Insightful investor podcast interviewed Greg Bond, CIO of Man Group 20260609

 说话人
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说话人1
00:00:01
Welcome to the insightful investor podcast，a weekly series that seeks to share industry investment and market insights。Learn more about our show at insightful investor dot org。Today we're joined by Greg Bond，cio at man group，one of the world's largest hedge fund firms with 220 billion dollars in assets under management as of the end of March。The firm was founded way back in seventeen eighty three and now operates across fourteen countries of more than seventeen hundred employees.
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说话人1
00:00:34
In this conversation, we'll briefly explore man groups history, the evolution of systematic and discretionary investing, and Greg's perspectives on alpha technology and the future of active management. We're so pleased to have you join us, Greg. Thanks Alex, really happy to be here.
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说话人1
00:00:49
I'd like to first ask a few quick questions about your firm. So obviously, Man Group's history stretches back more than two centuries. How does leading a firm with. Of history shape the way you think about legacy stewardship innovation and the appropriate time horizon for decision making.
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说话人2
00:01:08
Yeah, we're very proud of that history having that history particularly you know going through the last several market inflection points. I think that gives us confidence that we can navigate through those things. Building hopefully robust infrastructure and importantly the culture, right? I think having you know different strategies work at different times and having some of that internal diversification, I think helps some of that where you're not just stuck in sort of one mode of investing. And that legacy is important to us going back through time, and then particularly over the last 2030 years where we've got you know a few businesses that have been running for that long and in sort of certain areas and really looking forward to the next phase and hopefully being well positioned to manage whatever happens next because there always seems to be something next of course.
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说话人1
00:01:52
Yeah, that's always the case. So when you zoom out over man groups evolution, what do you see as the pivotal moments or. Key decisions that most clearly defined what the firm is today.
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说话人2
00:02:03
Well, I think if I think about the firm and I grew up on the quant side of firm called Numeric, which we were acquired by Man Group back in 2014. And if I look in sort of the decades before that, before Numeric joined, I think it was really an evolution of just broader investment capabilities, whether it was sort of AHL on the trend side. What was GLG on the discretionary side, and then ultimately Newbery joining?
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说话人2
00:02:28
So I think the first phase, if you will, of that recent phase is really about building out the investment capabilities, leveraging you know a strong central tech platform, you know centralized operations, centralized sales. So I think from that side, it's a bit about kind of a business diversification piece. But what's really... I think evolved over the decade or so, you know, plus that we've been that I've been at the firm.
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说话人2
00:02:51
That's also evolved from a kind of a pure business diversification into more of a solution space focus for clients. Can we put those things together that we've built? Up a really good track record into solutions, into one strategy or a few strategies that they that will fit their needs, and so that much more I'd say cross collaboration across those engines. So think about it as man group rather than a GLG numeric or AHL. I think that one of the evolutions of the firm is people have thought about man group historically as sort of tier three different businesses, and the mindsets now shifted to this is man group and this is what we could do.
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说话人1
00:03:28
So for listeners who may be. Less familiar with main group. How would you describe its core identity? A little bit beyond.
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说话人2
00:03:35
what you just described. I lean towards alpha at scale. So it's important that we generate alpha. We aim for hopefully top quartile and everything that we do. But also do it in a way that actually helps investors at you know at scale, so that it's sort of the alpha times the assets, not just the alpha, not just the AUM, but really that focus on dollars of excess return, which is what which is what I call it, um, and maybe. People call it, and that way you can think about that solves real problems and different ways and different strategy sets work at different times.
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说话人2
00:04:08
But it's truly that alpha scale, and if your alpha scale that has downstream repercussions on how you think about technology, how you think about who you hire in the organization, how you organize compliance and legal. So I think one of the nice things about that strategy is it then dictates how you want to position. The rest of your firm and all the different activities that it does.
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说话人2
00:04:28
I think it's consistency in that messaging that's really, really powerful. And so that message has gone from a few different kinds of strategies. Now we've done a bit more work on in the private market side and just kind of leveraging that concept out into the outer world. And I think also just having that right culture and a very collaborative culture really to deliver that, you need to have that broad based kind of collaboration and creativity.
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说话人1
00:04:52
Alpha at scale sounds like a very reasonable objective, but in my experience, a lot of times those two compete. With one another, those two interests, because as you get larger and you scale, it's harder to generate alpha. So I think it is, even though it sounds very simple in terms of alpha at scale, in practice, it can be very challenging.
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说话人2
00:05:13
Well, yeah, I mean, my dorky answer to that is you could think about an objective function where there's kind of an optimal amount, right? You have one pressure, as you say, of. Of increased assets hurting the kind of your raw alpha, but at the same time your AUM is growing.
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说话人2
00:05:27
So there is conceptually at least a kind of an optimal point right where that's the right size of your firm depending how fast your alpha decays as you raise assets. And so that's important. That's things that that's things we look at.
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说话人2
00:05:39
It's a measurement. It has a lot of uncertainty around it. And if anything, maybe we lean a little bit towards the left side of that AUM curve, maybe running a little bit less AUM, it makes sure that we are delivering that alpha.
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说话人2
00:05:50
And so that's important. I think there's a few caveats to that broader statement. I think for sure individual strategies should decay at some rate with assets, but there are. places where economies of scale can't start to play into the equation that if you have more assets you can do more things on the technology side maybe you could hire additional diversifying capabilities so there is a little bit of the corporate question on that side as well particularly you put strategies together but i definitely agree at the individual strategy level that principle just definitely holds.
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说话人1
00:06:19
yes certainly you need assets to generate revenues so you can hire the best people. incorporate the best technology. So there's probably some sweet spot in there, as you described. And I guess you can also think of it in terms of alpha in percentage terms or alpha in dollar terms.
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说话人2
00:06:37
Correct. And I think, you know, I get the theoretical answers. You want to think about it in dollar terms, but clearly people in their own portfolios see those percentages and that and that's very critical.
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说话人2
00:06:45
So you need that's why ultimately my, you know, a simple answer about maximizing dollars of excess return is too simple. You need to again, lean back a little bit on what. What is the alpha? I mean, if we were generating one basis point on a trillion dollars of assets, that would be great, but I'm not sure.
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说话人2
00:07:00
On one basis, you know, it is sort of the extreme with a could be interesting outcome. So that's I think most importantly, well, it's a hard problem to crack and you're going to come out with something that's got some uncertainty around it. I think culturally, it's important that you think about it.
