Transcript: John Chisholm, Acadian Asset Management

 

The transcript from this week’s MIB: John Chisholm, Acadian Asset Management, is below

You can stream/download the full conversation, including the podcast extras on iTunesBloombergOvercast, and Stitcher. Our earlier podcasts can all be found at iTunesStitcherOvercast, and Bloomberg.

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ANNOUNCER: This is Masters in Business with Barry Ritzholtz on Bloomberg Radio.

BARRY RITHOLTZ, BLOOMBERG: This week on the podcast I have an extra special guest. His name is John Chisholm and you might not have heard of him, despite the fact that he is the Co-CEO and Former Chief Investment Officer of Acadian Asset Management, which runs nearly $100 billion in institutional money all around the world; most of it is here in the U.S., but a healthy chunk, about a third is overseas money.

He has a fascinating background; an aspiring rocket scientist who worked at the MIT Instrument Labs before taking a gig at State Street and then eventually him and his partners launched Acadian about 32 years ago. They are a quantitative shop and have a very, very interesting approach, combining essential fundamental factor models into a quantitative system and it’s really very, very interesting. They’ve put together quite a fascinating track record over time.

If you are at all interested in quantitative approaches, factor-based investing, Big Data, artificial intelligence — the way to approach markets from a data-driven perspective, then I think you’re going to find this conversation absolutely fascinating. So with no further ado, my interview with Acadian Asset Management’s John Chisholm.

(MUSIC)

My special guest this week is John Chisholm. He is the Co-CEO of Acadian Asset Management. Previously he was Chief Investment Officer. Acadian manages $86 billion in almost 75 countries around the world. He began as an analyst at State Street Bank. Previous to that he was a Systems Engineer at Draper Laboratories, which really is a great place to start. John Chisholm, welcome to Bloomberg.

JOHN CHISHOLM, CO-CEO, ACADIAN ASSET MANAGEMENT: Hi, Barry. Thanks. It’s great to be here.

RITHOLTZ: So Systems Engineer at Draper Laboratories, which became known as the MIT Instrumentation Lab — is that right? How did you find your way from MIT to the Instrumentation Labs?

CHISHOLM: When I went to college my passion, what I was excited about was really building or designing spaceships. This was in the early ’80s, so I —

RITHOLTZ: So you were a rocket scientist, is that what —

(CROSSTALK)

CHISHOLM: I was a — I was a wannabee, an aspiring rocket scientist.

(CROSSTALK)

Exactly. And so I got my undergraduate degree, but I found my senior year, I was, I’d gotten really interested in investing and I was spending a lot of time mostly just reading about investment, you know, whatever it was; journal, business publications, journals and, when I decided, okay, what do I want to do now? I thought, well, I’d probably want to go back to grad school. Do I want to do finance? Investing? Business? Or do I want to do aerospace?

And I had an opportunity to apply to several different programs, so I had both; I had a finance opportunity and an aerospace opportunity and I thought, why don’t I try to test both out? I’ll get a full-time job here in the Boston area — the only place — there’s not a lot of aerospace jobs in Boston —

RITHOLTZ: Mm-hmm.

CHISHOLM: Draper Labs is one that works on guidance systems. So if you’ve got a satellite or a missile, you try to figure out, where is it going to go? How does it get there? Before GPS…

(CROSSTALK)

— had a guidance system. So I worked there fulltime and I got a part-time job, like sort of after-hours job working with a fellow named Gary Bergstrom, who was later one of my co-founders at Acadian. He’d been a portfolio manager at Putnam —

RITHOLTZ: Mm-hmm.

CHISHOLM: And in the ’70s and then he left, sort of off on his own, consulting for money managers; consulted at the time, his big project when I was working with him was for State Street; later State Street Mobile Advisors.

RITHOLTZ: Mm-hmm.

CHISHOLM: And we helped build and design their first International Index Fund — and then later on some international active strategies. So that was sort of a part-time job. I went back to — that made me decide — that was more interesting than the aerospace stuff I was doing at the time —

(CROSSTALK)

— that made me decide —

(CROSSTALK)

— (INAUDIBLE) go back.

RITHOLTZ: So that’s why, when you said to go back, you really started your first fulltime job in finance was as an analyst for State Street? Is that right?

(CROSSTALK)

CHISHOLM: So the State Street job was also a part time — that was also while I was at school, working for them for like — there was some time off in January and then the spring semester I worked for them as a potential employer. But in the end, Gary’s goal was to launch an asset management firm. Myself and we had another colleague, Churchill Franklin and another colleague, Ron (ph) Frazier — they all came aboard, we all came together about the same time, around 1987, when I graduated — and so we launched Acadian as an active money manager at that point in time.

(CROSSTALK)

RITHOLTZ: So that’s 32 years ago — an active manager, as well as a heavily… influenced by quantitative strategies. Is that a fair statement?

(CROSSTALK)

CHISHOLM: We’re a quantitative manager. We were all, you know, my background, aerospace engineering — all quantitative — Gary’s background — Gary had gotten a Ph.D. from MIT. So we were all very quantitative, but Quant at the time was not as sophisticated as what Quant today is.

(CROSSTALK)

Right? There wasn’t any machine learning. There wasn’t any Big Data. There was, you know, Little Data. There were statistics.

(CROSSTALK)

Right? So you know, what’s the average payoff to value? And how do we build a portfolio that captures that payoff? Very simple quantitative tools that we used back in the middle ’80s.

RITHOLTZ: So do you consider yourselves today an active manager of quantitatively — like I think about firms like DFA or any of the pharma fringe-based — factor models and they’re somewhere between active and a quantitative screening approach — how would you describe Acadian?

CHISHOLM: Describe us as active. So most of what we’re doing is highly active, potentially high tracking error against a benchmark. We have the flexibility, so we can build low-tracking error strategies. This ties into this concept of capacity; how much money can you manage —

(CROSSTALK)

— and still expect to add the value your clients are looking for? And typically the more money you manage, the harder it is to add value. So at lower, at levels of active risk, lower expected value added, you can manage more money. There’s some clients who are happy hiring managers for that. They’re also usually happy paying lower fees.

RITHOLTZ: Mm-hmm.

CHISHOLM: Right. So you really have to trade off both from a perspective of adding value and from a perspective of running a business — where do you want to be? Most of our strategies are highly active, but we have some that are shading more towards enhanced index.

RITHOLTZ: Mm-hmm. When you say “enhanced index” you’re taking a basic index and then adding a little flavoring to it, to move it away from the benchmark?

CHISHOLM: Yeah. So for example, we might say “enhanced index” would be if we have a tracking error of less than two percent; if we have one-and-a-half percent tracking error, it just means what’s the standard deviation of the expected returns —

(CROSSTALK)

— versus the benchmark. One-and-a-half percent tracking error would be enhanced index strategy; you might only expect to get one-and-a-half percent excess return associated with that net of fees. If we had a more active strategy, we might expect, we might see four percent tracking error. We expect to get about two-and-a-half percent active return net of fees.

RITHOLTZ: Mm-hmm. How do you avoid the challenge as Bill Miller described it, of active managers who charge active fees, but are effectively are closet indexers? How do you clearly differentiate —

(CROSSTALK)

— yourself from that group?

