Speaker 1: Welcome to Inside Active, a podcast about active managers that
Speaker 1: goes beyond sound bites at headlines and looks deeper into
Speaker 1: the processes, challenges, and philosophies and security selection. I'm David Cote,
Speaker 1: I lead mutual fund and active research at Bloomberg Intelligence.
Speaker 1: Today my coast is Christopher Kaine, us quantitative strategist at
Speaker 1: Bloomberg Intelligence. Chris, thanks for joining me today.
Speaker 2: Thank you so much.
Speaker 3: David.
Speaker 1: So you recently wrote a note about how low vall
Speaker 1: strategies held up during last year's Taraft driven volatility, but
Speaker 1: they're not behaving the same way now, even with geopolitical
Speaker 1: risk and the headlines, so it's changed.
Speaker 2: Yeah, that's a good question, David. I'll be honest, I'm
Speaker 2: rather surprised about the factor movements so far this year.
Speaker 2: To be clear, you know, coming into the year, we
Speaker 2: had value as our top our top factor. Basically, value
Speaker 2: is very cheap when you look at cheap stocks versus
Speaker 2: expensive stocks, and had some positive trends in its favor,
Speaker 2: so we were positive on value. That said, I didn't
Speaker 2: know a war in the Middle East was going to
Speaker 2: break out. I'm I'm pretty surprised how well value has
Speaker 2: held up, and I'm also surprised how the defensive strategies
Speaker 2: just have not worked, things like low volatility, things like quality.
Speaker 2: They're not getting killed, but they're not getting that safe
Speaker 2: haven bid that you would think you would get when
Speaker 2: you have a geopolitical event like this. So I think
Speaker 2: you could you could kind of interpret it as the
Speaker 2: market shrugging office war or looking past it, but we
Speaker 2: have not seen the safe haven flows internally that you
Speaker 2: would expect.
Speaker 3: Interesting.
Speaker 1: Well, speaking of factors, I'd like to welcome Jeff Sheen
Speaker 1: to the podcast. Jeff is co CIO of Systematic Active
Speaker 1: Equity at black Rock, and I'm sure you can help
Speaker 1: us understand the benefits of factors and systematic equity and
Speaker 1: active management. So I want to thank you for joining us.
Speaker 3: Jeff, great to be here, Thanks very much for having me.
Speaker 1: So. Systematic active equity kind of sits in that middle
Speaker 1: ground between traditional discretionary investing and pure quantitative How do
Speaker 1: you define its core edge today? And you know what
Speaker 1: do you think people still misunderstanding about it? Yeah?
Speaker 4: I mean I think you know, systematic active equity type
Speaker 4: of strategy had been around for a long time. We
Speaker 4: actually just celebrated our forty year anniversary last year, and
Speaker 4: I'll see that's it's actually how it differentiates. It's certainly
Speaker 4: I think historically probably is when we think about discretionary
Speaker 4: it's very deep, could.
Speaker 3: Be in a particular name, could be in a particular.
Speaker 4: Country or sector, and systematic qualitative strategy is a little
Speaker 4: bit broad, you know, five miles wide, but a.
Speaker 3: Few feet deep.
Speaker 4: And I think, you know, to Christopher's earlier point around factories,
Speaker 4: that's sort of typically with people think about it. I
Speaker 4: actually think that the evolution today is such that I
Speaker 4: think there's an opportunity, especially given additional data, additional algorithms
Speaker 4: and technology, you can be both broad and deep at
Speaker 4: the same time. So it's a bit of an exciting
Speaker 4: moment for the evolution for systematic investment on a forwardlooking basis.
Speaker 4: And I think the other thing that I think maybe
Speaker 4: in terms of misconception, is also that historically there was
Speaker 4: certainly a bit of a feeling of systematic strategies could
Speaker 4: be a bit of a black box. People don't exactly
Speaker 4: understand their all these labeling, but people don't exactly what's
Speaker 4: going on. And to the point that you know, a
Speaker 4: minimum volatility strategy may not be as defensive as one thinks.
Speaker 4: I actually think that that when there's an inflation story,
Speaker 4: there's also an AI story that may, you know, maybe
Speaker 4: underneath a hood of that particular conundrum. But I think
Speaker 4: I think the exciting thing on a forward looking basis,
Speaker 4: given some of a new technology, is that I think
Speaker 4: you can also understand these strategies in a much more
Speaker 4: transparent and humanly understandable way. So I think that's probably
Speaker 4: one of the misconceptions people say, Oh, I understand the stock,
Speaker 4: but I'm not social about the factors. I'm certainly not
Speaker 4: social about quantitative methods. With the aid of AI and
Speaker 4: large language model, some of these transparencies may also be
Speaker 4: evolving a forarlooking basis as well.
Speaker 1: So when you kind of step back from the models
Speaker 1: and the signals, what are the persistent sources of alpha
Speaker 1: your team is really trying to capture, and you know,
Speaker 1: do you think those have kind of evolved over time?
