usually

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Speaker 1: happy paying lower fees. Right, so you really have to

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Speaker 1: trade off both from perspective of adding value and from

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Speaker 1: perspective of running a business. Where do you want to be?

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Speaker 1: Most of our strategy is highly active, but we have

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Speaker 1: some that are shading more towards UM enhanced index. When

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Speaker 1: you say enhanced index, you're taking a basic index and

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Speaker 1: then adding a little flavoring to it to move it

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Speaker 1: away from the benchmark. Yeah, we're so, for example, we

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Speaker 1: might say enhanced. The next would be if we have

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Speaker 1: tracking or of less than two percent, if we have

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Speaker 1: one and a half percent. Tracking are just means what's

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Speaker 1: the standard deviation of the expected returns versus the benchmark?

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Speaker 1: UM one and a half percent tracking er would be

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Speaker 1: enhanced index strategy, you might only expect to get one

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Speaker 1: and a half percent UM excess return associated with that

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Speaker 1: net of fees. If we had a more active strategy,

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Speaker 1: we might expect we might see four percent tracking er.

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Speaker 1: We'd expect to get about two and a half percent

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Speaker 1: active return neta fees. How do you avoid the challenge,

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Speaker 1: as as Bill Miller described it, of UM active managers

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Speaker 1: TUBJE active fees but are effectively our closet indexers. How

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Speaker 1: do you clearly differentiate yourself from that group. So, so

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Speaker 1: there's two parts to it. One is what what's under

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Speaker 1: our control? What we can do. We can build portfolios

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Speaker 1: that are active in the sense that they are very different,

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Speaker 1: they look different from the benchmark, they have UM high

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Speaker 1: higher levels of tracking, or they have high active share UM.

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Speaker 1: The other part of that isn't is the client's job.

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Speaker 1: So if the client hires twenty managers like that, they're

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Speaker 1: still getting close to back back to an index. Let's

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Speaker 1: talk a little bit about your quantitative approach, and I

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Speaker 1: was noticing on your website you describe a fourth step process,

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Speaker 1: and really what that is is strategy signal generation signal

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Speaker 1: consumption and then process can can you explain what that

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Speaker 1: means to to the perhaps the lay person who may

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Speaker 1: not be familiar with a quantitative approach. Absolutely, let me

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Speaker 1: let me start with a signal generation part, because that's

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Speaker 1: maybe the part that will be easiest for for people

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Speaker 1: to start with the basic ideas. There's different characteristics companies have.

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Speaker 1: Those characteristics can be on average predictive of returns. So,

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Speaker 1: for example, one characteristic is just is how expensive as

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Speaker 1: a company look on whatever metric P ratio? Right, So

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Speaker 1: you've got a company that has a P ratio of

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Speaker 1: eight and one that has a P ratio of forty.

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Speaker 1: That's all you knew about those companies? Which one would

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Speaker 1: you want to own? If you look at the last

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Speaker 1: fifty or sixty years globally, you'd say, I want to

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Speaker 1: own the P eight company right on average is going

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Speaker 1: to do better. The problem with that is you can

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Speaker 1: have a ten year stretch where the P forty company

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Speaker 1: kills the P A company like we've just had. We've

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Speaker 1: had you know, what would you rather own right the

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Speaker 1: last ten years? We'd rather row on Amazon or would

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Speaker 1: rather own P G n E? Right? I mean, so

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Speaker 1: those are the two biggest streames you can you can

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Speaker 1: really sorry I picked those out of it. Not hopefully

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Speaker 1: it's not quite, but it's true. Generally growth stocks have

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Speaker 1: done very well. Value stocks and stuff that joined up

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Speaker 1: for a decade. So how do you it around that? UM?

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Speaker 1: So let's say you believe on average, value is going

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Speaker 1: to outperform, like some people might not believe that. But

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Speaker 1: let's say I do. I want value in my portfolio,

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Speaker 1: but I don't want to underperform for ten years in

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Speaker 1: a row. What can I do? I can take other

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Speaker 1: characteristics that I believe are also predictive of return and

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Speaker 1: combine those with value. So I might say, for example,

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Speaker 1: UM quality measures, right, I want companies that are well managed.

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Speaker 1: How do you define well managed? Well? As a dozen definitions,

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Speaker 1: But let's say one definition is inventory turnover. Do you

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Speaker 1: have companies that turn over their inventory uh more frequently

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Speaker 1: than companies in the same industry? Right? And maybe that's

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Speaker 1: a signal that on average has some payoffs associated with

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Speaker 1: it for companies in many industries. So now I've got

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Speaker 1: value and I've got this. Let's say I think people

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Speaker 1: talk about a lot in markets as momentum, So I've

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Speaker 1: got momentum. Companies have good momentum um, they've been performing,

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Speaker 1: they've been performing their peers for the last you know,

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Speaker 1: six to twelve months. Maybe that's indicative that on average

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Speaker 1: in the future, they're likely outperformed for the next, say,

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Speaker 1: one to three months. I want to wrap that in.

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Speaker 1: So you combine all these different signals and you've got

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Speaker 1: what you historically people call a multi factor model. Right,

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Speaker 1: and so now, even if value does badly, maybe momentum

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Speaker 1: and quality and these other things do well enough to

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Speaker 1: allow you to still outperform, which is always the goal,

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Speaker 1: uh for for us and for our clients, and and

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Speaker 1: so that that's sort of the the genesis of signals.

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Speaker 1: It's just there are different types of characteristics that we

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Speaker 1: use to help predict company returns, and we then combine them.

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Speaker 1: So when you say signal consumption, a couple of pieces

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Speaker 1: of that One is how do you combine these things? Right?

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Speaker 1: Is the payoff to value the same as the payoff

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Speaker 1: to quality will probably not um. So you have to

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Speaker 1: figure out what do I expect to get If I'm

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Speaker 1: looking at it doesn't differ by the type of company

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Speaker 1: I'm looking at, is a tech company, maybe it has

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Speaker 1: different drivers overturn than a utility, And so I have

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Speaker 1: to mix the weight on those signals depending on what

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Speaker 1: kind of opening I'm evaluating. Uh, And so that's part

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Speaker 1: of consumption. Then the second part of consumption is how

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Speaker 1: do you implement that in a portfolio? Right, So, ultimately

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Speaker 1: I'm gonna hold stocks and a portfolio, I'll hold Amazon

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Speaker 1: or I won't. I'll hold pg n E or I won't.

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Speaker 1: What's my weight going to be? Hopefully it will be

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Speaker 1: last ten years, hopefully high on Amazon, lower zero. And

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Speaker 1: But but the idea is you've got to then turn

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Speaker 1: those expected returns that you're getting from the signal generation

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Speaker 1: part of your process into portfolio positions. At Acadian, we

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Speaker 1: use a pretty quantitative approach to do that as well.

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Speaker 1: We use what's called an optimizer that basically trades off

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Speaker 1: the return expectations we come up with from the signals

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Speaker 1: into and maps those into portfolio positions by trading those

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Speaker 1: off against transaction costs. So, if I'm trading again Amazon, Samsung,

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Speaker 1: a big liquid company, transaction costs are probably gonna be

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Speaker 1: pretty low, almost negligible. But if I'm trading a less

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Speaker 1: liquid company, that may be more efficiently priced than the

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Speaker 1: return up tratunity may be much greater. But I need

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Speaker 1: to now account for what's it going to cost to

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Speaker 1: get in the position and what's it going to cost

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Speaker 1: to get out of the position someday. So what you're

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Speaker 1: describing sounds a lot like traditional factor based investing. You're

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Speaker 1: describing um value, describing a momentum quality. When we talk

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Speaker 1: about liquidity, I always think about cap size. How does

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Speaker 1: your approach differ from factor investing? Or am I asking

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Speaker 1: that question wrong? Are you effectively a farmer French factor

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Speaker 1: type investor? Now that's it's a great question. Uh. And

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Speaker 1: in some ways our approach is very much like factor investing.

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Speaker 1: In other words, we consider these different signals. We consider

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Speaker 1: them to be types of factors um. What's different is

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Speaker 1: that we integrate the factors. So, for example, let's suppose

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Speaker 1: you've just been on momentum factor by itself. There's some

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Speaker 1: periods if you built a portfolio that has the most

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Speaker 1: attractive whatever it is, ten percent of uh, let's say

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Speaker 1: us amentum stocks and then the least to try active

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Speaker 1: or shorts, or you just went long the most attractive

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Speaker 1: ten percent, there are some periods where your portfolio beta.

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Speaker 1: Your sensitivity to market movements might be might be two,

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Speaker 1: so you might have a huge amount of volatility in

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Speaker 1: the portfolio. And there's other times when your sensitivity to

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Speaker 1: market movements might be very low, might be a bet

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Speaker 1: of point five um. Why does that matter? It impacts

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Speaker 1: how you can control risk. If you're doing these single

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Speaker 1: factor portfolios, for example, unless you're very careful about how

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Speaker 1: you build them, you are likely to take on all

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Speaker 1: kinds of unexpected risks in the construction of those portfolios.

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Speaker 1: With a multi factor approach, you're not beholding any one factor.

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Speaker 1: You've got all these different characteristics you can emphasize in

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Speaker 1: the portfolio, and you can trade them off, and it

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Speaker 1: allows you to manage risk better. Portfolio construction can be

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Speaker 1: a lot better than what people do when they're doing

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Speaker 1: When people talk about factor investing, if you look at

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Speaker 1: a typical factor e. T f UM, it's not built

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Speaker 1: in a very efficient way. It's more costly to investors

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Speaker 1: in ways that the investors can't see things like how

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Speaker 1: they trade the portfolio. So they simply take a rank

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Speaker 1: order of companies based on some factor, and they rebalanced.

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Speaker 1: They buy some of the most attractive ones that have

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Speaker 1: just gotten into that list and sell some of the

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Speaker 1: ones that have fallen out. That can be a lot

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Speaker 1: of turnover. And there may be times when you want

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Speaker 1: to hold onto something that's become less attractive because it

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Speaker 1: might be expensive to trade out of it and it

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Speaker 1: hasn't fallen that far right, So it's important to do

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Speaker 1: to be smart about how you use these factors. And

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Speaker 1: I'd say the keep one of the key things we

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Speaker 1: do is we worry a lot about the engineering of

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Speaker 1: our process. How do you put these factors together? How

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Speaker 1: do you minimize the slippage the transaction costs while still

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Speaker 1: getting exposure to the underlying factor in the portfolio? Quite fascinating.

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Speaker 1: Let's talk a little bit about what it's like to

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Speaker 1: run a global organization. You're headquartered in Boston. Congratulations on

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Speaker 1: the Patriots. Thank you. Um, you were at the game

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Speaker 1: you mentioned, Uh, I was at the game, first time

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Speaker 1: I've ever gone. So you don't know how long the

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Speaker 1: Patriots that this might be their last Super Bowl in

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Speaker 1: a while. Good idea to go. Yeah, now that makes

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Speaker 1: perfect sense. Um, you have affiliates in London and Singapore

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Speaker 1: and Tokyo and Sydney. What other countries are you located

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Speaker 1: in I mean, I know you have clients in We've

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Speaker 1: got clients probably thirty thirty five countries, but really they

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Speaker 1: coverage of Singapore, Sydney, Tokyo, London. We are thinking, because

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Speaker 1: it breaksit, we may need to open an office in Dublin, UM,

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Speaker 1: perhaps Amsterdam, but there's enough uncertainty there that we haven't

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Speaker 1: actually pulled the trigger yet Amsterdam. So so if people

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Speaker 1: I keep asking this, and I'm getting very different answers

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Speaker 1: from very different people. If Brexit happens hard or soft,

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Speaker 1: and London is no longer the central finance location for Europe,

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Speaker 1: where where does it go? Amsterdam doesn't really seem seem

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Speaker 1: like the place UM Geneva stud guard. I mean, I

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Speaker 1: can't Paris. Some people have floated, none of them seem

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Speaker 1: to make sense. I think Frankfort's got the economic in

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Speaker 1: many ways the commercial center of Europe. A lot of

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Speaker 1: people don't love Frankfort. Because I live near Frankfort for

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Speaker 1: a bunch of years. It's not always um doesn't have

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Speaker 1: the cultural reputation that Paris does. For example. That being said,

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Speaker 1: it's a very comfortable city to live in. So I

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Speaker 1: think Frankfort will do well. That all being said, you know,

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Speaker 1: if if we did something with another office, we're we're

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Speaker 1: just basically opening up an office to meet the regulatory requirements.

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Speaker 1: We still keep London, that would still be a major

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Speaker 1: center for portfolio management. Uh, you know, for our team,

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Speaker 1: for client service team. So your client base is uh

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Speaker 1: primarily institutional and public pension funds and other large investors.

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Speaker 1: Are they mostly US located? Are they around the world?