CHISHOLM: So there’s two parts to it. One is what’s under our control, what we can do. We can build portfolios that are active in the sense that they are very different; they look different from the benchmark. They have higher levels of tracking error. They have high active share.

RITHOLTZ: Mm-hmm.

CHISHOLM: The other part of that is the client’s job. So if the client hires 20 managers like that, they’re still getting close to back —

(LAUGHTER)

— back to an index.

RITHOLTZ: Let’s talk a little bit about your quantitative approach and I was noticing on your website, you describe a four-step process and really what that is, is strategy signal generation, signal consumption and then process.

CHISHOLM: Mm-hmm.

RITHOLTZ: Can you explain what that means? To the — perhaps the layperson who may not be familiar with a quantitative approach?

CHISHOLM: Absolutely. Let me start with the signal generation part, because that’s maybe the part that will be easiest for people to start with. The basic idea is there’s different characteristics companies have — those characteristics can be on average predictive of returns. So for example, one characteristic is just, is how expensive does a company look on whatever metric, P-Ratio, right, so you’ve got a company that has award P-Ratio of eight and one that has a P-Ratio of 40. That’s all you knew about those companies; which one would you want to own?

If you look at the last 50 or 60 years, globally, you’d say I want to own PE-8 company, right; on average it’s going to do better. The problem with that is, you can have a 10-year stretch where the PE-40 company kills the P-A company, like we just had. We had —

(CROSSTALK)

You know, Amazon (INAUDIBLE) —

(CROSSTALK)

RITHOLTZ: Value has not done well.

CHISHOLM: What would you rather own? Right? In the last 10 years, would you rather have owned Amazon or would you rather have owned PG&E?

(LAUGHTER)

Right? I mean.

RITHOLTZ: So those are the two biggest extremes you can really (INAUDIBLE) —

(CROSSTALK)

CHISHOLM: Sorry. I picked those out and they’re not, hopefully it’s not quite that bad.

(CROSSTALK)

RITHOLTZ: But it’s true generally. Growth stocks have done very well.

(CROSSTALK)

Value stocks (INAUDIBLE) join up for a decade.

CHISHOLM: So how do you get around that? So let’s say you believe on average value is going to outperform — like some people may not believe that, but let’s say I do — I want value in my portfolio, but I don’t want to underperform for 10 years in a row. What can I do? I can take other characteristics that I believe are also predictive of return and combine those with value. So I might say, for example, quality measures, right — I want companies that are well managed. How do you define well managed most, there’s dozens of definitions, but let’s say one definition is — our inventory turnover. We have companies that turn over their inventory more frequently than companies in the same industry.

RITHOLTZ: Mm-hmm.

CHISHOLM: Right? And maybe that’s a signal that on average has some payoff associated with it, for companies and many industries. So now I’ve got value and I’ve got this. Let’s say a thing people talked a lot about in the markets is momentum.

RITHOLTZ: Mm-hmm.

CHISHOLM: So I’ve got (INAUDIBLE) companies… have good momentum. They’ve been performing, they’ve been outperforming their peers for the last, you know, six to 12 months. Maybe that’s indicative that on average in the future they’ll likely outperform for the next say one to three months; I want to wrap that in. So you combine all these different signals and you’ve got, what — historically people call them “multi-factor model”.

RITHOLTZ: Right.

CHISHOLM: Right. And so now, even if value does badly, maybe momentum and quality and these other things do well enough to allow you to still outperform, which is always the goal for us and for our clients. And so that’s sort of the genesis of signals. It’s just, they’re different types of characteristics that we use to help predict company returns and we then combine them — so when you say “signal consumption” there’s a couple of pieces of that. One is, how do you combine these things? Right? Is they pay off the value the same as the payoff to quality? Well, probably not.

So you have to figure out — what do I expect to get if I’m looking and it doesn’t differ by the type of company I’m looking at — is it a tech company? Maybe it has different drivers of return than a utility and so I have to mix the weight on those signals, depending on what kind of company I’m evaluating. And so that’s part of consumption.

Then the second part of consumption is — how do you implement that in a portfolio? Right. So ultimately I’m going to hold stocks in a portfolio. I’ll hold Amazon or I won’t. I’ll hold PG&E or I won’t. What’s my weight going to be? Hopefully it’ll be — last 10 years hopefully high on Amazon, low or zero on P — but the idea is you’ve got to then turn those expected returns that you’re getting from the Signal Generation part of your process into portfolio positions.

At Acadian, we use a pretty quantitative approach to do that as well. We use what’s called an “optimizer” that basically trades off the return expectations we come up with from the signals —

RITHOLTZ: Mm-hmm.

CHISHOLM: — into and maps those into portfolio positions by trading those off against transaction costs. So if I’m trading, again, Amazon, Samsung… a big, liquid company, transactions costs are probably going to be pretty low.

RITHOLTZ: Right.

CHISHOLM: I mean, almost negligible. But if I’m trading a less liquid company that may be more (INAUDIBLE) officially priced than the return opportunity may be much greater — but I need to now account for… what’s it going to cost to get in the position and what’s it going to cost to get out of that position some day?

RITHOLTZ: So what you’re describing sounds a lot like traditional factor-based investing. You’re describing value, describing momentum, quality —

CHISHOLM: Yeah.

RITHOLTZ: When we talk about liquidity I always think about cap size. How does your approach differ from factor investing? Or, am I asking that question wrong? Are you effectively a pharma fringe factor type investor?

CHISHOLM: No, that’s a great question. And in some ways our approach is very much like factor investing. In other words, we consider these different signals; we consider them to be types of factors. What’s different is that we integrate the factors. So, for example, let’s suppose you just bet on a momentum factor by itself.

RITHOLTZ: Mm-hmm.

CHISHOLM: There’s some periods, if you built a portfolio that has the most attractive, whatever it is, 10 percent of let’s U.S. (INAUDIBLE) stocks and then the least attractive were shorts — or you just went long — most attractive 10 percent — there’s some periods where your portfolio beta, your sensitivity to market movements might be, might be two. So you might have a huge amount of volatility in the portfolio and there’s other times when your sensitivity to market movements might be very low, it might be a beta .5.

Why does that matter? It impacts height and control risk. If you’re doing these single factor portfolios, for example, unless you’re very careful about how you build them, you are likely to take on all kinds of unexpected risks in the construction of those portfolios.

With a multi-factor approach, you’re not beholding to any one factor; you’ve got all these different characteristics that you can emphasize in the portfolio and you can trade them off and it allows you to manage risk better.

Portfolio construction can be a lot better than what people do when they’re doing — when people talk about “factor investing” if you look at a typical factor ETF, it’s not built in a very efficient way; it’s most costly to investors in ways that investors can’t see; things like how they trade the portfolio. So they simply take a rank order of companies based on some factor —

RITHOLTZ: Mm-hmm.

CHISHOLM: — and they rebalance, they buy some of the most attractive ones that have just gotten into that list and sell some of the ones that have fallen out. That can be a lot of turnover. And there may be times when you want to hold onto something that’s become less attractive because it might be expensive to trade out of it and it hasn’t fallen that far. Right.

