Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: This week on the podcast, I have an extra special guest.
Speaker 1: His name is John Chisholm, and you might not have
Speaker 1: heard of him, despite the fact that he is the
Speaker 1: co CEO and former Chief Investment Officer of Acadian Asset Management,
Speaker 1: which runs nearly a hundred billion dollars in institutional money
Speaker 1: UH all around the world. Most of it is here
Speaker 1: in the US, but a healthy UH chunk about a
Speaker 1: third is overseas money. He has a fascinating background, UH
Speaker 1: an aspiring rocket scientist who worked at the m I
Speaker 1: t um Instrument Labs before taking a gig at State Street,
Speaker 1: and then eventually him and his partners launched Acadian about
Speaker 1: thirty two years ago. UH. They are a quantitative shop
Speaker 1: and have a very very UH interesting approach combining essentially
Speaker 1: fundamental factor models into a quantitative UH system and it's
Speaker 1: really very very interesting. They've put together quite a fascinating
Speaker 1: track record over time. If you are at all interested
Speaker 1: in quantitative approaches, factor based UH investing, big data, artificial intelligence,
Speaker 1: the way to approach markets UH from a data driven perspective,
Speaker 1: then I think you're gonna find this conversation absolutely fascinating. So,
Speaker 1: with no further ado, my interview with Acadian Asset Managements
Speaker 1: John Chisholm. My special guest this week is John Chisholm.
Speaker 1: He is the co CEO of Acadian Asset Management. Previously
Speaker 1: he was Chief Investment Officer. Acadian manages a six billion
Speaker 1: dollars in almost seventy countries around the world. UH. He
Speaker 1: began as an analyst at State Street Bank. Previous to that,
Speaker 1: he was a systems engineer at Draper Laboratories, which really
Speaker 1: is a great place to start. John Chisholm, Welcome to Bloomberg.
Speaker 1: Hi Berry, Thanks, it's great to be here. So, systems
Speaker 1: engineer at Draper Laboratories, which became known as the M
Speaker 1: I T Instrumentation Lab. Is that right? How did you
Speaker 1: find your way from M I T to uh, the
Speaker 1: instrumentation Labs. When I went to college, my passion, what
Speaker 1: I was excited about, was really building or designing spaceships.
Speaker 1: This was in the early eighties, So so you were
Speaker 1: a rocket science I was a lot of the aspiring
Speaker 1: rocket scientists exactly. And so I got my undergraduate degree,
Speaker 1: but I found my senior year I was gotten really
Speaker 1: interested investing, and I was spending a lot of time
Speaker 1: mostly just reading about investing, you know whatever. It was
Speaker 1: journal business publications, journals and uh. When I decided, okay,
Speaker 1: what do I want to do now? I thought, well,
Speaker 1: I probably want to go back to grad school. Do
Speaker 1: I want to do finance investing business or do I
Speaker 1: want to do um aerospace? And uh. I had had
Speaker 1: a an opportunity apply to several different programs, so I
Speaker 1: had both. I had a finance opportunity and an aerospace opportunity,
Speaker 1: and I thought, why don't I try to test both out.
Speaker 1: I'll get a full time job here in Boston area.
Speaker 1: The only place there's not a lot of airspace jobs
Speaker 1: in Boston A Draper Labs is one that works on
Speaker 1: guidance systems. So if you've got a satellite or a missile,
Speaker 1: you try to figure out where's it's, where's it going
Speaker 1: to go? How does it get there? Before GPS of
Speaker 1: headed guidance system. So uh, so I worked there full time,
Speaker 1: and I got a part time job, like sort of
Speaker 1: after hours job, working with a fellow named Gary Bergstrom,
Speaker 1: who was later one of my co founders at a Kadian.
Speaker 1: He had been a portfolio manager at Putnam and UH
Speaker 1: in the seventies and then he left sort of off
Speaker 1: on his own, consulting for money managers consulted at the time.
Speaker 1: As big project when I was working with them was
Speaker 1: for State Street later State Street Global Advisors, and we
Speaker 1: helped building design their first international index fund and then
Speaker 1: later on some international active strategies. So that was sort
Speaker 1: of a part time job. UM I went back to
Speaker 1: that made me decide that was more interesting than the
Speaker 1: aerospace stuff I was doing at the time. That made
Speaker 1: me decide to go back. So that's what when you
Speaker 1: said to go back, you really started. Your first full
Speaker 1: time job in finance was as an analyst for State Street.
Speaker 1: Is so, so the State Street job was a was
Speaker 1: also a part time That was also while I was
Speaker 1: at school um UH working for them for like there
Speaker 1: was some time off in January and then the spring semester.
Speaker 1: I worked for them as a potential employer. But in
Speaker 1: the end Gary's goal was to launch an asset management firm.
Speaker 1: UH myself and we had another colleague, Churchill Franklin, and
Speaker 1: another colleague, Ron Frasier. They all came aboard. We all
Speaker 1: came together about the same time around when I graduated
Speaker 1: and uh and so we launched a Kadian as an
Speaker 1: active money manager at that point. So that's thirty two
Speaker 1: years ago. An active manager as well as a heavily
Speaker 1: influenced by quantitative strategies? Is that affair the work? We're
Speaker 1: a quantitative manager. We were all you know, my background
Speaker 1: aerospace engineering, UM all all quantitative. Gary's background, Gary gotten
Speaker 1: a PhD from m I t uh. So we were
Speaker 1: all very quantitative. But quant at the time was not
Speaker 1: as sophisticated as what quant today is. Right, there wasn't
Speaker 1: any machine learning, there wasn't any big data. There was
Speaker 1: you know, little data. There were statistics, right, so you
Speaker 1: know what's the average payoff to value UM and ch
Speaker 1: how do we build a portfolio that captures that payoff?
Speaker 1: Very simple quantitative tools that we use back in the
Speaker 1: middle eighties. So do you consider yourselves today an active
Speaker 1: manager or quantitatively? Like I think about firms like um
Speaker 1: D f A or any of the FAMA French based
Speaker 1: UM factor models, and they're somewhere between active and a
Speaker 1: quantitative screening approach. How how would you describe a Kadian?
Speaker 1: Describe us as active? So most of what we're doing
Speaker 1: is highly active, potentially high tracking our against a benchmark.
Speaker 1: We we have the flexibility so we can build low
Speaker 1: tracking our strategies. UM. This ties into this concept of capacity.
Speaker 1: How much money can you manage and still expect to
Speaker 1: add the value your clients are looking for? And typically
Speaker 1: the more money you manage, the harder is to add value.
Speaker 1: UM So at lower levels of active risk, lower expected
Speaker 1: value added, you can manage more money. There's some clients
Speaker 1: who are happy hiring managers for that. They're also usually
Speaker 1: happy paying lower fees. Right, so you really have to
Speaker 1: trade off both from perspective of adding value and from
Speaker 1: perspective of running a business. Where do you want to be?
Speaker 1: Most of our strategy is highly active, but we have
Speaker 1: some that are shading more towards UM enhanced index. When
Speaker 1: you say enhanced index, you're taking a basic index and
Speaker 1: then adding a little flavoring to it to move it
Speaker 1: away from the benchmark. Yeah, we're so, for example, we
Speaker 1: might say enhanced. The next would be if we have
Speaker 1: tracking or of less than two percent, if we have
Speaker 1: one and a half percent. Tracking are just means what's
Speaker 1: the standard deviation of the expected returns versus the benchmark?
Speaker 1: UM one and a half percent tracking er would be
Speaker 1: enhanced index strategy, you might only expect to get one
Speaker 1: and a half percent UM excess return associated with that
Speaker 1: net of fees. If we had a more active strategy,
Speaker 1: we might expect we might see four percent tracking er.
Speaker 1: We'd expect to get about two and a half percent
Speaker 1: active return neta fees. How do you avoid the challenge,
Speaker 1: as as Bill Miller described it, of UM active managers
Speaker 1: TUBJE active fees but are effectively our closet indexers. How
Speaker 1: do you clearly differentiate yourself from that group. So, so
Speaker 1: there's two parts to it. One is what what's under
Speaker 1: our control? What we can do. We can build portfolios
Speaker 1: that are active in the sense that they are very different,
Speaker 1: they look different from the benchmark, they have UM high
Speaker 1: higher levels of tracking, or they have high active share UM.
Speaker 1: The other part of that isn't is the client's job.
Speaker 1: So if the client hires twenty managers like that, they're
Speaker 1: still getting close to back back to an index. Let's
Speaker 1: talk a little bit about your quantitative approach, and I
Speaker 1: was noticing on your website you describe a fourth step process,
Speaker 1: and really what that is is strategy signal generation signal
Speaker 1: consumption and then process can can you explain what that
Speaker 1: means to to the perhaps the lay person who may
Speaker 1: not be familiar with a quantitative approach. Absolutely, let me
Speaker 1: let me start with a signal generation part, because that's
Speaker 1: maybe the part that will be easiest for for people
Speaker 1: to start with the basic ideas. There's different characteristics companies have.