Speaker 4: I think, yeah, it's certainly evolved, And I think evolution
Speaker 4: is actually it's a feature of a successful strategy. Today's
Speaker 4: alpha is really going to be tomorrow's beta, maybe tomorrow's
Speaker 4: smart beta. So I think it's ultimately I think the
Speaker 4: edge here is about how can you innovate, how can
Speaker 4: you evolve? So that's, if you will, that the the
Speaker 4: secret sauce. It's trying to think about what can drive
Speaker 4: security selection, cross sectional returns, or macro returns. The drivers
Speaker 4: are as fundamentally different, you know. I think about back
Speaker 4: in nineteen eighty five when we first started trading to
Speaker 4: be able to get a price to book, price to
Speaker 4: earning and give it and yield information for five hundred
Speaker 4: stocks in S and P five hundred. That was big
Speaker 4: data machine learning back in nineteen eighty five. But fast
Speaker 4: forward today, I think on this point of evolution, it's
Speaker 4: certainly about the amount of data you can access, the
Speaker 4: amount of learners that you have in your system, and
Speaker 4: the type of execution you're going to have. So I
Speaker 4: also that maybe the core source of alpha is certainly
Speaker 4: anchored around innovation and an innovation today. I think it's
Speaker 4: also one of the most exciting times, if not the
Speaker 4: most exciting times over the last forty fifty years, is
Speaker 4: that the many innovations you can go for data learners execution.
Speaker 4: So I'm sure we'll dig into it, but I think
Speaker 4: it's it certainly has evolved quite a bit over the years.
Speaker 2: Jeff, I would love to give you get your opinion.
Speaker 2: You know, I cover you know, equity factors at BI,
Speaker 2: and I typically focus on the well known factors, you know,
Speaker 2: the value of mental quality, low volatility. One of the
Speaker 2: questions I constantly get from customers is can factor exposures
Speaker 2: be timed? And it's a bit of a uh, you know,
Speaker 2: controversial thing. Some people think they can, some people that
Speaker 2: they can't. I don't think it's not this similar to markets,
Speaker 2: O can you time the market? So I guess I'll
Speaker 2: ask you, like, do you think factor exposures could be
Speaker 2: timed in a somewhat reliable way? And what, if any
Speaker 2: does factor timing play in your process.
Speaker 3: That's a very very good point.
Speaker 4: The headline here is actually, I'll say that it's possible
Speaker 4: to time the factors. But at the same time, it's possible,
Speaker 4: but you've got to do it very carefully.
Speaker 3: That's probably the headline. I think.
Speaker 4: You know, when I say carefully, is that I think
Speaker 4: you know specifically, I mean, we are very familiar with
Speaker 4: the factory investment. You know, back when I talk about
Speaker 4: nineteen eighty five, it was a bunch of value factors
Speaker 4: and then you know, momentum becomes a big research topic
Speaker 4: for us. We've done quite a bit of work on that.
Speaker 4: And then earning quality, as you were mentioning, you know,
Speaker 4: certainly started around two thousand n Roun and some will come.
Speaker 4: Some of the blow ups there certainly caused people to
Speaker 4: think about think about this quality metric in a very
Speaker 4: careful way. And I would say that maybe right after
Speaker 4: the financial crisis, that's probably there is a bit of
Speaker 4: a watershed moment in terms of think about factor and
Speaker 4: factor timing financial crisis, a lot of these factoris actually
Speaker 4: had severe draw downs, and they all actually draw down together.
Speaker 4: So I would say that around that time there was
Speaker 4: a you know, twenty ten twenty eleven, there was a
Speaker 4: big debate in the industry that you know, are the
Speaker 4: old factor is going to come back and you just
Speaker 4: need to time them a little bit more, or their
Speaker 4: additional new new things that you should be looking at.
Speaker 4: We sort of took the second route, where we think
Speaker 4: that there were a lot of new innovations alternative data,
Speaker 4: machine learning AI that you can use for systematic investments.
Speaker 4: So we went for that sort of a route in
Speaker 4: a significant fashion. But when we look back at the
Speaker 4: you know the factor lend if you will. I think
Speaker 4: these are becoming very well known. Uh you know, drivers
Speaker 4: of across sectional return. They're pretty persistent drivers of return,
Speaker 4: even though the time variation to a point is becoming
Speaker 4: much more pronounced. And I think this attempt to time
Speaker 4: them is certainly I think it's you want to do
Speaker 4: it carefully in the sense that you want to identify
Speaker 4: the factor in a very careful fashion. Depending the definition,
Speaker 4: it can vary quite a bit. You want to InCor
Speaker 4: put it not only bottom up information, but you also
Speaker 4: want to look at top down information how the regime
Speaker 4: can change the factory rotation. And then last but not
Speaker 4: the least is also you've got to think about how
Speaker 4: to manage the risk of this kind of timing because
Speaker 4: by definition, it's a low breath type of strategy. It's
Speaker 4: a low information type of rotation. So you want to
Speaker 4: make sure that you manage the risk accordingly. And if
Speaker 4: you do this carefully, I think it's certain. We've certainly
Speaker 4: done you know this type of strategy in different form,
Speaker 4: and we think there's actually some promise, but it's not
Speaker 4: nearly as easy as people think it is.