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Speaker 1: What what your mix so are right now? Are mixes

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Speaker 1: price seventy thirty um US non US? Uh? We we

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Speaker 1: from a business for spective you know, as a firm,

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Speaker 1: you want to be diversified, you know, so having a

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Speaker 1: fair amount of non US exposure with our clients is

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Speaker 1: something we strive for. We think there's a lot of

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Speaker 1: great growth opportunities in terms of just the the growth

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Speaker 1: of pension markets, of of institutional investor markets in Asia,

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Speaker 1: for example, there's still growth in Australia, um in in

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Speaker 1: Europe as well, there's pockets. You know, a few years

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Speaker 1: ago Germany really didn't have defined benefit pension plans. Slowly

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Speaker 1: evolving a little bit, So there's there's definitely opportunities. Don't

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Speaker 1: most of Europe or don't doesn't much of Europe have

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Speaker 1: UM some sort of retirement system covered by the government.

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Speaker 1: How do you operate around that or is that their

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Speaker 1: social security and it doesn't take the place of a

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Speaker 1: real retirement. Yeah, I mean there's a lot of variation,

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Speaker 1: but a lot of Europe, you know, like the UK,

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Speaker 1: for example, you've got public pension plans, just like in

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Speaker 1: the US you've got uh State of California cows, cowpers

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Speaker 1: and calsters. Well, in in UK you've got um local

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Speaker 1: authority pension plans UM and you can sort of think

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Speaker 1: of them as the equivalent of public plans. Here in

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Speaker 1: the US you've got large companies based in the UK

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Speaker 1: that have private pension plans, so there's some state provision.

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Speaker 1: But again, just like in the US, you have sort

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Speaker 1: of security, but that doesn't exclude all these other types

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Speaker 1: of pension plans. So in your list of countries, I

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Speaker 1: didn't hear a whole lot In China. Is Hong Kong

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Speaker 1: attractive or is mainland China possible or if it has,

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Speaker 1: the government made it too challenging to set up shop there.

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Speaker 1: Now the government's actually moving to liberalize, so it had

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Speaker 1: been very difficult for a non local investors to invest

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Speaker 1: in in in Chinese assets and manage money for Chinese institutions. UM.

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Speaker 1: We do manage money for Hong Kong clients, but that's

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Speaker 1: sort of separate stile, a separate um regulatory structure. Is

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Speaker 1: that why you're located in Sydney for that part of

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Speaker 1: the world. Singapore is is we're serving right now, we're

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Speaker 1: serving Asia extrapan out of Singapore. UM. But but China

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Speaker 1: is liberalizing and there's plenty of non local managers now

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Speaker 1: setting up shop to manage money for the Chinese institutions

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Speaker 1: in China. The challenges in China you need some scale.

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Speaker 1: You need a partner because you can't touch the retail

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Speaker 1: market without a local partner. And there's only really four

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Speaker 1: big institutions you know that you've got that are like

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Speaker 1: sort of the equivalent of Cowper's for example. UM. So

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Speaker 1: that that that market, the institutional markets very narrow and

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Speaker 1: the rest of the market, the retail market. You need

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Speaker 1: a partner. If we found the right partner, I think

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Speaker 1: would be very excited about doing something in China that

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Speaker 1: we're certainly doing some work there. We've we've had some

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Speaker 1: people not fully full time based there, but spending a

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Speaker 1: lot of time in the market. But that's something remains

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Speaker 1: to be seen whether we'll find the right opportunity to

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Speaker 1: really be a player there. So you previously were chief

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Speaker 1: Investment Officer and now you're co CEO. I have so

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Speaker 1: many questions about both. So are you still working in

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Speaker 1: a ce IO capacity as well? No, so, so I'm

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Speaker 1: I'm still interested. I still go to Investment Policy Committee meetings.

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Speaker 1: But you know, if you're if you're taking out a

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Speaker 1: new role and you've picked someone to succeed you in

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Speaker 1: your job, you really need to give them the ability

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Speaker 1: to run that function. I've got a great successor, my successor,

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Speaker 1: Brendan Bradley's or CEE i O UM started last year.

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Speaker 1: As see. I always been with it Katian for a

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Speaker 1: long time and got complete confidence in his ability to

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Speaker 1: manage the investment function UM. I still participate in some

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Speaker 1: of the meetings and I I'm interested in the research

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Speaker 1: and I talked talked to lots of the investment professionals.

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Speaker 1: It's part of my job as CEO is being in touch.

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Speaker 1: You know, what does an investment firm do? We invest

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Speaker 1: for our clients, So it's still important. But Brennan is

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Speaker 1: managing and leading the investment team and you're a co CEO,

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Speaker 1: which sounds like it has a whole lot of complications

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Speaker 1: and issues that would come out of that dual CEO role.

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Speaker 1: At least we've seen that with public companies. How do

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Speaker 1: you navigate that? Is there a clear distinction between who

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Speaker 1: is running what? Tell us a little bit about your

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Speaker 1: co absolutely? Let me let me you that, um, because

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Speaker 1: it is a great question. Uh. We we went through

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Speaker 1: a succession process. Our former CEO was stepping down, was

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Speaker 1: retiring from the CEO role, and so myself and one

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Speaker 1: of my colleagues rosted out were internal candidates for the role.

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Speaker 1: We have a selection process where we had an equivalent

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Speaker 1: of executive committee. Essentially you think of as eight individuals

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Speaker 1: running the firm making that decision. We were two members

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Speaker 1: of that group, UM, and we we both shared our

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Speaker 1: views on what's our vision for a Kadian, where do

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Speaker 1: we want the firm to go, what would we like

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Speaker 1: to do differently with with our executive committee, UM, it

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Speaker 1: turned out what we were very well aligned in terms

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Speaker 1: of where we wanted a Katie and to go. So

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Speaker 1: when we looked at UM sort of are there situations

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Speaker 1: I have a lot of respect for Ross, my current

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Speaker 1: co CEO, he has I think a lot of respect

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Speaker 1: for me. He comes from a marketing client service background.

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Speaker 1: I come from the investment background. And we we both

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Speaker 1: wanted each other to remain at the firm, thought about

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Speaker 1: how can we do that, and we looked at examples

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Speaker 1: where there have been co CEO structures in the past

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Speaker 1: and other firms, the ones that worked relatively well. UM,

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Speaker 1: and there are some Um generally you had co CEOs

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Speaker 1: with highly aligned visions and that we're able to work

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Speaker 1: together to provide a single voice of the firm. Right,

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Speaker 1: So you don't want somebody coming to Ross getting one

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Speaker 1: answer and then coming to me and getting a different answer.

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Speaker 1: And we we thought, given the fact that we in

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Speaker 1: fact do have highly lined visions, we do have areas

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Speaker 1: of expertise that are complementary to each other. Uh, we

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Speaker 1: thought this is something that not only could we pull

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Speaker 1: it off, but it would actually be beneficial for Acadian.

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Speaker 1: So we're now a little over a year into the role. UM,

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Speaker 1: we think we're managing the firm effectively. We're getting feedback

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Speaker 1: from our team, UM that that's the case and UH

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Speaker 1: and I think it's working extremely well so far. UM.

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Speaker 1: We What we typically do is issue comes up, we

430
00:23:57,680 --> 00:24:00,760
Speaker 1: will discuss it together. We'll figure out where are we

431
00:24:00,880 --> 00:24:03,639
Speaker 1: what are we looking to do? Um. And there are

432
00:24:03,640 --> 00:24:05,760
Speaker 1: times when you know it's an area. Ross has a

433
00:24:05,760 --> 00:24:09,120
Speaker 1: lot of expertise in the fern him more, Um, there's

434
00:24:09,160 --> 00:24:10,520
Speaker 1: times when it's an area. I've got a lot of

435
00:24:10,520 --> 00:24:14,040
Speaker 1: expertise in the for me more. It's great when we

436
00:24:14,080 --> 00:24:15,760
Speaker 1: do need to be in two places at once. Right,

437
00:24:15,800 --> 00:24:18,479
Speaker 1: he can be in Tokyo and I can be in Boston,

438
00:24:19,000 --> 00:24:21,800
Speaker 1: or vice versa. UM. One of us can be doing

439
00:24:21,960 --> 00:24:24,200
Speaker 1: to meeting with some of our clients, another one can

440
00:24:24,240 --> 00:24:28,120
Speaker 1: be running internal meetings. So it really helps us, I think,

441
00:24:28,600 --> 00:24:31,440
Speaker 1: be do a more effective job of managing the firm

442
00:24:31,520 --> 00:24:34,440
Speaker 1: to have the structure we have. Quite interesting. So let's

443
00:24:34,480 --> 00:24:36,320
Speaker 1: talk a little bit about what's going on in the

444
00:24:36,359 --> 00:24:40,360
Speaker 1: marketplace today. Are you still seeing the same sort of

445
00:24:40,400 --> 00:24:45,359
Speaker 1: miss pricings and securities that perhaps we're so abundant a

446
00:24:45,440 --> 00:24:49,280
Speaker 1: decade ago? The miss prices have changed a lot. So

447
00:24:49,400 --> 00:24:52,359
Speaker 1: if we take any particular signal that we've used ten

448
00:24:52,440 --> 00:24:54,280
Speaker 1: years ago and we look at the payoff to that

449
00:24:54,320 --> 00:24:58,680
Speaker 1: signal today, it's lower today. Right. So typically, whether it's

450
00:24:59,359 --> 00:25:02,679
Speaker 1: inefficiency is being squeeze out of the market, it's arbitraged

451
00:25:02,760 --> 00:25:05,119
Speaker 1: by different types of investors, whatever it is. You know,

452
00:25:05,200 --> 00:25:08,879
Speaker 1: Typically the payoff to these characteristics decreases over time. So

453
00:25:08,920 --> 00:25:11,520
Speaker 1: as a result, we're in a way, we're on a treadmill.

454
00:25:11,560 --> 00:25:14,440
Speaker 1: We need to keep on finding new ideas to replace

455
00:25:14,480 --> 00:25:17,040
Speaker 1: the old ideas that aren't working as well anymore. So

456
00:25:17,480 --> 00:25:21,360
Speaker 1: when you say these the payoffs to these ideas um

457
00:25:21,760 --> 00:25:25,240
Speaker 1: decline over time, is that all these ideas? Is that

458
00:25:25,320 --> 00:25:27,879
Speaker 1: a function of we had a giant reset with the

459
00:25:27,880 --> 00:25:32,280
Speaker 1: financial crisis. Hey, anytime the markets lose of the value,

460
00:25:32,840 --> 00:25:35,320
Speaker 1: you have to think that some value is going to

461
00:25:35,400 --> 00:25:37,760
Speaker 1: be created and a lot of babies get thrown out

462
00:25:37,800 --> 00:25:40,480
Speaker 1: with the bathwater. Or is it just the nature of

463
00:25:40,560 --> 00:25:44,160
Speaker 1: every good idea eventually runs its course. I think it's

464
00:25:44,200 --> 00:25:46,800
Speaker 1: really a little bit of the reset idea. So there's

465
00:25:46,840 --> 00:25:49,560
Speaker 1: no there's there's definitely a pattern that differs a little

466
00:25:49,560 --> 00:25:52,000
Speaker 1: bit the rate of the client. And some of these

467
00:25:52,040 --> 00:25:55,880
Speaker 1: things accelerated during the financial crisimmediately after the financial crisis,

468
00:25:56,480 --> 00:25:59,560
Speaker 1: and the payoff to value is the biggest one where

469
00:25:59,560 --> 00:26:02,560
Speaker 1: it's clear really been the worst ten year period for

470
00:26:02,680 --> 00:26:07,360
Speaker 1: value globally post GFC that we've seen in the long

471
00:26:07,440 --> 00:26:09,840
Speaker 1: term history, whether it's the U S history or longer

472
00:26:09,880 --> 00:26:12,720
Speaker 1: history that's different. That all being said, a lot of

473
00:26:12,720 --> 00:26:15,680
Speaker 1: the factors, it's an average thing, right. There's some signals

474
00:26:15,720 --> 00:26:18,719
Speaker 1: that still work, you know today, not much worse than

475
00:26:18,760 --> 00:26:21,600
Speaker 1: they worked ten years ago, but the average signal, the

476
00:26:21,640 --> 00:26:25,520
Speaker 1: payoff decreases a little bit every year. M that's quite interesting.

477
00:26:26,240 --> 00:26:29,560
Speaker 1: Quant has been around for thirty forty years or so.

478
00:26:30,320 --> 00:26:33,159
Speaker 1: Do you think things are very different based on the

479
00:26:33,280 --> 00:26:36,480
Speaker 1: rise of you mentioned earlier, big data and artificial intelligence.

