Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: This week on the podcast, I have an extra special guest.
Speaker 1: His name is Matthew Grenade and he is a senior God.
Speaker 1: How do I describe his role? His title really doesn't
Speaker 1: do it justice. His official title is Chief Market Intelligence
Speaker 1: Officer at Point seventy two, follow the progression that has
Speaker 1: taken place. Stevie Cohen was running Sack Capital for a
Speaker 1: long time. That was eventually converted into a family office
Speaker 1: which was Point seventy two that reopened to outside investors
Speaker 1: last year in UM and Grenade has been working there
Speaker 1: for a good couple of years. Previously he was at
Speaker 1: Bridgewater with Ray Dalio. You'll hear all about that during
Speaker 1: our conversation, but more importantly, you'll hear about the intersection
Speaker 1: between man and machine, between the way models can be
Speaker 1: used to not only manage assets, but improve the entire process,
Speaker 1: along with a variety of big data and other approaches
Speaker 1: UH that are really quite fascinating if you are at
Speaker 1: all interested in quantitative investing, machine learning, hedge funds, UH,
Speaker 1: the state of investing today and what anybody who is
Speaker 1: pursuing alpha must do to stay current, then you're gonna
Speaker 1: find this to be an absolutely fascinating conversation. So, with
Speaker 1: no further ado, here is my conversation with Point seventy
Speaker 1: two's Matthew Grenade. My extra special guest this week is
Speaker 1: Matthew Grenade. He is the chief market intelligence officer at
Speaker 1: Point seventy two. That is Stevie Cohen's new hedge fund,
Speaker 1: which employs about out people and manages about thirteen billion dollars.
Speaker 1: Point seventy two asset management was converted into a hedge
Speaker 1: fund in and last year it reopened to external investors.
Speaker 1: Matthew comes to us by way of Bridgewater Associates Domino
Speaker 1: Data Lab, and he got his both undergraduate and graduate
Speaker 1: NBA at Harvard Business School, where at undergraduate he was
Speaker 1: the president of the Harvard Crimson. Matthew Grenade, Welcome to Bloomberg.
Speaker 1: Thank you for having me. So let's start. Let's start
Speaker 1: with the most unusual thing on your um resume. You're
Speaker 1: president of the Harvard Crimson, not exactly a hotbed of
Speaker 1: future hedge funds officers. How did that come about? What
Speaker 1: was that experience? Like, well, a couple of things, I
Speaker 1: mean in terms of that experience. Running in newspaper is
Speaker 1: one of the most amazing things in the world, and
Speaker 1: I got to run a small one at Harvard. But I,
Speaker 1: you know, I think it's just an incredible job because
Speaker 1: you're in the middle of so much information, You're helping
Speaker 1: shape the debate, you're investigating things, so many interesting people. Um,
Speaker 1: Harvard's an awesome place to do that at. And so
Speaker 1: there are there are a few jobs that I've loved
Speaker 1: as much as I loved that one. It was incredible
Speaker 1: how you get that job. There's sort of a couple
Speaker 1: of things. One, there's a bit of a path um so,
Speaker 1: it generally is a newsperson, a reporter. Um. So I
Speaker 1: was a reporter for my first couple of years, and
Speaker 1: then I was the head of the central what's called
Speaker 1: the central Administration beat that covers the president of the university.
Speaker 1: That's also kind of a traditional stepping stone. Then there's
Speaker 1: a process called the Turkey Shoot. Um. The Turkey Shoot
Speaker 1: runs for about a month leading up to Thanksgiving, where
Speaker 1: they picked the next president. There's all sorts of sort
Speaker 1: of arcane rules, but probably the most interesting is that
Speaker 1: every outgoing member of the paper gets to vote, and
Speaker 1: if more than three disagree, you're blackballed, and so you
Speaker 1: resually hold in uh sort of and you know, in
Speaker 1: sort of in veto mode for as long as that goes.
Speaker 1: And so the deliberations generally run about teen hours straight exactly,
Speaker 1: and then and then there's a big party and whatever
Speaker 1: sort of once the sort of unlocking happens. But I
Speaker 1: had I think six or seven opponents for the job,
Speaker 1: and uh, you know, you have to you know, a
Speaker 1: little politics, a little politics, a little message, a little
Speaker 1: of this, um, and that's that's how it works. But
Speaker 1: it was an amazing opportunity. So that's an unusual background
Speaker 1: as a journalist and someone who's publishing the paper to
Speaker 1: really being a data scientist for a financial services shop.
Speaker 1: How did that career path unwind? Well, they're probably more
Speaker 1: similar than you think, because I mean a lot of
Speaker 1: it comes down to information, collecting information, using information, UM.
Speaker 1: And so you know, I've always been someone who likes
Speaker 1: to know what's going on, um, you know, what's going
Speaker 1: on in the world. I like to sort of be
Speaker 1: ahead of other people and knowing things, and so that's
Speaker 1: the that's the similarity. But the you know, but the
Speaker 1: career arc was um, I went from from college to Mackenzie,
Speaker 1: was a business analyist there, uh, and then went to
Speaker 1: business school like you mentioned, and then ended up at Bridgewater.
Speaker 1: Um and which is also a fascinating place. Is a
Speaker 1: fascinating place. So I was there for six years, um
Speaker 1: and Uh, it's a phenomenal place to work. I'm a
Speaker 1: big fan of Ray Dalio. I find his philosophy just
Speaker 1: totally intriguing. I think Bridgewater kind of gets a bad rap.
Speaker 1: People have called it a cult and have criticized the
Speaker 1: radical transparency. You survived there for six years. Can't be
Speaker 1: all bad, right, how to be pretty good? No, it's
Speaker 1: not all bad at all. In fact, I think it's
Speaker 1: you know one uh, you know as investors go, um,
Speaker 1: you know they're they're as good as it gets um,
Speaker 1: and you know, just phenomenal at it. And look, I
Speaker 1: think the differences of the culture there get overstated, um,
Speaker 1: meaning the radical transparency and meaning like how different it
Speaker 1: is from you know. Look, I mean like you know,
Speaker 1: I would say everywhere I've ever worked McKenzie point seventy two, Domino, Bridgewater, Um,
Speaker 1: you know, they've all been ambitious people who are trying
Speaker 1: to get to the right answer. Who wanted to do
Speaker 1: great things. Um. And you know, like at core, like
Speaker 1: that's that's a lot of what Bridgewater is about. And
Speaker 1: you know, Ray and the team, they're are very thoughtful
Speaker 1: about ways to um, you know, just sort of apply
Speaker 1: certain ideas. Um. You know, like you want to. You
Speaker 1: always want to make sure you're getting the best opinions right,
Speaker 1: And so they're very explicit about you know, who should
Speaker 1: you listen to about things? But you know, I see,
Speaker 1: I see Steve asked that question all the time, you know,
Speaker 1: like why am I listening to you? You know, I
Speaker 1: should be listening to this person instead. Um. And so
Speaker 1: I think Bridgewater is great at sort of scaling it.
Speaker 1: But but um, but I think that the ideas are
Speaker 1: are not not quite as radical as the media would
Speaker 1: want you to believe. And then the transparency, Um, it's
Speaker 1: just great. I mean I love the idea. Yeah, I
Speaker 1: mean I was. I always just saying like it's a
Speaker 1: very clean place to live. And the reason it's a
Speaker 1: very clean place to live at Bridgewater is you just
Speaker 1: don't say things behind people's back. You just say things
Speaker 1: to their face. Um, and you're just He writes about
Speaker 1: that in his first book in a chapter where he
Speaker 1: describes raise people problem. I mean, most founders and chairman
Speaker 1: don't spend the chapter describing the wrong people person. That's
Speaker 1: fairly trying its parent. Yeah, I mean I think that's
Speaker 1: fairly transparent. And and that's just how you're expected to operate,
Speaker 1: you know. I mean, if you're gonna say something about Ray,
Speaker 1: you say it to him. And um and I have
Speaker 1: many stories of of of saying things to Ray that
Speaker 1: I think people would find not horrifying. There's they were
Speaker 1: me being honest and him and I trying to sort
Speaker 1: of work out differences. But you know, the only rule
Speaker 1: was just don't say it behind his back, and and
Speaker 1: that's you know, it's it's interesting that that's considered so radical,
Speaker 1: you know what I mean, It's not it's not that radical.