Speaker 1: Those characteristics can be on average predictive of returns. So,
Speaker 1: for example, one characteristic is just is how expensive as
Speaker 1: a company look on whatever metric P ratio? Right, So
Speaker 1: you've got a company that has a P ratio of
Speaker 1: eight and one that has a P ratio of forty.
Speaker 1: That's all you knew about those companies? Which one would
Speaker 1: you want to own? If you look at the last
Speaker 1: fifty or sixty years globally, you'd say, I want to
Speaker 1: own the P eight company right on average is going
Speaker 1: to do better. The problem with that is you can
Speaker 1: have a ten year stretch where the P forty company
Speaker 1: kills the P A company like we've just had. We've
Speaker 1: had you know, what would you rather own right the
Speaker 1: last ten years? We'd rather row on Amazon or would
Speaker 1: rather own P G n E? Right? I mean, so
Speaker 1: those are the two biggest streames you can you can
Speaker 1: really sorry I picked those out of it. Not hopefully
Speaker 1: it's not quite, but it's true. Generally growth stocks have
Speaker 1: done very well. Value stocks and stuff that joined up
Speaker 1: for a decade. So how do you it around that? UM?
Speaker 1: So let's say you believe on average, value is going
Speaker 1: to outperform, like some people might not believe that. But
Speaker 1: let's say I do. I want value in my portfolio,
Speaker 1: but I don't want to underperform for ten years in
Speaker 1: a row. What can I do? I can take other
Speaker 1: characteristics that I believe are also predictive of return and
Speaker 1: combine those with value. So I might say, for example,
Speaker 1: UM quality measures, right, I want companies that are well managed.
Speaker 1: How do you define well managed? Well? As a dozen definitions,
Speaker 1: But let's say one definition is inventory turnover. Do you
Speaker 1: have companies that turn over their inventory uh more frequently
Speaker 1: than companies in the same industry? Right? And maybe that's
Speaker 1: a signal that on average has some payoffs associated with
Speaker 1: it for companies in many industries. So now I've got
Speaker 1: value and I've got this. Let's say I think people
Speaker 1: talk about a lot in markets as momentum, So I've
Speaker 1: got momentum. Companies have good momentum um, they've been performing,
Speaker 1: they've been performing their peers for the last you know,
Speaker 1: six to twelve months. Maybe that's indicative that on average
Speaker 1: in the future, they're likely outperformed for the next, say,
Speaker 1: one to three months. I want to wrap that in.
Speaker 1: So you combine all these different signals and you've got
Speaker 1: what you historically people call a multi factor model. Right,
Speaker 1: and so now, even if value does badly, maybe momentum
Speaker 1: and quality and these other things do well enough to
Speaker 1: allow you to still outperform, which is always the goal,
Speaker 1: uh for for us and for our clients, and and
Speaker 1: so that that's sort of the the genesis of signals.
Speaker 1: It's just there are different types of characteristics that we
Speaker 1: use to help predict company returns, and we then combine them.
Speaker 1: So when you say signal consumption, a couple of pieces
Speaker 1: of that One is how do you combine these things? Right?
Speaker 1: Is the payoff to value the same as the payoff
Speaker 1: to quality will probably not um. So you have to
Speaker 1: figure out what do I expect to get If I'm
Speaker 1: looking at it doesn't differ by the type of company
Speaker 1: I'm looking at, is a tech company, maybe it has
Speaker 1: different drivers overturn than a utility, And so I have
Speaker 1: to mix the weight on those signals depending on what
Speaker 1: kind of opening I'm evaluating. Uh, And so that's part
Speaker 1: of consumption. Then the second part of consumption is how
Speaker 1: do you implement that in a portfolio? Right, So, ultimately
Speaker 1: I'm gonna hold stocks and a portfolio, I'll hold Amazon
Speaker 1: or I won't. I'll hold pg n E or I won't.
Speaker 1: What's my weight going to be? Hopefully it will be
Speaker 1: last ten years, hopefully high on Amazon, lower zero. And
Speaker 1: But but the idea is you've got to then turn
Speaker 1: those expected returns that you're getting from the signal generation
Speaker 1: part of your process into portfolio positions. At Acadian, we
Speaker 1: use a pretty quantitative approach to do that as well.
Speaker 1: We use what's called an optimizer that basically trades off
Speaker 1: the return expectations we come up with from the signals
Speaker 1: into and maps those into portfolio positions by trading those
Speaker 1: off against transaction costs. So, if I'm trading again Amazon, Samsung,
Speaker 1: a big liquid company, transaction costs are probably gonna be
Speaker 1: pretty low, almost negligible. But if I'm trading a less
Speaker 1: liquid company, that may be more efficiently priced than the
Speaker 1: return up tratunity may be much greater. But I need
Speaker 1: to now account for what's it going to cost to
Speaker 1: get in the position and what's it going to cost
Speaker 1: to get out of the position someday. So what you're
Speaker 1: describing sounds a lot like traditional factor based investing. You're
Speaker 1: describing um value, describing a momentum quality. When we talk
Speaker 1: about liquidity, I always think about cap size. How does
Speaker 1: your approach differ from factor investing? Or am I asking
Speaker 1: that question wrong? Are you effectively a farmer French factor
Speaker 1: type investor? Now that's it's a great question. Uh. And
Speaker 1: in some ways our approach is very much like factor investing.
Speaker 1: In other words, we consider these different signals. We consider
Speaker 1: them to be types of factors um. What's different is
Speaker 1: that we integrate the factors. So, for example, let's suppose
Speaker 1: you've just been on momentum factor by itself. There's some
Speaker 1: periods if you built a portfolio that has the most
Speaker 1: attractive whatever it is, ten percent of uh, let's say
Speaker 1: us amentum stocks and then the least to try active
Speaker 1: or shorts, or you just went long the most attractive
Speaker 1: ten percent, there are some periods where your portfolio beta.
Speaker 1: Your sensitivity to market movements might be might be two,
Speaker 1: so you might have a huge amount of volatility in
Speaker 1: the portfolio. And there's other times when your sensitivity to
Speaker 1: market movements might be very low, might be a bet
Speaker 1: of point five um. Why does that matter? It impacts
Speaker 1: how you can control risk. If you're doing these single
Speaker 1: factor portfolios, for example, unless you're very careful about how
Speaker 1: you build them, you are likely to take on all
Speaker 1: kinds of unexpected risks in the construction of those portfolios.
Speaker 1: With a multi factor approach, you're not beholding any one factor.
Speaker 1: You've got all these different characteristics you can emphasize in
Speaker 1: the portfolio, and you can trade them off, and it
Speaker 1: allows you to manage risk better. Portfolio construction can be
Speaker 1: a lot better than what people do when they're doing
Speaker 1: When people talk about factor investing, if you look at
Speaker 1: a typical factor e. T f UM, it's not built
Speaker 1: in a very efficient way. It's more costly to investors
Speaker 1: in ways that the investors can't see things like how
Speaker 1: they trade the portfolio. So they simply take a rank
Speaker 1: order of companies based on some factor, and they rebalanced.
Speaker 1: They buy some of the most attractive ones that have
Speaker 1: just gotten into that list and sell some of the
Speaker 1: ones that have fallen out. That can be a lot
Speaker 1: of turnover. And there may be times when you want
Speaker 1: to hold onto something that's become less attractive because it
Speaker 1: might be expensive to trade out of it and it
Speaker 1: hasn't fallen that far right, So it's important to do
Speaker 1: to be smart about how you use these factors. And
Speaker 1: I'd say the keep one of the key things we
Speaker 1: do is we worry a lot about the engineering of
Speaker 1: our process. How do you put these factors together? How
Speaker 1: do you minimize the slippage the transaction costs while still
Speaker 1: getting exposure to the underlying factor in the portfolio? Quite fascinating.