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说话人2
00:07:13
So I think that capacity question. Because it's very hard, you know, to go to an investor and sort of sell them one thing, and then ultimately it massively changes over time with the success of that strategy. So I think being very clear and articulate about how you think about capacity upfront.
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说话人2
00:07:30
And then the other thing that has been very beneficial for folks, and I think as we talk to clients, is that one of the questions around scale also amounts to how much volatility or tracking error does one want in a portfolio. So let's take a classic long-only portfolio: you want to hire manager to attack the MSCI World or S&P 500. And so you can have a very, very active strategy, you know, five percent kind of tracking error relative to that, or you can have.
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说话人2
00:08:00
A less active strategy, maybe running one or 2% tracking error, and those are fine. Fine, it's sort of that dial, but I think what's important is that the client's getting the same series of, let's say, alpha models or portfolio manager attention on the discretionary side, and then some of that downstream tracking everything could just be done through portfolio construction. So I think what we find very useful with investors as we sit down is to say here's the menu of alpha sources, alpha ideas.
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说话人2
00:08:27
Here's the kinds of volatilities that one could run or levered or less levered, let's say in the traditional hedge fund side. And let's just have an open conversation about what works. And then that also maps back to fees and other things. So having that dialogue, I think is really, really important.
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说话人1
00:08:41
So you mentioned AHL and AHL was founded long before systematic investing became mainstream. What would you tell us about AHL and the original insight that made this approach so powerful at the time?
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说话人2
00:08:53
It's kind of interesting, I think both AHL and then also numeric at about the same time sort of came up AHL very much on the trend. Following taking the you know taking advantage of the behavior of various commodity markets to trend behavioral reasons maybe there's some other sort of economic rationale and hedging behavior things that happen versus active and versus and hedging participants in those markets and so I think a lot of that is the is the insight hey see this behavior that's one thing and then also how do you actually monetize that and how do you build strategies that that are robust and obviously in the early days of some of these insights.
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说话人2
00:09:26
It's really the insight that drives everything, and then over time competition comes in, and you really have to think about really on execution: do I want to add more and more markets? So I think the key is the evolution not only of identifying what happened but the anomaly, but then also to add more capabilities, extend it across geographies, and I think even on the bottom-up equity side at numeric, it was really ah an insight around animals behavior around earnings announcements and. You know, typically analysts would would upgrade their estimates, but not all the way to where they think it should go.
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说话人2
00:09:56
They're waiting for other other analysts in the market. Change or maybe back in the old days waiting for that whisper number from the company, those kinds of things. And so you had an anomaly there. So that was essentially kind of a trend or momentum following but on analyst earnings analyst revisions that numerick was founded on.
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说话人2
00:10:12
So I think the stories there are quite similar in the sense there was an anomaly took advantage of it, but then to stay ahead of the game, it really means you have to morph and become stay innovative because I think there are a lot of trend followers that aren't around anymore. There's a lot of bottom up equity quant. That aren't around anymore, and it's really how do you evolve into the into the coming decades?
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说话人1
00:10:34
And do you think they're not around anymore because they try to stay true to what originally works, even though that ultimately did not work over time?
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说话人2
00:10:44
Well, there's a pressure, right? It's important that you have a strong culture, a strong idea of what you want to be as an organization. So it's not, hey, I'm doing trend and all of a sudden I'm going to.
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说话人2
00:10:54
COMPLETELY CHANGE INTO ANOTHER PRIVATE EQUITY OR SOMETHING, JUST BECAUSE IT SEEMS TO BE DIFFERENT, YOU KNOW. Given my philosophy, given where I want to play, what is the next thing in innovation? What makes sense? So maybe it's instead of looking at just analyst revision activity, I look at other stock fundamentals.
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说话人2
00:11:13
I start to bring another alternative data set. So it's all linked, and I think that that's important, and particularly in this day of AI. I find it's really important not to change everything.
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说话人2
00:11:25
Because of AI, but really how does AI fit your philosophy? And so as a manager of businesses, as you try to build capabilities, it's really that trade-off of how what is actually innovative in your current lane versus something that's unrelated and you don't have a lot of opportunity to add value. So it's a trade-off.
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说话人2
00:11:41
It's something you work on every day. Maybe sometimes you go a little too far in the diversification front. Or often what can happen in these strategies is that you talk to clients and your clients may not want change in their portfolios.
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说话人2
00:11:52
Maybe they hired you to be the trend manager or they hired you to be the value momentum quality kind of quant manager or a discretion. Style that's brought on, and so often you can get stuck not wanting to upset your clients either. So that's one of the pros of being in this business as long as we have is you've gone through multiple market environments. But that's also one of the downsides is maybe you have a lot of anchoring, and so that's really the hard part: what is the right amount of change? And we spend a lot of time thinking about.
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说话人1
00:12:18
Do you think trend following and systematic strategies have proven durable across decades and very different market regimes?
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说话人2
00:12:27
I think we've seen very good differential performance over different regimes. It's not that we get every regime correct. I mean, I think clearly, you know, around trend and some of the strategies there, we haven't really had a sustained equity drawdown since twenty twenty two.
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说话人2
00:12:40
So it's not a lot of what we've seen and about a lot of these V shaped recoveries have not necessarily been kind to trend, but barely starting. Q3 Q4 of last year, and then into this year we're seeing that kind of more normal behavior. I guess is what you you would expect from trend.
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说话人2
00:12:54
If you go back 2018, 19 and 20, what we would call the value winter on the on the system. Somatic equity side, right? There was some adjustments and things that needed to happen in the quant side, but it was also just a tough time for value based investing. So there's these different regimes.
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说话人2
00:13:10
I think one of the hard parts as a manager and a CIO of the firm is to differentiate, okay, is this strategy underperforming because there's just competitive decay, right? There's just a lot of people doing it, or is there just some cyclical event that's happening that makes it tough for these strategies? And so I think.
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说话人2
00:13:27
The cyclicality can often overwhelm that secular decay, and so it's just spending time thinking about environments: what can we do to be better? Undoubtedly there is kind of general decay. I think people get that in kind of the baseline signals and strategies, and you know our earnings revisions model that I talked about.
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说话人2
00:13:44
You trade it over months; you could wait for your analyst book to come in and type it in by hand. And now some of those things last two to three days, so. You just need to adapt. But in all of those tough periods of a certain strategy, you need to have strong reflection and see if they think you could do better. And maybe improve it and make it more robust the next time around.