Speaker 2: Yes, I want to echo that. You know, it's like
Speaker 2: Cliff Astas says sin a little. You know, it's like
Speaker 2: if you're going to do some sometime and maybe just
Speaker 2: just sin a little. I love that. So, you know, again,
Speaker 2: traditional equity factors like your value of momentums, et cetera.
Speaker 2: I would call them medium term horizon factors. You know,
Speaker 2: typically the rebalance every month coms of fundamental factors like
Speaker 2: quality or value. Could be a little bit lower frequency,
Speaker 2: like every quarter. You know, I'm not asking for secret
Speaker 2: sauce here, but like do you have do you incorporate
Speaker 2: other factors that rebounce maybe more frequently or even less
Speaker 2: frequently to that kind of more traditional cadence.
Speaker 4: Yeah, I mean that's a fascinating question. May I'll say
Speaker 4: that if I zoom it all slightly, I think it is.
Speaker 4: This kind of a medium frequency that you talked about
Speaker 4: is actually, in my mind, a very exciting place to
Speaker 4: be in the sense that a lot of the high
Speaker 4: frequency traders are essentially trading milliseconds intra day. So these
Speaker 4: are very much a technical oriented focus and that's a
Speaker 4: very exciting place.
Speaker 3: For a long time.
Speaker 4: And there are probably twenty thirty dominant players in there,
Speaker 4: and they've done very well, and there's a big competition
Speaker 4: going on. I think the exciting thing on for looking
Speaker 4: base is exactly to your point on this kind of
Speaker 4: a medium three quincy and medium investment horizon, I actually
Speaker 4: think that there's a this is uh, you know, if
Speaker 4: you will, I like to say that this is you
Speaker 4: need technology, computer science, engineering, but you also need the
Speaker 4: liberal arts. You also need economics and finance and understanding
Speaker 4: of policy and the geopolitics to really make that uh,
Speaker 4: to deliver consistent set of returns. So I think the
Speaker 4: nature of the game is actually quite different, and you know,
Speaker 4: in the high frequency or shorter horizon, you actually don't
Speaker 4: really need too much of a sensibility per se or
Speaker 4: economic intuition versus in the intermediate horizon to the you know,
Speaker 4: whether it's a value or momentum or minimum volatility, there
Speaker 4: is actually some economic story associated with it. So I
Speaker 4: think in that sense, I think the the big price
Speaker 4: I think in my mind on a forward looking basis
Speaker 4: is that, if you will, there are certain firms kind
Speaker 4: of sawt of the little bit of the high frequency
Speaker 4: trading game, and it's still very competitive and keep on evolving,
Speaker 4: But there's actually a set of very successful large incumbents.
Speaker 4: Now in this kind of medium horizon, I think the
Speaker 4: space is reasonably open, uh, discretionary, systematic, and you know
Speaker 4: maybe old fashion systematic, modern fashion, uh, you know, modern
Speaker 4: version of systematic. So that's a bit of a wide
Speaker 4: space that I think we'll see a bit more evolution
Speaker 4: forwardlooking basis.
Speaker 1: So one thing I think that's really interesting in systematic
Speaker 1: investing is, you know, I think a lot of folks
Speaker 1: see it as purely technical, but you know, there's still
Speaker 1: a research culture behind it. How do you foster idea
Speaker 1: generation and you know, intellectual diversity within you know, your
Speaker 1: systematic team.
Speaker 4: Yeah, that's I mean, David, that's probably one of the
Speaker 4: most important thing. Uh. You know, even though we're talking
Speaker 4: about numbers and uh, technology and systems, I think ultimately
Speaker 4: this is a people business. We want to make sure
Speaker 4: that we hire the best in the world. At the
Speaker 4: same time, I think from a cultural perspective, I'll say
Speaker 4: that two things that I think has actually made a
Speaker 4: big difference for us, just to sort of looking inside
Speaker 4: out a little bit. It's I think one part is
Speaker 4: actually this concept of collaboration. Now it's a little bit
Speaker 4: overused words to a certain extent. But I think you
Speaker 4: know what we did was, actually we provide a tremendous
Speaker 4: amount of transparency for folks who join us.
Speaker 3: So, you know, forty years.
Speaker 4: Of IP and people on day one would get a
Speaker 4: tremendous amount of transparency to how we do things, what
Speaker 4: we do, and so that they kind of realize that
Speaker 4: there are a lot of work that's already been done,
Speaker 4: lots of ideas have already been tried. It's very intimidating
Speaker 4: because when I first started, I kind of realized that, Okay,
Speaker 4: most of the question that I thought with smart questions
Speaker 4: have already been answered five years before I joined, so
Speaker 4: you really need to step up. So but that level
Speaker 4: of transparency really fostered the level of collaboration, making sure
Speaker 4: that there's actually a lot of disciplines involved in solving
Speaker 4: hard problems, and that there's actually a lot of people
Speaker 4: trying to help each other. So it's actually one big
Speaker 4: platform really leverage that. So so that that part, I
Speaker 4: think from a cultural perspective, really help help that. I
Speaker 4: think the second part that I think is also probably
Speaker 4: equally important, this is I'm going to speak a little
Speaker 4: bit from the San Francisco Silicon Value perspective is that
Speaker 4: this idea that the world can be a better place
Speaker 4: and whatever is being done, uh it's all good, but
Speaker 4: there will be something better and more and different. Uh.