Speaker 1: So now let's let's take this phote. You'll end up
Speaker 1: at at point seventy two. Your title is Chief Market
Speaker 1: Intelligence Officer. I've never even seen c M I O
Speaker 1: as a abbreviation. What does a c M I O do?
Speaker 1: That title was the title I had when I got there. Um,
Speaker 1: and I was really focused at that point on proprietary research.
Speaker 1: And so what we mean by that is how do
Speaker 1: we take UM data sets or surveys or web scraping
Speaker 1: or sort of all the different things you can do,
Speaker 1: UM and make that useful to our portfolio managers and analysts. UM.
Speaker 1: Since then, my job has evolved to include a couple
Speaker 1: other things. So I also oversee our central book at
Speaker 1: this point, UM, which is our sort of a systematic
Speaker 1: best ideas book we have and also receive venture capital.
Speaker 1: And we just haven't really changed the title. Quite fascinating.
Speaker 1: Let's talk a little bit about big data and machine
Speaker 1: learning and artificial intelligence. Help me make a little sense
Speaker 1: about those buzzwords which have come into vogue for a while.
Speaker 1: But but your shop has been using these things for
Speaker 1: for quite a while. UM. What's the state of the industry, uh,
Speaker 1: in terms of machine learning and big data and artificial intelligence? Well,
Speaker 1: I think the you know, the thing to sort of
Speaker 1: contextualize all those terms, UM. And you know, I agree
Speaker 1: with you, they're they're very buzzy. UM. But but the
Speaker 1: way I like to think about it as being model
Speaker 1: what I call model driven UM. And so you can
Speaker 1: talk about model driven businesses or model driven processes, and
Speaker 1: really the idea of a model is it takes in data.
Speaker 1: It could be big data, it could be not big data. UM.
Speaker 1: It runs a certain set of logic on that UM
Speaker 1: and then it produces a prediction of some variety UM.
Speaker 1: And you know, basically it tries to close the loop
Speaker 1: around that data so that you know, you're constantly improving
Speaker 1: the logic or the algorithms. And so Netflix is a
Speaker 1: model driven business intensents a model driven business UM. And
Speaker 1: obviously finance and and you know the hedge funds we're
Speaker 1: talking about there, you know, they're they're they're very model driven.
Speaker 1: What I would you know, what I would say is that,
Speaker 1: you know, the state of the industry, uh, in that
Speaker 1: regard is that UM, you know, these techniques are highly
Speaker 1: highly relevant to kind of almost everything we're doing, you know,
Speaker 1: whether it be extracting signal from data sets or you know,
Speaker 1: all the way up to making trading decisions. Uh. And
Speaker 1: so you know, we're investing, you know, like a lot
Speaker 1: of hedge funds were investing a lot in you know,
Speaker 1: people with the data science capabilities and with the machine
Speaker 1: learning capabilities as well. So ron course Ferry you famously said,
Speaker 1: torture the data long enough and it will confess to
Speaker 1: whatever you want. How do you avoid running into that
Speaker 1: problem of when you're building models and putting a ton
Speaker 1: of different quantitative information into it, how do you avoid
Speaker 1: that bad outcome of Hey, if we back test this
Speaker 1: enough and we make these tweaks, we could get this
Speaker 1: to say whatever we want. Yes, I think there's I
Speaker 1: think there's a couple of different ways you do that.
Speaker 1: I mean one is UM. You know, you want to
Speaker 1: have a fundamental intuition of some variety around what you're doing,
Speaker 1: you know, I mean, you're not just sort of running
Speaker 1: everything through a machine and some some people do, but
Speaker 1: but not not. That's not how I like to do it.
Speaker 1: You're not just sort of running everything through and sort
Speaker 1: of seeing, you know, seeing what fits, because to your point,
Speaker 1: something will fit UM, and it may be a real
Speaker 1: thing or it maybe you know, a very short lived
Speaker 1: thing UM. And then you know, you have to have
Speaker 1: a lot of discipline in terms of looking at your UM.
Speaker 1: You know it's called out a sample, uh sorry, basically
Speaker 1: in sample, out of sample and live UM. And what
Speaker 1: that basically means is where are you allowing yourself to
Speaker 1: to fit the parameters where you're sort of just looking
Speaker 1: at the results but still in a in a backwards
Speaker 1: looking way, and when are you sort of really trying
Speaker 1: it out? And you know, we have very strict rules
Speaker 1: about how we segment those different things before we start,
Speaker 1: you know, using you know, putting money against a certain strategy,
Speaker 1: so and out of sample, just to put a little
Speaker 1: flesh on that. If you're testing on a large cap us, hey,
Speaker 1: let's see how the status in the past. Let's see
Speaker 1: how it does overseas, not just the area you're looking
Speaker 1: forward to see if it's really something to the model.
Speaker 1: Is that a fair descriptor yeah. So let's say you
Speaker 1: were using um, you know, credit card data to trade Chipotle,
Speaker 1: you know, or something like that. Um. You know, what
Speaker 1: you would do is you would sort of, you know,
Speaker 1: you build some rules, um, and you would sort of
Speaker 1: fit those rules to some sub some set of data
Speaker 1: some time period, you know, three or four years. Then
Speaker 1: you would stop fitting the rules and you would sort
Speaker 1: of look at the next three or four years and
Speaker 1: sort of see it, does that those two match. Do
Speaker 1: they look the same or is the behavior very different?
Speaker 1: And then you would and then you would basically start
Speaker 1: running the model live from today and then see again
Speaker 1: if those match the other two periods and so you're
Speaker 1: looking sort of for a consistency across that and if
Speaker 1: you're not seeing that, then that's a good sign that
Speaker 1: you're overfitting it. It's also you know, because going back
Speaker 1: to my original point, you know, you want to think
Speaker 1: about whether or not there's a real intuition there. You know,
Speaker 1: I mean, should credit card and chipotle a make sense together? Right?
Speaker 1: It probably does because a lot of people use a
Speaker 1: credit card at chipole. But you know, if you were
Speaker 1: using uh, you know, credit card to trade ge you know,
Speaker 1: you might you might start scratching your head about what
Speaker 1: you're doing. Right, might just be a random correlation as
Speaker 1: opposed to a real causal relationship. So so let's talk
Speaker 1: about some of these unusual UM data sources. I know,
Speaker 1: alternative satellite data is all the rage these days. People
Speaker 1: are looking at parking lots, how filled they are. They're
Speaker 1: looking at how deep transport ships are sitting, uh in
Speaker 1: the water, how far below the waterline they might actually be.
Speaker 1: How esoteric can we get with these alternative types of data? Well,
Speaker 1: I think you can. I think you can get quite esoteric.
Speaker 1: I mean I think satellite, um you know, satellite has
Speaker 1: been around for a while and to your point. I mean,
Speaker 1: it's it's very widely used. Um. You know, you know
Speaker 1: what we think much more about now is um, you know,
Speaker 1: sort of much more specific data sets. UM. You know
Speaker 1: kind of that that give you, you you know, a read
Speaker 1: into a limited number of tickers, often via some sort
Speaker 1: of payment system or something like that. UM. And uh,
Speaker 1: you know I think that we're I think we're just
Speaker 1: you know, we're probably in the third inning of something
Speaker 1: or something like that in the in the in the
Speaker 1: data movement in investing. That's that's quite fascinating. So let's
Speaker 1: talk a little bit about complexity. You know, we could
Speaker 1: go back a hundred years and just look at Graham
Speaker 1: and Dodd simple p ratio and more expensive stocks over
Speaker 1: time perform less well and have lower expected turns than
Speaker 1: less expensive stocks. Are we running the risk of making
Speaker 1: things too complex? At at what point does complexity get
Speaker 1: outweighed by its own internal complications? Well, I think, um,
Speaker 1: you know, I think this goes back to the point
Speaker 1: I was making about, you know, about an intuition. Um.