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说话人1
00:14:03
It is a really interesting challenge because you start with the assumption that your insight should decay over time as it becomes more widely understood and implemented. But then you have cycles within that natural decay. And so you have to assess at every low point of that cycle whether the underperformance is temporary or if it's more permanent. And so that can be challenging to underwrite.
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说话人2
00:14:28
Well the funny part, and again, I make fun of our industry, because when when we're in a period of down, like sort of the downward side for a certain strategy, it's oh, well there been outflows in the space and you know there's just downward pressure because people are selling out of the strategy, but we never come to you and say hey, when all the inflflows are coming in, look at that, the tailwind that's from there. So some of that.
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说话人2
00:14:46
You need to be reflective on both sides when you're doing well and when you're doing poorly, and be honest about, you know, maybe you can estimate some of those effects of flows both on the good and the bad side. So I think one of the the keys. For us is how do we communicate from it in a transparent way to clients what we think the drivers of that performance are right? And I think that that's that can become more difficult with new technologies, other things that are coming across.
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说话人2
00:15:13
But I think we spend as much time building the technologies as we do sort of building the tools to explain what's going on. I can't come to you now and say well the AI model told me to do it. That's why we lost a bunch of money that.
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说话人2
00:15:26
Doesn't work with people, obviously for obvious reasons. So you need... And I think that's why you kind of tie him back to that strong philosophy. You can always anchor back to your philosophy: 'Here's what happened' and let's be very transparent.
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说话人2
00:15:38
I think that's whether that's a long-only strategy, a hedge fund strategy, or strategies of strategies — a lot of the multi-strategy work that we do to make sure that you can be transparent. You know, and that's also. Discretionary, it can be often a little bit easier because you can talk about individual stocks and bonds and other things that have been bought, but also making sure we can do.
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说话人1
00:15:57
At a high level, how does. Systematic discipline potentially improve investment outcomes. Not obviously, we know about mitigating behavioral biases and human emotion, but it can also enable scale, breadth, and more complex decision making. What would you talk about that?
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说话人2
00:16:14
Well, I think again there's two camps in the world, and I think they are converging this kind of discretionary versus systematic. They're both, they both have pros and cons. They seem to work a bit differently at different times, which is good, which is naturally diversifying. So.
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说话人2
00:16:28
Obviously，some of the benefits of systematic is that these are rules we can show you the rules，it has，it's well tested over various regimes and other things，but you know，it's not changing necessarily on a day to day basis or reacting in the short run。Immediately to serve macro shocks, whereas discretionary can do that. This is a bit more flexible.
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说话人2
00:16:48
And so that's the trade off between the two sides. You do get, you know, the back testability, other things that comes on the systematic side, but the trade off is somewhat on the short term decision making. And so you go through cycles.
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说话人2
00:17:00
I remember coming out of 2008, everybody hated systematic and everybody loved discretionary, and then you kind of move into the recent world where systematic's done quite well. We've also seen discretionary for strong performance as well. So these things run in cycles.
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说话人2
00:17:14
It's why I'm not in either camp on that side. I think there's a nice blend that can be had. I do worry bringing a bunch of discretionary into a systematic strategy or a bunch of systematic stuff into a discretionary strategy can be a little tricky.
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说话人2
00:17:27
Maybe eighty twenty twenty eighty, but never fifty fifty. And I think you'd rather as an investor sitting there to allocate, maybe you allocate. Discretionary systematic on your own rather than forcing you know this concept of quantamental and other things which can be a tricky phrase.
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说话人1
00:17:42
So you just mentioned this but man group has deliberately built both systematic and discretionary capabilities. Would you talk us through why you feel it's important to not choose just one of those philosophies?
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说话人2
00:17:54
What the original impetus I think was to have again back to a very strong technology platform Salesforce operation. All of that to be able to diversify across discretionary and systematic. I think that was sort of the general idea and over time adding more capabilities.
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说话人2
00:18:11
And I think where we're headed today is a world given the advancements of AI that you can start to see a bit of a convergence between the two approaches where. Discretionary managers can do a bit more back testing, a bit more deliberation on what and make it a bit more repeatable, incorporating new data concepts, all of that. I think you see this on the systematic side that you can build more intelligent models that can be a bit more reactive to the macro regime.
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说话人2
00:18:39
So I think we're in a fortunate position. That we've got both, and given the influx of these new technologies could be in a world and sort of going back against what I said earlier about fifty-fifty that you can't really distinguish between discretionary and systematic down the road. I mean this is sort of five to ten years because AI sits in the middle AI.
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说话人2
00:19:00
Sits in the middle, you start to move away. You know, one of the reasons systematic how people come up on the systematic side is they're typically generally more technical, and so they come out from that band. So discretionary is a bit more fundamental by nature, and think of it as sort of MBA PhD, you know kind of thing.
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说话人2
00:19:16
I'm an MBA, so I've grown up on the quant side, but I appreciate more fundamental analysis. And now, given that you've removed a bit of that basic, you know, you don't need that extreme technical background now. That you start to focus on people that are the most creative, and you know, obviously the best clients I see today are the ones that are the most creative, the ones that ask the right questions.
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说话人2
00:19:35
And that's exactly the same case on the discretionary side. So it's just now the toolkit has opened up on both sides. And that's important, I think. And that also kind of dictates how you think about hiring now and emphasizing a bit more that creativity, and which is always the hardest for me when I do interviews for folks, whether it's discretionary or systematic. If they've got a good resume in the. Of undergrad, you can see that they would be technically competent.
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说话人2
00:20:02
They would be good diligent builders of spreadsheets and analyzers of earnings reports, or they'd be good technologists or writing attacking new data sets those kinds of things. But it really takes two three four years to figure out if that person's going to actually be creative. And I'm wondering now if you can sort of emphasize a bit more on the probability of being creative rather than the sort of the floor ceiling effect where maybe in hiring that you want to get a good high floor for somebody so you really lean into their technical capabilities. Now maybe you kind of shoot a little bit more for the moon on the creative side and so you look for more ceiling. So these are really interesting strategic questions for folks on the hiring front and what happens ultimately with some of the AI technologies, large language models in particular.
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说话人1
00:20:45
Yeah, and it's interesting when you think about it from that perspective where you have the you have the kind of the fundamental analysis side and you add AI to that, and then you have the quantitative systematic side and you add AI to that, and how you could see how it. Could potentially benefit both sides.