480
00:26:36,800 --> 00:26:42,000
Speaker 1: How has that affected how Acadian approaches quant investing or

481
00:26:42,080 --> 00:26:46,840
Speaker 1: is that just something that is a background noise that

482
00:26:46,880 --> 00:26:50,800
Speaker 1: affects the market overall. Yeah, I think there's um there's

483
00:26:51,080 --> 00:26:54,560
Speaker 1: no question that the machine learning, art and big data,

484
00:26:54,680 --> 00:26:58,320
Speaker 1: artificial intelligence, those are early days, right. Those things are

485
00:26:58,480 --> 00:27:02,240
Speaker 1: starting to impact investors and how people invest. But we're

486
00:27:02,240 --> 00:27:04,719
Speaker 1: still in the early days of that in quant, let

487
00:27:04,760 --> 00:27:07,240
Speaker 1: alone in finance in general. And there's a lot more

488
00:27:07,280 --> 00:27:09,760
Speaker 1: to come. But I would say things have changed a

489
00:27:09,800 --> 00:27:13,919
Speaker 1: lot since the eighties and nineties, the the cerphification, the

490
00:27:14,280 --> 00:27:17,200
Speaker 1: not not so much big data, but just any kind

491
00:27:17,240 --> 00:27:19,679
Speaker 1: of data now is a lot more available than it

492
00:27:19,760 --> 00:27:21,959
Speaker 1: was then, So we have a lot more information and

493
00:27:22,040 --> 00:27:25,040
Speaker 1: quants can do things today that they couldn't do twenty

494
00:27:25,119 --> 00:27:27,439
Speaker 1: years ago. Fundamental investors could maybe do them for a

495
00:27:27,440 --> 00:27:30,960
Speaker 1: small group of companies, quants couldn't. Today we can look

496
00:27:31,000 --> 00:27:34,680
Speaker 1: at all these like we have industry specific information about

497
00:27:34,800 --> 00:27:36,800
Speaker 1: lots of companies that we just didn't have access to

498
00:27:36,920 --> 00:27:41,080
Speaker 1: twenty years ago. Is it the technology and database or

499
00:27:41,119 --> 00:27:44,880
Speaker 1: is it actually the specifics of the data itself that's

500
00:27:44,960 --> 00:27:47,600
Speaker 1: changed so much. It's both. So the technolog no question,

501
00:27:47,640 --> 00:27:51,240
Speaker 1: the technology and the database access um, the power and

502
00:27:51,280 --> 00:27:55,240
Speaker 1: speed of databases and of software and processing in general

503
00:27:55,280 --> 00:27:58,119
Speaker 1: has increased tremendously. That makes a lot of things easier

504
00:27:58,119 --> 00:28:02,960
Speaker 1: to do. Machine learning, those algorithms can be very computationally intensive,

505
00:28:03,520 --> 00:28:07,000
Speaker 1: and you hardware you had five years ago, you couldn't

506
00:28:07,000 --> 00:28:09,280
Speaker 1: do these things today. Today you can do them on

507
00:28:09,320 --> 00:28:11,520
Speaker 1: your on your laptop. In some cases might take a while,

508
00:28:11,640 --> 00:28:13,720
Speaker 1: but there's things you can do on your laptop. If not,

509
00:28:14,080 --> 00:28:16,960
Speaker 1: you go to Amazon Web Services and scale up processing

510
00:28:16,960 --> 00:28:19,800
Speaker 1: power and you've got everything you need in terms of

511
00:28:19,840 --> 00:28:23,840
Speaker 1: the sort of the processing computational aspect of things. So

512
00:28:23,920 --> 00:28:27,040
Speaker 1: that's changed a lot, but also the data itself today

513
00:28:27,119 --> 00:28:30,080
Speaker 1: is much broader than it was. I got started. You

514
00:28:30,160 --> 00:28:33,040
Speaker 1: got a p you gotta p B, you got a

515
00:28:33,080 --> 00:28:35,719
Speaker 1: price to cash flow. You've got a market cap, a price,

516
00:28:36,320 --> 00:28:38,240
Speaker 1: and maybe a divident deal thrown in, and that's there.

517
00:28:38,280 --> 00:28:41,880
Speaker 1: That's that was your data. That was like four ish

518
00:28:41,960 --> 00:28:45,640
Speaker 1: and then UM, shortly after you started getting analyst data electronically.

519
00:28:46,080 --> 00:28:48,280
Speaker 1: What what do you think about some of these alternative

520
00:28:48,360 --> 00:28:53,280
Speaker 1: data points that people are pulling from either satellite data, Hey,

521
00:28:53,320 --> 00:28:56,920
Speaker 1: here's all the ships moving oil around the world, UM,

522
00:28:57,040 --> 00:29:00,360
Speaker 1: or parking lot activity to determine how well read tailors

523
00:29:00,400 --> 00:29:05,440
Speaker 1: are doing. Is is any of that potentially useful and

524
00:29:05,520 --> 00:29:09,160
Speaker 1: valuable to investors? Or is it just um a bunch

525
00:29:09,240 --> 00:29:13,160
Speaker 1: of of geeks playing with some new tech toys and

526
00:29:13,160 --> 00:29:15,160
Speaker 1: and kind of having fun with it. It's both, so

527
00:29:15,200 --> 00:29:17,040
Speaker 1: that on the latter point, you know, we we have

528
00:29:17,080 --> 00:29:19,520
Speaker 1: an analyst, and if we have a satellite data project,

529
00:29:20,120 --> 00:29:22,160
Speaker 1: we have no problem getting somebody volunteer, put their hand

530
00:29:22,240 --> 00:29:23,440
Speaker 1: up and say I'd like to work on this. This

531
00:29:23,440 --> 00:29:27,440
Speaker 1: will be fun, right, So that's true. It's potentially valuable. Now,

532
00:29:27,520 --> 00:29:31,080
Speaker 1: whether it's actually valuable to any individual in any particular

533
00:29:31,080 --> 00:29:34,600
Speaker 1: investment firm depends on their style and their process. So

534
00:29:34,680 --> 00:29:36,720
Speaker 1: let me let me tell you what I mean by that. UM.

535
00:29:36,760 --> 00:29:39,400
Speaker 1: If you've got a satellite data, let's say you're getting

536
00:29:39,600 --> 00:29:43,280
Speaker 1: your parking lot, your infrared images UM, and you're getting

537
00:29:43,320 --> 00:29:47,520
Speaker 1: information about you know, parking lots UM. And that's if

538
00:29:47,520 --> 00:29:51,640
Speaker 1: you're following retailers and UM. Investing in retailers is a

539
00:29:51,680 --> 00:29:55,000
Speaker 1: big part of what you do that can be useful

540
00:29:55,040 --> 00:29:58,560
Speaker 1: in predicting over the short run revenues. You've got to

541
00:29:58,560 --> 00:30:00,680
Speaker 1: have a lot of infrastructure, you gotta know all the locations.

542
00:30:01,000 --> 00:30:03,560
Speaker 1: You've got to be able to aggregate that in quasi

543
00:30:03,640 --> 00:30:08,920
Speaker 1: real time, and satellite coverage at high resolution at a

544
00:30:09,400 --> 00:30:13,720
Speaker 1: quick successive short time intervals is still expensive. UM. So

545
00:30:13,720 --> 00:30:15,280
Speaker 1: you've got to figure out is it worth it to

546
00:30:15,320 --> 00:30:17,640
Speaker 1: your process to do that. If you only one percent

547
00:30:17,640 --> 00:30:20,880
Speaker 1: of the portfolio have that you have invested in retailers,

548
00:30:21,640 --> 00:30:24,080
Speaker 1: maybe it's not really going to move the needle that much, right,

549
00:30:25,080 --> 00:30:27,920
Speaker 1: So so let's talk about some other things that don't

550
00:30:27,920 --> 00:30:33,080
Speaker 1: involve satellites. This has been a let's call it a

551
00:30:33,280 --> 00:30:37,160
Speaker 1: typical UM political environment for the past couple of years,

552
00:30:37,200 --> 00:30:42,520
Speaker 1: not just the Trump presidency, but Brexit and the financial

553
00:30:42,600 --> 00:30:45,600
Speaker 1: crisis and the rise of the tea party. How does

554
00:30:45,640 --> 00:30:51,440
Speaker 1: a quant shop manage those sorts of non market inputs

555
00:30:51,640 --> 00:30:53,920
Speaker 1: or does it all just come out in the wash

556
00:30:54,040 --> 00:30:56,920
Speaker 1: and it's not really all that important. There's there's two

557
00:30:57,000 --> 00:30:59,560
Speaker 1: sides of this. One side is what I call risk management.

558
00:30:59,680 --> 00:31:02,880
Speaker 1: Right can you if you can observe some of these

559
00:31:03,000 --> 00:31:05,400
Speaker 1: risks and you don't observe them in a standard quant

560
00:31:05,480 --> 00:31:08,440
Speaker 1: risk model because they're the quant risk models are typically

561
00:31:08,480 --> 00:31:11,240
Speaker 1: backward looking. They're not forward looking. So you've got to,

562
00:31:11,400 --> 00:31:14,480
Speaker 1: as a professional investor, think, what are some of these

563
00:31:14,560 --> 00:31:17,080
Speaker 1: risks that maybe aren't priced into the risk models that

564
00:31:17,120 --> 00:31:20,240
Speaker 1: are looking at the historical data, but that could impact

565
00:31:20,240 --> 00:31:23,080
Speaker 1: the portfolio UM and let me give you an example

566
00:31:23,080 --> 00:31:27,560
Speaker 1: of such a risk. One one risk is UM. We

567
00:31:27,560 --> 00:31:30,760
Speaker 1: we managed one strategy that's a low volatility equity strategy.

568
00:31:30,760 --> 00:31:33,000
Speaker 1: So we're trying to do there is reduce the risk

569
00:31:33,040 --> 00:31:37,320
Speaker 1: of equity markets UM capuited benchmark. Let's say in the US,

570
00:31:37,400 --> 00:31:39,880
Speaker 1: it might have a twelve or fourteen vol and we

571
00:31:39,960 --> 00:31:43,000
Speaker 1: might want to produce a ten vol for example UM

572
00:31:43,360 --> 00:31:46,640
Speaker 1: and what that means is somebody gets the same risk

573
00:31:46,680 --> 00:31:49,000
Speaker 1: returned that that they get on count a capuited benchmark,

574
00:31:49,080 --> 00:31:51,240
Speaker 1: but they get it with less risk. That's very helpful

575
00:31:51,240 --> 00:31:54,720
Speaker 1: from asta allocation perspective. When you do that today, though,

576
00:31:54,720 --> 00:31:56,440
Speaker 1: what you're doing is you're taking on a lot of

577
00:31:56,480 --> 00:31:59,760
Speaker 1: interest rate risk because these lower risk companies typically tend

578
00:31:59,760 --> 00:32:02,640
Speaker 1: to be higher dividend companies. Companies are more sensitive to

579
00:32:02,680 --> 00:32:05,280
Speaker 1: interest rates. So if you're worried about a rising interest

580
00:32:05,360 --> 00:32:09,320
Speaker 1: rate environment, your historical risk model wouldn't say, constrain your

581
00:32:09,360 --> 00:32:13,320
Speaker 1: exposure to interest rates your sensitivity interest rates. But going

582
00:32:13,440 --> 00:32:16,480
Speaker 1: forward you might want to do that um in a

583
00:32:16,520 --> 00:32:20,440
Speaker 1: low volatility portfolio so that your volatility doesn't come out

584
00:32:20,640 --> 00:32:22,479
Speaker 1: much higher than you expect or your return is much

585
00:32:22,520 --> 00:32:24,960
Speaker 1: lower than you expect if interest rates do in fact

586
00:32:25,000 --> 00:32:29,760
Speaker 1: continue rising. So that's the risk management piece. You want

587
00:32:29,800 --> 00:32:32,280
Speaker 1: to anticipate certain risks and build that into your risk

588
00:32:32,320 --> 00:32:36,040
Speaker 1: controls that you apply to your strategies. The second pieces

589
00:32:36,240 --> 00:32:38,640
Speaker 1: can you use are there are other signals that help

590
00:32:38,680 --> 00:32:43,400
Speaker 1: you navigate from a return perspective, These kinds of you know,

591
00:32:43,480 --> 00:32:47,520
Speaker 1: macro events and uh you know, for example, volatility itself

592
00:32:47,520 --> 00:32:51,680
Speaker 1: can be an earlier warning signal. Right, every major devaluation

593
00:32:51,960 --> 00:32:56,560
Speaker 1: of currencies and emerging markets and many market breaks were

594
00:32:56,600 --> 00:33:00,320
Speaker 1: preceded by periods of rising volatility, rising by untill. He

595
00:33:00,320 --> 00:33:03,960
Speaker 1: also sometimes predicts more benign environments. But the point is,

596
00:33:04,320 --> 00:33:06,719
Speaker 1: if there's a signal there, maybe there's ways to predict

597
00:33:06,720 --> 00:33:10,000
Speaker 1: these these environments, and so are the top down part

598
00:33:10,040 --> 00:33:12,400
Speaker 1: of what we do tries to look at these macro

599
00:33:12,520 --> 00:33:16,480
Speaker 1: events um or potential macro events and figure out how

600
00:33:16,520 --> 00:33:19,480
Speaker 1: can we anticipate those and how can we position the

601
00:33:19,520 --> 00:33:23,560
Speaker 1: portfolios based on that anticipation. So you mentioned a rising

602
00:33:23,720 --> 00:33:27,719
Speaker 1: rate environment. Lots of folks have been focused on the

603
00:33:27,720 --> 00:33:31,880
Speaker 1: Federal Reserve and focused on are we gonna take a pause?