Speaker 1: And you know, at the end of the day a
Speaker 1: point of two. You know, we are we are fundamental investors,
Speaker 1: you know, we believe that Uh, that you know that
Speaker 1: companies ultimately, you know, trade on how they're doing as
Speaker 1: a business and the kind of cash flows they're going
Speaker 1: to produce UM and you know, everything we do, I mean,
Speaker 1: we will use very sophisticated data science to predict a
Speaker 1: revenue stream or something like that, but we're at core
Speaker 1: trying to do something fairly simple. You know, we're trying
Speaker 1: to understand what the revenues are, what the costs are,
Speaker 1: you know, what the growth profile of the earnings are UM,
Speaker 1: and you know, we never sort of lose that grounding
Speaker 1: UM and so you know, look, there's a lot of
Speaker 1: ways to make money in the markets UM, and I'm
Speaker 1: only I'm not an expert in a lot of them.
Speaker 1: I'm only familiar with some of them. But but for us,
Speaker 1: I think that grounding back to pretty simple principles U
Speaker 1: is very important and not something that we lose track of.
Speaker 1: It's interesting that you I think of you guys as
Speaker 1: a quant shop, but you keep referring to intuition. What's
Speaker 1: the intersection like between man machine? Is it really UM
Speaker 1: technology aiding human decision making or is it mostly hey,
Speaker 1: let's go and make the decisions and we'll just see
Speaker 1: what happens, so it points in me too. We do UM,
Speaker 1: we do UM. We do a mix of of three things.
Speaker 1: We have a very large discretionary business that's global long
Speaker 1: short equity you know, people driven its portfolio managers and
Speaker 1: analysts UM looking at some subset of the of stock
Speaker 1: universe UM, meeting with management teams, looking at data sets UH,
Speaker 1: and then making decisions in a in a fairly discretionary fashion. UM.
Speaker 1: We also have a systematic business that's running on algorithms UM.
Speaker 1: And then we have a people plus machine business, which
Speaker 1: is the one that I oversee, which is the you
Speaker 1: know what what what you call the central book earlier UM,
Speaker 1: where what we're doing there is we're looking at UM
Speaker 1: what the behavior of all the people is as one
Speaker 1: of the important inputs UM. But we're also looking at
Speaker 1: the data sets and we're running algorithms to essentially helped
Speaker 1: make decisions out of that. So one way of thinking
Speaker 1: about it is that historically Steve had a best ideas
Speaker 1: book that he he ran as a discretionary investor, and
Speaker 1: over time we've built that up into a systematic best
Speaker 1: ideas book UM. But but a lot of the input
Speaker 1: of that is from discretionary investors and so UM. So
Speaker 1: you know, one of the kind of key questions we're
Speaker 1: always asking is what are the people best at and
Speaker 1: what are the machines best at? And you know, our view,
Speaker 1: UM is that you know, in terms of of really
Speaker 1: being able to interpret fairly nuanced and complicated situations inside
Speaker 1: a specific company, that people are still um, really really good. UM.
Speaker 1: You know, there's other things that machines do very very well.
Speaker 1: But you know, if you're going to meet with the
Speaker 1: management team and interpret a large set of data that
Speaker 1: that has a lot of sort of nuanced and specifics
Speaker 1: to it, UM, the people still beat the machines at that.
Speaker 1: And so we have a you know, we have several
Speaker 1: hundred people that do that. Do you see that edge
Speaker 1: of humans over machines continuing indefinitely or at at some
Speaker 1: point in the future, will smart um computers and artificial
Speaker 1: intelligence be able to do that also well? And definitely
Speaker 1: is a very long time. So I'm gonna I'm not
Speaker 1: gonna I'm not gonna comment on indefinitely. What I will
Speaker 1: say is that our our thesis is a firm right
Speaker 1: now over the next call it you know, seven to
Speaker 1: ten years, is that UM, is that it is people
Speaker 1: plus machines UM, and that the people are very good
Speaker 1: at the nuanced situation, at the idea generation, at the
Speaker 1: interpreting the thin data at the synthesis UM. And that
Speaker 1: the machines are very good at conducting, UH, correcting for
Speaker 1: behavioral bias at portfolio construction, at trade execution. And you know,
Speaker 1: what we're trying to do is figure out how you
Speaker 1: marry those two up in a really smart way UM.
Speaker 1: And that that is essentially the you know, the next
Speaker 1: wave of hedge fund but UM. But you know, like
Speaker 1: where where we are ten or fifteen years in terms
Speaker 1: of what people can do versus machines, I don't think
Speaker 1: I can comment on that quite quite fascinating. Let's talk
Speaker 1: about the venture capital work you guys do. UM. What
Speaker 1: makes you different from traditional vcs? Well, I think a
Speaker 1: couple of things make us UM different than traditional vcs,
Speaker 1: But probably the most important is we we are extremely
Speaker 1: expertise focused in how we are designed, so UM, we
Speaker 1: have no generalists. UM. We have certain practice areas. Right now,
Speaker 1: we have three different three or four different practice areas UM,
Speaker 1: all of which are led by people who have worked
Speaker 1: in that space and invested in that space for quite
Speaker 1: some time, and kind of one of the standards I
Speaker 1: use is, you know, when when when portfolio companies are
Speaker 1: meeting with the investors on our team, do they believe
Speaker 1: that the person they're sitting across from is the one
Speaker 1: of the world's leading experts on the area that they're
Speaker 1: working in. UM. So that that's one difference. I think
Speaker 1: that the other different side point too is we're extremely
Speaker 1: outbound in how we operate. So one of our challenges was,
Speaker 1: you know, we don't have a we don't have a
Speaker 1: brand end NVC you know the way a sequoia does
Speaker 1: or something like that. And so, you know, one of
Speaker 1: the biggest concerns you gotta have in venture investing is
Speaker 1: adverse selection UH. And you probably don't want to be
Speaker 1: taking what's coming through the door. UM. So you know,
Speaker 1: what we focus on is um themes that we think
Speaker 1: are gonna be big money makers, where we think real
Speaker 1: change is happening, where technology is is um uh is
Speaker 1: driving really important impact UH. And then we go try
Speaker 1: to find the companies that we want to invest in
Speaker 1: and knock on their door proactively, look extremely proactive. Almost
Speaker 1: almost all of it is an outbound motion like ninety
Speaker 1: eight percent of it UM and then UM, and so
Speaker 1: that it would be the two big differences. I'd also
Speaker 1: say that UM, you know, you know, probably as firms go,
Speaker 1: our diligence is more intense than a lot of venture firms.
Speaker 1: I think that comes from Steve Um. You know Steve
Speaker 1: uh Um. Steve's one of Steve's sayings is do the
Speaker 1: work um. And you know, when we go into an
Speaker 1: investment committee to talk about something, uh, there's kind of
Speaker 1: only one answer, which is I did the work um.
Speaker 1: Otherwise the meaning's gonna end very soon. And so we
Speaker 1: we hold a pretty high bar in terms of the
Speaker 1: amount of research we're gonna do when we're looking into
Speaker 1: a company. So there would be the three things I
Speaker 1: point to. So, once you decide to make an investment
Speaker 1: in a startup or an existing company, how actively involved
Speaker 1: um with the corporate management are you? Are you guys
Speaker 1: they're giving them advice assistance? Or is it more of
Speaker 1: an arms length here's some money, now, now go do
Speaker 1: something great. It varies, but I would say we're fairly active.
Speaker 1: And the reason we end up being active is goes
Speaker 1: back to this expertise thing that I was describing, which
Speaker 1: is that, Um, you know, because the team is made
Speaker 1: up of people who are very deep experts, it tends
Speaker 1: to be that the entrepreneurs want them on the boards
Speaker 1: because you know, they're they're they're very useful and sort
Speaker 1: of sorting through the strategic questions and knowing where the
Speaker 1: business should go. Um. You know. It's interesting because when
Speaker 1: we started out, I was actually, uh pretty really sucked
Speaker 1: tot to take board seats because I actually, you know,
Speaker 1: I think it can be a bit of a distraction
Speaker 1: from doing the next investment. UM. But it turned out
Speaker 1: it was an important ask from a lot of our entrepreneurs.