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说话人2
00:21:02
Yeah, it's exciting. And so the key right now for organizations is to get those technologies in the hands of both sets. Don't limit it to just your quants, for example.
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说话人2
00:21:11
And let people experiment. And I think that's been our approach is let's make it easily accessible, whether it's more through a web based interface or a more technical interface. Ironically, I thought in terms of adoption, we've seen a lot more people do both.
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说话人2
00:21:27
I thought there'd be groups that would kind of just run to the more straightforward web based interface versus the hard code technical stuff. But a lot of people, because now it's easy to do, easier to do, people kind of lean to actually on both. They lean on both sides. And they also think it's important organization that you don't have vendor lock in on these models. I think every few months one firm is going to you know run to the lead with a different capability, and so you want to have an organization that can scale and change quickly as these new technologies come about and new evolutions of the models.
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说话人1
00:21:58
And do you think about. Two different sides helping train those models so they can benefit the entire firm.
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说话人2
00:22:04
So there's a couple of ways that that could happen. I think right now where we sit at these technologies, a lot of the IP is in the skill files, right? What you know, sort of the harness if you... They can they call them harnesses. The technology is the horse, but you need a harness on how to move and direct that horse, right? And these harnesses I call them skill files, other things market whatever the phrase is.
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说话人2
00:22:28
Where you go in that skill, how you want to think about a problem, how does man group want to think about the problem, how does a really good discretionary portfolio manager want to think about a problem so that when somebody is interacting or tackling a new problem, they already immediately as they sit down and interact with the large language model, there's all of this great IP from across the firm that the large language model already knows about. It knows that it. It understands in and out of sample testing, it understands being fooled by different regimes.
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说话人2
00:22:58
So I think that that. It's kind of boring in one way because it's not as cool as the actual technology, but just how do you go about researching different things and building things is hugely valuable. So I think that's where we've gone.
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说话人2
00:23:11
I technologies have gotten a lot better in the last six to nine months, a lot of excitement, a lot of what I called dashboard building, helping people automate their day to day lives and putting together manager performance, other things. But now it's in that next phase. And can we actually bring it to bear on actual problems, but in a way that you don't have to continually relearn? So I, for example, been working, you know, when I write something.
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说话人2
00:23:37
Or work or review a research proposal by somebody I've created my own, I guess Craig Bond like person in this large language model that will attack it with things that I would always ask. And then you can also augment it with things like: Hey, I want the best mathematician in the world to review this. I want a high-level multi-strat PM to review.
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说话人2
00:23:58
So you can create these. Personas, I guess nine or ten personas of things that you like and just have them review your work. And this is very basic stuff and I think anybody could do it, but once you've built that skill, I can then send that out to other people, you know, and I think that's very helpful organizationally that you have this concept across because anybody can sit down and build a dashboard.
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说话人2
00:24:21
Right, that's great. It's fun, but we don't need a hundred thousand dashboards that all do about the same thing. We actually need to bring it to bear on real problems.
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说话人1
00:24:27
As CIO, how do you personally think about diversification of ideas, not just diversification of assets or strategies?
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说话人2
00:24:35
One of the fears I have in our industry, there's a little bit of. I guess I call it FOMO, right? If you're missing out on various strategies, products, what might be out there, or you see a very successful firm and you want to copy that firm or go in that direction.
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说话人2
00:24:52
I think that's very hard to do. You can never quite observe the entire organization you're sitting externally. You can talk to people inside, outside, whatever it might be, but it.
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说话人2
00:25:00
That's really not the way to set a strategy. It's more about where do you want to be in the marketplace. I like our alpha at scale positioning, and if you take that, that makes some decisions internally very clear.
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说话人2
00:25:11
I also know over my career, some people work differently. Some people are very good in meetings and you know could be a twenty-person meeting and they are willing to pound the table and make their voice heard. Other people don't necessarily like to do interact that way.
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说话人2
00:25:25
Clearly, when the CIO is in the room or our CEO or whoever it might be, they can also dominate the conversation unknowingly, or maybe they feel like they have to dominate it because they're in the position that they are. So what we try to do in certain places is really make it much more collaborative, allow multiple ways for people to have their opinions heard, systematic engine bottom upside at numeric, for example, we use something called the expert panel. Where people will vote on an idea in an anonymous fashion, everybody sees the comments of what everybody's written, but they don't know who who wrote them, and then you can use that particularly.
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说话人2
00:26:00
Questions that are sort of 5545 in the voting to try to drive that to 9010 or 1090 right, and try to build some understanding seeing of the people's comments but not with the bias that can come across if somebody walks into the room. You know, if I said hey we got to go do this, those are some subtle things that you can do. A lot of it comes back to collaboration.
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说话人2
00:26:19
And the other thing I think philosophically for folks to have them work in different parts of the organization, right? So I think that also helps decision making. Because once you've seen the discretionary side, or the systematic side, or even within systematic the research versus the PM side, the analyst side of discretionary, that you start to get an appreciation for where other people are coming from in some of these discussions.
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说话人2
00:26:41
So I think flexibility, movement, collaborations, a lot of just talent development, growth focus, where they can they go in the organization. Sometimes again, as going back to my. When you hire people in an organization, it might take three or four years to figure out what the best fit is.
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说话人2
00:26:56
So maybe you move into a different part of the organization. So that's really. talents hr all of those things are as important as anything else because the people whether it's discretionary or a quant side of the business are super important.
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说话人1
00:27:09
And I guess increasingly AI may have a voice at the table as well.
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说话人2
00:27:14
That's interesting, is AI another employee? Right? You can think of it that way, or you know, some of these these large language models. I think what's important, I think there is a reinforcing effect because of the new technologies and sort of the older ways of doing things, because it might bring in insights that you just didn't observe. It can help push back, but I think when you again going back to design. Sort of the digital version of your organization, it needs to have a consistent culture with what you're doing on the organic side.
2
说话人2
00:27:47
So I think of this where one of the short-term applications and near-term applications is just on the research side, where historically organic researchers kind of digging away on ideas, but could you augment them with kind of their digital counterparts? The digital counterparts. Parts have been trained by the organic parts, right? And you can get the scaling effect whether that's systematic or discretionary.
2
说话人2
00:28:06
And so that's one way that organizations can scale. That's why I'm not... I don't think we can debate this five to ten years from now, but I don't think people are looking to do necessarily job reductions. It's more giving more power to people that are in the organization and getting uplift there because ultimately the scaling benefits. I think could be there if it's done correctly.