Speaker 1: Let's talk a little bit about what it's like to
Speaker 1: run a global organization. You're headquartered in Boston. Congratulations on
Speaker 1: the Patriots. Thank you. Um, you were at the game
Speaker 1: you mentioned, Uh, I was at the game, first time
Speaker 1: I've ever gone. So you don't know how long the
Speaker 1: Patriots that this might be their last Super Bowl in
Speaker 1: a while. Good idea to go. Yeah, now that makes
Speaker 1: perfect sense. Um, you have affiliates in London and Singapore
Speaker 1: and Tokyo and Sydney. What other countries are you located
Speaker 1: in I mean, I know you have clients in We've
Speaker 1: got clients probably thirty thirty five countries, but really they
Speaker 1: coverage of Singapore, Sydney, Tokyo, London. We are thinking, because
Speaker 1: it breaksit, we may need to open an office in Dublin, UM,
Speaker 1: perhaps Amsterdam, but there's enough uncertainty there that we haven't
Speaker 1: actually pulled the trigger yet Amsterdam. So so if people
Speaker 1: I keep asking this, and I'm getting very different answers
Speaker 1: from very different people. If Brexit happens hard or soft,
Speaker 1: and London is no longer the central finance location for Europe,
Speaker 1: where where does it go? Amsterdam doesn't really seem seem
Speaker 1: like the place UM Geneva stud guard. I mean, I
Speaker 1: can't Paris. Some people have floated, none of them seem
Speaker 1: to make sense. I think Frankfort's got the economic in
Speaker 1: many ways the commercial center of Europe. A lot of
Speaker 1: people don't love Frankfort. Because I live near Frankfort for
Speaker 1: a bunch of years. It's not always um doesn't have
Speaker 1: the cultural reputation that Paris does. For example. That being said,
Speaker 1: it's a very comfortable city to live in. So I
Speaker 1: think Frankfort will do well. That all being said, you know,
Speaker 1: if if we did something with another office, we're we're
Speaker 1: just basically opening up an office to meet the regulatory requirements.
Speaker 1: We still keep London, that would still be a major
Speaker 1: center for portfolio management. Uh, you know, for our team,
Speaker 1: for client service team. So your client base is uh
Speaker 1: primarily institutional and public pension funds and other large investors.
Speaker 1: Are they mostly US located? Are they around the world?
Speaker 1: What what your mix so are right now? Are mixes
Speaker 1: price seventy thirty um US non US? Uh? We we
Speaker 1: from a business for spective you know, as a firm,
Speaker 1: you want to be diversified, you know, so having a
Speaker 1: fair amount of non US exposure with our clients is
Speaker 1: something we strive for. We think there's a lot of
Speaker 1: great growth opportunities in terms of just the the growth
Speaker 1: of pension markets, of of institutional investor markets in Asia,
Speaker 1: for example, there's still growth in Australia, um in in
Speaker 1: Europe as well, there's pockets. You know, a few years
Speaker 1: ago Germany really didn't have defined benefit pension plans. Slowly
Speaker 1: evolving a little bit, So there's there's definitely opportunities. Don't
Speaker 1: most of Europe or don't doesn't much of Europe have
Speaker 1: UM some sort of retirement system covered by the government.
Speaker 1: How do you operate around that or is that their
Speaker 1: social security and it doesn't take the place of a
Speaker 1: real retirement. Yeah, I mean there's a lot of variation,
Speaker 1: but a lot of Europe, you know, like the UK,
Speaker 1: for example, you've got public pension plans, just like in
Speaker 1: the US you've got uh State of California cows, cowpers
Speaker 1: and calsters. Well, in in UK you've got um local
Speaker 1: authority pension plans UM and you can sort of think
Speaker 1: of them as the equivalent of public plans. Here in
Speaker 1: the US you've got large companies based in the UK
Speaker 1: that have private pension plans, so there's some state provision.
Speaker 1: But again, just like in the US, you have sort
Speaker 1: of security, but that doesn't exclude all these other types
Speaker 1: of pension plans. So in your list of countries, I
Speaker 1: didn't hear a whole lot In China. Is Hong Kong
Speaker 1: attractive or is mainland China possible or if it has,
Speaker 1: the government made it too challenging to set up shop there.
Speaker 1: Now the government's actually moving to liberalize, so it had
Speaker 1: been very difficult for a non local investors to invest
Speaker 1: in in in Chinese assets and manage money for Chinese institutions. UM.
Speaker 1: We do manage money for Hong Kong clients, but that's
Speaker 1: sort of separate stile, a separate um regulatory structure. Is
Speaker 1: that why you're located in Sydney for that part of
Speaker 1: the world. Singapore is is we're serving right now, we're
Speaker 1: serving Asia extrapan out of Singapore. UM. But but China
Speaker 1: is liberalizing and there's plenty of non local managers now
Speaker 1: setting up shop to manage money for the Chinese institutions
Speaker 1: in China. The challenges in China you need some scale.
Speaker 1: You need a partner because you can't touch the retail
Speaker 1: market without a local partner. And there's only really four
Speaker 1: big institutions you know that you've got that are like
Speaker 1: sort of the equivalent of Cowper's for example. UM. So
Speaker 1: that that that market, the institutional markets very narrow and
Speaker 1: the rest of the market, the retail market. You need
Speaker 1: a partner. If we found the right partner, I think
Speaker 1: would be very excited about doing something in China that
Speaker 1: we're certainly doing some work there. We've we've had some
Speaker 1: people not fully full time based there, but spending a
Speaker 1: lot of time in the market. But that's something remains
Speaker 1: to be seen whether we'll find the right opportunity to
Speaker 1: really be a player there. So you previously were chief
Speaker 1: Investment Officer and now you're co CEO. I have so
Speaker 1: many questions about both. So are you still working in
Speaker 1: a ce IO capacity as well? No, so, so I'm
Speaker 1: I'm still interested. I still go to Investment Policy Committee meetings.
Speaker 1: But you know, if you're if you're taking out a
Speaker 1: new role and you've picked someone to succeed you in
Speaker 1: your job, you really need to give them the ability
Speaker 1: to run that function. I've got a great successor, my successor,
Speaker 1: Brendan Bradley's or CEE i O UM started last year.
Speaker 1: As see. I always been with it Katian for a
Speaker 1: long time and got complete confidence in his ability to
Speaker 1: manage the investment function UM. I still participate in some
Speaker 1: of the meetings and I I'm interested in the research
Speaker 1: and I talked talked to lots of the investment professionals.
Speaker 1: It's part of my job as CEO is being in touch.
Speaker 1: You know, what does an investment firm do? We invest
Speaker 1: for our clients, So it's still important. But Brennan is
Speaker 1: managing and leading the investment team and you're a co CEO,
Speaker 1: which sounds like it has a whole lot of complications
Speaker 1: and issues that would come out of that dual CEO role.
Speaker 1: At least we've seen that with public companies. How do
Speaker 1: you navigate that? Is there a clear distinction between who
Speaker 1: is running what? Tell us a little bit about your
Speaker 1: co absolutely? Let me let me you that, um, because
Speaker 1: it is a great question. Uh. We we went through
Speaker 1: a succession process. Our former CEO was stepping down, was
Speaker 1: retiring from the CEO role, and so myself and one
Speaker 1: of my colleagues rosted out were internal candidates for the role.
Speaker 1: We have a selection process where we had an equivalent
Speaker 1: of executive committee. Essentially you think of as eight individuals
Speaker 1: running the firm making that decision. We were two members
Speaker 1: of that group, UM, and we we both shared our
Speaker 1: views on what's our vision for a Kadian, where do
Speaker 1: we want the firm to go, what would we like
Speaker 1: to do differently with with our executive committee, UM, it
Speaker 1: turned out what we were very well aligned in terms
Speaker 1: of where we wanted a Katie and to go. So
Speaker 1: when we looked at UM sort of are there situations
Speaker 1: I have a lot of respect for Ross, my current
Speaker 1: co CEO, he has I think a lot of respect
Speaker 1: for me. He comes from a marketing client service background.
Speaker 1: I come from the investment background. And we we both
Speaker 1: wanted each other to remain at the firm, thought about
Speaker 1: how can we do that, and we looked at examples
Speaker 1: where there have been co CEO structures in the past
Speaker 1: and other firms, the ones that worked relatively well. UM,
Speaker 1: and there are some Um generally you had co CEOs
Speaker 1: with highly aligned visions and that we're able to work
Speaker 1: together to provide a single voice of the firm. Right,
Speaker 1: So you don't want somebody coming to Ross getting one
Speaker 1: answer and then coming to me and getting a different answer.
Speaker 1: And we we thought, given the fact that we in
Speaker 1: fact do have highly lined visions, we do have areas
Speaker 1: of expertise that are complementary to each other. Uh, we
Speaker 1: thought this is something that not only could we pull
Speaker 1: it off, but it would actually be beneficial for Acadian.
Speaker 1: So we're now a little over a year into the role. UM,
Speaker 1: we think we're managing the firm effectively. We're getting feedback
Speaker 1: from our team, UM that that's the case and UH
Speaker 1: and I think it's working extremely well so far. UM.