Speaker 1: So we do end up taking a lot of board seats,
Speaker 1: which means we're pretty involved. And we talked earlier about
Speaker 1: the quantitative approach UM point seventy two. Often employees, how
Speaker 1: much big data do you bring to bear when trying
Speaker 1: to make a decision about either an area to invest
Speaker 1: in or a specific company. Very little, very little, very little. Uh.
Speaker 1: You know. Part part of it is the areas we're
Speaker 1: investing in. I mean, we're generally investing in enterprise companies
Speaker 1: uh in their early stage, and so you know, lots
Speaker 1: of times they'll have three or four customers UM, and
Speaker 1: there isn't a whole lot to sort of, you know,
Speaker 1: torture the data for UM. Doesn't mean we don't do research.
Speaker 1: We do a tremendous amount of research, but it tends
Speaker 1: to be more interviews with people and UM, you know,
Speaker 1: you know, customer follow ups with customers and probing on
Speaker 1: how you know how a certain product works UM or
Speaker 1: market sizing exercises or things like that UM. But we've
Speaker 1: not brought a lot of the of the of the
Speaker 1: big data to bear on on venture UM though I
Speaker 1: do think you know, in the consumer space there could
Speaker 1: be opportunities for that UM, and that that might be
Speaker 1: something we explore down the road. So this might be
Speaker 1: a little bit of a weird question. But how challenging
Speaker 1: is it two manage two distinct businesses with two very
Speaker 1: different approaches. One is so quantitative and data intensive, the
Speaker 1: other seems to be a little more intuitive and subjective.
Speaker 1: Do you find any sort of when you switch hats?
Speaker 1: Is that a little bit different to get into that
Speaker 1: a little bit challenging to get into that different headspace?
Speaker 1: I wouldn't say so. I think the similarity between both
Speaker 1: of them is that in both cases. You know, we're
Speaker 1: very process driven. UM. You know in in the process
Speaker 1: looks different in each case. But UH, you know, I'm
Speaker 1: I'm a very big believer and I think this comes
Speaker 1: from my my Bridgewater training UM in sort of process
Speaker 1: over outcomes. UH. And you know you have to you know,
Speaker 1: you have to think ahead of time about how you're
Speaker 1: going to approach a problem and why that's going to
Speaker 1: give you an advantage in uh in your approach um
Speaker 1: and on on on both sides of the business UM
Speaker 1: that I'm involved with. You know, that's how we how
Speaker 1: we come at it. Uh. And you know when we
Speaker 1: have very elaborate uh sort of you know, predesigned sort
Speaker 1: of ways that we're going to develop algorithms, and we
Speaker 1: have very uh clear ways that we're gonna make investment
Speaker 1: decisions on the venture side. UM. And so for me
Speaker 1: as a as a manager of both of those areas,
Speaker 1: that's mainly what I'm trying to do is make sure
Speaker 1: that process is really solid um and UH. And and
Speaker 1: that's that's the similarity. How how significant uh portion of
Speaker 1: the point seventy two book are the venture sides. So
Speaker 1: the venture investments are all Steve's personal investments. UM, so
Speaker 1: there's not point they're not well, I mean we use
Speaker 1: it's points a ventures, we use the brand, but it's
Speaker 1: not it's not in the fund. Uh, it's it's Steve's
Speaker 1: personal money. UM. And it's you know, it's it's not
Speaker 1: it's not super large. I mean it's a it's a
Speaker 1: couple hundred million. So now I have to ask the
Speaker 1: obvious question, if it's Steve's personal money, is there a
Speaker 1: different UM thought process in terms of an exit. How
Speaker 1: does that pressure or how does that structure affect how
Speaker 1: you approach it or is it just a continuum across everything.
Speaker 1: And his philosophy is the same whether it's public or
Speaker 1: private investments. I think his philosophy is very similar across both.
Speaker 1: You know, he is he is an I r R
Speaker 1: focused investor UM. And you know he has a hedge
Speaker 1: fund that does well and produces a good return every year. UM.
Speaker 1: And you know he expects us to be the same,
Speaker 1: to bring the same discipline to the private investments. And
Speaker 1: so you know, we think about I r rs, We
Speaker 1: think about exits, we think when we can get cash
Speaker 1: back out, we think about applying leverage. We you know,
Speaker 1: we think about all these different things UM. But but
Speaker 1: it all comes back to you know, producing a you know,
Speaker 1: a good rate of return UM and that's that's that's
Speaker 1: how he thinks of about the world. Quite fascinating. So
Speaker 1: you mentioned traditional UM forms of fundamental analysis. What what
Speaker 1: metrics do you find important? Lots of people have talked
Speaker 1: about price the book, and then it seems to have
Speaker 1: fallen a little bit out of favor. Other people are
Speaker 1: looking at various forms of valuation. What's the most important
Speaker 1: fundamental approaches that that point seventy two is considering. It's
Speaker 1: just very so widely. I mean, you know, we're trading
Speaker 1: you know, in the U S we're trading almost eight
Speaker 1: hundred names, and we also trade in Asia and Europe,
Speaker 1: and so, uh, you know, there's I can't give sort
Speaker 1: of a one size fits all answer to that question
Speaker 1: because there's there's so many different sort of subsegments. So
Speaker 1: following up on that, you have written that investing changes
Speaker 1: over time and it's the role of the portfolio manager
Speaker 1: to adapt to those changes. How have you seen recent
Speaker 1: changes in the marketplace and what sort of adaptations do
Speaker 1: people have to make? Well, I think, you know, I
Speaker 1: think it's some of the things we're talking about them.
Speaker 1: I think the UM you know, the explosion of big
Speaker 1: data or what we call alternative data UM is you know,
Speaker 1: a big impact. Uh. You know, it used to be
Speaker 1: that most of the investing was a conversation between the investor,
Speaker 1: the company and the cell side UM. And now you
Speaker 1: know you have UM. You know, just you know, whether
Speaker 1: it be credit card or geolocation or email receipts or
Speaker 1: all these different satellite like you were talking about UM.
Speaker 1: You know, all these different things that you know that
Speaker 1: you can you can bring to bear. So I think
Speaker 1: that's a really important trend. I think the other important trend,
Speaker 1: like we're talking about earlier, is is people plus machine.
Speaker 1: You know, what what are machines good at versus what
Speaker 1: are people good at? UM? You know, machines uh, quite
Speaker 1: good at UM at repetitive math and complicated math, and
Speaker 1: UM you know have a lot to bring to bear
Speaker 1: in terms of portfolio construction and trading and and and
Speaker 1: those sorts of areas UM. So those are probably the
Speaker 1: two most important trends that that we're seeing and thinking about.
Speaker 1: Quite interesting. So you you talked earlier about the pursuit
Speaker 1: of alpha for a lot of the hedge fund industry,
Speaker 1: this has been a rough decade. Alpha has been hard
Speaker 1: to come by. Lots and lots of other hedge funds
Speaker 1: have had a hard time meaning their benchmark. Two questions
Speaker 1: that come from that, what's behind alpha's um elusiveness these days?
Speaker 1: And what must elusive alpha? You haven't thought of that previously?