604
00:33:32,240 --> 00:33:36,440
Speaker 1: I imagine that your shop doesn't spend a whole lot

605
00:33:36,480 --> 00:33:39,600
Speaker 1: of time struggling with that that it should end up

606
00:33:39,640 --> 00:33:41,600
Speaker 1: in the data, and it's not the sort of thing

607
00:33:42,000 --> 00:33:44,560
Speaker 1: that you have to play macro tourists or or am

608
00:33:44,600 --> 00:33:46,600
Speaker 1: I giving you guys too much credit? No, you know

609
00:33:46,680 --> 00:33:48,840
Speaker 1: you're giving us just the right amount of credit here

610
00:33:48,880 --> 00:33:52,040
Speaker 1: the very the you need to play the game that

611
00:33:52,080 --> 00:33:54,840
Speaker 1: you're good at um and so we don't want to

612
00:33:54,880 --> 00:33:57,080
Speaker 1: do we don't want to try to do things that

613
00:33:57,120 --> 00:33:59,680
Speaker 1: we've got other people who are much better at it

614
00:33:59,680 --> 00:34:04,120
Speaker 1: than we are and predicting UM rates using it the

615
00:34:04,160 --> 00:34:06,320
Speaker 1: FED what what's the FED going to do? That's not

616
00:34:06,360 --> 00:34:08,560
Speaker 1: all as a quant manager, that's not really what we're

617
00:34:08,600 --> 00:34:11,000
Speaker 1: good at, right, So you're absolutely right there. What we

618
00:34:11,040 --> 00:34:12,640
Speaker 1: would do is we would say, let's just look at

619
00:34:12,920 --> 00:34:16,000
Speaker 1: what's happening with you know, the short term rates, long

620
00:34:16,080 --> 00:34:18,239
Speaker 1: term rates, what's happening with the yield curve. Those can

621
00:34:18,280 --> 00:34:20,560
Speaker 1: be signals that we use in a model, but we're

622
00:34:20,560 --> 00:34:23,720
Speaker 1: not trying to really forecast the direction of interest rates

623
00:34:24,040 --> 00:34:27,200
Speaker 1: per se through FED statements or through other kinds of

624
00:34:27,680 --> 00:34:31,040
Speaker 1: actions like that. Um, it just means trying to do

625
00:34:31,239 --> 00:34:33,719
Speaker 1: what what where we think our edges and really trying

626
00:34:33,719 --> 00:34:35,840
Speaker 1: to focus on that in terms of the things we

627
00:34:35,880 --> 00:34:38,640
Speaker 1: actively do in the portfolio. So you mentioned your models.

628
00:34:38,800 --> 00:34:42,040
Speaker 1: When I was perusing the various offerings you have for

629
00:34:42,120 --> 00:34:47,520
Speaker 1: institutional clients, there are thirty something different models, maybe even more.

630
00:34:48,160 --> 00:34:50,960
Speaker 1: How do you develop different ideas? How do you express

631
00:34:51,040 --> 00:34:55,920
Speaker 1: them in a portfolio? Is it strictly math or there

632
00:34:55,960 --> 00:34:59,160
Speaker 1: are other guiding principles that affect that. The first first

633
00:34:59,160 --> 00:35:02,000
Speaker 1: step is always is there a you think it as

634
00:35:02,040 --> 00:35:06,480
Speaker 1: a story, it's really hypothesis of why a particular characteristics

635
00:35:06,520 --> 00:35:08,440
Speaker 1: related to return? Why is it? How can it be used?

636
00:35:08,480 --> 00:35:11,200
Speaker 1: To predict returns. What is it? What's the inefficiency that

637
00:35:11,239 --> 00:35:15,759
Speaker 1: we're capturing? And if we have that, then the next

638
00:35:15,760 --> 00:35:18,279
Speaker 1: step is, okay, now let's spend some time looking at

639
00:35:18,280 --> 00:35:21,000
Speaker 1: the data and figuring out how do we best um

640
00:35:21,440 --> 00:35:23,520
Speaker 1: create how do we best capture that inefficiency, how do

641
00:35:23,560 --> 00:35:26,960
Speaker 1: we best measure it? Uh, So, you know, we might

642
00:35:26,960 --> 00:35:30,480
Speaker 1: have an efficiency related to momentum um. And back in

643
00:35:30,760 --> 00:35:32,719
Speaker 1: the seven back in the eighties, you know, you had

644
00:35:32,719 --> 00:35:36,959
Speaker 1: some papers about price momentum and they basically said, Okay,

645
00:35:37,000 --> 00:35:39,640
Speaker 1: the best way to capture price momentum at the time

646
00:35:40,320 --> 00:35:44,360
Speaker 1: is uh, sort of a twelve month trailing risk adjusted

647
00:35:44,600 --> 00:35:48,680
Speaker 1: return price return. That's your best momentum measure. Since then,

648
00:35:49,000 --> 00:35:50,839
Speaker 1: a lot of things have changed. We've got a lot

649
00:35:50,880 --> 00:35:53,520
Speaker 1: better understanding what drives momentum, you know, what's what are

650
00:35:53,520 --> 00:35:57,560
Speaker 1: the inefficiencies we're capturing with it, and a lot more

651
00:35:57,600 --> 00:36:01,320
Speaker 1: ability to um turn that into different kinds of signals.

652
00:36:01,960 --> 00:36:04,560
Speaker 1: And today, in addition, we've got machine learning, so we

653
00:36:04,600 --> 00:36:07,200
Speaker 1: can put in all the historical prices and say, okay,

654
00:36:07,400 --> 00:36:11,320
Speaker 1: machine learning algorithm, what do you think the best predictor

655
00:36:11,520 --> 00:36:15,719
Speaker 1: of return is based on past price moves? And when

656
00:36:15,760 --> 00:36:18,080
Speaker 1: you do that, you have to be careful because machine

657
00:36:18,160 --> 00:36:20,760
Speaker 1: learning is one way to do what's called overfitting a problem,

658
00:36:20,880 --> 00:36:22,400
Speaker 1: you know, where you you have a great solution of

659
00:36:22,440 --> 00:36:24,839
Speaker 1: the past, but it doesn't work in the future. UM.

660
00:36:24,920 --> 00:36:28,680
Speaker 1: One of my colleagues, Michael bat Nick, once observed, the

661
00:36:28,760 --> 00:36:31,879
Speaker 1: best track record of any model is the last ten

662
00:36:32,000 --> 00:36:35,000
Speaker 1: years something something to that effect. Does that sound about

663
00:36:35,400 --> 00:36:40,279
Speaker 1: So every model, you know, every model implicitly has some

664
00:36:40,360 --> 00:36:43,719
Speaker 1: potential for some degree of overfitting associated with it. We

665
00:36:43,760 --> 00:36:46,040
Speaker 1: try to guard against that. We have various, you know,

666
00:36:46,080 --> 00:36:50,160
Speaker 1: statistical procedures that we follow, UM in various research procedures

667
00:36:50,160 --> 00:36:52,040
Speaker 1: we followed to try to avoid that, but it does

668
00:36:52,120 --> 00:36:55,800
Speaker 1: creep in no question about that. We have been speaking

669
00:36:55,800 --> 00:36:59,880
Speaker 1: with John Chisholm. He is the co CEO and former

670
00:37:00,040 --> 00:37:04,919
Speaker 1: Chief Investment Officer for Acadian Asset Management. If you enjoy

671
00:37:05,000 --> 00:37:07,480
Speaker 1: this conversation, we'll be sure and come back and check

672
00:37:07,520 --> 00:37:10,040
Speaker 1: out the podcast extras, where we keep the tape rolling

673
00:37:10,320 --> 00:37:14,080
Speaker 1: and continue to discuss all things quant You can find

674
00:37:14,120 --> 00:37:19,440
Speaker 1: that at iTunes, overcast, Stitcher, Bloomberg dot com, wherever final

675
00:37:19,520 --> 00:37:23,520
Speaker 1: podcasts are sold. We love your comments, feedback and suggestions

676
00:37:23,800 --> 00:37:27,520
Speaker 1: right to us at m IB podcast at Bloomberg dot net.

677
00:37:28,000 --> 00:37:30,560
Speaker 1: You can check out my daily column on Bloomberg dot

678
00:37:30,560 --> 00:37:34,000
Speaker 1: com slash Opinion. Follow me on Twitter at rit Halts.

679
00:37:34,440 --> 00:37:37,360
Speaker 1: I'm Barry Ri Halts. You're listening to Masters in Business

680
00:37:37,520 --> 00:37:53,400
Speaker 1: on Bloomberg Radio. Welcome to the podcast. John, Thank you

681
00:37:53,440 --> 00:37:55,200
Speaker 1: so much for doing this. I've been looking forward to

682
00:37:55,239 --> 00:37:58,600
Speaker 1: this for a while. We were having a conversation in

683
00:37:58,680 --> 00:38:01,320
Speaker 1: my office and on the way out the door, someone

684
00:38:01,360 --> 00:38:05,279
Speaker 1: said who you who you say interviewing today? I said, oh,

685
00:38:05,360 --> 00:38:09,480
Speaker 1: John Chiselm of a Kadian asset management and the person said, oh,

686
00:38:09,480 --> 00:38:12,400
Speaker 1: I've never heard of them. Uh do they manage any money?

687
00:38:12,440 --> 00:38:15,279
Speaker 1: And my auntswer was spitting distance from a hundred billion

688
00:38:15,360 --> 00:38:21,440
Speaker 1: dollars and that sort of shock some people. How do you, um,

689
00:38:21,520 --> 00:38:24,200
Speaker 1: how do you feel about being a little below the

690
00:38:24,320 --> 00:38:26,640
Speaker 1: radar and why are you sort of poking your head

691
00:38:26,640 --> 00:38:29,600
Speaker 1: out from from below the radar? So in general, it's

692
00:38:29,719 --> 00:38:31,439
Speaker 1: we think it's good to run a little bit below

693
00:38:31,480 --> 00:38:34,640
Speaker 1: the radar. Right. There's there's elements of First of all,

694
00:38:34,640 --> 00:38:36,720
Speaker 1: you can only manage so much money and still add value.

695
00:38:37,040 --> 00:38:40,600
Speaker 1: You just have to be careful managing capacity. Um. And

696
00:38:41,239 --> 00:38:45,560
Speaker 1: we also if you're a big name in the industry, uh,

697
00:38:45,760 --> 00:38:48,840
Speaker 1: you get more press attention. That's in one way that's good,

698
00:38:49,200 --> 00:38:51,640
Speaker 1: but in another way it can also be detrimental depending

699
00:38:51,640 --> 00:38:54,239
Speaker 1: on what's the type of attention. UM and a lot

700
00:38:54,239 --> 00:38:57,880
Speaker 1: of investors, a lot of institutions especially, want managers that

701
00:38:57,920 --> 00:39:01,240
Speaker 1: are very careful to focus us on maintaining their ability

702
00:39:01,239 --> 00:39:03,960
Speaker 1: to add value for clients by not getting too big. Right.

703
00:39:03,960 --> 00:39:06,360
Speaker 1: We all know managers that have grown and grown and

704
00:39:06,400 --> 00:39:08,800
Speaker 1: then at some point they just couldn't add value anymore.

705
00:39:08,800 --> 00:39:10,840
Speaker 1: They just got too big to happen. Then they shrink

706
00:39:10,840 --> 00:39:14,880
Speaker 1: and shrink, as we've seen with a number of famous

707
00:39:14,880 --> 00:39:18,080
Speaker 1: hedge fund managers the past decade or so. Exactly. We

708
00:39:18,080 --> 00:39:20,399
Speaker 1: we just like to be maybe a little bit um

709
00:39:20,640 --> 00:39:22,640
Speaker 1: less volatile in terms of our business than that. And

710
00:39:22,640 --> 00:39:25,000
Speaker 1: it's just best. It's best for our team, it's best

711
00:39:25,040 --> 00:39:27,799
Speaker 1: for our clients. UM. And those are really the key

712
00:39:27,840 --> 00:39:31,480
Speaker 1: considerations typically. Now sticking the head out part is it

713
00:39:31,560 --> 00:39:33,600
Speaker 1: is important, I think to have some degree in name

714
00:39:33,640 --> 00:39:38,480
Speaker 1: recognition because a we want talented people, and if your

715
00:39:38,760 --> 00:39:41,759
Speaker 1: potential employees don't know you are who you are, then

716
00:39:41,840 --> 00:39:46,479
Speaker 1: you may not be their first place of employment of choice. UM.