1
说话人1
00:28:29
Would you share some insight about how? Or what effective collaboration may look like when you're also trying to preserve independent thinking and low correlation across viewpoints.
2
说话人2
00:28:41
Yeah, this is a really interesting debate, right? And I think they're different and they're different models. There's some people are highly siloed. That's been very successful. I think people that have been on the very end of the very sharp end of collaboration have also been successful. So I'm not sure there's.
2
说话人2
00:28:55
One right answer depends on what your organizational philosophy is. I think it's important. And that if you're going to be in a siloed world, don't hire a bunch of collaborative people.
2
说话人2
00:29:04
And if you're in a collaborative world, not to hire people that are really into their into their PNL and you know, more on the kind of called mercenary side, whatever you want to describe it. Again, all reasonable models, all reasonable human behavior, everything, you know, is shown a lot of success. I think we've leaned more on the on the collaboration side.
2
说话人2
00:29:20
I think part of that is just. THE EVOLUTION OF THE FIRM. I, I do go back and forth with myself if, if I have one idea I want to explore as a firm. Is it better to have two teams work on it, and they may come up with a better answer sort of individually that you then bring back together? But the opportunity cost is we could have looked at two ideas.
2
说话人2
00:29:40
And is that better to have one team per idea, and then you can do two ideas, or do you have two teams on one idea, which means one idea, and sort of this. concept of scale. So that's a question.
1
说话人1
00:29:53
So you talked about this earlier, but multi strategy investing, it's become one of the dominant models in hedge funds. From your perspective, what problem does a multi strategy platform solve that perhaps singles strategy funds may struggle with?
2
说话人2
00:30:08
It's an interesting thing because you said multi strategy platform, and I think that's very much how people think about multi strats today, kind of capital M capital S. I bring together several hundred portfolio managers, whatever it might be, and I think that is one model, and that's been a very successful model, but I think it's not the only way.
2
说话人2
00:30:26
To think about multi strategies, like move into little M little S, having multiple strategies in one vehicle, one fund, what have you, has some advantages that investors and allocators can't necessarily do on their own. Okay? So forget what's inside the multi-strat for a second because I think that's you know there's differentiation there, but just the structure.
2
说话人2
00:30:45
So one as an individual allocator maybe you could allocate to five to ten hedge funds. On your own, right? And that's people have done that in the past.
2
说话人2
00:30:55
You know, fund to funds have come in to help manage some of that. But what are the downsides? Well, you have. To get to know five to ten portfolio managers and firms very well, it's a little bit hard maybe to move capital across those five to ten strategies. Maybe there's lockups. It's also just everybody has investment committees.
2
说话人2
00:31:12
It's also hard that hopefully these five to ten hedge funds that you're hiring have low correlation with each other. That's great, but if I put them together, maybe I don't have enough volatility to make it worth my while, or at least whatever dollars I'm putting in, maybe not getting as much efficiency. And what you'd maybe like to do is leverage that, but that can be hard if you've got five to ten separate.
2
说话人2
00:31:32
A fund investments. And so the theoretical benefits of multi strat little and little S is that okay, well you can hire a firm to go out and internally build those capabilities internally find. YOU KNOW, A SOURCE ADDITIONAL UNCORRELATED CONCEPTS AND THAT COULD BE DISCRETIONARY, IT COULD BE SYSTEMATIC, IT COULD BE ALL KINDS OF.
2
说话人2
00:31:54
different ideas，and so the pros are that you can bring that together，that the manager of that fund. Can then allocate quite quickly across new managers, different risk regimes, other things. They can see the whole risk of the portfolio in one fell swoop, rather than sort of relying on monthly reports and then trying to aggregate.
2
说话人2
00:32:12
So you've got a lot of hyper detail and what's in the portfolio, you can be quite nimble. And then importantly, if it is uncorrelated, you can also that manager can manage the leverage of that fund so that you get your. Volatility back up to what you may have had on a single fund before, maybe five or six percent, whatever that might be.
2
说话人2
00:32:28
In certain cases, maybe you want even more volatility and sort of moving that would depend on some of the content. Hey, maybe you want to take that multi-strat and put it on top of the S&P 500 and like a portable alpha type. So there's a lot of flexibility.
2
说话人2
00:32:41
So the downside obviously relying on this manager to put this together in a very efficient way. It also you lose some maybe some transparency, or what if you're buying into a multi-strat what's actually in there? And then there's this other part around you know cost and what is the the cost of of supporting that that infrastructure.
2
说话人2
00:33:00
So there's a lot of pros. There can be cons, but I think the important part is there's not just one way to do multi strat. And I think what you're seeing in the place today in the spectrum is differentiation of what's there.
2
说话人2
00:33:13
Some are a bit more quant, maybe some are more discretionary, some are more liquid, some are less liquid. So that profile has changed and augmented. And I think the people that. might be getting in trouble or have had difficulty in the space is really going back to that fomo side of just trying to find a firm that they want to emulate and go out and copy that exactly. I think if they have some self-reflection about where you want to fit in that spectrum.
1
说话人1
00:33:35
I know technology has always been a part of man group's dna. How do you think about technology as a strategic advantage rather than just a tool?
2
说话人2
00:33:44
So going back to my early days of my career, I wrote cases and did work with Michael Porter at Harvard Business School around strategies in the competition and strategy group at Harvard Business School and. And one of his big points was there's a difference between operational effectiveness. Strategy, you know, operational effectiveness is doing things better and better.
2
说话人2
00:34:04
Strategy is actually making choices, you know, which products and services, how do you want to think about the market pricing volume? All of those kinds of things because it's hard to do everything. And so if you... The technology in and of itself is not necessarily a competitive advantage, right? Everybody's going to continually invest there, get better and better, right? I think if you go way back to the.
2
说话人2
00:34:25
The Japanese auto manufacturers in the eighties, right? They were very, very good at operational effectiveness. That was that operational effectiveness was quickly copied around the world, right? So it's hard to maintain that.
2
说话人2
00:34:35
So the key is how does the technology fit into the strategy? And therefore, you make choices on your tech platform, right? If it's a single strategy fund, the firm has one fund. Right, if they have multiple PMs, but it's basically one fund that leads to one level of complexity, right? Sort of a narrower complexity in terms of what the product that you're supporting.
2
说话人2
00:35:00
But maybe you need to have broader capabilities for all the PMs on your platform, if you're offering many different kinds of strategies, different asset classes, and you're doing a lot of bespoke work for clients that has a different impact and concept on your tech platform. So it's really important that you align those, and particularly now with the development of AI. That you need perfect alignment and that allows you then to have AI and large language models digested more efficiently through the organization.