So it’s important to do — to be smart about how you use these factors and I’d say the key, one of the key things we do is we worry a lot about the engineering of our process; how do you put these factors together? How do you minimize the slippage, the transactions costs, while still getting exposure to the underlying factor in the portfolio?

RITHOLTZ: Quite fascinating. Let’s talk a little bit about what it’s like to run a global organization. Your headquartered in Boston. Congratulations on the Patriots.

CHISHOLM: Thank you.

RITHOLTZ: You were at the game, you mentioned?

CHISHOLM: I was at the game; first time I’ve ever gone. You don’t know how long the Patriots… you know, this might be their last Super Bowl in a while. It seemed like —

(CROSSTALK)

RITHOLTZ: I’ve heard that before.

CHISHOLM: — good idea to go.

RITHOLTZ: Yeah, no. That makes perfect sense. You have affiliates in London and Singapore and Tokyo and Sydney — what other countries are you located in?

(CROSSTALK)

I know you have clients in —

CHISHOLM: We’ve got clients in probably 30, 35 countries, but really, the coverage is Singapore, Sydney, Tokyo, London — we are thinking, because of Brexit, we —

(CROSSTALK)

— may need to open an office in Dublin, perhaps Amsterdam, but there’s enough uncertainty there that we haven’t actually pulled the trigger yet.

RITHOLTZ: Amsterdam. So. So if people — I keep asking this and I’m getting very different answers from very different people — if Brexit happens, hard or soft — and London is no longer the central finance location for Europe, where does it go? Amsterdam doesn’t really seem like the place. Geneva, Stuttgart, I mean, I can’t… Paris, some people have floated. None of them seem to make sense.

CHISHOLM: I think Frankfurt’s got the economic, in many ways, the commercial center of Europe — a lot of people don’t love Frankfurt because I lived near Frankfurt for a bunch of years; it’s not always, it doesn’t have the cultural reputation that Paris, does, for example.

(CROSSTALK)

That being said, it’s a very comfortable city to live in. So I think Frankfurt will do well. That all being said, you know, if we did something with another office, we’re just basically opening up an office to meet the regulatory requirements.

RITHOLTZ: Right.

CHISHOLM: We’d still keep London. That would still be a major center for portfolio management, you know, for our team, for Client Service Team.

RITHOLTZ: So your client base is primarily institutional and public pension funds and other large investors — are they mostly U.S.-located? Are they around the world? What’s your mix?

CHISHOLM: So our… right now our mix is probably 70/30, U.S./non-U.S.

RITHOLTZ: Mm-hmm.

CHISHOLM: We, from a business perspective, you know, as a firm you want to be diversified. You know, so having a fair amount of non-U.S. exposure with our clients is something we strive for. We think there’s a lot of great growth opportunities, in terms, they’re just the growth of pension markets of institutional investor markets… in Asia, for example —

RITHOLTZ: Mm-hmm.

CHISHOLM: There’s still growth in Australia. In Europe, as well, there’s pockets — you know, a few years ago Germany really didn’t have the fine benefit and pension plans, slowly evolving a little bit. So —

(CROSSTALK)

— there’s definitely opportunities.

RITHOLTZ: Don’t most of Europe or doesn’t much of Europe have some sort of a retirement system covered by the government? How do you operate around that?

(CROSSTALK)

Or is that their Social Security and it doesn’t take the place of a real retirement?

CHISHOLM: Yeah. I mean, there’s a lot of variation, but a lot of Europe, you know, like the U.K., for example, you’ve got public pension plan, just like in the U.S., you’ve got —

(CROSSTALK)

— State of California, CalPERS and CalSTRS. In U.K. you’ve got Local Authority Pension Plans. And you can sort of think of them as the equivalent of public plans here in the U.S.

RITHOLTZ: Mm-hmm.

CHISHOLM: You’ve got large companies based in the U.K. that have private pension plans. So there’s some state provision, but again, just like in the U.S., you have Social Security, but that doesn’t exclude all these other types of pension plans.

RITHOLTZ: So in your list of countries, I didn’t hear a whole lot in China. Is Hong Kong attractive? Or is Mainland China possible? Or has the government made it too challenging to set up shop there?

CHISHOLM: No, the government’s actually moving to liberalize, so it had been very difficult for a non-local investor —

(CROSSTALK)

RITHOLTZ: A shares, B shares.

CHISHOLM: Invested in Chinese assets and to manage money for Chinese institutions — we do manage money for Hong Kong clients, but that’s sort of a separate regulatory structure.

RITHOLTZ: Is that why you’re located in Sydney? For that part of the world?

CHISHOLM: Singapore is where —

(CROSSTALK)

— We’re serving — right now serving —

(CROSSTALK)

— Asia, ex-Japan, out of Singapore.

RITHOLTZ: Mm-hmm.

CHISHOLM: But China is liberalizing and there’s plenty of non-local managers now setting up shop to manage money for Chinese institutions in China. The challenge is in China you need some scaling — (INAUDIBLE) partner, because you can’t touch the retail market without a local partner.

RITHOLTZ: Right.

CHISHOLM: And there’s only really four big institutions, you know, that you’ve got better, like sort of the equivalent of a CalPERS, for example. So that that market, the institutional market’s very narrow and the rest of the market, the retail market, you need a partner. If we found the right partner, I think we’d be very excited about doing something in China and we’re certainly doing some work there.

We’ve had some people, not fully, full-time based there, but spending a lot of time in the market. But that’s something that remains to be seen, whether we’ll find the right opportunity to really be a player there.

RITHOLTZ: So you previously were Chief Investment Officer and now you’re Co-CEO; I have so many questions about both. So are you still working in a CIO capacity as well?

CHISHOLM: No. So I’m still interested, I still go to Investment Policy Committee Meetings, but you know, if you’re taking on a new role and you’ve picked someone to succeed you in your job, you really need to give them the ability to run that —

(CROSSTALK)

— function. You’ve got a great successor — my successor, Brendan Bradley is our CIO.

RITHOLTZ: Mm-hmm.

CHISHOLM: Started last year. As CIO he’s been with Acadian for a long time. And I’ve got complete confidence in his ability to manage the investment function. I still participate in some of the meetings and I’m interested in the research and I talk to lots of the investment professionals; it’s part of my job as CEO is being in touch. You know, what does an investment firm do?

(CROSSTALK)

We invest for our clients. So it’s still important. But Brendan is managing and leading the Investment Team.

RITHOLTZ: And you’re a Co-CEO, which sounds like it has a whole lot of complications and issues that would come out of dual-CEO role, at least we’ve seen that with public companies. How do you navigate that? Is there a clear distinction between who is running what? Tell us a little bit about your Co-CEO.

(CROSSTALK)

CHISHOLM: Yeah, absolutely. Let me do that. Because it is a great question. We went through a succession process; our former CEO was stepping down; was retiring from the CO role. And so myself and one of my colleagues, Ross Dowd, were internal candidate for the role. We have a selection process where we had an equivalent of an executive committee — essentially you think of it as individuals running the firm. Making that decision, we were two members of that group and we both shared our views on what’s our vision for Acadian? Where do we want the firm to go? What would we like to do differently… with our executive committee?