Speaker 1: We What we typically do is issue comes up, we
Speaker 1: will discuss it together. We'll figure out where are we
Speaker 1: what are we looking to do? Um. And there are
Speaker 1: times when you know it's an area. Ross has a
Speaker 1: lot of expertise in the fern him more, Um, there's
Speaker 1: times when it's an area. I've got a lot of
Speaker 1: expertise in the for me more. It's great when we
Speaker 1: do need to be in two places at once. Right,
Speaker 1: he can be in Tokyo and I can be in Boston,
Speaker 1: or vice versa. UM. One of us can be doing
Speaker 1: to meeting with some of our clients, another one can
Speaker 1: be running internal meetings. So it really helps us, I think,
Speaker 1: be do a more effective job of managing the firm
Speaker 1: to have the structure we have. Quite interesting. So let's
Speaker 1: talk a little bit about what's going on in the
Speaker 1: marketplace today. Are you still seeing the same sort of
Speaker 1: miss pricings and securities that perhaps we're so abundant a
Speaker 1: decade ago? The miss prices have changed a lot. So
Speaker 1: if we take any particular signal that we've used ten
Speaker 1: years ago and we look at the payoff to that
Speaker 1: signal today, it's lower today. Right. So typically, whether it's
Speaker 1: inefficiency is being squeeze out of the market, it's arbitraged
Speaker 1: by different types of investors, whatever it is. You know,
Speaker 1: Typically the payoff to these characteristics decreases over time. So
Speaker 1: as a result, we're in a way, we're on a treadmill.
Speaker 1: We need to keep on finding new ideas to replace
Speaker 1: the old ideas that aren't working as well anymore. So
Speaker 1: when you say these the payoffs to these ideas um
Speaker 1: decline over time, is that all these ideas? Is that
Speaker 1: a function of we had a giant reset with the
Speaker 1: financial crisis. Hey, anytime the markets lose of the value,
Speaker 1: you have to think that some value is going to
Speaker 1: be created and a lot of babies get thrown out
Speaker 1: with the bathwater. Or is it just the nature of
Speaker 1: every good idea eventually runs its course. I think it's
Speaker 1: really a little bit of the reset idea. So there's
Speaker 1: no there's there's definitely a pattern that differs a little
Speaker 1: bit the rate of the client. And some of these
Speaker 1: things accelerated during the financial crisimmediately after the financial crisis,
Speaker 1: and the payoff to value is the biggest one where
Speaker 1: it's clear really been the worst ten year period for
Speaker 1: value globally post GFC that we've seen in the long
Speaker 1: term history, whether it's the U S history or longer
Speaker 1: history that's different. That all being said, a lot of
Speaker 1: the factors, it's an average thing, right. There's some signals
Speaker 1: that still work, you know today, not much worse than
Speaker 1: they worked ten years ago, but the average signal, the
Speaker 1: payoff decreases a little bit every year. M that's quite interesting.
Speaker 1: Quant has been around for thirty forty years or so.
Speaker 1: Do you think things are very different based on the
Speaker 1: rise of you mentioned earlier, big data and artificial intelligence.
Speaker 1: How has that affected how Acadian approaches quant investing or
Speaker 1: is that just something that is a background noise that
Speaker 1: affects the market overall. Yeah, I think there's um there's
Speaker 1: no question that the machine learning, art and big data,
Speaker 1: artificial intelligence, those are early days, right. Those things are
Speaker 1: starting to impact investors and how people invest. But we're
Speaker 1: still in the early days of that in quant, let
Speaker 1: alone in finance in general. And there's a lot more
Speaker 1: to come. But I would say things have changed a
Speaker 1: lot since the eighties and nineties, the the cerphification, the
Speaker 1: not not so much big data, but just any kind
Speaker 1: of data now is a lot more available than it
Speaker 1: was then, So we have a lot more information and
Speaker 1: quants can do things today that they couldn't do twenty
Speaker 1: years ago. Fundamental investors could maybe do them for a
Speaker 1: small group of companies, quants couldn't. Today we can look
Speaker 1: at all these like we have industry specific information about
Speaker 1: lots of companies that we just didn't have access to
Speaker 1: twenty years ago. Is it the technology and database or
Speaker 1: is it actually the specifics of the data itself that's
Speaker 1: changed so much. It's both. So the technolog no question,
Speaker 1: the technology and the database access um, the power and
Speaker 1: speed of databases and of software and processing in general
Speaker 1: has increased tremendously. That makes a lot of things easier
Speaker 1: to do. Machine learning, those algorithms can be very computationally intensive,
Speaker 1: and you hardware you had five years ago, you couldn't
Speaker 1: do these things today. Today you can do them on
Speaker 1: your on your laptop. In some cases might take a while,
Speaker 1: but there's things you can do on your laptop. If not,
Speaker 1: you go to Amazon Web Services and scale up processing
Speaker 1: power and you've got everything you need in terms of
Speaker 1: the sort of the processing computational aspect of things. So
Speaker 1: that's changed a lot, but also the data itself today
Speaker 1: is much broader than it was. I got started. You
Speaker 1: got a p you gotta p B, you got a
Speaker 1: price to cash flow. You've got a market cap, a price,
Speaker 1: and maybe a divident deal thrown in, and that's there.
Speaker 1: That's that was your data. That was like four ish
Speaker 1: and then UM, shortly after you started getting analyst data electronically.
Speaker 1: What what do you think about some of these alternative
Speaker 1: data points that people are pulling from either satellite data, Hey,
Speaker 1: here's all the ships moving oil around the world, UM,
Speaker 1: or parking lot activity to determine how well read tailors
Speaker 1: are doing. Is is any of that potentially useful and
Speaker 1: valuable to investors? Or is it just um a bunch
Speaker 1: of of geeks playing with some new tech toys and
Speaker 1: and kind of having fun with it. It's both, so
Speaker 1: that on the latter point, you know, we we have
Speaker 1: an analyst, and if we have a satellite data project,
Speaker 1: we have no problem getting somebody volunteer, put their hand
Speaker 1: up and say I'd like to work on this. This
Speaker 1: will be fun, right, So that's true. It's potentially valuable. Now,
Speaker 1: whether it's actually valuable to any individual in any particular
Speaker 1: investment firm depends on their style and their process. So
Speaker 1: let me let me tell you what I mean by that. UM.
Speaker 1: If you've got a satellite data, let's say you're getting
Speaker 1: your parking lot, your infrared images UM, and you're getting
Speaker 1: information about you know, parking lots UM. And that's if
Speaker 1: you're following retailers and UM. Investing in retailers is a
Speaker 1: big part of what you do that can be useful
Speaker 1: in predicting over the short run revenues. You've got to
Speaker 1: have a lot of infrastructure, you gotta know all the locations.
Speaker 1: You've got to be able to aggregate that in quasi
Speaker 1: real time, and satellite coverage at high resolution at a
Speaker 1: quick successive short time intervals is still expensive. UM. So
Speaker 1: you've got to figure out is it worth it to
Speaker 1: your process to do that. If you only one percent
Speaker 1: of the portfolio have that you have invested in retailers,
Speaker 1: maybe it's not really going to move the needle that much, right,
Speaker 1: So so let's talk about some other things that don't
Speaker 1: involve satellites. This has been a let's call it a
Speaker 1: typical UM political environment for the past couple of years,
Speaker 1: not just the Trump presidency, but Brexit and the financial
Speaker 1: crisis and the rise of the tea party. How does
Speaker 1: a quant shop manage those sorts of non market inputs
Speaker 1: or does it all just come out in the wash
Speaker 1: and it's not really all that important. There's there's two
Speaker 1: sides of this. One side is what I call risk management.
Speaker 1: Right can you if you can observe some of these
Speaker 1: risks and you don't observe them in a standard quant
Speaker 1: risk model because they're the quant risk models are typically
Speaker 1: backward looking. They're not forward looking. So you've got to,
Speaker 1: as a professional investor, think, what are some of these
Speaker 1: risks that maybe aren't priced into the risk models that
Speaker 1: are looking at the historical data, but that could impact
Speaker 1: the portfolio UM and let me give you an example
Speaker 1: of such a risk. One one risk is UM. We
Speaker 1: we managed one strategy that's a low volatility equity strategy.
Speaker 1: So we're trying to do there is reduce the risk
Speaker 1: of equity markets UM capuited benchmark. Let's say in the US,
Speaker 1: it might have a twelve or fourteen vol and we
Speaker 1: might want to produce a ten vol for example UM
Speaker 1: and what that means is somebody gets the same risk
Speaker 1: returned that that they get on count a capuited benchmark,
Speaker 1: but they get it with less risk. That's very helpful
Speaker 1: from asta allocation perspective. When you do that today, though,
Speaker 1: what you're doing is you're taking on a lot of
Speaker 1: interest rate risk because these lower risk companies typically tend
Speaker 1: to be higher dividend companies. Companies are more sensitive to
Speaker 1: interest rates. So if you're worried about a rising interest
Speaker 1: rate environment, your historical risk model wouldn't say, constrain your
Speaker 1: exposure to interest rates your sensitivity interest rates. But going
Speaker 1: forward you might want to do that um in a
Speaker 1: low volatility portfolio so that your volatility doesn't come out
Speaker 1: much higher than you expect or your return is much
Speaker 1: lower than you expect if interest rates do in fact
Speaker 1: continue rising. So that's the risk management piece. You want
Speaker 1: to anticipate certain risks and build that into your risk
Speaker 1: controls that you apply to your strategies. The second pieces
Speaker 1: can you use are there are other signals that help
Speaker 1: you navigate from a return perspective, These kinds of you know,
Speaker 1: macro events and uh you know, for example, volatility itself
Speaker 1: can be an earlier warning signal. Right, every major devaluation
Speaker 1: of currencies and emerging markets and many market breaks were
Speaker 1: preceded by periods of rising volatility, rising by untill. He
Speaker 1: also sometimes predicts more benign environments. But the point is,
Speaker 1: if there's a signal there, maybe there's ways to predict
Speaker 1: these these environments, and so are the top down part
Speaker 1: of what we do tries to look at these macro
Speaker 1: events um or potential macro events and figure out how
Speaker 1: can we anticipate those and how can we position the
Speaker 1: portfolios based on that anticipation. So you mentioned a rising
Speaker 1: rate environment. Lots of folks have been focused on the
Speaker 1: Federal Reserve and focused on are we gonna take a pause?