717
00:39:46,560 --> 00:39:48,520
Speaker 1: When there's an opportunity that might be a great fit

718
00:39:48,560 --> 00:39:51,719
Speaker 1: for them. So there's an element of that, and also

719
00:39:51,800 --> 00:39:54,000
Speaker 1: we're doing a number of new things that we haven't

720
00:39:54,040 --> 00:39:57,320
Speaker 1: been doing before. One of them is we've built a

721
00:39:57,440 --> 00:40:01,440
Speaker 1: multi asset strategy. So historically we've been primarily an equity firm.

722
00:40:01,760 --> 00:40:04,480
Speaker 1: We have a multi as strategy today that has about

723
00:40:04,520 --> 00:40:07,520
Speaker 1: a little over a year live track record. It's done

724
00:40:07,640 --> 00:40:11,000
Speaker 1: very well relative to many of its peers. It's a

725
00:40:11,080 --> 00:40:14,400
Speaker 1: very quantitative approach, is very consistent with our philosophy, but

726
00:40:14,480 --> 00:40:20,160
Speaker 1: it invests in equities, fixed income, currency, commodities, and options,

727
00:40:20,680 --> 00:40:24,080
Speaker 1: and the goal there is to create a income return

728
00:40:24,160 --> 00:40:26,319
Speaker 1: stream that's much more stable than what you get from

729
00:40:26,360 --> 00:40:29,000
Speaker 1: an equity market beta. You know, that doesn't go up

730
00:40:29,000 --> 00:40:31,000
Speaker 1: and down every time the market equity markets go up

731
00:40:31,000 --> 00:40:35,480
Speaker 1: and down, but that provides a fairly consistent typically, for example,

732
00:40:35,520 --> 00:40:39,480
Speaker 1: one version of strategy cash plus five return. So not

733
00:40:39,600 --> 00:40:42,920
Speaker 1: quite risk parity, but it's not risk parity because we're

734
00:40:42,920 --> 00:40:46,440
Speaker 1: not necessarily investing in equal risk proportions. It's it's really

735
00:40:46,719 --> 00:40:49,200
Speaker 1: you can think of it more as it's related to

736
00:40:49,239 --> 00:40:52,560
Speaker 1: this concept that there's certain efficiencies that operate not just

737
00:40:52,600 --> 00:40:56,759
Speaker 1: inequities but also in in other asset classes um but

738
00:40:56,800 --> 00:40:59,760
Speaker 1: it also relates to specific expertise in these other asset

739
00:40:59,760 --> 00:41:03,920
Speaker 1: class is that there's individual drivers saying commodities UM that

740
00:41:04,000 --> 00:41:07,279
Speaker 1: are fairly unique there and there you can capture them

741
00:41:07,280 --> 00:41:10,560
Speaker 1: through these return models and in turn gets some significant

742
00:41:10,640 --> 00:41:13,480
Speaker 1: value added from that area, which you don't get in

743
00:41:13,560 --> 00:41:16,440
Speaker 1: a lot of these so called alternative risk premium strategies.

744
00:41:16,840 --> 00:41:21,200
Speaker 1: So you mentioned capacity, you're at eight six billion. How

745
00:41:21,280 --> 00:41:24,920
Speaker 1: much more capacity is there? You are you in broad

746
00:41:25,080 --> 00:41:29,040
Speaker 1: areas and equities and countries that have a lot of

747
00:41:29,080 --> 00:41:31,439
Speaker 1: a lot more headroom or do you do you see

748
00:41:31,480 --> 00:41:34,279
Speaker 1: limitations not too far down the road. It varies. So

749
00:41:34,600 --> 00:41:38,880
Speaker 1: the emerging markets, for example, strategies is closed new clients

750
00:41:38,960 --> 00:41:41,759
Speaker 1: when if we if a client withdraw some money, will

751
00:41:41,960 --> 00:41:45,320
Speaker 1: add some money for any existing client more capability to invest,

752
00:41:45,800 --> 00:41:49,880
Speaker 1: but we're closed their frontier markets is closed. Emerging markets

753
00:41:49,920 --> 00:41:53,480
Speaker 1: small cap is closed. Are non US small cap again

754
00:41:53,640 --> 00:41:57,920
Speaker 1: subject to some reallocation when there's flows out UM is

755
00:41:57,960 --> 00:42:01,719
Speaker 1: also closed UM. But we have capacity in areas like

756
00:42:01,760 --> 00:42:06,440
Speaker 1: global like our manage of volatility strategies, this multi asset strategy.

757
00:42:06,640 --> 00:42:08,200
Speaker 1: So we what we do is we have a very

758
00:42:08,239 --> 00:42:10,960
Speaker 1: specific process to measure how much money can we invest

759
00:42:11,239 --> 00:42:14,359
Speaker 1: and still meet our investment objective in each strategy, and

760
00:42:14,400 --> 00:42:17,000
Speaker 1: when we hit that number, we close the strategy. If

761
00:42:17,040 --> 00:42:19,239
Speaker 1: we've got headroom, we tell the clients here's how much

762
00:42:19,239 --> 00:42:21,480
Speaker 1: headroom we have. There's how much we expect to be

763
00:42:21,520 --> 00:42:23,760
Speaker 1: able to add before we have to close the strategy.

764
00:42:24,040 --> 00:42:28,240
Speaker 1: And I've seen, um, some of your long short UH

765
00:42:28,360 --> 00:42:32,359
Speaker 1: portfolios are one ninety over thirty or something like that.

766
00:42:32,719 --> 00:42:34,879
Speaker 1: Am I? Am I getting that more or less right? Yeah,

767
00:42:34,880 --> 00:42:36,640
Speaker 1: we have a variety. We have some that are pure

768
00:42:37,120 --> 00:42:40,480
Speaker 1: um market neutral, so they're equal sides long short. We

769
00:42:40,520 --> 00:42:45,080
Speaker 1: have some that are one thirty thirty so d long short,

770
00:42:45,680 --> 00:42:48,520
Speaker 1: and then we have some other variations as well. We

771
00:42:48,600 --> 00:42:51,239
Speaker 1: have a we call it diverse fight Alpha strategy that's

772
00:42:51,239 --> 00:42:55,719
Speaker 1: a slightly different ratio as well. But essentially, all these

773
00:42:55,760 --> 00:42:59,400
Speaker 1: strategies the ideas take advantage of the inefficiencies on the

774
00:42:59,400 --> 00:43:03,200
Speaker 1: short side and the unattractive companies that we follow. And alright,

775
00:43:03,200 --> 00:43:06,640
Speaker 1: so one thirty thirty is the long short as opposed

776
00:43:06,680 --> 00:43:13,760
Speaker 1: to fully market neutral, which is its leveraged. So effectively,

777
00:43:13,760 --> 00:43:16,360
Speaker 1: we have a global leverage market neutral strategy that's about

778
00:43:16,640 --> 00:43:22,839
Speaker 1: two long short. Quite quite interesting. Um, let me go

779
00:43:22,960 --> 00:43:26,000
Speaker 1: through some of the questions we didn't get at get

780
00:43:26,040 --> 00:43:30,240
Speaker 1: to during the broadcast portion before I get to my

781
00:43:30,400 --> 00:43:34,120
Speaker 1: favorite questions, UM, And there was one that I thought

782
00:43:34,200 --> 00:43:36,600
Speaker 1: was kind of interesting, and I pulled this off of

783
00:43:37,280 --> 00:43:41,440
Speaker 1: either your website or something you had written. Quote documented

784
00:43:41,719 --> 00:43:48,160
Speaker 1: recurring behavioral errors drive irrational actions in financial markets, behaviors

785
00:43:48,160 --> 00:43:53,120
Speaker 1: that are often contrary to investors best interests. How does

786
00:43:53,160 --> 00:43:56,879
Speaker 1: your firm uh use your understanding of this to help

787
00:43:56,920 --> 00:44:00,880
Speaker 1: manage money? So this goes back to how do we

788
00:44:01,600 --> 00:44:04,760
Speaker 1: come up with these signals? So, for example, one behavior

789
00:44:04,880 --> 00:44:08,400
Speaker 1: Lewer is investors typically are overconfident in their ability to

790
00:44:08,400 --> 00:44:12,720
Speaker 1: predict future growth rates. So if you're buying growth stocks

791
00:44:12,719 --> 00:44:15,640
Speaker 1: in the tech bubble, UH, and you're looking at companies

792
00:44:15,680 --> 00:44:20,040
Speaker 1: that are growing their earnings at or more a year

793
00:44:20,080 --> 00:44:24,600
Speaker 1: or higher UM, those companies were trading in some cases

794
00:44:24,600 --> 00:44:28,000
Speaker 1: at multiples north of hundred on earnings on current earnings.

795
00:44:28,680 --> 00:44:31,840
Speaker 1: And if those companies had continued growing their earnings at

796
00:44:31,880 --> 00:44:35,840
Speaker 1: those very high rates for twenty years, that would have

797
00:44:35,840 --> 00:44:39,320
Speaker 1: been a reasonable price to pay. What happened is investors

798
00:44:39,320 --> 00:44:41,880
Speaker 1: didn't realize that, yeah, they can grow their earnings at

799
00:44:41,920 --> 00:44:44,920
Speaker 1: that rate maybe for one year, three years, four years,

800
00:44:45,360 --> 00:44:48,719
Speaker 1: it's very hard to do that for twenty years UM,

801
00:44:48,840 --> 00:44:52,279
Speaker 1: and so that overconfidence, I think is one of the

802
00:44:52,320 --> 00:44:54,480
Speaker 1: key drivers of why you see in the law term

803
00:44:54,640 --> 00:44:58,719
Speaker 1: value UM working effectively. What's happened the last ten years

804
00:44:58,719 --> 00:45:01,799
Speaker 1: that's interesting is two things. One is that actually there

805
00:45:01,840 --> 00:45:04,200
Speaker 1: were some companies that actually did grow their earnings for

806
00:45:04,400 --> 00:45:06,960
Speaker 1: at really high rates for a long time. So typically

807
00:45:06,960 --> 00:45:10,640
Speaker 1: people think of the Internet services, you know, the Googles

808
00:45:10,640 --> 00:45:14,200
Speaker 1: and Amazons and so on. Those companies have been tremendously

809
00:45:14,239 --> 00:45:17,200
Speaker 1: successful for a while. Albeit you're starting to see a

810
00:45:17,200 --> 00:45:20,200
Speaker 1: few cracks in those growth rates now for some of

811
00:45:20,200 --> 00:45:22,799
Speaker 1: these some of these companies. And the other thing that's

812
00:45:22,840 --> 00:45:27,960
Speaker 1: happened is just a general repricing within valuation. So UM

813
00:45:28,120 --> 00:45:31,319
Speaker 1: you had a certain level of dispersion where value was

814
00:45:31,360 --> 00:45:35,160
Speaker 1: so successful from say two thousand one to two thousand seven,

815
00:45:35,840 --> 00:45:40,880
Speaker 1: that the dispersion of valuation multiple shrank and as a result,

816
00:45:41,320 --> 00:45:43,480
Speaker 1: you didn't the expensive companies weren't really that much more

817
00:45:43,480 --> 00:45:47,759
Speaker 1: expensive then the slower growing and expensive companies, and that

818
00:45:47,800 --> 00:45:51,320
Speaker 1: gave a little bit of tail wind growth over that period.

819
00:45:51,640 --> 00:45:54,439
Speaker 1: I think we've pretty much worked off all that, all

820
00:45:54,480 --> 00:45:58,680
Speaker 1: that dispersion UH, or rather the tightening of dispersion, so

821
00:45:58,719 --> 00:46:02,120
Speaker 1: we're back to more normal level dispersion now, so at

822
00:46:02,120 --> 00:46:04,879
Speaker 1: a kadie and we'd expect going forward that you're more

823
00:46:05,000 --> 00:46:08,239
Speaker 1: likely to have at some point soon whether soon as

824
00:46:08,280 --> 00:46:11,040
Speaker 1: you know, next month, next year, but not not in

825
00:46:11,080 --> 00:46:13,760
Speaker 1: six or seven years. Sometimes sooner than that. We expect

826
00:46:13,760 --> 00:46:17,319
Speaker 1: to see value reassert itself, and so we continue to

827
00:46:17,520 --> 00:46:21,280
Speaker 1: have some component of our of our factors focused on valuation.

828
00:46:21,560 --> 00:46:28,160
Speaker 1: So q FO fair to say that was value reasserting itself. Yeah,

829
00:46:28,239 --> 00:46:32,120
Speaker 1: I think actually a Q four was just for us

830
00:46:32,160 --> 00:46:35,080
Speaker 1: for a Kadian particular or not a great quarter um

831
00:46:35,160 --> 00:46:38,720
Speaker 1: And it was partly that actually value in some markets

832
00:46:38,760 --> 00:46:42,239
Speaker 1: didn't pay off well, but it was also partly um uh.