It turned out, what, we were very well aligned in terms of where we wanted Acadian to go. So when we looked at sort of, are there situations — I have a lot of respect for Ross, my current CEO; he has I think a lot of respect for me. He comes from a marketing client service background. I come from the investment background. And we both wanted each other to remain at the firm, thought about, how can we do that?

And we looked at examples where there had been Co-CEO structures in the past at other firms. The ones that worked relatively well, and there are some — generally you had Co-CEOs with highly-aligned visions and that were able work together to provide a single voice to the firm. Right?

(CROSSTALK)

So you don’t want somebody coming to Ross and getting one answer and then coming to me and getting a different answer.

RITHOLTZ: Right.

CHISHOLM: And we thought, given the fact that we in fact do have highly aligned visions, we do have areas of expertise that are complementary to each other. We thought, this is something that not only could we pull it off, but that it would actually be beneficial for Acadian. So we’re now a little over a year into the role. We think we’re managing the firm effectively. We’re getting feedback from out team that that’s the case. And I think it’s working extremely well so far.

We… what we typically do is — issue comes up, we will discuss it together. We’ll figure out where we — what are we looking to do? And there are times when, you know, it’s an area Ross has a lot of expertise in — defer to him more. There’s times when it’s an area I’ve got a lot of expertise in, defer me more. It’s great when we do need to be in two places at once, right — he can be in Tokyo and I can be in Boston or vice versa. One of us can be doing, so I’m meeting with some of our clients; another one can be running internal meetings. So it really helps us, I think be, do a more effective job of managing the firm, to have the structure we have.

RITHOLTZ: Quite interesting. So let’s talk a little bit about what’s going on in the marketplace today. Are you still seeing the same sort of mispricings in securities that perhaps were so abundant a decade ago?

CHISHOLM: The misprices have changed a lot. So if we take any particular signal that we’ve used 10 years ago and we look at the payoff to that signal today, it’s lower today.

RITHOLTZ: Mm-hmm.

CHISHOLM: Right? So typically, whether it’s inefficiencies being squeezed out of market, it’s arbitrage by different types of investors; whatever it is, you know, typically the payoff to these characteristics decreases over time.

RITHOLTZ: Mm-hmm.

CHISHOLM: So as a result, we’re… in a way, we’re on a treadmill. We need to keep on finding new ideas to replace the old ideas that aren’t working as well anymore.

RITHOLTZ: So when you say these… the payoffs to these ideas decline over time, is that all of these ideas? Is that a function of, we had a giant reset with the financial crisis? Hey, anytime the markets lose 57 percent of their value, you have to think that some value is going to be created and a lot of babies get thrown out with the bath water — or is it just the nature of every good idea eventually runs its course?

CHISHOLM: I think it’s really a little bit of the reset idea, so there’s no — there’s definitely a pattern that differs a little bit — the rate of decline in some of these things, accelerated during the financial crisis, immediately after the financial crisis. And the pay off to value is the biggest one, where it’s clearly been the worst 10-year period for value globally, post GFC, that we’ve seen in the long-term history — whether it’s the U.S. history or longer history, that’s different.

That all being said, a lot of the factors, it’s an average thing. Right? There are some signals that still work, you know, today, not much worse than they were 10 years ago. But the average signal, the payoff increases a little bit every year.

RITHOLTZ: Hmm. That’s quite interesting. Quant has been around for 30, 40 years or so. Do you think things are very different based on the rise of, you mentioned earlier, Big Data and artificial intelligence — how has that affected how Acadian approaches Quant Investing? Or is that just something that… is a background noise that affects the market overall?

CHISHOLM: Yeah, I think there’s no question that the machine learning (INAUDIBLE) — and Big Data, artificial intelligence — those are early days, right. Those things are starting to impact investors and how people invest. But we’re still in the early days of that in Quant, let alone in finance in general and there’s a lot more to come.

But I would say things have changed a lot since the ’80s and ’90s. The sophistication, be of… not so much Big Data, but just any kind of data now is a lot more available —

(CROSSTALK)

— than it was then. So we have a lot more information and quants can do things today that they couldn’t do 20 years ago; fundamental investors could maybe do them for a small group of companies, quants couldn’t. Today we can look at all these, like we have industry-specific information about lots of companies that we just didn’t have access to 20 years ago.

RITHOLTZ: Is it the technology and database? Or is it actually the specifics of the data itself that’s changed so much?

CHISHOLM: Yeah, it’s both. So the — no question, the technology and the database access, power and speed of databases and of software and processing in general has increased tremendously; that makes a lot of things easier to do.

RITHOLTZ: Mm-hmm.

CHISHOLM: Machine learning, those algorithms can be very computationally intensive. And you, with the hardware you had 25 years ago, you couldn’t do these things today. Today you can do them on your, on your laptop, in some cases.

RITHOLTZ: Mm-hmm.

CHISHOLM: It might take a while, but there’s things you can do on your laptop. If not, you go to Amazon Web Services and scale up processing power and you’ve got everything you need — in terms of the — sort of the processing computational aspect of things. So that’s changed a lot.

But also the data itself today is much broader than it was. I got started, you got a P.E., you got a P.B., you got a price-to-cash flow, you’ve got a market cap, a price and maybe a dividend yield thrown in. And that’s… that was your data.

RITHOLTZ: Right.

CHISHOLM: That was like 1984ish. And then shortly after you started getting Analyst Data electronically.

RITHOLTZ: But what do you think about some of these alternative data points that people are pulling from either the satellite data — hey, here’s all the ships moving oil around the world? Or parking lot activity, to determine how well retailers are doing — is any of that potentially useful and valuable to investors? Or is it just a bunch of geeks playing with some —

(LAUGHTER)

— new tech toys and kind of having fun with it?

CHISHOLM: It’s both.

(LAUGHTER)

So on the latter point, you know, we have an analyst and if we have a satellite data project, we have no problem getting somebody to volunteer, put their hand up and say I’d like to work on this; this will be fun. Right?

(CROSSTALK)

So that’s true. It’s potentially valuable. Now whether it’s actually valuable to any individual and any particular investment firm depends on their style and their process. So let me tell you what I mean by that. If you’ve got a satellite data, let’s say, and you’re getting, you know, a parking lot — your infrared images — and you’re getting information about, you know, parking lots. And that’s — if you’re following retailers and investing in retailers is a big part of what you do, that can be useful in predicting over the short run, revenues.

You’ve got to have a lot of infrastructure; you’ve got to know all the locations. You’ve got to be able to aggregate that in quasi real time. And satellite coverage at high resolution, at quick, successive, short time intervals —

RITHOLTZ: Mm-hmm.

CHISHOLM: — is still expensive. So you’ve got to figure out, is it worth it to your process to do that? If you’re only one percent of their portfolio have, that you have invested in retailers, maybe it’s not really going to move the needle that much. Right?

RITHOLTZ: Mm-hmm. So, so let’s talk about some other things that don’t involve satellites. This has been a… let’s call it atypical political environment for the past couple of years; not just the Trump presidency, but Brexit and the financial crisis and the rise of the Tea Party — how does a quant shop manage those sorts of non-market inputs? Or does it all just come out in the wash and it’s not really all that important?