Speaker 1: I imagine that your shop doesn't spend a whole lot
Speaker 1: of time struggling with that that it should end up
Speaker 1: in the data, and it's not the sort of thing
Speaker 1: that you have to play macro tourists or or am
Speaker 1: I giving you guys too much credit? No, you know
Speaker 1: you're giving us just the right amount of credit here
Speaker 1: the very the you need to play the game that
Speaker 1: you're good at um and so we don't want to
Speaker 1: do we don't want to try to do things that
Speaker 1: we've got other people who are much better at it
Speaker 1: than we are and predicting UM rates using it the
Speaker 1: FED what what's the FED going to do? That's not
Speaker 1: all as a quant manager, that's not really what we're
Speaker 1: good at, right, So you're absolutely right there. What we
Speaker 1: would do is we would say, let's just look at
Speaker 1: what's happening with you know, the short term rates, long
Speaker 1: term rates, what's happening with the yield curve. Those can
Speaker 1: be signals that we use in a model, but we're
Speaker 1: not trying to really forecast the direction of interest rates
Speaker 1: per se through FED statements or through other kinds of
Speaker 1: actions like that. Um, it just means trying to do
Speaker 1: what what where we think our edges and really trying
Speaker 1: to focus on that in terms of the things we
Speaker 1: actively do in the portfolio. So you mentioned your models.
Speaker 1: When I was perusing the various offerings you have for
Speaker 1: institutional clients, there are thirty something different models, maybe even more.
Speaker 1: How do you develop different ideas? How do you express
Speaker 1: them in a portfolio? Is it strictly math or there
Speaker 1: are other guiding principles that affect that. The first first
Speaker 1: step is always is there a you think it as
Speaker 1: a story, it's really hypothesis of why a particular characteristics
Speaker 1: related to return? Why is it? How can it be used?
Speaker 1: To predict returns. What is it? What's the inefficiency that
Speaker 1: we're capturing? And if we have that, then the next
Speaker 1: step is, okay, now let's spend some time looking at
Speaker 1: the data and figuring out how do we best um
Speaker 1: create how do we best capture that inefficiency, how do
Speaker 1: we best measure it? Uh, So, you know, we might
Speaker 1: have an efficiency related to momentum um. And back in
Speaker 1: the seven back in the eighties, you know, you had
Speaker 1: some papers about price momentum and they basically said, Okay,
Speaker 1: the best way to capture price momentum at the time
Speaker 1: is uh, sort of a twelve month trailing risk adjusted
Speaker 1: return price return. That's your best momentum measure. Since then,
Speaker 1: a lot of things have changed. We've got a lot
Speaker 1: better understanding what drives momentum, you know, what's what are
Speaker 1: the inefficiencies we're capturing with it, and a lot more
Speaker 1: ability to um turn that into different kinds of signals.
Speaker 1: And today, in addition, we've got machine learning, so we
Speaker 1: can put in all the historical prices and say, okay,
Speaker 1: machine learning algorithm, what do you think the best predictor
Speaker 1: of return is based on past price moves? And when
Speaker 1: you do that, you have to be careful because machine
Speaker 1: learning is one way to do what's called overfitting a problem,
Speaker 1: you know, where you you have a great solution of
Speaker 1: the past, but it doesn't work in the future. UM.
Speaker 1: One of my colleagues, Michael bat Nick, once observed, the
Speaker 1: best track record of any model is the last ten
Speaker 1: years something something to that effect. Does that sound about
Speaker 1: So every model, you know, every model implicitly has some
Speaker 1: potential for some degree of overfitting associated with it. We
Speaker 1: try to guard against that. We have various, you know,
Speaker 1: statistical procedures that we follow, UM in various research procedures
Speaker 1: we followed to try to avoid that, but it does
Speaker 1: creep in no question about that. We have been speaking
Speaker 1: with John Chisholm. He is the co CEO and former
Speaker 1: Chief Investment Officer for Acadian Asset Management. If you enjoy
Speaker 1: this conversation, we'll be sure and come back and check
Speaker 1: out the podcast extras, where we keep the tape rolling
Speaker 1: and continue to discuss all things quant You can find
Speaker 1: that at iTunes, overcast, Stitcher, Bloomberg dot com, wherever final
Speaker 1: podcasts are sold. We love your comments, feedback and suggestions
Speaker 1: right to us at m IB podcast at Bloomberg dot net.
Speaker 1: You can check out my daily column on Bloomberg dot
Speaker 1: com slash Opinion. Follow me on Twitter at rit Halts.
Speaker 1: I'm Barry Ri Halts. You're listening to Masters in Business
Speaker 1: on Bloomberg Radio. Welcome to the podcast. John, Thank you
Speaker 1: so much for doing this. I've been looking forward to
Speaker 1: this for a while. We were having a conversation in
Speaker 1: my office and on the way out the door, someone
Speaker 1: said who you who you say interviewing today? I said, oh,
Speaker 1: John Chiselm of a Kadian asset management and the person said, oh,
Speaker 1: I've never heard of them. Uh do they manage any money?
Speaker 1: And my auntswer was spitting distance from a hundred billion
Speaker 1: dollars and that sort of shock some people. How do you, um,
Speaker 1: how do you feel about being a little below the
Speaker 1: radar and why are you sort of poking your head
Speaker 1: out from from below the radar? So in general, it's
Speaker 1: we think it's good to run a little bit below
Speaker 1: the radar. Right. There's there's elements of First of all,
Speaker 1: you can only manage so much money and still add value.
Speaker 1: You just have to be careful managing capacity. Um. And
Speaker 1: we also if you're a big name in the industry, uh,
Speaker 1: you get more press attention. That's in one way that's good,
Speaker 1: but in another way it can also be detrimental depending
Speaker 1: on what's the type of attention. UM and a lot
Speaker 1: of investors, a lot of institutions especially, want managers that
Speaker 1: are very careful to focus us on maintaining their ability
Speaker 1: to add value for clients by not getting too big. Right.
Speaker 1: We all know managers that have grown and grown and
Speaker 1: then at some point they just couldn't add value anymore.
Speaker 1: They just got too big to happen. Then they shrink
Speaker 1: and shrink, as we've seen with a number of famous
Speaker 1: hedge fund managers the past decade or so. Exactly. We
Speaker 1: we just like to be maybe a little bit um
Speaker 1: less volatile in terms of our business than that. And
Speaker 1: it's just best. It's best for our team, it's best
Speaker 1: for our clients. UM. And those are really the key
Speaker 1: considerations typically. Now sticking the head out part is it
Speaker 1: is important, I think to have some degree in name
Speaker 1: recognition because a we want talented people, and if your
Speaker 1: potential employees don't know you are who you are, then
Speaker 1: you may not be their first place of employment of choice. UM.
Speaker 1: When there's an opportunity that might be a great fit
Speaker 1: for them. So there's an element of that, and also
Speaker 1: we're doing a number of new things that we haven't
Speaker 1: been doing before. One of them is we've built a
Speaker 1: multi asset strategy. So historically we've been primarily an equity firm.
Speaker 1: We have a multi as strategy today that has about
Speaker 1: a little over a year live track record. It's done
Speaker 1: very well relative to many of its peers. It's a
Speaker 1: very quantitative approach, is very consistent with our philosophy, but
Speaker 1: it invests in equities, fixed income, currency, commodities, and options,
Speaker 1: and the goal there is to create a income return
Speaker 1: stream that's much more stable than what you get from
Speaker 1: an equity market beta. You know, that doesn't go up
Speaker 1: and down every time the market equity markets go up
Speaker 1: and down, but that provides a fairly consistent typically, for example,
Speaker 1: one version of strategy cash plus five return. So not
Speaker 1: quite risk parity, but it's not risk parity because we're
Speaker 1: not necessarily investing in equal risk proportions. It's it's really
Speaker 1: you can think of it more as it's related to
Speaker 1: this concept that there's certain efficiencies that operate not just
Speaker 1: inequities but also in in other asset classes um but
Speaker 1: it also relates to specific expertise in these other asset
Speaker 1: class is that there's individual drivers saying commodities UM that
Speaker 1: are fairly unique there and there you can capture them
Speaker 1: through these return models and in turn gets some significant
Speaker 1: value added from that area, which you don't get in
Speaker 1: a lot of these so called alternative risk premium strategies.