Speaker 1: And what do active managers have to do to stay
Speaker 1: relevant and at the top of their game? Yeah, well, Steve,
Speaker 1: Steve always jokes that he'd just like to go back
Speaker 1: to the nineties, Um, you know, when it was easy,
Speaker 1: when it was when it was a lot easier. And
Speaker 1: uh and look, I mean, you know, success straws competition,
Speaker 1: that's just capitalism. And I think that um, you know,
Speaker 1: you know, I think there's not a whole lot of
Speaker 1: mystery to why it's harder. I think it's harder mainly
Speaker 1: because a lot more people are doing it. Um, you know,
Speaker 1: there's there's certain i'd say, sort of boogemen in the market,
Speaker 1: you know, like et F flows and things like that
Speaker 1: that people also talk about. But but I think the
Speaker 1: core thing that makes Alpha harder is just, you know,
Speaker 1: the scale at which it all takes place today. Um,
Speaker 1: and you know, I think in terms of of of
Speaker 1: maintaining an advantage. UM. You know, I uh, I remember
Speaker 1: the very first time I met Steve, I asked him
Speaker 1: the question of how he had been able to sustain
Speaker 1: his fund for so long UM and he's at such
Speaker 1: a high level, and he said, well, because I've rebuilt
Speaker 1: it four or five times UM. And you know, and
Speaker 1: and you know, the point he made is that this
Speaker 1: is just a constantly changing game that's always attracting competitors.
Speaker 1: And if you think that whatever success you have today
Speaker 1: is going to be true tomorrow, you are really naive UM.
Speaker 1: And so you know, there's it's part of what I
Speaker 1: like so much about working with him, And there's just
Speaker 1: a restless energy to him because he knows that that's
Speaker 1: what's required to continue to survive. And so that's how
Speaker 1: we approach the firm we have UM. You know, always
Speaker 1: you know, tons of new initiatives and experiments going on,
Speaker 1: and things will succeed or you know, and things will
Speaker 1: fail and will kill them, and things that will succeed
Speaker 1: will scale UM. But that but you know, I think
Speaker 1: his his view, and I agree with it, is that
Speaker 1: it's that you know, it's that activity that's how you
Speaker 1: maintain an advantage um, because the business you know, in
Speaker 1: three or four years isn't gonna look anything like it does,
Speaker 1: you know, three or four years prior to now. So
Speaker 1: Michael Mobison calls that the paradox of skill, that the
Speaker 1: success of the hedge fund industry and other sectors of
Speaker 1: finance have attracted so many intelligent, talented people that the
Speaker 1: easy money has gone away and it's becomes so much harder. Well,
Speaker 1: that's what makes it fun, right, I mean, that's what
Speaker 1: makes it. It's the you know, it's the competitive drive
Speaker 1: and the and the knowing that the bar is always
Speaker 1: going up. Um. You know, it's that challenge that I
Speaker 1: think draws a lot of people to the industry to
Speaker 1: to say the very least. So look around at some
Speaker 1: of the other hedge funds out there, like the Show
Speaker 1: or Citadel or Renaissance Technologies, and they were pretty early
Speaker 1: onto the high frequency trading and other computer driven UH approaches.
Speaker 1: Is that anything that is UH in point seventy two's
Speaker 1: field of interest or is that something that hey, let
Speaker 1: the computer driven guys do that. You have your own
Speaker 1: specific skill set, So we don't do any high frequency trading. UM,
Speaker 1: we do a fair bit of computer driven trading in
Speaker 1: our systematic unit, and then in some of the units
Speaker 1: I oversee their systematic as well, so driven by computers. Um.
Speaker 1: But uh, but but nothing that would constitute high frequency um. Uh.
Speaker 1: You know, it's certainly an area where a bunch of
Speaker 1: people made a bunch of money, but it wasn't something
Speaker 1: that we did. One of the things I didn't ask
Speaker 1: you earlier but is relevant here is the Domino Data Lab.
Speaker 1: What was the thinking behind that? And how have you
Speaker 1: used that experience at Bridgewater and at point seventy two? Yes,
Speaker 1: So the thinking behind that was really sort of two
Speaker 1: big ideas. One was that we were moving to a
Speaker 1: model driven world, um, where you know, we're algorithms that
Speaker 1: were trained, fed and trained data that made predictions or
Speaker 1: decisions for businesses. That that was going to be a
Speaker 1: very important um thing that took place, and so you know,
Speaker 1: you see the rise of Netflix and Amazon and all
Speaker 1: these things that I would call model driven businesses. Uh.
Speaker 1: And then the second sort of big idea was that,
Speaker 1: um that as that happened, the people who did that work,
Speaker 1: the data scientist needed a system of record. So salespeople
Speaker 1: work in salesforce HR people work in work day. There
Speaker 1: was not an equivalent for data science, and so we
Speaker 1: were building and in our building, uh, the system of
Speaker 1: record for UM for data scientists and and those were
Speaker 1: those were really the two big big ideas behind it.
Speaker 1: And whatever happens to Domino Data LAMB. Does it still exist?
Speaker 1: It still exists doing great UM you know, just you know,
Speaker 1: continues to grow leaps and bounds. I'm on the board. UM.
Speaker 1: It's still an independent company, still an independent company backed
Speaker 1: by Sequoia and COT primarily UM and some others actually
Speaker 1: including Bloomberg, Beta, UM and uh, you know, and it's uh,
Speaker 1: it's it's been. It's been very successful. And probably one
Speaker 1: of the most interesting things about it is just the
Speaker 1: diversity of industries now that are representing the client base.
Speaker 1: You know, it started out a lot of finance firms,
Speaker 1: insurance firms were interested, but now we have everything from
Speaker 1: retailers to grocery stores, to auto makers to pharmaceutical makers.
Speaker 1: Because you know, basically the thesis we were betting on
Speaker 1: was that the world was going to become model driven.
Speaker 1: And this is a tool set. This is a tool
Speaker 1: set to help track how effectively you're deploying your model.
Speaker 1: It's a it's a tool set that um you know, basically,
Speaker 1: data scientists build their models using the languages and tools
Speaker 1: they want in Domino, and then Domino revisions those things.
Speaker 1: Means they keep track of the data and the code
Speaker 1: and the results, and then you can also publish out
Speaker 1: so you can run models from that, and so it's
Speaker 1: sort of the your central repository, your system of record
Speaker 1: for models. Quite interesting, and I keep coming back to
Speaker 1: the idea of of man and machine. When you're evaluating talent,
Speaker 1: be it a startup management team or a a potential
Speaker 1: higher or a portfolio manager, how much of that is
Speaker 1: data driven and how much of that is your own
Speaker 1: human intuition? Well, in in people processes, you know, look,
Speaker 1: I think there's still a lot of human intuition into it. Uh.
Speaker 1: We we do try to be as rigorous and as
Speaker 1: systematic as possible. And what I mean by that is,
Speaker 1: you know, we we try to start with the job
Speaker 1: and the outcomes we expect. And as you think about
Speaker 1: those outcomes, what capabilities are required? And you think about
Speaker 1: those capabilities, you know, what's the best way to evaluate
Speaker 1: those capabilities? I personally don't like interviews. I don't think
Speaker 1: they're particularly useful. UM. I think that work samples and
Speaker 1: projects and these sort of and more testing and those
Speaker 1: sorts of things are very valuable. UM. But you know,
Speaker 1: obviously there's also you know, you do need to meet
Speaker 1: the people. And that's that's a part of it. Um
Speaker 1: by it for us, the hiring process or the evaluation
Speaker 1: process of people adventure, UM, you know, just has a
Speaker 1: certain methodical nous to it. That's that's very important, quite
Speaker 1: quite fascinating. We have been speaking with Matthew Grenade. He
Speaker 1: is the chief market intelligence officer at Point seventy two. UH.
Speaker 1: If you enjoy this conversation, we'll be sure and come
Speaker 1: back for the podcast extras, where we keep the tape
Speaker 1: rolling and continue discussing all things quant and hedge fund investing.
Speaker 1: We love your comments, feedback and suggestions. You can write
Speaker 1: to us at m IB podcast at Bloomberg dot net.
Speaker 1: Be sure and check out my daily column at Bloomberg
Speaker 1: dot com slash opinion. You can follow me on Twitter
Speaker 1: at rid Holts. I'm Barry Ridholts. You're listening to Masterson Business.