2
说话人2
00:35:27
So just be on the lookout for people radically changing their their tech stack somehow because of AI. No, no, it's only the tech stack should evolve with the strategy and not the other way around. And I think that's important.
2
说话人2
00:35:41
I think concepts around being flexible with your technology, and let's not forget, even with all of the great AI technology that have been developed, this goes back to your data management. That's the most important thing, and I think eventually you'll see a lot of AI models that will be open sourced other things. So it really sits back on how your your data is stated.
2
说话人2
00:36:00
Managed and protected. And that moat, I think, that kind of competitive advantage will sit there for firms that are very, very good with their data. So let's not forget about that kind of very old school block and tackling thing is how do I manage all of the data I have organizationally?
1
说话人1
00:36:13
I guess one way to think about the potential impact of AI is on one hand, it may help you do what you're already doing, producing the same thing more efficiently. And on the other, maybe it helps you create new things to produce and new alpha to generate.
2
说话人2
00:36:29
And I think that's where we're, you know, in terms of some of the training and as we go through in the broader company, how people think about AI not as exactly that productivity piece, but also kind of a partner in the journey along researcher creative endeavors. So can you create goes back to my persona concept, right? And that sort of research partner learning how I mean the prompt engineering is really, really critical.
2
说话人2
00:36:51
How you interact, you can't just say make me make me more alpha right? That doesn't really work very well. It's like a thought partner right? That has.
2
说话人2
00:37:01
That's a great way to describe it, and I think that takes some thinking in how to do that and which task, which model you want to use for a given task. There's all of that kind of interesting stuff, but even the models that are now whatever one or two generations old are still really, really good. So yeah, I think we just need to be better users of those technologies, I think.
1
说话人1
00:37:22
Yeah, it is interesting. Like the way you frame the question, I think is perfect, which is if you just go to an AI and say, 'Create help me figure out how to create more alpha,' and you go to a person and you say, 'Help me create more alpha,' but you put the two together and you say, 'Let's solve this problem together,' and they go back and forth with idea sharing and challenging one another. You could see why adding the two together could be more beneficial.
2
说话人2
00:37:47
And what's really, really important I find when I do this is let the technology know as much about you as possible. Load up as many documents, things you've written, how your firm approaches whatever the topics are, because that context. is very very important，and I think even in certain cases，you know，some of the newer models，you can actually talk into the large language model，so talking，you get more context than maybe you're just sitting there and typing，so，but more information you give it。THE MORE LIKELY YOU'RE GOING TO GET AN ANSWER THAT'S PRODUCTIVE.
1
说话人1
00:38:15
AND SO WHEN YOU THINK ABOUT AI AND LARGE LANGUAGE MODELS, WHAT EXCITES YOU THE MOST AT A CONCEPTUAL LEVEL?
2
说话人2
00:38:21
WELL AGAIN, WHEN IT FIRST CAME OUT. you know i was particularly on the systematic side maybe less excited only in the sense that you we already do a lot of reviews of of analyst report it's been using ai for many many years in some ways it's letting the competition catch up correct and then i'd say over the last eighteen months. This kind of partner or thought partner part.
2
说话人2
00:38:42
It's really, really improved partly because the technology has also the way we use it has improved and interacting with it. So that's I think very exciting. The ability to use it to help you think and go through problems and also try things you may not have been able to do in the past because it would have taken too much time.
2
说话人2
00:38:57
So just one of the things I like to do. Approach problems is like this concept of ensemble thinking, which is you bring different ways of doing it together and sort of average across them rather than picking one way or another to do it. That could be a portfolio allocation question.
2
说话人2
00:39:11
It could be a model question. It could be how do we want to allocate, you know, across discretionary manager, whatever it might be. And so. It allows you to kind of open up.
2
说话人2
00:39:21
Your mind a bit on how to approach it, and I also find it's very good as you go through recording your thoughts in that research process of kind of creating a live document or living document as you attack these things, because then you can go back and look at it: How did I get to this point? That's exciting for folks. And I think the.
2
说话人2
00:39:41
The question, and as we get the models get better and better, at what point do you switch to the outsource? You take an open source model, bring it in-house, and then start to train your own things. I think the smaller models trained over your own work is actually might be a very productive thing going forward.
2
说话人2
00:40:00
Which you could see in the next couple of years, rather than off the shelf. And I know a lot of the providers out there building very specific agents, you know finance agents, other things. But you could imagine a world where you don't necessarily need that. You can the models that are open source are powerful enough that you can actually build something for your own own use.
1
说话人1
00:40:20
Yeah, if you can incorporate some of your proprietary insights into the model that others don't have access to, you could see how that could create an edge.
2
说话人2
00:40:30
Absolutely. I think we've got a lot of history doing that. I mean, I think a lot of the firms that do. Traditional kind of machine learning and other things, they often start with the standard open source package or whatever, and then you put your own IP around it.
2
说话人2
00:40:45
And I think the other nice thing is you do bring in somebody's models on discretionary side in particular, right? You can start to train that a bit more with your human data, right? And I think that's to be very reinforcing for a portfolio manager on discretionary side.
1
说话人1
00:40:58
Man, if you think about AI feeds. UM DATA AND IF WHAT IS PUBLICLY AVAILABLE ALL OF A SUDDEN IS EASILY ACCESSIBLE BY EVERYONE, THEN IT'S THE DIFFERENTIATED DATA THAT COULD BE THE SOURCE OF FUTURE ALPHA.
2
说话人2
00:41:11
DIFFERENTIATED DATA. UM ONE OF THE QUESTIONS I WE TALK ABOUT QUITE A BIT IS WILL AI LET PEOPLE MAKE BETTER OR WORSE DECISIONS? Right on the one hand, you should make better decisions, but then you can have more people trying to make decisions in the space because it is so democratized.
2
说话人2
00:41:29
And I don't know how it's going to affect ultimately policy making decisions as well, which ultimately have a huge impact on the market. So it's not clear to me that we'll make better or worse decisions. It might create more alpha opportunities because of that. So that's one way to think about it because I do get questions around is everybody just going to think the same way. And I think well your point having more data, different data that's going to help. But also, the way you deploy the technology is just a lot of different ways to do it that will create some dispersion, and there will be bad uses of the technology in terms of bad decision making that could come up come out of it. So it's going to be a very interesting, you know, next decade.