CHISHOLM: I mean, there’s two sides of this. One side is what I call “risk management” right, so can you — if you can observe some of these risks and you don’t observe them in a standard quant risk model because they’re — the quant risk models are typically backward-looking. They’re not forward-looking. So you’ve got a, as a professional investor think, what are some of these risks that maybe aren’t priced into the risk models that are looking at the historical data, but that could impact the portfolio — and let me give you an example of such a risk.

One risk is — we managed one strategy that’s a low volatility equity strategy, so what we’re trying to do there is reduce the risk of equity markets — a cap-weighted benchmark, let’s say had a U.S. might have a 12 to 14 vol and we might want to produce a 10 vol, for example. And what that means is that somebody gets the same risk return that they get on a cap-weighted benchmark, but they get it with less risk. That’s very helpful from an asset allocation perspective.

When you do that today, though, what you’re doing is you’re taking on a lot of interest rate risk, because these lower-risk companies typically tend to be higher dividend companies, companies that are more sensitive to interest rates. So if you’re worried about a rising interest rate environment, your historical risk model wouldn’t say, constrain your exposure to interest rates, your sensitive interest rates — but going forward you might want to do that in a low volatility portfolio so that your volatility doesn’t come out much higher than you expect, or your return is much lower than you expect — if interest rates do in fact continue rising. So that’s the risk management piece. You want to anticipate certain risks and build that into your risk controls that you apply to your strategies.

The second piece is, can you use, are there other signals that help you navigate from a return perspective, these kinds of, you know, macro events? And you know, for example, volatility itself can be an early warning signal. Right, every major evaluation of currencies in emerging markets and many market breaks were preceded by periods of rising volatility.

Rising volatility also sometimes predicts more benign environments, but the point is, if there’s a signal there, maybe there’s ways to predict these environments and so our — or the top/down part of what we do, tries to look at these macro events or potential macro events and figure out how can we anticipate those and how can we position the portfolios based on that anticipation?

RITHOLTZ: So you mentioned a rising rate environment. Lots of folks have been focused on the Federal Reserve and focused on are we going to take a pause. I imagine that your shop doesn’t spend a whole lot of time struggling with that, that it should end up in the data and it’s not the sort of thing that you have to play macro tourist or am I giving you guys too much credit?

CHISHOLM: No, no. You’re giving us just the right about of credit here. The – Barry, you need to play the game that you’re good at. And so, we don’t want to do – we don’t want to try to do things that we’ve got other people who are much better at it than we are. And predicting rates using the Fed, what’s the Fed’s going to do, that’s not – as a client manager, that’s not really what we’re good at, right?

So you’re absolutely right. There what we would do is we would say let’s just look at what’s happening with the – you know, the short-term rates, long-term rates, what’s happening with the yield curve. Those can be signals that we use in a model, but we’re not trying to really forecast the direction of interest rates per se through Fed statements or through other kinds of, you know, actions like that.

It just means trying to do what – where we think out edge is and really trying to focus on that in terms of the things we actively do in the portfolio.

RITHOLTZ: So you mentioned your models. When I was pursuing the various offerings that you have for institutional clients, there are 30 something different models, maybe even more. How do you develop different ideas? How do you express them in a portfolio? Is it strictly math or are there other guiding principles that affect that?

CHISHOLM: The first step is always is there – you can think of this as a story. It’s really a hypothesis of why a particular characteristic’s related to a return, why is – how can it be used to predict returns, what’s the inefficiency that we’re capturing. And if we have that, then the next step is, OK, now let’s spend some time looking at the data and figuring out how do we best create – how do we best capture that efficiency, how do we best measure it.

So, you know, we might have an efficiency related to momentum, and back in the – back in the 80s, you know, you had some papers about price momentum, and they basically said, OK, the best way to capture price momentum at the time is sort of a 12 month trailing, risk adjusted return – price return. That’s your best momentum measure.

Since then, a lot of things have changed. We’ve got a lot better understanding of what drives momentum, you know, what are the inefficiencies that we’re capturing with it, and a lot more ability to turn that into different kinds of signals. And today in addition we’ve got machine learning, so we can put in all the historical prices and say, OK, machine learning algorithm, what do you think the best predictor of return is based on past price moves?

And when you do that, you have to be careful because machine learning is one way to do what’s called over fitting a problem where you’re – you have a great solution in the past but it doesn’t work in the future.

RITHOLTZ: One of my colleagues, Michael Batnick, once observed the best track record of any model is the last 10 years, something to that effect. Does that sound about right?

CHISHOLM: Yes, so every model – you know, every model implicitly has some potential for some degree of over fitting associated with it. We try to guard against that. We have various, you know, statistical procedures that we follow and various research procedures we follow to try to avoid that, but it does creep in. No question about that.

RITHOLTZ: We have been speaking with John Chisholm. He is the Co-CEO and former Chief Investment Officer for Acadian Asset Managmet. If you enjoy this conversation, well, be sure and come back and check out the podcast extras where we keep the tape rolling and continue to discuss all things quan (ph). You can find that at iTunes, Overcast, Stitcher, bloomberg.com, wherever finer podcasts are sold. We love your comments, feedback and suggestions. Write to us at mibpodcast@bloomberg.net. You can check out my daily column on Bloomberg.com/opinion. Follow me on Twitter @Ritholtz. I’m Barry Ritholtz. You’re listening to masters in business on Bloomberg radio.

(COMMERCIAL BREAK)

Welcome to the podcast. John, thank you so much for doing this. I’ve been looking forward to this for awhile. We were having a conversation in my office and on the way out the door someone said who are you — who are you interviewing today. I said John Chisholm of Acadian Asset Management.

And the person said, I’ve never heard of them. Do they manage any money? And my answer was a spitting distance from $100 billion and that sort of shocked some people. How do you — how do you feel about being a little below the radar and why are you sort of poking your head out from — from below the radar?

CHISHOLM: So in general it’s — we think it’s good to turn run a little bit below the radar. Right. There’s — there’s elements of — first of all you can only manage so much money and still add value.

So you just have to careful managing capacity and we also — if you’re a big name in the industry, you get more press attention that’s — in one that’s good but in another way it can also be detrimental depending on what’s the type of attention.

And a lot of investors, a lot of institutions especially want managers that are very careful to focus on maintaining their ability to add value for clients by not getting too big. Right. We all know managers that have grown and grown and then at some point they — they just couldn’t add value anymore. They just got too big to add value.

RITHOLTZ: And then they shrank and shrank as we’ve — as we’ve seen with a number of famous hedge fund managers the past decade or so.

CHISHOLM: Exactly. We — we just like to be maybe a little bit less volatile in terms of our business in that and that’s just best — it’s best for our team, it’s best for our clients, and those are really the key considerations typically.

Now sticking the head out part is — it is important, I think to have some degree name recognition because A, we want talented people and if your potential employees don’t know who you are, then you may not be there first place of employment of choice when there’s an opportunity that might be a great fit for them.

So there’s an element of that. And also we’re doing a number of new things that we haven’t been doing before. One of them is we’ve built a multi asset strategy. So historically we’ve primarily an equity firm. We have a multi asset strategy today that has about a little over a year live track record.