833
00:46:42,440 --> 00:46:45,560
Speaker 1: Smaller companies in general, especially in the US and emerging

834
00:46:45,600 --> 00:46:49,960
Speaker 1: markets did poorly the relative larger companies. And we have

835
00:46:50,120 --> 00:46:54,120
Speaker 1: in our portfolios a fair amount of exposure too smaller

836
00:46:54,120 --> 00:46:56,919
Speaker 1: and medium sized companies because typically that's where we see

837
00:46:56,920 --> 00:47:00,600
Speaker 1: the general inefficiencies any kind of fact or we see

838
00:47:00,600 --> 00:47:03,359
Speaker 1: those as being greater in that area than they are

839
00:47:03,360 --> 00:47:05,880
Speaker 1: in the very large cap companies. So what hurt us

840
00:47:05,880 --> 00:47:08,640
Speaker 1: in the fourth quarter a little bit of value, but

841
00:47:08,719 --> 00:47:13,000
Speaker 1: primarily just the risk of small versus large, biting us

842
00:47:13,800 --> 00:47:17,600
Speaker 1: you hinted earlier at E T F s UM sometimes

843
00:47:17,600 --> 00:47:23,400
Speaker 1: being less efficient than other ways of expressing the same strategies. However,

844
00:47:23,440 --> 00:47:25,800
Speaker 1: go back a decade or two and there were certain

845
00:47:25,800 --> 00:47:30,600
Speaker 1: strategies that you can only get through expensive alternative investments.

846
00:47:30,640 --> 00:47:33,640
Speaker 1: You're paying two and twenty for certain strategies that you

847
00:47:33,680 --> 00:47:36,759
Speaker 1: can now pay on a fifty basis points and an

848
00:47:36,760 --> 00:47:40,440
Speaker 1: eight dollar transaction fee. So what do you make of

849
00:47:40,480 --> 00:47:45,160
Speaker 1: this landscape and what does this mean for quantitative strategies

850
00:47:45,200 --> 00:47:48,800
Speaker 1: eventually migrating to some of these low cost products. I

851
00:47:49,400 --> 00:47:51,520
Speaker 1: think that we've seen that trend and there's a good

852
00:47:51,560 --> 00:47:54,840
Speaker 1: reason for it. Right. Investors should be looking for what's

853
00:47:54,880 --> 00:47:57,120
Speaker 1: the if I want to get a certain return and

854
00:47:57,160 --> 00:47:59,480
Speaker 1: risk stream, what's the least expensive way for me to

855
00:47:59,520 --> 00:48:02,359
Speaker 1: do that? And it's been great for investors the fact

856
00:48:02,400 --> 00:48:06,480
Speaker 1: that there has been pricing pressure on the esset management

857
00:48:06,520 --> 00:48:09,839
Speaker 1: side of the business. That's actually a great thing for investors, right.

858
00:48:09,920 --> 00:48:12,520
Speaker 1: It forces the investment managers to be more efficient, It

859
00:48:12,640 --> 00:48:16,399
Speaker 1: pushes the overpriced products away from you know, it makes

860
00:48:16,400 --> 00:48:19,960
Speaker 1: them less viable, and it allows strategies that can be

861
00:48:20,040 --> 00:48:24,120
Speaker 1: run inexpensively. But still provide value to do well in

862
00:48:24,120 --> 00:48:27,960
Speaker 1: the marketplace. So great for investors, tougher for asset managers.

863
00:48:28,080 --> 00:48:29,720
Speaker 1: Is not as easy to make money now as asset

864
00:48:29,760 --> 00:48:32,080
Speaker 1: manager as it was, you know, ten or fifteen years ago.

865
00:48:32,120 --> 00:48:35,960
Speaker 1: We've seen margins for the esset management business gets squeezed

866
00:48:36,000 --> 00:48:38,960
Speaker 1: a little bit over the last few years, no, no

867
00:48:39,000 --> 00:48:41,880
Speaker 1: doubt about that. What as long as we're talking about

868
00:48:42,120 --> 00:48:45,520
Speaker 1: UM indexes and ETFs and price squeezes, what do you

869
00:48:45,560 --> 00:48:49,520
Speaker 1: make of the argument that some of this movement away

870
00:48:49,520 --> 00:48:54,399
Speaker 1: from active management into passive is distorting prices. I don't

871
00:48:54,400 --> 00:48:58,000
Speaker 1: think we're there yet, UM, in terms I do believe. Look,

872
00:48:58,000 --> 00:49:00,400
Speaker 1: there's a value to price discovery. If you had a

873
00:49:00,440 --> 00:49:04,520
Speaker 1: hundred percent of of every know, all assets were pastively managed, UM,

874
00:49:04,560 --> 00:49:06,920
Speaker 1: you wouldn't have a mechanism of price discovery. But you

875
00:49:06,920 --> 00:49:10,120
Speaker 1: don't need you don't need, you know, as assets active

876
00:49:10,120 --> 00:49:13,000
Speaker 1: management to get the price discovery process to work. UM.

877
00:49:13,040 --> 00:49:16,160
Speaker 1: I think there's been various academic work on this. Andrew

878
00:49:16,200 --> 00:49:19,440
Speaker 1: Low right in your backyard, and and there's folks at

879
00:49:19,440 --> 00:49:23,040
Speaker 1: Harvard and and generally they what they come up with

880
00:49:23,760 --> 00:49:27,440
Speaker 1: is that you can have a greater level of passive

881
00:49:27,440 --> 00:49:30,680
Speaker 1: management than we have today and still get the you know,

882
00:49:30,719 --> 00:49:34,520
Speaker 1: the social benefits if you will, of the price discovery process.

883
00:49:35,080 --> 00:49:37,239
Speaker 1: Quite quite interesting. I know I only have you for

884
00:49:37,320 --> 00:49:40,040
Speaker 1: a limited amount of time. Let me jump to some

885
00:49:40,120 --> 00:49:43,279
Speaker 1: of my favorite questions we ask all our guests. UM,

886
00:49:43,400 --> 00:49:47,040
Speaker 1: tell us the most important thing that people don't know about,

887
00:49:47,120 --> 00:49:53,920
Speaker 1: John Chisholm. Wow. Um, that's a tough one. And you

888
00:49:53,920 --> 00:49:56,759
Speaker 1: know it's funny because I know you gave me the

889
00:49:56,840 --> 00:49:59,520
Speaker 1: questions in advance. So that's that's the one where I

890
00:49:59,560 --> 00:50:00,960
Speaker 1: looked at and I was like, I don't know if

891
00:50:00,960 --> 00:50:02,840
Speaker 1: I have anything there, and I skipped it. So I

892
00:50:02,880 --> 00:50:05,520
Speaker 1: did not pre didn't think. I did not come up

893
00:50:05,560 --> 00:50:08,799
Speaker 1: with an answer to that that particular question. UM. I

894
00:50:08,800 --> 00:50:12,560
Speaker 1: would say a couple of things. One is, um, I

895
00:50:12,640 --> 00:50:15,480
Speaker 1: love I love asset management. That's probably I love investing.

896
00:50:15,719 --> 00:50:18,880
Speaker 1: That's probably not a that's something that's some of the

897
00:50:18,920 --> 00:50:21,640
Speaker 1: people who work with me know pretty well. But it

898
00:50:21,719 --> 00:50:24,160
Speaker 1: may be something that you know, the you're listening audience

899
00:50:24,200 --> 00:50:28,160
Speaker 1: maybe doesn't appreciate as much. Uh. And um. The other

900
00:50:28,200 --> 00:50:30,640
Speaker 1: thing that maybe it's something that's maybe not really directly

901
00:50:30,680 --> 00:50:35,920
Speaker 1: work related is um. Uh. Two things in terms of

902
00:50:35,960 --> 00:50:39,040
Speaker 1: leisure activities, I love ultimate frisbee. Ultimate frisbee is a

903
00:50:39,040 --> 00:50:40,359
Speaker 1: great sport. I don't know if you know what it is.

904
00:50:40,480 --> 00:50:42,919
Speaker 1: Of course I know. I went to college at stony Brook.

905
00:50:43,440 --> 00:50:46,960
Speaker 1: Ultimate frisbie was a huge thing on campus back in

906
00:50:47,080 --> 00:50:50,959
Speaker 1: the nineteen hundreds when I went to school, so so same.

907
00:50:51,080 --> 00:50:53,880
Speaker 1: You know, I actually went to high school at Bronx

908
00:50:53,880 --> 00:50:58,080
Speaker 1: Science and the Bronx here, and uh it was it wasn't.

909
00:50:58,120 --> 00:50:59,480
Speaker 1: I was not the Ultimate team, but that's where I

910
00:50:59,480 --> 00:51:01,200
Speaker 1: started playing with some of the guys on the team.

911
00:51:01,239 --> 00:51:02,959
Speaker 1: And then I played a little bit in college, played

912
00:51:02,960 --> 00:51:06,040
Speaker 1: after college, and you know, now there's a over in

913
00:51:06,080 --> 00:51:08,120
Speaker 1: Boston area. There's no over forty league. I still get

914
00:51:08,120 --> 00:51:09,520
Speaker 1: a chance to go out and play every now and then.

915
00:51:10,160 --> 00:51:13,839
Speaker 1: It's not a contact sports, so you know, it's not

916
00:51:13,920 --> 00:51:16,799
Speaker 1: like rugby exactly. That's that's the beauty of it, I think,

917
00:51:16,880 --> 00:51:18,920
Speaker 1: is that you get a great exercise. It's a lot

918
00:51:18,960 --> 00:51:22,120
Speaker 1: of fun, it's very social and um and you don't

919
00:51:22,239 --> 00:51:24,960
Speaker 1: kill yourself. It's not like I played basketball typically once

920
00:51:24,960 --> 00:51:29,240
Speaker 1: a week as well. And I'll tell you after alsop

921
00:51:29,280 --> 00:51:31,520
Speaker 1: and left and right, I'm limping, you know, for like

922
00:51:31,560 --> 00:51:33,359
Speaker 1: three or four days, so I can start walking well

923
00:51:33,400 --> 00:51:36,080
Speaker 1: again and that does not happen after ultimate that. That's

924
00:51:36,200 --> 00:51:39,160
Speaker 1: very funny. So the next question was a question I

925
00:51:39,239 --> 00:51:41,799
Speaker 1: used to use as a throwaway to just do a

926
00:51:41,840 --> 00:51:45,520
Speaker 1: mic check, but the answers have been so interesting I

927
00:51:45,560 --> 00:51:49,239
Speaker 1: decided to ask it while we were recording. Tell us

928
00:51:49,560 --> 00:51:53,680
Speaker 1: what was your first car? The make, model, and year.

929
00:51:54,239 --> 00:51:58,040
Speaker 1: If I'm not a car guy, but I do remember,

930
00:51:58,080 --> 00:52:01,560
Speaker 1: it was a Mazda GLC cost about used to cost

931
00:52:01,640 --> 00:52:04,840
Speaker 1: about seven eight hundred dollars and it ran about like

932
00:52:04,880 --> 00:52:06,920
Speaker 1: it cost seven or eight hundred dollars. This is probably

933
00:52:07,480 --> 00:52:11,399
Speaker 1: eight four ish. Um. It had a nice little stick

934
00:52:11,440 --> 00:52:14,920
Speaker 1: shift in the wherever and we call those, by the

935
00:52:14,920 --> 00:52:20,040
Speaker 1: way today those are millennial anti theft devices. I like that.

936
00:52:20,120 --> 00:52:22,560
Speaker 1: I like that description. Um. And it was probably I

937
00:52:22,600 --> 00:52:24,560
Speaker 1: was probably because I had an eighty four is already used.

938
00:52:24,560 --> 00:52:26,480
Speaker 1: It's probably like a nineteen eight. I don't I don't

939
00:52:26,480 --> 00:52:28,600
Speaker 1: even know, but probably nineteen eighty or something like that

940
00:52:28,719 --> 00:52:32,080
Speaker 1: or seventy nine. That's interesting. Um, tell us about some

941
00:52:32,120 --> 00:52:35,359
Speaker 1: of your mentors who helped guide your career. Yeah, I'd

942
00:52:35,400 --> 00:52:37,400
Speaker 1: have to say there's there's really some of some of

943
00:52:37,440 --> 00:52:39,400
Speaker 1: my partners at a Katie and some of my co founders,

944
00:52:39,400 --> 00:52:43,920
Speaker 1: so Gary Brookstrom. Um, you know he Uh I started

945
00:52:44,280 --> 00:52:46,680
Speaker 1: my first part time job in asset management was working

946
00:52:46,719 --> 00:52:50,560
Speaker 1: with him, and uh so he was very important. We

947
00:52:50,560 --> 00:52:53,640
Speaker 1: were a development stage company, so there were lots of

948
00:52:54,040 --> 00:52:55,839
Speaker 1: you know, syncratic things we didn't have, like an HR

949
00:52:55,920 --> 00:52:58,480
Speaker 1: department we didn't have. But but Gary was really was

950
00:52:58,520 --> 00:53:01,799
Speaker 1: also really passionate about investing. Um. He's retired now, but

951
00:53:01,800 --> 00:53:05,600
Speaker 1: he's still invests. So I would say Gary. My other colleague,

952
00:53:05,680 --> 00:53:08,160
Speaker 1: Ron Fraser, who was a portfolio manager at Putnam before

953
00:53:08,160 --> 00:53:10,200
Speaker 1: he came to join us as one of the fourth

954
00:53:10,280 --> 00:53:14,520
Speaker 1: Health co founders. Ron is a true gentleman, an investment professional.