1
说话人1
00:42:04
I think the interesting part about what you just described is, I think it's important to keep in mind that better and worse decisions are relative metrics, and you know, you can you can argue better decisions are made today than 50 years ago, but it's relative to the higher level of expectations. So so maybe AI allows us to in. In absolute terms make better decisions, but you have to think of it relative and is it better relative to others? Is it worse relative to.
2
说话人2
00:42:34
others with a higher bar? The society benefits from better decision making in general, but the dispersion, you know, the sort of it makes it harder and harder for the people to add value above and beyond that. Exactly. And that's the nature of what we do, which is why it makes it fun.
1
说话人1
00:42:49
So the other thing that I think is interesting is we obviously live in a period of great uncertainty, and oftentimes investors feel very uncomfortable in periods like that. Do you think that can actually. Be healthy for active management.
2
说话人2
00:43:02
I think we're in a really good environment for active management. I think you could argue that, you know, coming out of the GSC and the lower interest rate environment, you know, there wasn't a lot of dispersion in different things, different classes, you know, cash was zero and it was really, hey, I should just maybe buy equities and it'll be fine. And what we've seen as we've gone into a different rate regime.
2
说话人2
00:43:24
We've seen better opportunities for alpha. So I think that that also manifests in better risk management hopefully, right? So I think one of the reasons you see some of the data maybe on some of the multi strats have done quite well is that ability maybe to do both the alpha and the risk management a bit more effectively in the market.
2
说话人2
00:43:42
Of course, that's brought in a lot of extra capital into the space as well. So you have that competitive dynamic, but. Generally across many sets of strategies, alpha has been pretty good the last several years. That can change year to year, but I would say the opportunity for active management is very good today.
1
说话人1
00:44:00
So if you look at today's macro environment, what characteristics tend to create the richest opportunity set for alpha?
2
说话人2
00:44:06
I think it's always the opportunity, but also the danger in some of this. I think the kind of continual shorter term shifts from risk on to risk off, right? That's dominated, you know, the last several years, obviously.
2
说话人2
00:44:20
You go back 24, 25 about inflation as well, which I think you're ending up in around periods where if your portfolio, if you're up at night worried about the next. Earnings print from a large cap S&P 500 company or the CPI print or a Federal Reserve decision, then your portfolio is probably not as well diversified as you'd like it. So I kind of use that as my backdrop to make sure that going into these events and other things that you may lose some in some parts of your portfolio, maybe win in others.
2
说话人2
00:44:51
It's not going to be a. Kind of what we call a left tail disaster. All of a sudden, you have a very, very tough, you know, day or two of performance. And so that's. Guiding principles is about just risk balance, less about trying to pick individual strategies that will be the best in this particular environment. It's more about being being a bit more balanced, so that does create opportunities because there is so much information flow in the market on a day-to-day basis.
2
说话人2
00:45:17
You've got the rise of of a lot of retail investing coming into the space. There's just a lot of interesting pieces that are different today than there were 10 years ago. I do worry that people. A lot of investors today were not around in 2008. But you sort of what's going on back then when credit could sell off and there's distress and you know the markets are yeah so I think just remembering or at least if you didn't live through that then when you're thinking about asset allocation or risk at least have some of that history in mind and what's what can happen because. That's those are hard lessons, and I think people generationally maybe that they forget that like every ten to fifteen years you need, you need a reminder of some of that.
1
说话人1
00:46:02
Yeah, it is interesting how living through it is so different from just reading about it because you have to, it has to hit you hard for those lessons to stick, and it kind of enforces a certain risk discipline that unless you've been through that or if it happened too long ago, those memories fade.
2
说话人2
00:46:21
exactly exactly.
1
说话人1
00:46:23
So one observation that I've had is talent competition in the industry is pretty intense beyond compensation. What did the best investors really want from a platform or firm?
2
说话人2
00:46:34
Again, I think it comes up to what does the portfolio manager sort of their style, what do they want to go towards. And so I think you've got a group that have been very successful clearly on the portage based silo based approach. Other people are looking for a more collaborative type setup, so I think that's interesting.
2
说话人2
00:46:51
Now again, if you're aiming for collaborative, it's hard to have 10 portfolio managers on the platform doing the same thing, right? That's sort of. is against.
2
说话人2
00:47:00
It's the opposite of the thesis. So maybe if you're more collaborative, maybe if one or two PMs in each of the buckets, not ten to fifteen. So that sets up your model goes back to strategy, organizational design, how you want to think about it.
2
说话人2
00:47:12
I think stability of the platform, I think the use of technology, are they getting access to the great. Bating capabilities, what what are our firms' prime broker relationships like, so they get good, you know, margin? So all of that package that as they sit from the O outside and looking in, and I don't think it's always about compensation or expected compensation.
2
说话人2
00:47:31
I think, particularly as the multirap models evolved over time, people are seasoneded with it. people may start to value some of the other stability points, collaborative points a bit differently than the pure pod base. But again, people have different preferences and.
2
说话人2
00:47:45
And you can do that, I think it's just. You not only do you need a strategy when you go out to your clients about being differentiated, I think you need to have a similar strategy when try to recruit talent. What do you stand for as an organization? And that can be tricky, you know, uh... But you try to. Have every you need everything aligned.
1
说话人1
00:48:02
yeah? And ultimately it comes down to alpha. So if you're join, if you join a firm that gives you the best opportunity they generate alpha, ultimately that leads your compensation as well.
2
说话人2
00:48:12
Correct? No alpha, no business right?
1
说话人1
00:48:15
So that's right.
2
说话人2
00:48:16
which is the way it should be right because the.
1
说话人1
00:48:18
clients are paying the fees they should earn alpha over time and and that that feeds the is the net alpha that matters. Yes.
2
说话人2
00:48:25
that's right.
1
说话人1
00:48:26
For younger listeners considering a career in finance, why do you believe creativity is becoming as important as technical skill? You touched on this a little bit earlier.
2
说话人2
00:48:35
The technical barriers have dropped significantly. I don't think that still means you need some, you know, some technology background in terms of some basic data science work. And I think you're seeing that whether it's in the more traditional technical fields or even humanities, social sciences, they all have a little bit of a quant track, I'll call it, again, sort of rudimentary programming.