It’s done very well relative to many of its peers. It’s a very quantitative approach. It’s very consistent with our philosophy but it invest in equities, fixed income, currency, commodities and options, and the goal there is to create a income return stream that’s much more stable than what you get from a equity market beta you know that doesn’t go up and down every time the market — the equity markets go up and down.

But that provides you a fairly consistent — typically for table (ph) one version of the strategy, cash plus five for return.

RITHOLTZ: So not quite risk parity but definitely …

CHISHOLM: It’s — it’s not — it’s not risk parity because we’re not necessarily investing equal risk portions. It’s –it’s really — you can think of it more as it’s related to this concept that there’s certain inefficiencies that operate not just in equities but also in other asset classes. But it also related to specific expertise in these other asset classes, that there’s individual driver sane (ph) commodities that are fairly unique there, and they’re – you can capture them through these return models and in turn get some significant value added from that area which you don’t get in a lot of these so-called alternative risk premia strategies.

RITHOLTZ: So you mentioned capacity. You’re at $86 billion. How much more capacity is there? Are you in broad areas and equities and countries that have a lot of – a lot more headroom or do you see limitations not too far down the road?

CHISHOLM: It varies. So the emerging markets, for example, strategies is closed to new clients. When – if we – if a client withdraws some money, we’ll add some money for – to give the existing client more capability to invest, but we’re closed there. Frontier markets is closed. Emerging markets small cap is close. Our non-U.S. small cap, again, subject to some reallocation when there’s flows out is also closed.

RITHOLTZ: But –

CHISHOLM: We have capacity in areas like global, like our managed volatility strategies, this multi-asset strategy. So we – what we do is we have a very specific process to measure how much money can we invest and still meet our investment objective in each strategy. And when we hit that number, we close the strategy. If we’ve got headroom, we tell the clients here’s how much headroom we have. Here’s how much we expect to be able to add before we have to close this strategy.

RITHOLTZ: And I’ve seen some of your long-short portfolios are 190 over 30 or something like that. Am I getting that more or less right?

CHISHOLM: Yes, we have a variety. We have some that are pure market neutral, so they’re equal sides long-short. We have some that are 130/30, so 130 percent long, 30 percent short. And then we have some other variations as well. We have a – we call it a risk flight alpha strategy. It has a slightly different ratio as well. But essentially all these strategies the idea is take advantage of the inefficiencies on the short side and the unattractive companies that we follow.

RITHOLTZ: And – all right. So 130/30 is the long-short as opposed to fully market neutral which is 50/50 or –

CHISHOLM: Or – or –

RITHOLTZ: – 100 or –

CHISHOLM: – or it’s levered, so effectively we have a global levered market neutral strategy that’s about 200 percent long, 200 percent short.

RITHOLTZ: Yes. Quite interesting. Let me go through some of the questions we didn’t get at – get to during the broadcast portion before I get to my favorite questions, and there was one that I thought was kind of interesting, and I pulled this off of either your website or something you had written. Quote, “documented recurring behavioral errors drive irrational actions in financial markets, behaviors that are often contrary to investors’ best interests.” How does your firm use your understanding of this to help manage money?

CHISHOLM: Yes. So this goes back to how do we come up with these signals? So for example, one behavioral error is investors typically are overconfident in their ability to predict future growth rates. So if you’re buying growth stocks in the tech bubble and you’re looking at companies that are growing their earnings at 20 percent, 25 percent, 30 percent or more a year or higher, those companies we’re trading in some cases at multiples north of 100 on earnings – on current earnings, and if those companies had continued growing their earnings at those very high rates for 15, 20 years, that would have been a reasonable price to pay.

What happened is investors didn’t realize that, yes, they can grow their earnings at that rate maybe for one year, three years, four years. It’s very hard to do that for 20 years. And so, that over confidence I think is one of the key drivers of why you see in the long-term value working effectively.

What’s happened in the last ten years that’s interesting is two things, one is that actually there were some companies that actually did grow their earnings at really high rates for a long time.

So, typically people think of the internet services, the Googles and Amazons and so on. Those companies have been tremendously successful for a while. I’ll be it; you’re starting to see a few cracks in those growth rates now for some of these companies. And the other thing that’s happened is just a general repricing within valuation.

So, you had a certain level of dispersion where value was so successful from say 2001 to 2007 that the dispersion of valuation multiples shrank and as a result, you didn’t — the expensive companies weren’t really that much mote expensive than the slower growing inexpensive companies.

And that gave a little of a tailwind of growth over that period. I think we’ve pretty much worked off all that dispersion or rather the tightening of dispersion. So, we’re back to more normal levels of dispersion now.

So at Acadian we’d expect going forward that you’re more likely to have at some point soon whether soon as next month, next year. but not in six or seven year, sometimes sooner than that. We expect to see value reassert itself. And so we continue to have some component of our factors focus on valuation.

RITHOLTZ: So, Q4 2018 fair to say that was value reasserting itself?

CHRISHOLM: Yes, I think actually Q4 was for us for a (inaudible) in particular not a great quarter. And it was partly that actually value in some markets didn’t pay off well but it was also partly smaller companies in general, especially in the U.S. and emerging market did poorly relative to larger companies.

And we have in our portfolios a fair amount of exposure to smaller medium size companies because typically that’s where we see the general inefficiencies, any kind of factor.

We see those as being greater in that area than they are in the great large cap companies. So, what hurt us in the fourth quarter, a little bit of value but primarily just the risk of small versus large biting us.

RITHOLTZ: You hinted earlier at ETFs sometimes being less efficient than other ways of expressing the same strategies. However, go back a decade or two and there were certain strategies that you can only get through expensive alternative investments.

You were paying (inaudible) 20 for certain strategies that you can now pay, I don’t know, 50 basis points and an $8 transaction fee. So, what do you make of this landscape and what does this mean for quantitative strategies eventually migrating to some of these low cost products.

CHRISHOLM: Oh, I think that we’ve seen that trend. And there’s a good reason for it, right? Investors should be looking for what’s the — if I want to get a certain return of risk screen, what’s the least expensive way fro me to do that.

And it’s been great for investors, the fact that there has been pricing pressure on the estimate management side of the business. That’s actually a great thing for investors, right? It forces the investment managers to be more efficient. It pushes the overprice products away from — it makes them less viable.

And it allows strategies that can be run inexpensively but still provide value to do well in the marketplace, so great for inventors, tougher for asset managers. It’s not as easy to make money now as asset mangers as it was 10 or 15 years ago. We’ve seen margins for the asset management business get squeezed a little bit over the last few years.

RITHOLTZ: No doubt about that. As long as we’re talking about indexes and ETFs and price squeezes, what do you make of the argument that some of this movement away from active management and to passive is distorting prices?

CHRISHOLM: I don’t think we’re there yet in terms — I do believe, look, there’s a value to price discovery. If you had 100 percent of every — you know all assets were passively managed, you wouldn’t have a mechanism to place discovery (ph).

But you don’t need — you don’t need you know 70 percent of assets active management to get the price of discovery process to work. I think — there’s been various academic work on this.

RITHOLTZ: And drew low (ph), right in your backyard …

CHISHOLM: And they’re low and — and there’s folks at Harvard and — and generally they — what they come up with is that you can have greater level of passive management than we have today and still get the — you know the social benefits if you will of — of the price discovery process.