955
00:53:15,280 --> 00:53:19,160
Speaker 1: Taught me a lot about how to treat other people. UM,

956
00:53:19,239 --> 00:53:21,400
Speaker 1: And so I would say that would be another another

957
00:53:21,480 --> 00:53:23,319
Speaker 1: one of the folks that I learned a lot from

958
00:53:23,320 --> 00:53:26,840
Speaker 1: when I first came into the business. Quite quite intriguing. Um.

959
00:53:26,920 --> 00:53:30,480
Speaker 1: What about investors who influenced the way you approached the

960
00:53:30,520 --> 00:53:34,399
Speaker 1: world of investment? I think Ben Graham, you know, sort

961
00:53:34,400 --> 00:53:36,879
Speaker 1: of the value part of that at Principle and again

962
00:53:36,920 --> 00:53:39,680
Speaker 1: even though value hasn't been great the last ten years, UM,

963
00:53:39,760 --> 00:53:42,160
Speaker 1: just the way he thought about, Um, how do you

964
00:53:42,200 --> 00:53:44,400
Speaker 1: make an investment decision? Uh, you know, a lot of

965
00:53:44,440 --> 00:53:49,680
Speaker 1: things came from Ben Graham. Um, I'd say he's important, Um,

966
00:53:49,760 --> 00:53:52,279
Speaker 1: and then I would say there's people outside of investment,

967
00:53:52,640 --> 00:53:55,320
Speaker 1: outside invest Mary who but who have lessons for investing.

968
00:53:55,400 --> 00:53:57,600
Speaker 1: So Michael Lewis, you know when he wrote back in

969
00:53:57,640 --> 00:54:01,560
Speaker 1: the eighties, he wrote Liars Poker, and and uh, that

970
00:54:01,600 --> 00:54:04,319
Speaker 1: book actually, even though it's not technically an investing book,

971
00:54:04,320 --> 00:54:06,360
Speaker 1: it's certainly not a textbook, but has a lot of

972
00:54:06,400 --> 00:54:10,480
Speaker 1: interesting information that someone who's coming into the investment industry

973
00:54:10,520 --> 00:54:12,719
Speaker 1: for the first time, you know, it's a great book

974
00:54:12,719 --> 00:54:14,439
Speaker 1: to read, or was certainly at the time a great

975
00:54:14,480 --> 00:54:17,480
Speaker 1: book to read. So I found that that's another example

976
00:54:17,520 --> 00:54:19,279
Speaker 1: of something where you can learn a lot even though

977
00:54:19,320 --> 00:54:23,439
Speaker 1: it's not technically an investment book. Speaking of books, let's

978
00:54:23,440 --> 00:54:25,319
Speaker 1: talk about some of your favorite books. What do you

979
00:54:25,360 --> 00:54:28,040
Speaker 1: read for fun? What do you read for work? Investing?

980
00:54:28,080 --> 00:54:32,080
Speaker 1: Non investing? Fiction? Nonfiction? Yeah, so I like two days

981
00:54:32,200 --> 00:54:35,560
Speaker 1: nonfiction books can be great. Um. I mentioned Michael Lewis

982
00:54:35,719 --> 00:54:39,040
Speaker 1: Liars Poker. His new book is a book about actually

983
00:54:39,080 --> 00:54:43,640
Speaker 1: the transfer power between them you know it. Okay, So

984
00:54:43,880 --> 00:54:47,239
Speaker 1: I've read that and again that's the stories there. It's

985
00:54:47,239 --> 00:54:50,319
Speaker 1: just it's interesting because it's his way. He has this

986
00:54:50,440 --> 00:54:52,399
Speaker 1: way of getting into you know, sort of getting into

987
00:54:52,440 --> 00:54:55,359
Speaker 1: the detail situation learning it's enough about the milieu and

988
00:54:55,640 --> 00:54:58,880
Speaker 1: talking to enough people, and then it's both humorous and

989
00:54:58,960 --> 00:55:01,680
Speaker 1: as you say, har flank, but it's it's educational too.

990
00:55:01,760 --> 00:55:03,560
Speaker 1: You know, you've learn a lot. So so that that

991
00:55:03,560 --> 00:55:06,399
Speaker 1: would be an example of a nonfiction type of nonfiction book.

992
00:55:06,600 --> 00:55:11,000
Speaker 1: I love the way he finds these eclectic characters and

993
00:55:11,040 --> 00:55:14,520
Speaker 1: the story is always unwound through these unusual people, the

994
00:55:14,560 --> 00:55:17,319
Speaker 1: person from the weather channel, and it's just that's a

995
00:55:17,320 --> 00:55:20,879
Speaker 1: fascinating book. And then the the personal stuff, I would

996
00:55:20,920 --> 00:55:27,040
Speaker 1: say would be UM. I read some occasionally, not not

997
00:55:27,760 --> 00:55:30,960
Speaker 1: it's not a huge volume nowadays, but consistently over the

998
00:55:31,040 --> 00:55:34,600
Speaker 1: last years. I'll try to find some science fiction stories.

999
00:55:34,640 --> 00:55:37,000
Speaker 1: And by sense fiction I mean not um fantasy. I

1000
00:55:37,000 --> 00:55:39,279
Speaker 1: guess this is the aerospace engineering me not sort of

1001
00:55:39,320 --> 00:55:42,239
Speaker 1: the fantasy version, but the sort of hard science like

1002
00:55:42,280 --> 00:55:48,000
Speaker 1: the three that would be one or um Alice. Redemption.

1003
00:55:48,160 --> 00:55:52,480
Speaker 1: There's a Redemption Space series that I'm currently reading, Redemption Space.

1004
00:55:52,480 --> 00:55:57,879
Speaker 1: Who's the author there? Okay, you Alistair, So I've I've

1005
00:55:57,920 --> 00:55:59,800
Speaker 1: just finished the first one of the series. There's a

1006
00:55:59,840 --> 00:56:03,120
Speaker 1: but about a six book series, and I'm embarked on

1007
00:56:03,120 --> 00:56:05,120
Speaker 1: the second and I'll have to get back to you

1008
00:56:05,200 --> 00:56:10,879
Speaker 1: on Alice. Uh if you could do Redemption. Uh, let's

1009
00:56:10,920 --> 00:56:14,400
Speaker 1: see what Google has to say about this Redemption arc

1010
00:56:15,160 --> 00:56:19,279
Speaker 1: by Alistair Reynolds. Reynolds, sorry Reynolds, the second book in

1011
00:56:19,320 --> 00:56:23,520
Speaker 1: the Revelation Space series. Revelation Space is the name of

1012
00:56:23,520 --> 00:56:26,840
Speaker 1: this series. Okay, absolutely, And so you're going to go

1013
00:56:26,880 --> 00:56:31,360
Speaker 1: sci fi a feeling very predictable that that would be

1014
00:56:31,400 --> 00:56:35,080
Speaker 1: an example of that's when I'm reading it's a dollar series.

1015
00:56:35,080 --> 00:56:38,200
Speaker 1: I think they started. I started writing those around you know,

1016
00:56:38,320 --> 00:56:40,520
Speaker 1: twenty years ago or eighteen years ago. But that's an

1017
00:56:40,520 --> 00:56:42,600
Speaker 1: example of the kind of sort of it's a little

1018
00:56:42,600 --> 00:56:44,920
Speaker 1: bit harder science fiction with a lot of speculative stuff,

1019
00:56:45,360 --> 00:56:48,120
Speaker 1: and it's kind of fun um to just think about

1020
00:56:48,360 --> 00:56:51,239
Speaker 1: technology and the impact technology can have in the very

1021
00:56:51,239 --> 00:56:54,520
Speaker 1: long term. And I find certain types of science fiction writers.

1022
00:56:54,560 --> 00:56:56,520
Speaker 1: Another example would be that's a little older, would be

1023
00:56:56,960 --> 00:57:02,719
Speaker 1: Larry because I knew you could things exactly. Uh, those

1024
00:57:02,800 --> 00:57:07,720
Speaker 1: those books, the whole series of Ringworld, um from Niven.

1025
00:57:08,239 --> 00:57:10,840
Speaker 1: He was amazing. Yeah. I had a great way of

1026
00:57:10,880 --> 00:57:13,280
Speaker 1: coming up with these ideas and then sort of making

1027
00:57:13,600 --> 00:57:17,200
Speaker 1: I mean, I think the quality of the of his

1028
00:57:17,280 --> 00:57:19,800
Speaker 1: writing over time varied a little bit, but certainly the

1029
00:57:19,920 --> 00:57:23,160
Speaker 1: examples like Ring World, um, A Moot in God's Eye

1030
00:57:23,520 --> 00:57:25,800
Speaker 1: that you co wrote with Jerry Purnell, those are examples

1031
00:57:25,800 --> 00:57:28,080
Speaker 1: of books that, uh, you know, there's a lot of

1032
00:57:28,080 --> 00:57:31,240
Speaker 1: creative thinking, and they're entertaining stories as well. They start

1033
00:57:31,280 --> 00:57:33,560
Speaker 1: with the framework and then the characters and the and

1034
00:57:33,640 --> 00:57:35,920
Speaker 1: the plot really move along. Any other ones do you

1035
00:57:35,960 --> 00:57:38,160
Speaker 1: want to mention before we move on that that's a

1036
00:57:38,320 --> 00:57:41,320
Speaker 1: that's a really good collection. I'm a giant Larry Niven face,

1037
00:57:41,760 --> 00:57:43,440
Speaker 1: and I had a feeling. I had a feeling you

1038
00:57:43,480 --> 00:57:48,840
Speaker 1: were you were heading in that direction. Um, so what

1039
00:57:48,840 --> 00:57:50,680
Speaker 1: what excites you right now? What about the world of

1040
00:57:50,720 --> 00:57:54,920
Speaker 1: investing has you really enthusiastic looking forward to the future.

1041
00:57:55,280 --> 00:57:57,840
Speaker 1: I think I mentioned earlier were early days with respect

1042
00:57:57,840 --> 00:58:00,320
Speaker 1: to things like machine learning and big data, and I

1043
00:58:00,360 --> 00:58:04,000
Speaker 1: think there's a potential for significant transformation. So you've got

1044
00:58:04,000 --> 00:58:07,720
Speaker 1: this historical division of quant and you know traditional or

1045
00:58:07,720 --> 00:58:11,920
Speaker 1: fundamental investors, where the quants go broad but maybe not

1046
00:58:12,000 --> 00:58:15,120
Speaker 1: that deep, and the traditional investors go very deep but

1047
00:58:15,160 --> 00:58:18,120
Speaker 1: they may not be quite as broad. Um. I think

1048
00:58:18,160 --> 00:58:19,880
Speaker 1: we're at a point where we're gonna be able to

1049
00:58:19,920 --> 00:58:24,479
Speaker 1: start going broad and deep because of these these kinds

1050
00:58:24,560 --> 00:58:26,240
Speaker 1: of both the on the data side and then the

1051
00:58:26,280 --> 00:58:29,000
Speaker 1: ability to interpret the data using machine learning. You have

1052
00:58:29,040 --> 00:58:32,080
Speaker 1: to be very careful with machine learning. It's prone to overfitting,

1053
00:58:32,160 --> 00:58:35,520
Speaker 1: so you've got to build in some safeguards to avoid that.

1054
00:58:35,920 --> 00:58:38,240
Speaker 1: And we're still learning best practices. You know, what are

1055
00:58:38,280 --> 00:58:42,560
Speaker 1: what are the best techniques to use UM in driverless cars?

1056
00:58:42,680 --> 00:58:46,960
Speaker 1: People talk about neural nets. Neural nets you know, can

1057
00:58:47,000 --> 00:58:51,320
Speaker 1: easily find the best fit to historic data UM, but

1058
00:58:51,560 --> 00:58:55,960
Speaker 1: not always guaranteed to outperform UM just a standard linear

1059
00:58:56,000 --> 00:59:00,560
Speaker 1: statistical model historic with with future data. And so I

1060
00:59:00,560 --> 00:59:03,120
Speaker 1: think there's a lot of opportunity there for us to

1061
00:59:03,120 --> 00:59:05,120
Speaker 1: to learn and do better in that area, and that

1062
00:59:05,120 --> 00:59:07,800
Speaker 1: that kind of stuff is very exciting both for me

1063
00:59:07,880 --> 00:59:10,960
Speaker 1: and it turns out when you talk to young people

1064
00:59:10,960 --> 00:59:14,200
Speaker 1: coming into the quantitative research area, that's that those are

1065
00:59:14,200 --> 00:59:16,320
Speaker 1: the kinds of things they're excited and working on. I

1066
00:59:16,320 --> 00:59:19,160
Speaker 1: can imagine. So tell us about a time you failed

1067
00:59:19,400 --> 00:59:22,160
Speaker 1: and what you learned from the experience. Yeah, I mean,

1068
00:59:22,200 --> 00:59:25,280
Speaker 1: there's there's probably plenty of plenty of areas. One area

1069
00:59:25,600 --> 00:59:27,960
Speaker 1: would be uh in many ways. I was kind of lucky.