2
说话人2
00:48:56
Using data to answer questions，so I participate whatever your field is，and I'm. Big fan of multidisciplinary, doesn't have to be finance, doesn't have to be economics. It can be a lot of different fields, but even whatever fields you choose, having there is a little bit of a in all of those fields a track that's a bit more quantitative.
2
说话人2
00:49:14
I think exploring that world is quite helpful. It gives you some skills that allows you to at least when you're working in this new environment of large language models that you're. ABLE TO KIND OF UNDERSTAND WHAT THE CODE IS DOING AND NOT DOING, SO YOU KIND OF PUSH BACK ON IT.
2
说话人2
00:49:29
I THINK THAT'S IMPORTANT, BUT I ALSO THINK BROAD EXPERIENCE IS REALLY, REALLY HELPFUL. SO AGAIN, MULTIDISCIPLINARY, MULTIPLE MAJORS, MULTIPLE MINORS, ALL OF THOSE KINDS OF THINGS ARE REALLY, REALLY GOOD FOR F. And if you tie that in with a little bit of technical, then it really opens up what you can do.
2
说话人2
00:49:47
And again, that's where you're going to go in the discretionary world or the systematic world, right? I think even on the discretionary side, there are you still have to run a spreadsheet or however you want to run it, model companies. It's just very specific. It's very narrow in terms of the company. Your following versus the broader set on quant side, but I think that's really helpful for folks on both whatever career path they pick.
1
说话人1
00:50:06
So when you reflect on your own career path, what non-obvious experiences ended up shaping how you think as an investor and leader?
2
说话人2
00:50:14
When I came out of college, I wasn't exactly sure. What I wanted to do, but I did know that I wanted to do different things and just and see. So part of the career path went from investment banking.
2
说话人2
00:50:26
I worked at Walt Disney in the Strat Planning Group. I worked for Michael Porter in the Competition and Strategy Group. I start, I joined right out of business school, I start up quant shop, which I was doing everything from coding to trading to talking to clients, and then ultimately joining numeric back all the way back in 2003.
2
说话人2
00:50:47
So I think having different experiences is really useful, different contexts. I took a two or three month break from numeric and did some work with the Boston Red Sox for a little bit. And my. Here is evolved.
2
说话人2
00:51:01
I was a researcher on the quant side, then ran the hedge funds, was the director of research, did a lot more on the discretionary side as part of our development of cross firm capabilities, and then moving into the broader man group CIO role last summer. So part of it is getting different experiences of different parts of the organization is really helpful because that allows you to. Two more things, but at the same time, it's okay to be an expert in one particular area.
2
说话人2
00:51:27
So there's a couple of different models for folks. If they love being a portfolio manager covering US financial stocks and they really get it, that's great. If you want to be more on the managerial side, organizational design, all that, there's other paths. So I think kind of find what you like to do. If you can, and if in when in doubt, then try different things.
1
说话人1
00:51:45
Are you able to share your experience with the Red Sox? I think that would be interesting.
2
说话人2
00:51:49
Oh yeah, I know. I was there's a on the elementary base that kind of like had quant there at the time. This is ah... this is quite a while ago, so I think it's okay to talk about. I was brought in to work a bit. Strategy，so you know baseball，you have uh amateur draft every year，and and sort of looking at that，so I spent a few weeks，I was an intern，I was punching taking my card and punching in every day，um，so it was a lot of fun。
2
说话人2
00:52:13
you do realize you know in well sports is fun sports analytics is fun there's only about there's only thirty baseball teams right and there's one per city it's a very rewarding career but you're also very the alpha there is definitely if you're not winning you might you know not be there as much so i think that was fun for me because i was a bit of an outsider doing it and having fun whereas if you're living it day to day it could be it's just like running running money in the real world right same thing and it's probably more popular today than it was back then. Yeah, I mean quite frankly, if there had been more moneyball type stuff, maybe that's what I would have ended up doing coming out of college. Who knows? It's very interesting.
2
说话人2
00:52:47
Maybe we've gone too far that way though. I think again, I'm back to discretionary and systematic, and I think maybe the systematic parts dominate a little too much. We need to go back to good old fundamental scouting, but that.
2
说话人2
00:53:00
What do I know? The pendulum swings back and forth. Yeah, so I think maybe it's gone way too because it did the other way. So hopefully bring it back a little bit.
1
说话人1
00:53:07
Hello Greg, this has been a fun conversation. A lot of great insights. I appreciate you joining us. Thank you so much. Oh yeah, thanks Alex. It was a lot of fun. Thank you.
1
说话人1
00:53:22
Important information. This podcast is provided for informational purposes only. It should not be considered legal, tax, investment or business advice. It is not a solicitation recommendation or endorsement.
1
说话人1
00:53:33
All opinions expressed by participants are their own and do not necessarily reflect the views of the evoke advisors division of Mai capital management LLC or evoke its affiliates or any companies mentioned. Information shared has not been independently verified by Mai or its affiliates. Mai Capital Management LLC, or Mai, is registered with the US Securities and Exchange Commission SEC, which does not imply any particular level.
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说话人1
00:54:00
Of skill or training, certain information contained herein has been obtained from third-party sources and such information has not been independently verified. No representation, warranty or undertaking expressed or implied is given to the accuracy or completeness of such information by any person. While such resources are believed to be reliable, Evok does not assume any responsibility for the accuracy or completeness of such information.
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说话人1
00:54:23
Evok does not undertake any obligation to update the information contained herein. As of any feature date, the content is intended for a general audience and does not constitute a recommendation to buy or sell securities or adopt any investment strategy. Any examples or scenarios discussed are illustrative only, involve risks and uncertainties, and do not guarantee future results.
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说话人1
00:54:46
Nontraditional assets carry significant risks and may not be suitable for all investors. Decisions should be based on individual objectives, risk tolerance, and circumstances. Statements herein are general and may not reflect an individuals or entities specific.
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说话人1
00:55:00
circumstances or applicable laws, which vary by jurisdiction. Further, speaker's views are personal and may differ from evoke and Mai recommendations and are not specific investment advice and do not consider client objectives, risk tolerance and diversification. Guests may have current or past relationships with evoke and Mai, its affiliates or the host, including as clients, service providers or business partners.
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说话人1
00:55:24
Participation does not constitute an endorsement or testimonial. No compensation has been paid or received for guest participation unless disclosed. Mai and its affiliates may have business relationships with entities mentioned in this podcast, which could create potential conflicts of interest. These relationships may include advisory services, investment management, or other arrangements. Mai seeks to manage such conflicts consistent with its fiduciary obligations and policies.