Speaker 4: So if you will this kind of outlier type of thinking,
Speaker 4: I think it's actually very central to our culture. It's
Speaker 4: not just to say, oh, everybody's actually done this kind
Speaker 4: of things before and we're just going to execute a
Speaker 4: little bit better.
Speaker 3: No, I mean we want to actually.
Speaker 4: Think from the basic principle and to see if we
Speaker 4: can do things fundamentally different on a forwardlooking basis. So
Speaker 4: I think these are the two things devid to a point.
Speaker 4: I think from the people talent and the cultural perspective,
Speaker 4: I think if you can get smart people to collaborate
Speaker 4: and then really allow them but also push them to
Speaker 4: really go for a different way of thinking, a different
Speaker 4: way of investing, I think that's actually been very much
Speaker 4: central to our DNA.
Speaker 2: I think it's an interesting comment about the transparency because
Speaker 2: that really gets people up the speed. And like you said,
Speaker 2: I feel like us quants answer the same question in
Speaker 2: five you know many different ways. You're so kind of
Speaker 2: building on these questions, so you know, there's these well
Speaker 2: known factors that I call open secrets and finance that
Speaker 2: people know the value moments and quality low of all
Speaker 2: you know, generally like what percent didch what you say
Speaker 2: of your research is involved in those factors, trying to
Speaker 2: refine them, trying to get the best combination, trying to
Speaker 2: even time them, versus trying to find new things that
Speaker 2: are bespoke that people might not know about or are
Speaker 2: not well known to add value.
Speaker 4: Yeah, I mean I think today I'll say very little
Speaker 4: to try to refine the open secret type of factors.
Speaker 4: I think this is actually also we've been on this
Speaker 4: journey for sixteen seventeen years, so it's really around twenty
Speaker 4: nine twenty ten. You know, our global head of research,
Speaker 4: Run Kong, back in twenty ten basically say you know what,
Speaker 4: we are not going to do value version number eleven.
Speaker 4: We're not going to do quality version seventeen. And you know,
Speaker 4: while there are other people who may want to do that,
Speaker 4: we're going to go on a completely different route, which
Speaker 4: is to look for new signals, new ideas that nobody
Speaker 4: has actually really thought about before. You know, simple things
Speaker 4: like you know, if you look at the job posting
Speaker 4: data in the United States, there are you know, millions
Speaker 4: of these job posting data. You know, that's a you
Speaker 4: know on companies website, on aggregator's database. And if you
Speaker 4: have that data set, you can essentially get a sense
Speaker 4: of Okay, is this company hiring and what type of
Speaker 4: skill set are they hiring? Modern skill sets versus old
Speaker 4: fashioned skill set. We have this signal idea which is
Speaker 4: jokingly called lung Python short Excel. You know, people who
Speaker 4: are hiring more Python oriented skill sets are more ready
Speaker 4: for the machine learning AI world and versus actually quite
Speaker 4: like Excel. But we're bet against the company that's hiring
Speaker 4: heavily into Excel oriented skill set. So you can actually
Speaker 4: have a new data set and come up with new factors,
Speaker 4: if you will, new signals and asking a bunch of
Speaker 4: questions you never thought about asking. And so we've been
Speaker 4: on that journey for sixteen seventeen years and fast for
Speaker 4: today it is you know, one thy five hundred signals
Speaker 4: or factors if you will, far far beyond the traditional,
Speaker 4: open secret type of traditional factors. And that really allow
Speaker 4: us to you know, really tap into the data engineering piece,
Speaker 4: the different learner piece, the some of the new new
Speaker 4: things in terms of you know, natural language processing, and
Speaker 4: fast forward today large language model. So I think once
Speaker 4: you open it up, you do realize the world is
Speaker 4: very big and there are many many places to go.
Speaker 4: And I think given the development of AI today, I'll
Speaker 4: see that that number is going to go skyrocket even
Speaker 4: more on our forelooking basis, just given the technology is
Speaker 4: getting a lot better.
Speaker 1: So, I mean, it's obviously fascinating what's happened with AI
Speaker 1: and data. And if we just think about data, what
Speaker 1: do you think the real edges today? Is it having
Speaker 1: unique data or you know, being better at extracting signals
Speaker 1: from widely available data.
Speaker 4: Yeah, I mean, David, that's a very good question. I
Speaker 4: mean I would say that we probably don't necessarily see
Speaker 4: too much of a trade off. I think on one
Speaker 4: side of it is we are going for more and
Speaker 4: more data. You know, the number is like we have
Speaker 4: thirty thousand data sets on the platform, and that number
Speaker 4: I think is going to explode even more, especially in
Speaker 4: the world of synthetic data on top of you know,
Speaker 4: if you will sensor based or quote unquote real data.
Speaker 4: So I think that data explosion is going to continue.