Speaker 1: I'm Bloomberg rad Ye. Welcome to the podcast. Matthew thank
Speaker 1: you so much for doing this. UM. I've been looking
Speaker 1: forward to this conversation. I have followed Stevie Cohen's career
Speaker 1: from AFAR for since the nineties, and I find him
Speaker 1: to be an absolutely intriguing individual, both as a investor
Speaker 1: and an art collector, and a person who has managed
Speaker 1: to um thrive despite a lot of really fascinating challenges. So, UM,
Speaker 1: when we first made contact with your office, I was
Speaker 1: really excited about this. UM, so thank you for doing this.
Speaker 1: One of the things we did not get to talk
Speaker 1: about during the broadcast portion was the OpEd that you
Speaker 1: and Steve wrote in the Wall Street Journal. And UM,
Speaker 1: it's not software is eating the world, it's models will
Speaker 1: run the world. Tell us a little bit about that.
Speaker 1: So so Mark Andresen wrote a piece several years ago
Speaker 1: and called software is Eating the World, and it's basically
Speaker 1: the idea that software is going to change every business. UM,
Speaker 1: and Steve and I were thinking about, you know, kind
Speaker 1: of what's the equivalent today, because I think that was
Speaker 1: written almost seven or eight years ago. Uh. And you know,
Speaker 1: the thing that we zero in on was this idea
Speaker 1: that that really models we're going to change the change
Speaker 1: the business landscape and you know, you know the idea
Speaker 1: of a model um, you know, think about Netflix, right.
Speaker 1: So I think Netflix is a great model driven business
Speaker 1: where you know, eight percent of the content consumption there
Speaker 1: comes from their recommendation engine, right, and so basically what
Speaker 1: they're what they're trying to do is they're trying to
Speaker 1: build the best recommend or possible. There you know, you're
Speaker 1: signing up, they're taking in data about you there, you
Speaker 1: know your zip code, and but then they watch everything
Speaker 1: you do. They watch you know how you um, you
Speaker 1: know what shows do you jump on right away? Which
Speaker 1: shows do you finish? Which shows do you not? And
Speaker 1: that lets them recommend better and better content for you.
Speaker 1: And then basically at the core of their business is
Speaker 1: this engine that's that's you know, holding or basically recommending
Speaker 1: content for you, um that you're going to enjoy more
Speaker 1: and more. And now they're using that same data in
Speaker 1: that same approach to build content as well. Um. So
Speaker 1: I think we think about that as a model driven
Speaker 1: business and it's a it's a really sort of powerful
Speaker 1: mode because once you get the loop going where you're
Speaker 1: collecting the data and seeing the outcomes that you're driven,
Speaker 1: you're driving you can make the model better and better. Um.
Speaker 1: And so you know we in the in the out
Speaker 1: ed what we talk about is uh, some public and
Speaker 1: some private companies. Um that Uh you know that that
Speaker 1: our model driven and and and some of the implications
Speaker 1: of this trend um and um and so Yeah, it
Speaker 1: was a fun piece to write. Yeah, and it's still
Speaker 1: available if you anybody wants to go see it. Models
Speaker 1: will run the world. It's in the Wall Street Journal. UM.
Speaker 1: So when you see something like and Reeson's peace, Uh,
Speaker 1: software is in in the world. I want to say
Speaker 1: that he's half right. Software had started to eat the world.
Speaker 1: But we run into problems all the time. That software,
Speaker 1: it only gets you half the way there. And and
Speaker 1: the entire infrastructure of everything from the hardware to the
Speaker 1: network too, everything else that's involved has to work seamlessly.
Speaker 1: Doesn't quite feel like we're in the future yet. How
Speaker 1: do you am I overstating that or how do you
Speaker 1: how do you perceive the world where you know, a
Speaker 1: robot butler doesn't take you to work each day, but
Speaker 1: it's not too far off in the future. I can't
Speaker 1: remember who said it, but somebody said, uh, you know,
Speaker 1: the future is here. It's just unevenly distributed, you know.
Speaker 1: William Gibson, Yeah, I think there's I think there's a
Speaker 1: lot of truth to that, you know. I mean when
Speaker 1: you're in uh, you know, San Francisco and you you know,
Speaker 1: you see the self driving cars that you know, Cruise
Speaker 1: and and Google and others are making. Um, you know,
Speaker 1: then that that feels that feels very in the future. Uh.
Speaker 1: And then you know, like you said, you look at
Speaker 1: some other industries and you sort of scratch your head
Speaker 1: about you know, why can't I get a good cell
Speaker 1: signal in Manhattan? It's exactly why point why can I
Speaker 1: maintain the still signal on the train back to back
Speaker 1: to Connecticut? Um? But um so I certainly, I certainly
Speaker 1: agree that it's it's unevenly distributed. But but you know,
Speaker 1: there's also a tremendous amount of very exciting things happening.
Speaker 1: Um and uh And and look, that's what makes the
Speaker 1: venture investing so much fun, you know, is seeing all
Speaker 1: that and being involved in that world, having that view
Speaker 1: of upcoming technologies. How does it affect the way you
Speaker 1: look at the world of existing public companies. That's a
Speaker 1: great question. I look, I think, um, uh, you know,
Speaker 1: it makes you um much more skeptical about their advantages
Speaker 1: and about the durability of their um of their moats
Speaker 1: quote unquote right uh and um, you know, you look
Speaker 1: at how fast the change has happened in retail and
Speaker 1: how and how deep and dramatic some of that took place, um,
Speaker 1: you know, and you go back and you look at
Speaker 1: some of these companies and all the moats they were
Speaker 1: talking about and the customer loyalty, and then you know,
Speaker 1: um and so you know, one of the things we
Speaker 1: try to do at at points of ME two is
Speaker 1: we we try to sort of cross pollinate some of
Speaker 1: the big thematic learnings um from the venture work in
Speaker 1: with them, in with the public market investors. We had
Speaker 1: a dinner a few months ago on robotics UM, and
Speaker 1: we had through four CEOs of robotics companies, and we
Speaker 1: had our industrial a couple of our industrials pms, and
Speaker 1: our healthcare pms, you know, and it's essentially a discussion,
Speaker 1: you know, exactly along the lines you said of you know,
Speaker 1: how is how is robotics going to And obviously there's
Speaker 1: gonna be a bunch of private companies that get created,
Speaker 1: but it's also going to really change, you know, in
Speaker 1: those two areas. You know a lot of companies as well.
Speaker 1: How often do you guys have dinners like that? It
Speaker 1: sounds like that's an intriguing evening. We do them about
Speaker 1: once a month. We're doing one tonight actually um and
Speaker 1: uh um, you know it's what's the topic tonight tonight?
Speaker 1: Topics actually talent evaluation. So Angela Duckworth is going to join.
Speaker 1: Um wrote a book, Grita. Have you gotten to Have
Speaker 1: you read that yet? I have read gret And how
Speaker 1: do you like it? I think it's great. It's been
Speaker 1: at the top of a number of people's lists for
Speaker 1: for quite a while. Yeah, I have a you know,
Speaker 1: I think it's a it's an interesting way to sort
Speaker 1: of think about, you know, why people are successful. Also,
Speaker 1: as a parent, it's something you know, you you you
Speaker 1: think a lot about, uh, you know, what can you
Speaker 1: actually teach your kids? And you know how and and
Speaker 1: you know, probably at the top of my list of
Speaker 1: things I realized my children to have and to learn. Um.
Speaker 1: And so we have we have rules now about sticking
Speaker 1: with things and stuff like that, largely because of her books.
Speaker 1: So that's that's quite fascinating. UM, I could talk about
Speaker 1: this stuff forever, but I know only have you for
Speaker 1: a finite amount of time, and I wanted to get
Speaker 1: to my favorite questions. UM so let me jump right
Speaker 1: into this, So feel free to answer these as longer
Speaker 1: as short as you want. These are pretty straightforward, um,
Speaker 1: but they usually are a little uh insightful into who
Speaker 1: you are. Tell us the first car you ever owned,
Speaker 1: you're making model? It was a Volvo S forty two thousand,
Speaker 1: sort of, the two door with the hatchback. Is that
Speaker 1: the one I'm I'm thinking of it? Four door? It was?