Speaker 1: So you mentioned capacity, you're at eight six billion. How
Speaker 1: much more capacity is there? You are you in broad
Speaker 1: areas and equities and countries that have a lot of
Speaker 1: a lot more headroom or do you do you see
Speaker 1: limitations not too far down the road. It varies. So
Speaker 1: the emerging markets, for example, strategies is closed new clients
Speaker 1: when if we if a client withdraw some money, will
Speaker 1: add some money for any existing client more capability to invest,
Speaker 1: but we're closed their frontier markets is closed. Emerging markets
Speaker 1: small cap is closed. Are non US small cap again
Speaker 1: subject to some reallocation when there's flows out UM is
Speaker 1: also closed UM. But we have capacity in areas like
Speaker 1: global like our manage of volatility strategies, this multi asset strategy.
Speaker 1: So we what we do is we have a very
Speaker 1: specific process to measure how much money can we invest
Speaker 1: and still meet our investment objective in each strategy, and
Speaker 1: when we hit that number, we close the strategy. If
Speaker 1: we've got headroom, we tell the clients here's how much
Speaker 1: headroom we have. There's how much we expect to be
Speaker 1: able to add before we have to close the strategy.
Speaker 1: And I've seen, um, some of your long short UH
Speaker 1: portfolios are one ninety over thirty or something like that.
Speaker 1: Am I? Am I getting that more or less right? Yeah,
Speaker 1: we have a variety. We have some that are pure
Speaker 1: um market neutral, so they're equal sides long short. We
Speaker 1: have some that are one thirty thirty so d long short,
Speaker 1: and then we have some other variations as well. We
Speaker 1: have a we call it diverse fight Alpha strategy that's
Speaker 1: a slightly different ratio as well. But essentially, all these
Speaker 1: strategies the ideas take advantage of the inefficiencies on the
Speaker 1: short side and the unattractive companies that we follow. And alright,
Speaker 1: so one thirty thirty is the long short as opposed
Speaker 1: to fully market neutral, which is its leveraged. So effectively,
Speaker 1: we have a global leverage market neutral strategy that's about
Speaker 1: two long short. Quite quite interesting. Um, let me go
Speaker 1: through some of the questions we didn't get at get
Speaker 1: to during the broadcast portion before I get to my
Speaker 1: favorite questions, UM, And there was one that I thought
Speaker 1: was kind of interesting, and I pulled this off of
Speaker 1: either your website or something you had written. Quote documented
Speaker 1: recurring behavioral errors drive irrational actions in financial markets, behaviors
Speaker 1: that are often contrary to investors best interests. How does
Speaker 1: your firm uh use your understanding of this to help
Speaker 1: manage money? So this goes back to how do we
Speaker 1: come up with these signals? So, for example, one behavior
Speaker 1: Lewer is investors typically are overconfident in their ability to
Speaker 1: predict future growth rates. So if you're buying growth stocks
Speaker 1: in the tech bubble, UH, and you're looking at companies
Speaker 1: that are growing their earnings at or more a year
Speaker 1: or higher UM, those companies were trading in some cases
Speaker 1: at multiples north of hundred on earnings on current earnings.
Speaker 1: And if those companies had continued growing their earnings at
Speaker 1: those very high rates for twenty years, that would have
Speaker 1: been a reasonable price to pay. What happened is investors
Speaker 1: didn't realize that, yeah, they can grow their earnings at
Speaker 1: that rate maybe for one year, three years, four years,
Speaker 1: it's very hard to do that for twenty years UM,
Speaker 1: and so that overconfidence, I think is one of the
Speaker 1: key drivers of why you see in the law term
Speaker 1: value UM working effectively. What's happened the last ten years
Speaker 1: that's interesting is two things. One is that actually there
Speaker 1: were some companies that actually did grow their earnings for
Speaker 1: at really high rates for a long time. So typically
Speaker 1: people think of the Internet services, you know, the Googles
Speaker 1: and Amazons and so on. Those companies have been tremendously
Speaker 1: successful for a while. Albeit you're starting to see a
Speaker 1: few cracks in those growth rates now for some of
Speaker 1: these some of these companies. And the other thing that's
Speaker 1: happened is just a general repricing within valuation. So UM
Speaker 1: you had a certain level of dispersion where value was
Speaker 1: so successful from say two thousand one to two thousand seven,
Speaker 1: that the dispersion of valuation multiple shrank and as a result,
Speaker 1: you didn't the expensive companies weren't really that much more
Speaker 1: expensive then the slower growing and expensive companies, and that
Speaker 1: gave a little bit of tail wind growth over that period.
Speaker 1: I think we've pretty much worked off all that, all
Speaker 1: that dispersion UH, or rather the tightening of dispersion, so
Speaker 1: we're back to more normal level dispersion now, so at
Speaker 1: a kadie and we'd expect going forward that you're more
Speaker 1: likely to have at some point soon whether soon as
Speaker 1: you know, next month, next year, but not not in
Speaker 1: six or seven years. Sometimes sooner than that. We expect
Speaker 1: to see value reassert itself, and so we continue to
Speaker 1: have some component of our of our factors focused on valuation.
Speaker 1: So q FO fair to say that was value reasserting itself. Yeah,
Speaker 1: I think actually a Q four was just for us
Speaker 1: for a Kadian particular or not a great quarter um
Speaker 1: And it was partly that actually value in some markets
Speaker 1: didn't pay off well, but it was also partly um uh.
Speaker 1: Smaller companies in general, especially in the US and emerging
Speaker 1: markets did poorly the relative larger companies. And we have
Speaker 1: in our portfolios a fair amount of exposure too smaller
Speaker 1: and medium sized companies because typically that's where we see
Speaker 1: the general inefficiencies any kind of fact or we see
Speaker 1: those as being greater in that area than they are
Speaker 1: in the very large cap companies. So what hurt us
Speaker 1: in the fourth quarter a little bit of value, but
Speaker 1: primarily just the risk of small versus large, biting us
Speaker 1: you hinted earlier at E T F s UM sometimes
Speaker 1: being less efficient than other ways of expressing the same strategies. However,
Speaker 1: go back a decade or two and there were certain
Speaker 1: strategies that you can only get through expensive alternative investments.
Speaker 1: You're paying two and twenty for certain strategies that you
Speaker 1: can now pay on a fifty basis points and an
Speaker 1: eight dollar transaction fee. So what do you make of
Speaker 1: this landscape and what does this mean for quantitative strategies
Speaker 1: eventually migrating to some of these low cost products. I
Speaker 1: think that we've seen that trend and there's a good
Speaker 1: reason for it. Right. Investors should be looking for what's
Speaker 1: the if I want to get a certain return and
Speaker 1: risk stream, what's the least expensive way for me to
Speaker 1: do that? And it's been great for investors the fact
Speaker 1: that there has been pricing pressure on the esset management
Speaker 1: side of the business. That's actually a great thing for investors, right.
Speaker 1: It forces the investment managers to be more efficient, It
Speaker 1: pushes the overpriced products away from you know, it makes
Speaker 1: them less viable, and it allows strategies that can be
Speaker 1: run inexpensively. But still provide value to do well in
Speaker 1: the marketplace. So great for investors, tougher for asset managers.
Speaker 1: Is not as easy to make money now as asset
Speaker 1: manager as it was, you know, ten or fifteen years ago.
Speaker 1: We've seen margins for the esset management business gets squeezed
Speaker 1: a little bit over the last few years, no, no
Speaker 1: doubt about that. What as long as we're talking about
Speaker 1: UM indexes and ETFs and price squeezes, what do you
Speaker 1: make of the argument that some of this movement away
Speaker 1: from active management into passive is distorting prices. I don't
Speaker 1: think we're there yet, UM, in terms I do believe. Look,
Speaker 1: there's a value to price discovery. If you had a
Speaker 1: hundred percent of of every know, all assets were pastively managed, UM,
Speaker 1: you wouldn't have a mechanism of price discovery. But you
Speaker 1: don't need you don't need, you know, as assets active
Speaker 1: management to get the price discovery process to work. UM.
Speaker 1: I think there's been various academic work on this. Andrew
Speaker 1: Low right in your backyard, and and there's folks at
Speaker 1: Harvard and and generally they what they come up with
Speaker 1: is that you can have a greater level of passive
Speaker 1: management than we have today and still get the you know,
Speaker 1: the social benefits if you will, of the price discovery process.