Speaker 4: On the other side of it, I think given the
Speaker 4: more and more data that's available. I think it's also
Speaker 4: extraordinarily important to think about what are the interesting questions
Speaker 4: we're asking to this particular data set. To your point,
Speaker 4: extract more information from a particular data set. And I
Speaker 4: think that super exciting area because I think the quality
Speaker 4: of the questions in this kind of a big data
Speaker 4: machine learning era is very very important. And this ability
Speaker 4: to ask interesting questions I think now can also empowered
Speaker 4: by generative AI, so you can actually, you know, we
Speaker 4: have an interesting research which is actually essentially co working
Speaker 4: with large large language model try to extract more interesting
Speaker 4: questions that we have not thought about asking for a
Speaker 4: particular data set. So it is actually data to answer
Speaker 4: your question is actually broader but also deeper, more data sets.
Speaker 4: But it's also trying to make sure that we ask
Speaker 4: deeper questions to each individual data sets. And I think
Speaker 4: this is we can talk more about it. But I
Speaker 4: think this concept of scale in alpha generation, I think
Speaker 4: it's becoming increasingly critical. You need scale in data, you
Speaker 4: need scale in inside generation, you need scale in compute,
Speaker 4: you need scale in implementation. So how do you leverage
Speaker 4: the scale for innovation for alpha I think it's really
Speaker 4: going to be a bit of a defining theme in
Speaker 4: the years to come, you know.
Speaker 2: Jeff, you know obviously whatever you're willing to share here,
Speaker 2: but you know, kind of going back to the factor
Speaker 2: timing question, you know, and then let's just talk about
Speaker 2: traditional factors. And you said some of the extent of
Speaker 2: is like bottom up processes, or's top down processes. So
Speaker 2: let's focus on top down. So, like, if you can
Speaker 2: give us maybe just an idea of like the general
Speaker 2: things that maybe you could use the time factors, like
Speaker 2: is it the momentum of the factor, the evaluation differences,
Speaker 2: is some kind of economic regime, anything you could share
Speaker 2: that would be very valuable.
Speaker 4: Yeah, I mean I give you maybe one specific example
Speaker 4: that I find it to be to be interesting, right,
Speaker 4: I mean, I think you mentioned regime, and you know,
Speaker 4: this is always an interesting concept in the sense that intuitively,
Speaker 4: with the twenty twenty hindsight, you know, where one can
Speaker 4: write us story about a particular environment happens. Certain you know,
Speaker 4: strategy is supposed to work, and then they work, and
Speaker 4: it's very comforting to see. But ACCENTI, how do you
Speaker 4: define a regime and how do you link the regime
Speaker 4: to the factor performance in a rotational sense? So the
Speaker 4: interesting work that we've done I think I'm happy to
Speaker 4: share is actually try to define the regime in a
Speaker 4: way that that's much more machine learning driven. Is to say,
Speaker 4: let's look at everything going on in the world, you know,
Speaker 4: different asset prices, different economic variables, and you can actually
Speaker 4: use a machine learning algorithm to essentially define what type
Speaker 4: of environment that is given all the you know, if
Speaker 4: you will time serious data that you observe in a
Speaker 4: particular period, and then you can actually you look at
Speaker 4: sort of you know, we use a sort of fancy
Speaker 4: Euclidian distance term, but you can look at how similar
Speaker 4: this regime is to the history that we've actually seen.
Speaker 4: Some people say, well, if all your price is going
Speaker 4: to so this way, it looks like nineteen seventies, or
Speaker 4: if there's actually a private credit issue, this may look
Speaker 4: like a Carbinger of you know, two thousand and seven,
Speaker 4: two thousand and eight mortgage crisis. So whatever the talk
Speaker 4: that may be, I think you can actually verify it
Speaker 4: with a more machine learning algorithm to say, okay, let
Speaker 4: me actually look at the Euclidan distance between this and
Speaker 4: to see how is that defining a particular regime in
Speaker 4: a systematic fashion and then you can link that to
Speaker 4: the factory performance to say, this is a regime, how
Speaker 4: did this factor do? Allow the machine to learn the
Speaker 4: factory performance in different regimes once you define it, and
Speaker 4: then you know, once the market price start to move,
Speaker 4: you can identify the regime and then in relation to
Speaker 4: that factory performance from a timing perspective. So that's maybe
Speaker 4: one specific example that I think is very intuitive, but
Speaker 4: it's actually what human we as a human well look
Speaker 4: at market do anyway, but it's actually in this where
Speaker 4: you can quantify it, you can systematize it, and you
Speaker 4: can also do it in a bit of unbiased way.
Speaker 1: So it sounds like AI machine learning or really making
Speaker 1: things a lot more efficient for you. Are you also
Speaker 1: or do you think it's also genuinely improving investment outcomes too?
Speaker 4: Yeah, I mean I think I think it's definitely we
Speaker 4: see it, you know, David to a point on two front.
Speaker 4: One is actually certainly about efficiency in the sense that
Speaker 4: the average researchers are becoming a lot more productive, the
Speaker 4: average portfolio managers are becoming much more capable of managing
Speaker 4: many more portfolios than before. So there is a scaling,
Speaker 4: there's a bit of efficiency gain aspect of it. And
Speaker 4: the second part, executly to your point is also I
Speaker 4: do think that from a performance perspective is that once
Speaker 4: you have a big stack in terms of data, in
Speaker 4: terms of learners, in terms of assystems and secution, it
Speaker 4: is actually possible to drive the diversification. From alpha perspective.