1070
00:59:28,640 --> 00:59:31,760
Speaker 1: I went to a good school. Um, I was good

1071
00:59:31,800 --> 00:59:35,760
Speaker 1: at taking exams. UM got a job that you know,

1072
00:59:35,800 --> 00:59:39,120
Speaker 1: we had the aerospace job, but the investment job, and

1073
00:59:39,160 --> 00:59:41,800
Speaker 1: that turned into a company. And I've been very fortunate

1074
00:59:41,840 --> 00:59:43,959
Speaker 1: in the people I've worked with, so I've always things

1075
00:59:43,960 --> 00:59:47,680
Speaker 1: are kind of work, been kind of successful. And when

1076
00:59:47,760 --> 00:59:53,400
Speaker 1: it came time to go through the CEO search process, UM,

1077
00:59:53,440 --> 00:59:54,920
Speaker 1: you know, one of the things we did is we

1078
00:59:54,960 --> 00:59:58,280
Speaker 1: took these I guess you administer different kinds of not

1079
00:59:58,320 --> 01:00:00,520
Speaker 1: just personality exams, but they're sort of invent tories of

1080
01:00:00,560 --> 01:00:04,680
Speaker 1: your managerial leadership capabilities, and so what when you take

1081
01:00:04,680 --> 01:00:06,840
Speaker 1: one of these, they ask you to rate yourself, and

1082
01:00:06,840 --> 01:00:08,680
Speaker 1: then all your peers and all your colleagues at the

1083
01:00:08,680 --> 01:00:11,080
Speaker 1: company do the same thing and even sort of compare.

1084
01:00:11,120 --> 01:00:14,160
Speaker 1: Here's where I think I am. And I'm doing this

1085
01:00:14,200 --> 01:00:15,840
Speaker 1: as a gesture, but I'll explain in a minute for

1086
01:00:15,880 --> 01:00:18,400
Speaker 1: your audience, here's where everybody else thinks I am. So

1087
01:00:18,480 --> 01:00:20,760
Speaker 1: it's very humbling to find out I had a very

1088
01:00:20,840 --> 01:00:25,600
Speaker 1: high opinion of my strategic thinking and UM, my ability

1089
01:00:25,680 --> 01:00:28,959
Speaker 1: to bring people to a consensus or to pull behind

1090
01:00:28,960 --> 01:00:32,480
Speaker 1: a decision, and UM, some of my colleagues observed that

1091
01:00:33,040 --> 01:00:35,440
Speaker 1: there were aspects of my decision making that, you know,

1092
01:00:35,640 --> 01:00:38,760
Speaker 1: they didn't appreciate as much potentially as I would have

1093
01:00:38,760 --> 01:00:41,680
Speaker 1: thought they might have. Um And so that was humbling,

1094
01:00:41,760 --> 01:00:44,520
Speaker 1: but it was also great because you know, really hearing

1095
01:00:44,600 --> 01:00:48,880
Speaker 1: other people's honest feedback is something that not everybody gets easily.

1096
01:00:49,240 --> 01:00:51,120
Speaker 1: And this is sort of an anonymous process, so it's

1097
01:00:51,120 --> 01:00:54,200
Speaker 1: a little filtered, but I can sort of see, here

1098
01:00:54,200 --> 01:00:56,000
Speaker 1: are some areas where I actually, you know, could be

1099
01:00:56,040 --> 01:00:58,720
Speaker 1: doing better than I than I was. UM. So I

1100
01:00:59,360 --> 01:01:01,080
Speaker 1: an area where I what I think of as failed

1101
01:01:01,120 --> 01:01:05,800
Speaker 1: as um my self image was miscalibrated relative to where

1102
01:01:05,800 --> 01:01:09,360
Speaker 1: everybody else was. On the plus side, that's a that's

1103
01:01:09,360 --> 01:01:13,280
Speaker 1: a learning opportunity because you can say, Okay, here's some

1104
01:01:13,320 --> 01:01:15,040
Speaker 1: things I could work on. I can try to do better.

1105
01:01:15,520 --> 01:01:17,640
Speaker 1: And if even if I can't do better, because I

1106
01:01:17,680 --> 01:01:19,520
Speaker 1: am who I am, maybe it's good to have an

1107
01:01:19,520 --> 01:01:22,840
Speaker 1: appreciation for some of my shortcomings. I love the way

1108
01:01:22,880 --> 01:01:26,680
Speaker 1: you phrase that, as an engineer would my self image

1109
01:01:26,720 --> 01:01:30,480
Speaker 1: was miscalibrated with the with the rest of the rest

1110
01:01:30,480 --> 01:01:33,200
Speaker 1: of the office. That's funny. Um So, if a millennial

1111
01:01:33,320 --> 01:01:35,440
Speaker 1: or recent college grad came to you and said they

1112
01:01:35,440 --> 01:01:39,840
Speaker 1: were considering a career in either quantitative research or asset management.

1113
01:01:40,200 --> 01:01:43,040
Speaker 1: What sort of advice would you give them. I'd say,

1114
01:01:43,640 --> 01:01:45,840
Speaker 1: if you're if this is something that you're excited and

1115
01:01:45,880 --> 01:01:50,360
Speaker 1: you're interested in, Absolutely it can still be a tremendously exciting,

1116
01:01:50,400 --> 01:01:53,880
Speaker 1: rewarding career. I do think it's very different than the

1117
01:01:54,000 --> 01:01:57,560
Speaker 1: environment that I faced three years ago. Right when you're

1118
01:01:57,680 --> 01:02:00,600
Speaker 1: entering something that's sort of new and green field, you know,

1119
01:02:00,640 --> 01:02:03,600
Speaker 1: there's not a lot of established players. Um, you've got

1120
01:02:03,640 --> 01:02:06,840
Speaker 1: a lot of opportunity. I mean, it could go completely astray,

1121
01:02:06,920 --> 01:02:08,640
Speaker 1: in which case you have to go to Plan B.

1122
01:02:09,280 --> 01:02:12,160
Speaker 1: But um, you've got a lot of opportunity. We've got

1123
01:02:12,200 --> 01:02:15,520
Speaker 1: a more mature industry now, there's lots of established competitors,

1124
01:02:16,160 --> 01:02:18,800
Speaker 1: and so it's harder to come in and have an immediate,

1125
01:02:18,960 --> 01:02:23,240
Speaker 1: big impact on a firm or established investment process. Um.

1126
01:02:23,520 --> 01:02:25,080
Speaker 1: You know, it's going to take more work and it's

1127
01:02:25,120 --> 01:02:27,800
Speaker 1: gonna take some time. UM, so you've got to be

1128
01:02:27,800 --> 01:02:30,200
Speaker 1: prepared for that. You know, if if you're if you

1129
01:02:30,240 --> 01:02:33,400
Speaker 1: want to, um, you know, develop the next great idea,

1130
01:02:33,480 --> 01:02:36,520
Speaker 1: there's still scope to do that, but um, you're doing

1131
01:02:36,520 --> 01:02:39,160
Speaker 1: it within the context typically of of a bigger existing

1132
01:02:39,760 --> 01:02:44,160
Speaker 1: process and firm. Another area though, might be fintech. So fintech,

1133
01:02:44,200 --> 01:02:48,000
Speaker 1: you know, the retail investors I think still are not sure.

1134
01:02:48,320 --> 01:02:50,959
Speaker 1: Fees have come down somewhat. You've got lots of index funds,

1135
01:02:51,000 --> 01:02:53,200
Speaker 1: you've got a t f s, but they're still not

1136
01:02:53,280 --> 01:02:55,680
Speaker 1: served as well in terms of the sort of advice

1137
01:02:56,000 --> 01:03:00,560
Speaker 1: and planning portion as they potentially could be. And so

1138
01:03:00,880 --> 01:03:04,000
Speaker 1: you know, some of these fintech companies, I think there

1139
01:03:04,040 --> 01:03:07,520
Speaker 1: there are some potentially disruptive ideas that either we are

1140
01:03:07,560 --> 01:03:09,320
Speaker 1: seeing or some of them may pan out, some of

1141
01:03:09,320 --> 01:03:11,360
Speaker 1: them may not, but that may be an interesting area

1142
01:03:11,360 --> 01:03:15,120
Speaker 1: as well to consider beyond pure investment management. Quite quite intriguing.

1143
01:03:15,720 --> 01:03:18,480
Speaker 1: UH and our final question, what do you know about

1144
01:03:18,480 --> 01:03:22,440
Speaker 1: the world of quantitative investing today? You wish you knew

1145
01:03:22,480 --> 01:03:25,520
Speaker 1: when you were starting out thirty years ago. There's a

1146
01:03:25,520 --> 01:03:28,280
Speaker 1: couple of lessons. One is the importance of risk control.

1147
01:03:28,440 --> 01:03:31,920
Speaker 1: I mentioned um, if you're just betting on a single factor,

1148
01:03:31,960 --> 01:03:34,800
Speaker 1: single signal, there could be a lot of risk associated

1149
01:03:35,360 --> 01:03:38,560
Speaker 1: with that exposure and a portfolio you need to manage

1150
01:03:38,600 --> 01:03:41,800
Speaker 1: that risk effectively. That's really important. A second thing is

1151
01:03:42,320 --> 01:03:46,080
Speaker 1: the payoffs to factors can change a lot over time.

1152
01:03:46,760 --> 01:03:49,840
Speaker 1: I think I intellectually, I think my I and my

1153
01:03:49,880 --> 01:03:53,720
Speaker 1: colleagues appreciated that. But there may be ways to manage

1154
01:03:53,760 --> 01:03:58,240
Speaker 1: those uh, the expectation of those payoffs using models that

1155
01:03:58,280 --> 01:04:01,600
Speaker 1: help predict how well values going work or quality or

1156
01:04:01,760 --> 01:04:05,160
Speaker 1: momentum is going to work and uh. And so the

1157
01:04:05,200 --> 01:04:08,400
Speaker 1: importance of having such models and incorporating them into your

1158
01:04:08,440 --> 01:04:12,560
Speaker 1: process is something I'd love to appreciate, Say before two

1159
01:04:12,560 --> 01:04:17,280
Speaker 1: thousand eight, for example, quite fascinating. Thank you, John for

1160
01:04:17,360 --> 01:04:20,520
Speaker 1: being so generous with your time. We have been speaking

1161
01:04:20,520 --> 01:04:24,600
Speaker 1: with John Chisholm. He is the co CEO and former

1162
01:04:24,680 --> 01:04:29,440
Speaker 1: chief investment officer for a Kadian asset Management. If you

1163
01:04:29,520 --> 01:04:32,040
Speaker 1: enjoyed this conversation, we'll be sure to look up an

1164
01:04:32,040 --> 01:04:36,560
Speaker 1: inch or down an inch on Apple iTunes or wherever

1165
01:04:36,840 --> 01:04:39,680
Speaker 1: final podcasts are sold and you can see the other

1166
01:04:40,000 --> 01:04:43,000
Speaker 1: let's call it two hundred and thirty or so such

1167
01:04:43,080 --> 01:04:47,360
Speaker 1: conversations we've had. We love your comments, feedback and suggestions.

1168
01:04:47,400 --> 01:04:50,960
Speaker 1: Please write to us at m IB podcast at Bloomberg

1169
01:04:51,000 --> 01:04:54,280
Speaker 1: dot net. If you enjoy this conversation, go to Apple

1170
01:04:54,360 --> 01:04:57,520
Speaker 1: iTunes and be sure to give us a five star rating. UH.

1171
01:04:57,720 --> 01:05:01,280
Speaker 1: Check out my daily column on Bomberg dot Com. Follow

1172
01:05:01,320 --> 01:05:04,160
Speaker 1: me on Twitter at rid Halts. I would be remiss

1173
01:05:04,240 --> 01:05:07,080
Speaker 1: if I did not thank the crack staff who helps

1174
01:05:07,120 --> 01:05:11,000
Speaker 1: put together this conversation each week. Medina Partwana is our

1175
01:05:11,040 --> 01:05:15,280
Speaker 1: producer and Charles Volmer is our returning audio engineer an

1176
01:05:15,280 --> 01:05:19,400
Speaker 1: all time champion. Taylor Riggs is our booker slash producer.

1177
01:05:19,960 --> 01:05:24,200
Speaker 1: Uh Attica val Brunn is our project manager. Michael Batnick

1178
01:05:24,280 --> 01:05:27,600
Speaker 1: is my head of research. I'm Barry Ridholts. You've been

1179
01:05:27,640 --> 01:05:30,640
Speaker 1: listening to Masters in Business on Bloomberg Radio