Speaker 1: Quite quite interesting. I know I only have you for
Speaker 1: a limited amount of time. Let me jump to some
Speaker 1: of my favorite questions we ask all our guests. UM,
Speaker 1: tell us the most important thing that people don't know about,
Speaker 1: John Chisholm. Wow. Um, that's a tough one. And you
Speaker 1: know it's funny because I know you gave me the
Speaker 1: questions in advance. So that's that's the one where I
Speaker 1: looked at and I was like, I don't know if
Speaker 1: I have anything there, and I skipped it. So I
Speaker 1: did not pre didn't think. I did not come up
Speaker 1: with an answer to that that particular question. UM. I
Speaker 1: would say a couple of things. One is, um, I
Speaker 1: love I love asset management. That's probably I love investing.
Speaker 1: That's probably not a that's something that's some of the
Speaker 1: people who work with me know pretty well. But it
Speaker 1: may be something that you know, the you're listening audience
Speaker 1: maybe doesn't appreciate as much. Uh. And um. The other
Speaker 1: thing that maybe it's something that's maybe not really directly
Speaker 1: work related is um. Uh. Two things in terms of
Speaker 1: leisure activities, I love ultimate frisbee. Ultimate frisbee is a
Speaker 1: great sport. I don't know if you know what it is.
Speaker 1: Of course I know. I went to college at stony Brook.
Speaker 1: Ultimate frisbie was a huge thing on campus back in
Speaker 1: the nineteen hundreds when I went to school, so so same.
Speaker 1: You know, I actually went to high school at Bronx
Speaker 1: Science and the Bronx here, and uh it was it wasn't.
Speaker 1: I was not the Ultimate team, but that's where I
Speaker 1: started playing with some of the guys on the team.
Speaker 1: And then I played a little bit in college, played
Speaker 1: after college, and you know, now there's a over in
Speaker 1: Boston area. There's no over forty league. I still get
Speaker 1: a chance to go out and play every now and then.
Speaker 1: It's not a contact sports, so you know, it's not
Speaker 1: like rugby exactly. That's that's the beauty of it, I think,
Speaker 1: is that you get a great exercise. It's a lot
Speaker 1: of fun, it's very social and um and you don't
Speaker 1: kill yourself. It's not like I played basketball typically once
Speaker 1: a week as well. And I'll tell you after alsop
Speaker 1: and left and right, I'm limping, you know, for like
Speaker 1: three or four days, so I can start walking well
Speaker 1: again and that does not happen after ultimate that. That's
Speaker 1: very funny. So the next question was a question I
Speaker 1: used to use as a throwaway to just do a
Speaker 1: mic check, but the answers have been so interesting I
Speaker 1: decided to ask it while we were recording. Tell us
Speaker 1: what was your first car? The make, model, and year.
Speaker 1: If I'm not a car guy, but I do remember,
Speaker 1: it was a Mazda GLC cost about used to cost
Speaker 1: about seven eight hundred dollars and it ran about like
Speaker 1: it cost seven or eight hundred dollars. This is probably
Speaker 1: eight four ish. Um. It had a nice little stick
Speaker 1: shift in the wherever and we call those, by the
Speaker 1: way today those are millennial anti theft devices. I like that.
Speaker 1: I like that description. Um. And it was probably I
Speaker 1: was probably because I had an eighty four is already used.
Speaker 1: It's probably like a nineteen eight. I don't I don't
Speaker 1: even know, but probably nineteen eighty or something like that
Speaker 1: or seventy nine. That's interesting. Um, tell us about some
Speaker 1: of your mentors who helped guide your career. Yeah, I'd
Speaker 1: have to say there's there's really some of some of
Speaker 1: my partners at a Katie and some of my co founders,
Speaker 1: so Gary Brookstrom. Um, you know he Uh I started
Speaker 1: my first part time job in asset management was working
Speaker 1: with him, and uh so he was very important. We
Speaker 1: were a development stage company, so there were lots of
Speaker 1: you know, syncratic things we didn't have, like an HR
Speaker 1: department we didn't have. But but Gary was really was
Speaker 1: also really passionate about investing. Um. He's retired now, but
Speaker 1: he's still invests. So I would say Gary. My other colleague,
Speaker 1: Ron Fraser, who was a portfolio manager at Putnam before
Speaker 1: he came to join us as one of the fourth
Speaker 1: Health co founders. Ron is a true gentleman, an investment professional.
Speaker 1: Taught me a lot about how to treat other people. UM,
Speaker 1: And so I would say that would be another another
Speaker 1: one of the folks that I learned a lot from
Speaker 1: when I first came into the business. Quite quite intriguing. Um.
Speaker 1: What about investors who influenced the way you approached the
Speaker 1: world of investment? I think Ben Graham, you know, sort
Speaker 1: of the value part of that at Principle and again
Speaker 1: even though value hasn't been great the last ten years, UM,
Speaker 1: just the way he thought about, Um, how do you
Speaker 1: make an investment decision? Uh, you know, a lot of
Speaker 1: things came from Ben Graham. Um, I'd say he's important, Um,
Speaker 1: and then I would say there's people outside of investment,
Speaker 1: outside invest Mary who but who have lessons for investing.
Speaker 1: So Michael Lewis, you know when he wrote back in
Speaker 1: the eighties, he wrote Liars Poker, and and uh, that
Speaker 1: book actually, even though it's not technically an investing book,
Speaker 1: it's certainly not a textbook, but has a lot of
Speaker 1: interesting information that someone who's coming into the investment industry
Speaker 1: for the first time, you know, it's a great book
Speaker 1: to read, or was certainly at the time a great
Speaker 1: book to read. So I found that that's another example
Speaker 1: of something where you can learn a lot even though
Speaker 1: it's not technically an investment book. Speaking of books, let's
Speaker 1: talk about some of your favorite books. What do you
Speaker 1: read for fun? What do you read for work? Investing?
Speaker 1: Non investing? Fiction? Nonfiction? Yeah, so I like two days
Speaker 1: nonfiction books can be great. Um. I mentioned Michael Lewis
Speaker 1: Liars Poker. His new book is a book about actually
Speaker 1: the transfer power between them you know it. Okay, So
Speaker 1: I've read that and again that's the stories there. It's
Speaker 1: just it's interesting because it's his way. He has this
Speaker 1: way of getting into you know, sort of getting into
Speaker 1: the detail situation learning it's enough about the milieu and
Speaker 1: talking to enough people, and then it's both humorous and
Speaker 1: as you say, har flank, but it's it's educational too.
Speaker 1: You know, you've learn a lot. So so that that
Speaker 1: would be an example of a nonfiction type of nonfiction book.
Speaker 1: I love the way he finds these eclectic characters and
Speaker 1: the story is always unwound through these unusual people, the
Speaker 1: person from the weather channel, and it's just that's a
Speaker 1: fascinating book. And then the the personal stuff, I would
Speaker 1: say would be UM. I read some occasionally, not not
Speaker 1: it's not a huge volume nowadays, but consistently over the
Speaker 1: last years. I'll try to find some science fiction stories.
Speaker 1: And by sense fiction I mean not um fantasy. I
Speaker 1: guess this is the aerospace engineering me not sort of
Speaker 1: the fantasy version, but the sort of hard science like
Speaker 1: the three that would be one or um Alice. Redemption.
Speaker 1: There's a Redemption Space series that I'm currently reading, Redemption Space.
Speaker 1: Who's the author there? Okay, you Alistair, So I've I've
Speaker 1: just finished the first one of the series. There's a
Speaker 1: but about a six book series, and I'm embarked on
Speaker 1: the second and I'll have to get back to you
Speaker 1: on Alice. Uh if you could do Redemption. Uh, let's
Speaker 1: see what Google has to say about this Redemption arc
Speaker 1: by Alistair Reynolds. Reynolds, sorry Reynolds, the second book in
Speaker 1: the Revelation Space series. Revelation Space is the name of
Speaker 1: this series. Okay, absolutely, And so you're going to go
Speaker 1: sci fi a feeling very predictable that that would be
Speaker 1: an example of that's when I'm reading it's a dollar series.
Speaker 1: I think they started. I started writing those around you know,
Speaker 1: twenty years ago or eighteen years ago. But that's an
Speaker 1: example of the kind of sort of it's a little
Speaker 1: bit harder science fiction with a lot of speculative stuff,
Speaker 1: and it's kind of fun um to just think about
Speaker 1: technology and the impact technology can have in the very
Speaker 1: long term. And I find certain types of science fiction writers.
Speaker 1: Another example would be that's a little older, would be
Speaker 1: Larry because I knew you could things exactly. Uh, those
Speaker 1: those books, the whole series of Ringworld, um from Niven.