Speaker 4: You know, clearly multi strategy hedge fund have been very
Speaker 4: successful in general in the hedge fund industry a quarter
Speaker 4: of the assets and growing very rapidly. I think that's
Speaker 4: also there's a lesson learned there, which is really if
Speaker 4: you deploy scale across different parts, across different technologies. We
Speaker 4: have a little bit different way of looking at it,
Speaker 4: but it's ultimately it is about driving that scale in
Speaker 4: the type of strategies horizon insights, and that's where you
Speaker 4: can drive both the consistency uh, but also the differentiation UH.
Speaker 4: And I think that's if you will, the the from
Speaker 4: an investment outcome perspective, also, the consistency and differentiation are
Speaker 4: probably two most important, UH, two important metrics you want
Speaker 4: to you know, all perform consistently from a sharper information
Speaker 4: racial perspective, but you also want to make sure that
Speaker 4: when there's a market warble, when there's all your price spike,
Speaker 4: or when there's certain things happening in the market that's
Speaker 4: impacting active managers at large, that you have a differentiated
Speaker 4: way of looking at the world, that's the strategy is
Speaker 4: actually performing better than some of the competitors. So I
Speaker 4: think that's yes. So the short answer to that is,
Speaker 4: so we've certainly seen that, you know, not only this year,
Speaker 4: but also over the years, and I think this idea
Speaker 4: that you can build scale underneath it, and you can
Speaker 4: build that consistency and differentiation even more on a forelooking
Speaker 4: basis using these modern tools. That's certainly a very high
Speaker 4: confidence view that I have.
Speaker 2: You know, let me ask you another kind of long
Speaker 2: standing quant debate that everyone has, right, and that's around
Speaker 2: the interpretability of signals or factors, you know, the traditional
Speaker 2: factor world. You know, we have all these thoughts about
Speaker 2: why the traditional factors work. There's behavioral reasons, there's risk
Speaker 2: based reasons, there's economic intuition stories as to why momentumcial
Speaker 2: work value should work. And then I see other people say,
Speaker 2: you know, if you find the signal that's statistically significant,
Speaker 2: that's robust, but you're not really sure why it's working,
Speaker 2: you could still use it. What is What is your
Speaker 2: opinion on that?
Speaker 4: The opinion is both. I guess that's that's that's short answer.
Speaker 4: I think we have been a very much in the
Speaker 4: first camp. We quite sensibility. So this is the idea
Speaker 4: that an idea needs to make economic sense or intuitive
Speaker 4: sense for us to use it. So that's been a
Speaker 4: long run tradition for close to forty years, and that's
Speaker 4: still very much well in the life. At the same time,
Speaker 4: I think, you know, we've been working with some of
Speaker 4: the AI related the professors and at Stamford, at Berkeley
Speaker 4: and other universities, and I think, you know, there's this
Speaker 4: professor Stephen Boyd who at Stamford who works with us.
Speaker 4: Back fifteen sixteen years ago, he told me, he said,
Speaker 4: maybe you want to let the data to speak a
Speaker 4: bit more, and this idea that when we use machine learning,
Speaker 4: it is actually open minded learning using machine and that
Speaker 4: data intelligence should come into the process without too much
Speaker 4: of a human.
Speaker 3: Intervention, if you will.
Speaker 4: And that part, certainly, you know, was a bit of
Speaker 4: a shock to some of economists and finance oriented people
Speaker 4: fifteen sixteen years ago to say, I mean that doesn't
Speaker 4: make any sense or doesn't make any sort of long term,
Speaker 4: long term reason, but I would say that that evolution
Speaker 4: has happened for us. So the answer is both. There
Speaker 4: are plenty of things that we do that has a
Speaker 4: very strong economic inquation and reason, But then there are
Speaker 4: stuff that's actually a little bit more machine intelligence that
Speaker 4: we set it up such that we intentionally want to
Speaker 4: get data, let data to shine through the in this
Speaker 4: of an AI machine learning world, and I think that's
Speaker 4: especially important. You know, we talked about horizon earlier. If
Speaker 4: I were to do stuff at the millisecond high frequency space,
Speaker 4: the economic intuition.
Speaker 3: Matters much less.
Speaker 4: It's very much letting the data speak, because in milliseconds, economics,
Speaker 4: geopolitics matter very little on a day to day basis.
Speaker 4: And at the same time, on this kind of a
Speaker 4: one month two month investment horizon, it's actually quite important
Speaker 4: to get that economic intuition. But we're also living in
Speaker 4: a world where there's abundance of abundance of data, abundance
Speaker 4: of machine learning algorithm, so letting that to speak is
Speaker 4: also very important. So I think, yeah, we are we
Speaker 4: are a little bit of a school of we got
Speaker 4: to use both and.
Speaker 3: Both can be helpful.