RITHOLTZ: Quite interesting. I know I only have you for a limited amount of time. Let me jump to some of my favorite questions we ask all our guest. Tell us the most important thing that people don’t know about John Chisholm.

CHISHOLM: Wow. That’s a tough one. And you know it’s funny because I — I know you gave me the questions in advance. So that’s — that’s the one where I looked at it and I was like I don’t know if I have anything and you know I skipped it. So I did not pre — I did not think about …

RITHOLTZ: Right. Did not do your homework.

CHISHOLM: I did not come up with an answer to that — that particular question. I would say a couple of things. One is I love — I love asset management that’s probably — I love investing. That’s probably not a — that’s something that some of the people who work with me know pretty well but it may be something that you know your listening audience maybe doesn’t appreciate as much.

And the other thing that maybe is something that’s maybe not really directly work related is two things in terms of leisure activities. I love ultimate Frisbee. Ultimate Frisbee is a great sport. I don’t know if you know what it is.

RITHOLTZ: Of course I know. I went to college in Stony Brook.

CHISHOLM: All right.

RITHOLTZ: Ultimate Frisbee was a huge thing on campus back in the 1900s when I went to school.

CHISHOLM: I — so — so same. You know I actually went to high school at Bronx Science in the Bronx here. And it was — it — I wasn’t — I wasn’t on the ultimate team but that’s where I started playing with some of the guys on the team.

And then I played a little bit in college, played after college, and you know now there’s an over — in the Boston area there’s an over 40 league. I still get a chance to go out and play every now and again.

RITHOLTZ: Well, it’s not a contact sport. So over 40 — you know it’s not like rugby.

CHISHOLM: Exactly. That’s — so that’s the beauty of that I think is that you get all the great exercise, it’s a lot of fun. It’s very social. And — and you don’t kill yourself. It’s not like — I play basketball typically once a week as well. And I’ll tell you I — after basketball …

RITHOLTZ: ACLs popping left and right.

CHISHOLM: I’m — I’m limping, you know, for like three or four days so I can start walking well again and that does not happen after ultimate.

RITHOLTZ: That — that’s very funny. So the next question was a question I used to use as a throw away to just do a mike check but the answers have been so interesting I decided to ask it while we’re recording. Tell us what was your first car? The make, model, and year if you remember.

CHISHOLM: Sure. I’m not a car guy but I do remember it was a Mazda GLC. It cost about — used it cost about $700, $800 and it ran about like it cost $700 or $800. This is probably 1984-ish. It had a nice little stick shift in the whatever.

RITHOLTZ: Right. We — we call those — by the way, today those are millennial anti-theft devices.

CHISHOLM: I like that. I like that description. And it was probably — yes, it was probably — since I had it in 84’ it was already used. It was probably like a 1980 — I don’t — I don’t even but probably a 1980 or something like that or 79 …

RITHOLTZ: That’s interesting. Tell us about some of your mentors. Who helped guide your career?

CHISHOLM: Yes, I’d have to say there’s — there’s really some of — some of my partners at Acadian and most of my co-founders. So Gary Bergstrom, you know he — I started — my — my first part time job in asset management was working with him and so he was very important.

We were a development stage company. So there were lots of idiosyncratic things. We didn’t have like an H.R. department, we didn’t’ have — but — but Gary was really — is also really passionate about investing.

He’s retired now, but he still invests. So I’d say, “Gary, my colleague, Ron Frazier (ph), who was a portfolio manager at Putnam before he came to join us as one of the fourth co-founders, Ron is a true gentleman and investment professional. He taught me a lot about how to treat other people.” And so, I would say that would be another – another one of the folks that I learned a lot from when I first came into the business.

RITHOLTZ: Quite – quite intriguing. What about investors? Who influenced the way you approach the world of investment?

CHISHOLM: I think Ben Graham (ph). You know, so the value part of that principal, and again, even though value hasn’t been great the last 10 years, just he way he thought about how do you make an investment decision, a lot of things came from Ben Graham (ph). I say he’s important. And then I would say there’s people out of investment – outside of (inaudible) who have lessons for investing. So Michael Lewis, you know, when he wrote back in the ‘80s, he wrote “Liar’s Poker.”

RITHOLTZ: Sure.

CHISHOLM: And that book, actually, even though it’s not technically an investing book, it’s certainly not a textbook, but it has a lot of interesting information that, someone who’s coming into the investment industry for the first time, you know, it’s a great book to read – or it was certainly, at the time, a great book to read. So I found that’s another example of something where you can learn a lot, even though it’s not technically an investment book.

RITHOLTZ: Speaking of books, let’s talk about some of your favorite books. What do you read for fun? What do you read for work, investing, non-investing, fiction, non-fiction?

CHISHOLM: Yes. So I like (inaudible). Non-fiction books can be great. I mentioned Michael Lewis, “Liar’s Poker.” His new book is a book about, actually, the transfer of power between —

RITHOLTZ: The fifth column.

CHISHOLM: You know it, OK. So I’ve read that, and again, the stories are – it’s interesting because he has this way – he has this way of getting into – you know, sort of getting into the details of a situation and learning enough about the (ph) middle U (ph) and talking to enough people. And then it’s both humorous and, as you say, powerful (ph), but it’s educational, too.

RITHOLTZ: Totally.

CHISHOLM: You know, you learn a lot. So – so that would an example of a non-fiction – type of non-fiction book.

RITHOLTZ: I love the way he finds these eclectic characters, and the story is always unwound through these unusual people – the person from the weather channel. And it’s just – that’s a fascinating book.

CHISHOLM: And the personal stuff, I would say, would be – I read some occasionally, not – it’s not a huge volume nowadays, but consistently, over the last 20, 30 years, I’ll try to find some science fiction stories. And by science fiction, I mean not fantasy. I guess this is the aerospace engineer in me, not sort of the fantasy version but the – the sort of hard science –

RITHOLTZ: Like “The Three-Body Problem” or –

CHISHOLM: Yes, that would be one, or “Redemption.” There’s a “Redemption Space” series that I’m currently reading.

RITHOLTZ: “Redemption Space,” who’s the author of that?

CHISHOLM: OK. You’ve – Alastair – so I’ve just finished the first one of the series. There’s about – it’s about a six-book series, and I’m embarked on the second. And I’ll have to get back to you on – if you could do “Redemption –

RITHOLTZ: Let’s see what Google has to say about this. “Redemption Ark” Alistair Reynolds.

CHISHOLM: Reynolds. Sorry, Reynolds.

RITHOLTZ: The second book in the “Revelation Space” series.

CHISHOLM: Yes.

RITHOLTZ: All right.

CHISHOLM: So “Revelation Space” is the name of the series.

RITHOLTZ: OK, I –

CHISHOLM: Yes, absolutely. And so –

RITHOLTZ: I knew you were going to go sci-fi. I had a feeling.

CHISHOLM: Very predictable. That would be an example of – that’s a – what I’m – so it’s an older series. I think they started – started writing those around, you know, 20 years ago or 18 years ago. But that’s an example of the kind of sort of – it’s a little bit harder science fiction with a lot of speculative stuff thrown in.

RITHOLTZ: Right.