Speaker 1: It was it was a new model year. Um, so, yeah,
Speaker 1: it was a four door. It was blue. What's the
Speaker 1: most important thing people don't know about Matthew Grenade? Um.
Speaker 1: People are usually surprised to learn that I'm from the South, Um,
Speaker 1: you know, having gone to Harvard twice and worked at
Speaker 1: hedge funds and things like that, and uh, and my
Speaker 1: family has been from the South from for a very
Speaker 1: long time. Um. You have the slightest wisp of an accent,
Speaker 1: but not a heavy the slightest whisp. And then you know,
Speaker 1: and and and I think, uh, you know, certainly affects
Speaker 1: my my manners and that kind of thing. So are
Speaker 1: you a courtly southern gentleman? Is that I wouldn't go
Speaker 1: that far. But but but but my my my mom
Speaker 1: raised me right, she would say, so, So, tell us
Speaker 1: about some of your early mentors. Who are the people
Speaker 1: who helped guide your career. Yeah so, um uh so
Speaker 1: Bo Jones, who was publisher of the Washington Post. Um
Speaker 1: he had been a president of the Crimson as well.
Speaker 1: Um he uh. I worked for him for a summer
Speaker 1: and uh, you know, one of the things, one of
Speaker 1: the things that a couple of things very interesting about
Speaker 1: working from one was, you know, he and and Don
Speaker 1: Graham um in the Graham family in general sort of
Speaker 1: really understood the ecosystem of their business well and and
Speaker 1: sort of how all the parts interconnected. Um uh in
Speaker 1: the in sort of you know, the basically how the
Speaker 1: subscription revenue, um you know was important, but you didn't
Speaker 1: want to You wanted to make sure that you kept
Speaker 1: that price low enough. You have the advertisers, and they'd
Speaker 1: a very holistic way of thinking about the business. And
Speaker 1: then the second uh thing that I thought they you know,
Speaker 1: they're very principal based leaders. Um. You know, a new
Speaker 1: a newsrooms of place, things can run quite a mock
Speaker 1: and and the Washington Post has the backs of their
Speaker 1: reporters and that was always interesting to watch. UM. Another
Speaker 1: would be Tom Barkin, who Um, Tom uh is now
Speaker 1: president of the Richmond Fed UM and on the FOMC
Speaker 1: at the moment, but he was a very senior partner
Speaker 1: at McKenzie UH and one of the people who I
Speaker 1: worked with the closest and most when I was there
Speaker 1: right out of college. UM. And you know, I think
Speaker 1: the thing Tom taught me was the uh sort of
Speaker 1: seeing the essence of the of a problem. UM. You know,
Speaker 1: when you're when you're first out of school and and
Speaker 1: you can think of a two thousand analyzes to do,
Speaker 1: you know, let's do all these things. And Tom Tom
Speaker 1: was great at knowing what the what the right question
Speaker 1: was to ask and the and the right one to answer.
Speaker 1: So what investors influenced the way you look at markets
Speaker 1: and your approach to deploying risk capital. Well, look, I
Speaker 1: mean it's it's really the two I've worked you know,
Speaker 1: closely with. It would be it would be Ray and
Speaker 1: Steve Um. And that's quite a pair of mentors his uh.
Speaker 1: You know, with Ray, I think UM sort of two
Speaker 1: big lessons. One is um um being systematic, being process
Speaker 1: driven that you know, you don't you don't look at outcomes,
Speaker 1: you look at how you got to those outcomes. Uh.
Speaker 1: And then also being fundamental um. And you know, as
Speaker 1: we're talking about earlier, in the world of data science,
Speaker 1: you can torture the data to say anything, and so
Speaker 1: you really have to think about how the how the
Speaker 1: world actually works and why what you're finding matters. Um.
Speaker 1: And then with Steve UM, you know, it's it's the
Speaker 1: sort of tenacity to to really dig in and do
Speaker 1: the work, you know, which, as I mentioned, is one
Speaker 1: of the things he he says over and over UM. Uh.
Speaker 1: You know, you you don't go talk to Steve about
Speaker 1: a name or a venture, investment, or a new strategy
Speaker 1: without having sort of turned it over a hundred different ways. Um.
Speaker 1: And you know his bar for just having you dig
Speaker 1: deep is very high. Um. And Uh, there's probably the
Speaker 1: lessons I've learned most from those guys. So we mentioned, um,
Speaker 1: grit tell us about some of your favorite books fiction,
Speaker 1: non fiction, FINANCEI related whatever. Yeah, So, UM, I mean
Speaker 1: some of my favorite books of all times. Uh, let's see.
Speaker 1: So and just so you know, just so you know,
Speaker 1: the feedback I get on this question is consistently the
Speaker 1: most asked about question, and people say to me, I'm
Speaker 1: always looking for a well thought out suggestion for a book,
Speaker 1: and it's my favorite question you ask people because I've
Speaker 1: created a reading list off of that question, so it's
Speaker 1: not just a random Hey, what are you thinking about
Speaker 1: the books people recommend? Other people say, he seems like
Speaker 1: an intelligent guy. I want to read the books that
Speaker 1: he likes to read. So I'm just I'm just annotating
Speaker 1: before you. So let's try to do three from fairly
Speaker 1: diverse areas. So uh, so you know more finance data science. E.
Speaker 1: I love super Forecasters, which you know is basically tetlock,
Speaker 1: which talks about how you, you know, essentially get good predictions.
Speaker 1: And he's spent his life studying how you get good
Speaker 1: predictions or someone in the markets. You know, it's it's
Speaker 1: it's critical. Um, then let's go outside of investing in
Speaker 1: financing those sorts of things. One of my favorite sort
Speaker 1: of historical books is Wild Swans UM Wild swan Swands,
Speaker 1: which chronicles the life of three women in China and
Speaker 1: the twentieth century. UM. I think I think China is
Speaker 1: such an interesting story because it just you know, it's, it's,
Speaker 1: it's there's been so much dramatic change. And you look
Speaker 1: at those three lives and uh you know, uh, you know,
Speaker 1: one of which is a fair bit of which has
Speaker 1: been on the cultural revolution, and you sort of think
Speaker 1: the world you're living in is the world you're living in,
Speaker 1: and it can just change so dramatically. I want to
Speaker 1: make sure I have the right book Wild Swans Three
Speaker 1: Daughters of China by Jung Chang. Is that it? That's
Speaker 1: it quite interesting? Uh. And then we'll go for a classic,
Speaker 1: uh I Um, I love The Tempest by Shakespeare. Um,
Speaker 1: and uh you know it's where I me there's a
Speaker 1: lot of things goes on go on in that book,
Speaker 1: but uh, that's where you know he he wrote, you
Speaker 1: know what was past his prologue, UM, which I think
Speaker 1: is really true. The past is prologue could really be
Speaker 1: the slogan for anybody who creates models. So so that
Speaker 1: works out. That works out pretty well. Also, UM, tell
Speaker 1: us about a time you failed and what you learned
Speaker 1: from the experience. There's been a bunch, but uh, you know,
Speaker 1: well I'll do this one. So um, Before we started
Speaker 1: Domino Data Labs, my co founders and either two of us,
Speaker 1: three of us total, all all of us from from Bridgewater.