Speaker 1: So if we think about taking these signals and you know,
Speaker 1: putting them into a portfolio, you know, two different managers
Speaker 1: can look at the exact same signals and you know,
Speaker 1: might end up with two very very different portfolios. So
Speaker 1: how much of you know, I guess alpha or like
Speaker 1: an edge could come from portfolio construction once you have
Speaker 1: those signals.
Speaker 3: I would say very important.
Speaker 4: Portfolio construction is something that I think clearly mein variance
Speaker 4: optimization has been around for decades and it's a common
Speaker 4: practice for a lot of the systematic investors. And I
Speaker 4: think what we've actually done is also try to put
Speaker 4: quite a bit of innovation on back front. You know,
Speaker 4: machine learning, newer network there's actually been you know, deep learning,
Speaker 4: there's actually been quite a bit of innovation that's been
Speaker 4: that has happened that can be applied to portfolio construction
Speaker 4: as well. So I think you can almost think about
Speaker 4: min variance optimization as your basic benchmark, and it has
Speaker 4: its own pluses, it has its own minuses, and some
Speaker 4: of its modern techniques using machine learning at least, you
Speaker 4: know for us has you're proven to be very additive
Speaker 4: above and beyond, and once you get into that world.
Speaker 4: You also realize that there's a lot of flexibility in
Speaker 4: using some of these modern tools, and the search space
Speaker 4: is a lot larger, you know mein variance optimization from
Speaker 4: a grading descent perspective sometimes can narrow you into a
Speaker 4: corner solution, a local maximum pretty quickly, and the search
Speaker 4: space may not be as large as flexible. And once
Speaker 4: you adopt, you know, some of these modern tools, I
Speaker 4: think a more simulation based approach also becomes a lot
Speaker 4: more important daily To answer your question is like, you
Speaker 4: can do this, you know certain methods, a certain way
Speaker 4: of doing it, but how do you know it is
Speaker 4: a best and a much more simulation oriented approach. Using
Speaker 4: these modern methods allow us to really to get a
Speaker 4: bit of a sense of what the search space is.
Speaker 4: So I would say that the application of a machine
Speaker 4: learning A lot of people think about this essentially it's
Speaker 4: a fancy large language model and that's about it. But
Speaker 4: I would say that this is actually what I would
Speaker 4: like to call it, a big umbrella AI. AI can
Speaker 4: be applied not only to the large language model, which
Speaker 4: is certainly very exciting, but it can also apply to
Speaker 4: a profolio construction risk management simulation. Many things. So that's
Speaker 4: so we certainly want to use a full stack of
Speaker 4: AI as opposed to just one narrow application.
Speaker 2: Jeff, I want to ask you about like kind of
Speaker 2: like the risk side, specifically factor risk model. So like
Speaker 2: when you construct your portfolio and you have these longs
Speaker 2: and shorts, do you run it through any kind of
Speaker 2: factor risk model to see kind of like your maybe
Speaker 2: unintended bets that you're making and hedge them out or
Speaker 2: do you not do that?
Speaker 1: Oh?
Speaker 4: Yeah, definitely. We certainly look at the factory exposure. And
Speaker 4: I think from a risk management perspective, I think I
Speaker 4: think it's certainly we understand the factor potential, but also
Speaker 4: the potential risk associated with factors and also the time
Speaker 4: varying nature of this. But at the same time, I
Speaker 4: think if that's all the world that you're living, then
Speaker 4: the factory risk model becomes very important, and how do
Speaker 4: you model that? I'll see that from our perspective is
Speaker 4: actually we're trying to narrow that particular factor lens. It's
Speaker 4: still you know, in our legacy, in our history. We
Speaker 4: certainly want to look at it that way. A lot
Speaker 4: of market participants certainly look at it that way. But
Speaker 4: if you open up some of its potential for IDROO
Speaker 4: syncretic you know, security selection that's above and beyond factors.
Speaker 4: Once you open that up, you realize that there's actually
Speaker 4: a lot more interesting things above and beyond the factors,
Speaker 4: and some of them may carry it to your point,
Speaker 4: some factory loading to it. You may want to, you know,
Speaker 4: model it out and making sure you hedge it and
Speaker 4: make sure you don't you know, you don't you're getting
Speaker 4: the purefied exposure. But I see that's the overall risk
Speaker 4: management is certainly think about the factor risk exposure carefully
Speaker 4: and try to moderate it. For us, it's actually trying
Speaker 4: to be differentiated. It's actually just basically not have too
Speaker 4: much in this kind of factory loading and factory risk.
Speaker 4: But then it opens up this new world of IDEO
Speaker 4: syncretic risk taking. You know, this idea of you can
Speaker 4: be broad and deep. So this you know, factories is
Speaker 4: probably only broad but not very deep. And maybe the
Speaker 4: fundamental discretion investors are only deep but not necessarily broad.
Speaker 4: And we're trying to do both. And that space, in
Speaker 4: my mind is a rich opportunity set from an alpha
Speaker 4: generation perspective.
Speaker 1: So how do you think about or I should say, how,
Speaker 1: how should allocators think about that in terms of systematic
Speaker 1: equity alongside discretionary strategies. Do you see them as conplimary
Speaker 1: or you know, is their overlap at all?