CHISHOLM: It’s kind of fun to just think about technology and the impact technology can have in the very long-term. And I find certain types of science fiction writers. Another example would be – that’s a little older would be Larry Niven.

RITHOLTZ: Larry Niven, Ringworld engineer.

CHISHOLM: Yes, exactly.

RITHOLTZ: I knew you –

CHISHOLM: Those kinds of things. Exactly.

RITHOLTZ: Those – those books, the whole series of Ringworld from Niven, he was amazing.

CHISHOLM: Yes, he had a great way of coming up with these ideas and then sort of making – I mean, I think the quality of his writing over time varied a little bit, but certainly the examples like Ringworld, A Mote in God’s Eye that he co-wrote with Jerry Pournelle, those are examples of books that, you know, there’s a lot of creative thinking and they’re entertaining stories as well.

RITHOLTZ: They start with the framework and then the characters –

CHISHOLM: Yes.

RITHOLTZ: – and the plot really move along. Any other ones you want to mention before we move on?

CHISHOLM: (inaudible)

RITHOLTZ: That’s a really good collection. I am a giant Larry Niven fan –

CHISHOLM: OK, good to know that.

RITHOLTZ: – and I had a feeling – I had a feeling you were heading in that direction. So what – what excites you right now? What about the world of investing has you really enthusiastic looking forward to the future?

CHISHOLM: I think I mentioned earlier we’re early days with respect to things like machine learning and big data. And I think there’s a potential for significant transformation. So you’ve got this historical division of quant and, you know, traditional or fundamental investors where the quants go broad but maybe not that deep, and the traditional investors go very deep, but they may be not quite as broad. I think we’re at a point where we’re going to be able to start going broad and deep because of these kinds of – both on the data side and the ability to interpret the data using machine learning. You have to be very careful with machine learning. It’s prone to over fitting, so you’ve got to build in some safe guards to avoid that, and we’re still learning best practices. What are the best techniques to use?

In driverless cars, people talk about neural nets. Neural nets, you know, can easily find the best fit to historic data, but not always guaranteed to outperform just a standard linear statistical model historically with future data. And so, I think there’s a lot of opportunity there for us to learn and do better in that area, and that kind of stuff is very exciting both for me, and it turns out when you talk to young people coming into the quantitative research area, that’s – those are the kinds of things they’re excited and working on.

RITHOLTZ: I can imagine. So tell us about a time you failed and what you learned from the experience.

CHISHOLM: Yes, I mean, there’s probably plenty of areas. One area would be the many ways I was kind of lucky. I went to a good school, I was good at taking exams, got a job that, you know, we had the aerospace job, the investment job, and that turned into a company, and I’ve been very fortunate in the people I’ve worked with. So I’ve always – things have kind of worked. I’ve been kind of successful.

And when it came time to go through the CEO search process, you know, one of the things that we did is we took these I guess you administer different kinds of – they’re not just personality exams, but they’re sort of inventories of your managerial leadership capabilities. And so, when you take one of these, they ask you rate yourself, and then all of your peers and all of your colleagues at the company do the same thing. And you can sort of compare here’s where I think I am and like – I’m doing this as a gesture, but I’ll explain in a minute for your audience – here’s where everybody else thinks I am.

So it’s very humbling to find out I had a very high opinion of my strategic thinking and my ability to, you know, bring people to a consensus or to pull behind a decision. And some of my colleagues observed that there were aspect of my decision making that, you know, they didn’t appreciate as much potentially as I would have thought they might have.

And so, that was humbling, but it was also great because, you know, really hearing other people’s honest feedback is something that not everybody gets easily, and this was sort of an anonymous process so it was a little filtered, but you can sort see here’s some areas where I actually, you know, could be doing better than I was.

So I had an area where I think of as failed is my self image was miscalibrated relative to where everybody else was. The plus side, that’s a learning opportunity because you can say, OK, here’s some things I could work on, I could try to do better. And if – even if I can’t do better because I am who I am, maybe it’s good to have an appreciation for some of my shortcomings.

ROTHOLTZ: I love the way you phrase that as an engineer would. My self image was misclaibrated with the rest of the office. That’s funny. So if a millennial or a recent college grad came to you and said they were considering a career in either quantitative research or asset management, what sort of advice would you give them?

CHISHOLM: I’d say if you’re – is this is something that you’re excited in, you’re interested in, absolutely. It can still be a tremendously exciting and rewarding career. I do think it’s very different than the environment that I faced 30 years ago, right? When you’re entering something that’s sort of new and green field, you know, there’s not a lot of established players. You’ve got a lot of opportunity. I mean, it could go completely astray, in which case you have to go to plan B, but you’ve got a lot of opportunity.

We’ve got a more mature industry now. There’s lots of established competitors. And so, it’s harder to come in and have an immediate big impact on a firm or an established investment process. You know, it’s going to take more work and it’s going to take some time. So you’ve got to be prepared for that.

If you’re – if you want to, you know, develop the next great idea, there’s still scope to do that, but you’re doing it within the context typically of a bigger existing process or firm. Another area, though, might be fintech. So fintech, you know, the retail investors I think still are not – sure, fees have come down somewhat. You’ve got lots of index funds. You’ve got ETFs, but they’re still not served as well in terms of the sort of advice and planning portion as they potentially could be.

And so, you know, in some of these fintech companies, I think there are some potentially disruptive ideas that either we are seeing or some of them may pan out, some of them may not, but that may be an interesting area as well to consider beyond put investment management.

RITHOLTZ: Quite intriguing. And our final question, what do you know about the world of quantitative investing today you wish you knew when you were starting out 30 years ago?

CHISHOLM: There’s a couple of lessons. One is the importance of risk control. I mentioned if you’re just betting on a single factor, a single signal, there could be a lot of risk associated with that exposure in a portfolio. You need to manage that risk effectively. That’s really important.

Second thing is the payouts to factors can change a lot over time. I think I intellectually – I think I and my colleagues appreciate that, but there may be ways to manage those – the expectation of those payoffs using models that help predict how well value’s going to work or quality or momentum is going to work. And so, the importance of having such models and incorporating them into your process is something I would love to appreciate, say, before 2008 for example.

RITHOLTZ: Quite fascinating. Thank you, John, for being so generous with your time. We have been speaking with John Chisholm. He is the Co-CEO and former Chief Investment Officer for Acadian Asset Management.

If you enjoyed this conversation, well, be sure and look up and inch or down an inch on Apple iTunes or wherever finer podcasts are sold, and you can see the other, let’s call it 230 or so such conversations we’ve had.

We love your comments, feedback, and suggestions. Please write to us at mibpodcast@bloomberg.net. If you enjoyed this conversation, go to Apple iTunes and be sure to give us a five star rating. Check out my daily column on Bloomberg.com. Follow me on Twitter @Ritholtz. I would be remiss if I did not thank the crack (ph) staff who puts together this conversation this week.

Madena Parwana is our producer and Charles Vollmer is our returning audio engineer and all time champion. Taylor Riggs is our booker slash producer. Atika Valbrun is our project manager. Michael Batnick is my head of research. I’m Barry Ritholtz. You’ve been listening to Masters in Business on Bloomberg Radio.

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