Speaker 1: We started a previous business called Cerebro UH and Cerebro
Speaker 1: was in the talent evaluation space, and so I was
Speaker 1: trying to sort of figure out smarter ways to help
Speaker 1: companies assess their talent. And we had some great clients
Speaker 1: UH in tech, mainly technology firms UH, and we mainly
Speaker 1: had leaders from the business lines, and so we would
Speaker 1: sort of do this work, they would love it, and
Speaker 1: then we would get past to the recruiting department and
Speaker 1: the project would just die. And we did this like
Speaker 1: over and over and over again. UM. And what we
Speaker 1: finally realized was realized a couple of things. One was
Speaker 1: that at a micro level, that the incentives between the
Speaker 1: recruiters and the business people were very different. That the
Speaker 1: recruiters wanted to put people in seats and that the UH,
Speaker 1: and that the business people wanted to have great people
Speaker 1: in those seats. But then more deeply, what we learned
Speaker 1: was that we actually had no idea what we were
Speaker 1: doing UM, and that you know, that we were really
Speaker 1: trying to build a business in an area that we
Speaker 1: weren't experts in and that you you know that that
Speaker 1: is starting a business is just so so so hard
Speaker 1: in like a thousand different ways, and uh, you know,
Speaker 1: and so you have to you have to take advantages
Speaker 1: where you can. And so what we uh, you know
Speaker 1: what we've we started asking ourselves, so what do we
Speaker 1: actually know about? And in those areas of what we
Speaker 1: actually know about, where are their actual problems? And that
Speaker 1: led us do Domino in the data science space. So
Speaker 1: so you come from the school of Ray Dalio's use
Speaker 1: failure as a learning experience to improve your next uh,
Speaker 1: your next attempt at whatever it is. Oh. Absolutely, so
Speaker 1: he told me a funny story about the inside of
Speaker 1: his uh of his book with with the failure cycle,
Speaker 1: and someone who will remain nameless said to him, Ray,
Speaker 1: what sort of signature is that? They obviously hadn't read
Speaker 1: the book, but quite quite hilarious. Um, so tell us
Speaker 1: what you do for fun when you're out of the office.
Speaker 1: Would you do to kickback, relax, have a good time. Um.
Speaker 1: I like to cook um. And this is like going
Speaker 1: back to being from the South. So my my grandmother
Speaker 1: taught me to cook um and uh, and so my
Speaker 1: wife and I will throw parties and we'll cook, in
Speaker 1: particular fried chicken and things like that, and that's probably
Speaker 1: what I enjoy. You work off a cookbookers at all
Speaker 1: grandma's recipes, it's usually a combination. Um. So I like to,
Speaker 1: you know, kind of mix in some more modern cooking
Speaker 1: with some of the more traditional recipes. So give us
Speaker 1: a few dishes. Uh. Well, you know, a traditional dinner
Speaker 1: party would be, um, you know, fried chicken with macaroni
Speaker 1: and cheese and biscuits and blueberry cobbler. But southern, real
Speaker 1: southern cooking. But I'll also do you know, like maybe
Speaker 1: some molecular astronomy with like a watermelon drop or something. Um.
Speaker 1: So you gotta keep it, keep it modern. But um,
Speaker 1: did you see Nathan Revold's Get gast Row cookbook? I
Speaker 1: have all those it's supposed to be a fascinating Have
Speaker 1: you tried any of those dishes? So he has so
Speaker 1: he has his his five volumes, five or six volume
Speaker 1: set that's very intense and completely overwhelming. And then he
Speaker 1: has a home version, um, which I have done a
Speaker 1: couple of things out of the home version. Do they work?
Speaker 1: They work? Um? But he's he's he's much more serious
Speaker 1: than I am. So he's he's he's very intense yet
Speaker 1: to say, to say the least. So what are you
Speaker 1: most excited about within the financial industry today? Well, I
Speaker 1: think you know the thing that the most interesting question
Speaker 1: right now is the people plus machine question. You know, what,
Speaker 1: what are the people good at? How do you the
Speaker 1: most out of them? How do you um uh, how
Speaker 1: do how do you think about those capabilities? And how
Speaker 1: do you couple those with what machines are good at? Um?
Speaker 1: And I um, you know, I think that, Like I said,
Speaker 1: I think the next generation hedge fund is going to
Speaker 1: be a mixture of those two things and um. And
Speaker 1: that's a it's a really it's hard in a lot
Speaker 1: of ways, but it's a very exciting question. So a
Speaker 1: millennial or a recent college grad comes up to you
Speaker 1: and says they're interested in a career in either investing
Speaker 1: or quant what sort of career advice would you give them? Well,
Speaker 1: I'm not sure it would be so specific to any field.
Speaker 1: I mean, I think, uh, I think the career advice
Speaker 1: I would give and I'm I'm not a huge fan
Speaker 1: of giving advice, but since I'm on the show and
Speaker 1: on the spot. Um. Look, I Number one would be, um, hm,
Speaker 1: set your goals as preposterously as you can set them. Um,
Speaker 1: you will continuously surprise yourself and what you can do.
Speaker 1: And UM I think uh, um you know so so
Speaker 1: aim big and dream really big. Um. So that would
Speaker 1: be one I think. Second, Um, the is work hard. Um.
Speaker 1: The you know, no no one I've ever met, uh
Speaker 1: doesn't know, no one, no one who I've ever worked for,
Speaker 1: you know, Ray, Steve, these guys, none of them are slackers. Um.
Speaker 1: You know, I mean Steve starts every he starts the
Speaker 1: week on Sunday morning at um and you know, uh,
Speaker 1: and and that's when that's when the then he works
Speaker 1: all day Sunday and he works a fair bit today
Speaker 1: Saturday and so um, so you know, I think I
Speaker 1: think it would be to set really almost preposterous goals. Uh,
Speaker 1: you know, be willing to work really really hard. And
Speaker 1: then I think the third would probably be, um, you know,
Speaker 1: love what you do. Um. I've also never really met
Speaker 1: someone who was successful who didn't really love what they did. Um.
Speaker 1: And I think you know, Steve Jobs had something that
Speaker 1: he said I think in the Stanford commincement speech. It's
Speaker 1: like if you haven't found, if you haven't found what
Speaker 1: you love yet, just keep looking. UM. And I think
Speaker 1: that's I think it's right. I think all those things
Speaker 1: are true, good good advice. UM. And our final question,
Speaker 1: what is it that you know about the world of
Speaker 1: investing today? Did that you wish you knew twenty years
Speaker 1: or so when you were first getting out of college? Well,
Speaker 1: stay long, Microsoft right, that that was a good time
Speaker 1: to not not panic, right exactly, But I mean we're
Speaker 1: as opposed to crystal ball, more processed. Absolutely. Look, I
Speaker 1: think the UM uh, you know, I think I think
Speaker 1: one of the most interesting things is just how different um,
Speaker 1: different periods of time will feel and be UM. And
Speaker 1: this goes a little bit too, you know what has
Speaker 1: passed his prologue and using history and things like that,
Speaker 1: you know, I mean, UM, when you know I graduated
Speaker 1: in from college in two thousand, you know that was
Speaker 1: the just the bubble was peaking and UM and the
Speaker 1: tech bubble, and that sort of felt one very one
Speaker 1: certain way. And then you know, you get to two
Speaker 1: thousand eight and you're just in a very very different regime.
Speaker 1: And I think, UM, I think the differences between these
Speaker 1: regimes and how what's gonna work in these regimes is
Speaker 1: quite different. Um. You know, you really have to kind
Speaker 1: of get your your head around that, um and and
Speaker 1: kind of really appreciate that quite quite fascinating. We have
Speaker 1: been speaking with Matthew Grenade. He is the chief market
Speaker 1: intelligence officer at Point seventy two, where he also oversees
Speaker 1: their main book as well as helping to manage their
Speaker 1: venture capital business. If you enjoy this conversation, we'll be
Speaker 1: sure and look up an inch or down an inch
Speaker 1: on Apple iTunes, overcast at your Bloomberg dot com wherever
Speaker 1: final podcasts are sold and you can see any of
Speaker 1: the other let's call it two dred and forty or
Speaker 1: so past conversations we have had. We love your comments,
Speaker 1: feedback and suggestions right to us at m IB podcast
Speaker 1: at Bloomberg dot net. I would be remiss if I
Speaker 1: did not thank the crack staff that helps put together
Speaker 1: this conversation each week. Medina Parwana is my producer slash
Speaker 1: audio engineer. Taylor Riggs is our booker. Attica val Broun
Speaker 1: is our project manager. Michael Batnick is my head of research.
Speaker 1: I'm Barry Riholts. You've been listening to Masters in Business
Speaker 1: on Bloomberg Radio