Speaker 4: I think it's it's a sort of maybe our I'll
Speaker 4: step back a little bit. I'll see that this definition
Speaker 4: of systematic versus discretionary certainly is very relevant and probably
Speaker 4: will be relevant. But from allocator perspective, I would also
Speaker 4: think about maybe one additional lens, which is trying to
Speaker 4: think about modern versus the traditional. Uh there are you know,
Speaker 4: modern tradition, I mean discretionary managers and then their old
Speaker 4: fashioned systematic quantitative investors. And I think this concept of
Speaker 4: being modern, I think in the world of AI development
Speaker 4: is extraordinarily important. So I think at the from allocator perspective,
Speaker 4: I'll say that's a try to get active managers that
Speaker 4: are actually moderan on both front. Now, I mean, eventually
Speaker 4: is there going to be a you know, more AI
Speaker 4: adoptions for the discretionary managers and more AI you know
Speaker 4: adoption in the systematic managers. And therefore this concept of
Speaker 4: being both broad and h and deep all converge into
Speaker 4: one thing. There could be but I think this is
Speaker 4: how modern you are. I think it's a pretty important
Speaker 4: concept from allocator perspective. I think one more lens, given
Speaker 4: you know what Christopher was talking about earlier. I also
Speaker 4: think that this factor versus pure alpha piece is also
Speaker 4: very important. What you realize is that when you have
Speaker 4: multiple strategies, when you put them together, I using credit,
Speaker 4: alpha sometimes actually gets diversified away and what you are
Speaker 4: left with is actually a bit of a compounded factory exposure.
Speaker 4: This is a you know, very important concept from a
Speaker 4: central desk management, from a multi strategy of the perspective,
Speaker 4: but I think from allocator perspective it's also very important.
Speaker 4: You don't want to just hire ten managers, but all
Speaker 4: you have left is essentially momentum bed and that's a
Speaker 4: very expensive way to get momentum exposure. So how do
Speaker 4: you make sure that the portfolio from a CAT perspectives
Speaker 4: been driven by idosyncratic security selection in a very diversified
Speaker 4: fashion and be constraining some of the factory exposure and
Speaker 4: knowing some of the all alativity associated with it. I
Speaker 4: think that's a very important question for the educator as well.
Speaker 1: And you know, as more capital continues to flow in
Speaker 1: these type of systematic strategies. How do you think about
Speaker 1: crowding in you know, the potential durability of alpha.
Speaker 4: I think it's certainly something that we worry about deeply,
Speaker 4: having gone through August O seven. Uh, you know, I
Speaker 4: was certainly with the firm back I joined two thousand
Speaker 4: and four, So I think some of this crowding issue, certainly,
Speaker 4: you know, was a lived experience and certainly very much important, uh,
Speaker 4: to to be cognizant of. At the same time, I
Speaker 4: think I'm actually reasonably optimistic from a capacity from a
Speaker 4: you know, diversity of the strategy perspective, in the sense
Speaker 4: that I think the modern methods and the availability of
Speaker 4: the data and how you know, you can mean we
Speaker 4: were talking about how to use this data, it really
Speaker 4: becomes much more of an open ended question. Uh. It's
Speaker 4: actually I think it's time for creativity. It's a time
Speaker 4: for uh, if you will, liberal arts thinking on this.
Speaker 4: So if you can do that, there's actually a lot
Speaker 4: of diversity thinking that you can actually get to drive
Speaker 4: alpha stream that could be you know, much less uh,
Speaker 4: you know, get you get you into some of these
Speaker 4: crowded trades and can open up capacity, and so I
Speaker 4: think the if a scale player can play the creativity game,
Speaker 4: I actually think that the capacity will be large, ELPA
Speaker 4: will be differentiated and you can actually avoid some of
Speaker 4: the crowded traits. Now, to build that, I think you
Speaker 4: you really need to build a large scale platform that
Speaker 4: is ready to innovate at that scale. So innovation at
Speaker 4: scale is easily said than done. But I do think
Speaker 4: that for the platform that I can do that. I
Speaker 4: think on a forward looking basis, this is actually a
Speaker 4: very exciting period, if not the most exciting over the
Speaker 4: last forty fifty years, and that's going to drive longer
Speaker 4: term durability to the alpha generation. You deliver that consistency
Speaker 4: but also differentiation.
Speaker 1: That's great. Unfortunately we need to end here, but this
Speaker 1: was a lot of fun. Jeff, thank you again for
Speaker 1: joining us today.
Speaker 3: Thank you both. It's great to be here.
Speaker 1: And Chris, thank you again for being my co host.
Speaker 2: Thank you, Thank you so much. Javis is great.
Speaker 1: Also want to thank our listeners. If you liked the episode,
Speaker 1: please share, subscribe, and leave a review. If you'd like
Speaker 1: to see more of our research on the terminal, go
Speaker 1: to bi fund, Go for fund and Active Research in
Speaker 1: Bisto x En go for equity strategy research until our
Speaker 1: next episode. This is David com but inside active.
Speaker 2: It has Torso
Speaker 3: Town to sup