Speaker 1: He was amazing. Yeah. I had a great way of
Speaker 1: coming up with these ideas and then sort of making
Speaker 1: I mean, I think the quality of the of his
Speaker 1: writing over time varied a little bit, but certainly the
Speaker 1: examples like Ring World, um, A Moot in God's Eye
Speaker 1: that you co wrote with Jerry Purnell, those are examples
Speaker 1: of books that, uh, you know, there's a lot of
Speaker 1: creative thinking, and they're entertaining stories as well. They start
Speaker 1: with the framework and then the characters and the and
Speaker 1: the plot really move along. Any other ones do you
Speaker 1: want to mention before we move on that that's a
Speaker 1: that's a really good collection. I'm a giant Larry Niven face,
Speaker 1: and I had a feeling. I had a feeling you
Speaker 1: were you were heading in that direction. Um, so what
Speaker 1: what excites you right now? What about the world of
Speaker 1: investing has you really enthusiastic looking forward to the future.
Speaker 1: I think I mentioned earlier were early days with respect
Speaker 1: to things like machine learning and big data, and I
Speaker 1: think there's a potential for significant transformation. So you've got
Speaker 1: this historical division of quant and you know traditional or
Speaker 1: fundamental investors, where the quants go broad but maybe not
Speaker 1: that deep, and the traditional investors go very deep but
Speaker 1: they may not be quite as broad. Um. I think
Speaker 1: we're at a point where we're gonna be able to
Speaker 1: start going broad and deep because of these these kinds
Speaker 1: of both the on the data side and then the
Speaker 1: ability to interpret the data using machine learning. You have
Speaker 1: to be very careful with machine learning. It's prone to overfitting,
Speaker 1: so you've got to build in some safeguards to avoid that.
Speaker 1: And we're still learning best practices. You know, what are
Speaker 1: what are the best techniques to use UM in driverless cars?
Speaker 1: People talk about neural nets. Neural nets you know, can
Speaker 1: easily find the best fit to historic data UM, but
Speaker 1: not always guaranteed to outperform UM just a standard linear
Speaker 1: statistical model historic with with future data. And so I
Speaker 1: think there's a lot of opportunity there for us to
Speaker 1: to learn and do better in that area, and that
Speaker 1: that kind of stuff is very exciting both for me
Speaker 1: and it turns out when you talk to young people
Speaker 1: coming into the quantitative research area, that's that those are
Speaker 1: the kinds of things they're excited and working on. I
Speaker 1: can imagine. So tell us about a time you failed
Speaker 1: and what you learned from the experience. Yeah, I mean,
Speaker 1: there's there's probably plenty of plenty of areas. One area
Speaker 1: would be uh in many ways. I was kind of lucky.
Speaker 1: I went to a good school. Um, I was good
Speaker 1: at taking exams. UM got a job that you know,
Speaker 1: we had the aerospace job, but the investment job, and
Speaker 1: that turned into a company. And I've been very fortunate
Speaker 1: in the people I've worked with, so I've always things
Speaker 1: are kind of work, been kind of successful. And when
Speaker 1: it came time to go through the CEO search process, UM,
Speaker 1: you know, one of the things we did is we
Speaker 1: took these I guess you administer different kinds of not
Speaker 1: just personality exams, but they're sort of invent tories of
Speaker 1: your managerial leadership capabilities, and so what when you take
Speaker 1: one of these, they ask you to rate yourself, and
Speaker 1: then all your peers and all your colleagues at the
Speaker 1: company do the same thing and even sort of compare.
Speaker 1: Here's where I think I am. And I'm doing this
Speaker 1: as a gesture, but I'll explain in a minute for
Speaker 1: your audience, here's where everybody else thinks I am. So
Speaker 1: it's very humbling to find out I had a very
Speaker 1: high opinion of my strategic thinking and UM, my ability
Speaker 1: to bring people to a consensus or to pull behind
Speaker 1: a decision, and UM, some of my colleagues observed that
Speaker 1: there were aspects of my decision making that, you know,
Speaker 1: they didn't appreciate as much potentially as I would have
Speaker 1: thought they might have. Um And so that was humbling,
Speaker 1: but it was also great because you know, really hearing
Speaker 1: other people's honest feedback is something that not everybody gets easily.
Speaker 1: And this is sort of an anonymous process, so it's
Speaker 1: a little filtered, but I can sort of see, here
Speaker 1: are some areas where I actually, you know, could be
Speaker 1: doing better than I than I was. UM. So I
Speaker 1: an area where I what I think of as failed
Speaker 1: as um my self image was miscalibrated relative to where
Speaker 1: everybody else was. On the plus side, that's a that's
Speaker 1: a learning opportunity because you can say, Okay, here's some
Speaker 1: things I could work on. I can try to do better.
Speaker 1: And if even if I can't do better, because I
Speaker 1: am who I am, maybe it's good to have an
Speaker 1: appreciation for some of my shortcomings. I love the way
Speaker 1: you phrase that, as an engineer would my self image
Speaker 1: was miscalibrated with the with the rest of the rest
Speaker 1: of the office. That's funny. Um So, if a millennial
Speaker 1: or recent college grad came to you and said they
Speaker 1: were considering a career in either quantitative research or asset management.
Speaker 1: What sort of advice would you give them. I'd say,
Speaker 1: if you're if this is something that you're excited and
Speaker 1: you're interested in, Absolutely it can still be a tremendously exciting,
Speaker 1: rewarding career. I do think it's very different than the
Speaker 1: environment that I faced three years ago. Right when you're
Speaker 1: entering something that's sort of new and green field, you know,
Speaker 1: there's not a lot of established players. Um, you've got
Speaker 1: a lot of opportunity. I mean, it could go completely astray,
Speaker 1: in which case you have to go to Plan B.
Speaker 1: But um, you've got a lot of opportunity. We've got
Speaker 1: a more mature industry now, there's lots of established competitors,
Speaker 1: and so it's harder to come in and have an immediate,
Speaker 1: big impact on a firm or established investment process. Um.
Speaker 1: You know, it's going to take more work and it's
Speaker 1: gonna take some time. UM, so you've got to be
Speaker 1: prepared for that. You know, if if you're if you
Speaker 1: want to, um, you know, develop the next great idea,
Speaker 1: there's still scope to do that, but um, you're doing
Speaker 1: it within the context typically of of a bigger existing
Speaker 1: process and firm. Another area though, might be fintech. So fintech,
Speaker 1: you know, the retail investors I think still are not sure.
Speaker 1: Fees have come down somewhat. You've got lots of index funds,
Speaker 1: you've got a t f s, but they're still not
Speaker 1: served as well in terms of the sort of advice
Speaker 1: and planning portion as they potentially could be. And so
Speaker 1: you know, some of these fintech companies, I think there
Speaker 1: there are some potentially disruptive ideas that either we are
Speaker 1: seeing or some of them may pan out, some of
Speaker 1: them may not, but that may be an interesting area
Speaker 1: as well to consider beyond pure investment management. Quite quite intriguing.
Speaker 1: UH and our final question, what do you know about
Speaker 1: the world of quantitative investing today? You wish you knew
Speaker 1: when you were starting out thirty years ago. There's a
Speaker 1: couple of lessons. One is the importance of risk control.
Speaker 1: I mentioned um, if you're just betting on a single factor,
Speaker 1: single signal, there could be a lot of risk associated
Speaker 1: with that exposure and a portfolio you need to manage
Speaker 1: that risk effectively. That's really important. A second thing is
Speaker 1: the payoffs to factors can change a lot over time.
Speaker 1: I think I intellectually, I think my I and my
Speaker 1: colleagues appreciated that. But there may be ways to manage
Speaker 1: those uh, the expectation of those payoffs using models that
Speaker 1: help predict how well values going work or quality or
Speaker 1: momentum is going to work and uh. And so the
Speaker 1: importance of having such models and incorporating them into your
Speaker 1: process is something I'd love to appreciate, Say before two
Speaker 1: thousand eight, for example, quite fascinating. Thank you, John for
Speaker 1: being so generous with your time. We have been speaking
Speaker 1: with John Chisholm. He is the co CEO and former
Speaker 1: chief investment officer for a Kadian asset Management. If you
Speaker 1: enjoyed this conversation, we'll be sure to look up an
Speaker 1: inch or down an inch on Apple iTunes or wherever
Speaker 1: final podcasts are sold and you can see the other
Speaker 1: let's call it two hundred and thirty or so such
Speaker 1: conversations we've had. We love your comments, feedback and suggestions.
Speaker 1: Please write to us at m IB podcast at Bloomberg
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Speaker 1: Check out my daily column on Bomberg dot Com. Follow
Speaker 1: me on Twitter at rid Halts. I would be remiss
Speaker 1: if I did not thank the crack staff who helps
Speaker 1: put together this conversation each week. Medina Partwana is our
Speaker 1: producer and Charles Volmer is our returning audio engineer an
Speaker 1: all time champion. Taylor Riggs is our booker slash producer.
Speaker 1: Uh Attica val Brunn is our project manager. Michael Batnick
Speaker 1: is my head of research. I'm Barry Ridholts. You've been
Speaker 1: listening to Masters in Business on Bloomberg Radio