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Matthew Granade Discusses Quantitative Investment (Podcast) · Masters in Business

0:02
Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
0:09
Speaker 1: This week on the podcast, I have an extra special guest.
0:13
Speaker 1: His name is Matthew Grenade and he is a senior God.
0:17
Speaker 1: How do I describe his role? His title really doesn't
0:21
Speaker 1: do it justice. His official title is Chief Market Intelligence
0:26
Speaker 1: Officer at Point seventy two, follow the progression that has
0:31
Speaker 1: taken place. Stevie Cohen was running Sack Capital for a
0:34
Speaker 1: long time. That was eventually converted into a family office
0:38
Speaker 1: which was Point seventy two that reopened to outside investors
0:43
Speaker 1: last year in UM and Grenade has been working there
0:49
Speaker 1: for a good couple of years. Previously he was at
0:52
Speaker 1: Bridgewater with Ray Dalio. You'll hear all about that during
0:55
Speaker 1: our conversation, but more importantly, you'll hear about the intersection
1:01
Speaker 1: between man and machine, between the way models can be
1:05
Speaker 1: used to not only manage assets, but improve the entire process,
1:11
Speaker 1: along with a variety of big data and other approaches
1:16
Speaker 1: UH that are really quite fascinating if you are at
1:20
Speaker 1: all interested in quantitative investing, machine learning, hedge funds, UH,
1:26
Speaker 1: the state of investing today and what anybody who is
1:30
Speaker 1: pursuing alpha must do to stay current, then you're gonna
1:33
Speaker 1: find this to be an absolutely fascinating conversation. So, with
1:38
Speaker 1: no further ado, here is my conversation with Point seventy
1:41
Speaker 1: two's Matthew Grenade. My extra special guest this week is
1:49
Speaker 1: Matthew Grenade. He is the chief market intelligence officer at
1:54
Speaker 1: Point seventy two. That is Stevie Cohen's new hedge fund,
1:58
Speaker 1: which employs about out people and manages about thirteen billion dollars.
2:04
Speaker 1: Point seventy two asset management was converted into a hedge
2:08
Speaker 1: fund in and last year it reopened to external investors.
2:14
Speaker 1: Matthew comes to us by way of Bridgewater Associates Domino
2:19
Speaker 1: Data Lab, and he got his both undergraduate and graduate
2:23
Speaker 1: NBA at Harvard Business School, where at undergraduate he was
2:28
Speaker 1: the president of the Harvard Crimson. Matthew Grenade, Welcome to Bloomberg.
2:33
Speaker 1: Thank you for having me. So let's start. Let's start
2:35
Speaker 1: with the most unusual thing on your um resume. You're
2:39
Speaker 1: president of the Harvard Crimson, not exactly a hotbed of
2:44
Speaker 1: future hedge funds officers. How did that come about? What
2:49
Speaker 1: was that experience? Like, well, a couple of things, I
2:52
Speaker 1: mean in terms of that experience. Running in newspaper is
2:55
Speaker 1: one of the most amazing things in the world, and
2:57
Speaker 1: I got to run a small one at Harvard. But I,
3:00
Speaker 1: you know, I think it's just an incredible job because
3:02
Speaker 1: you're in the middle of so much information, You're helping
3:05
Speaker 1: shape the debate, you're investigating things, so many interesting people. Um,
3:10
Speaker 1: Harvard's an awesome place to do that at. And so
3:12
Speaker 1: there are there are a few jobs that I've loved
3:14
Speaker 1: as much as I loved that one. It was incredible
3:17
Speaker 1: how you get that job. There's sort of a couple
3:19
Speaker 1: of things. One, there's a bit of a path um so,
3:22
Speaker 1: it generally is a newsperson, a reporter. Um. So I
3:24
Speaker 1: was a reporter for my first couple of years, and
3:26
Speaker 1: then I was the head of the central what's called
3:29
Speaker 1: the central Administration beat that covers the president of the university.
3:33
Speaker 1: That's also kind of a traditional stepping stone. Then there's
3:35
Speaker 1: a process called the Turkey Shoot. Um. The Turkey Shoot
3:38
Speaker 1: runs for about a month leading up to Thanksgiving, where
3:40
Speaker 1: they picked the next president. There's all sorts of sort
3:42
Speaker 1: of arcane rules, but probably the most interesting is that
3:45
Speaker 1: every outgoing member of the paper gets to vote, and
3:48
Speaker 1: if more than three disagree, you're blackballed, and so you
3:51
Speaker 1: resually hold in uh sort of and you know, in
3:54
Speaker 1: sort of in veto mode for as long as that goes.
3:57
Speaker 1: And so the deliberations generally run about teen hours straight exactly,
4:02
Speaker 1: and then and then there's a big party and whatever
4:04
Speaker 1: sort of once the sort of unlocking happens. But I
4:06
Speaker 1: had I think six or seven opponents for the job,
4:09
Speaker 1: and uh, you know, you have to you know, a
4:11
Speaker 1: little politics, a little politics, a little message, a little
4:14
Speaker 1: of this, um, and that's that's how it works. But
4:17
Speaker 1: it was an amazing opportunity. So that's an unusual background
4:20
Speaker 1: as a journalist and someone who's publishing the paper to
4:24
Speaker 1: really being a data scientist for a financial services shop.
4:28
Speaker 1: How did that career path unwind? Well, they're probably more
4:32
Speaker 1: similar than you think, because I mean a lot of
4:34
Speaker 1: it comes down to information, collecting information, using information, UM.
4:39
Speaker 1: And so you know, I've always been someone who likes
4:40
Speaker 1: to know what's going on, um, you know, what's going
4:42
Speaker 1: on in the world. I like to sort of be
4:44
Speaker 1: ahead of other people and knowing things, and so that's
4:46
Speaker 1: the that's the similarity. But the you know, but the
4:48
Speaker 1: career arc was um, I went from from college to Mackenzie,
4:53
Speaker 1: was a business analyist there, uh, and then went to
4:55
Speaker 1: business school like you mentioned, and then ended up at Bridgewater.
4:59
Speaker 1: Um and which is also a fascinating place. Is a
5:02
Speaker 1: fascinating place. So I was there for six years, um
5:05
Speaker 1: and Uh, it's a phenomenal place to work. I'm a
5:08
Speaker 1: big fan of Ray Dalio. I find his philosophy just
5:11
Speaker 1: totally intriguing. I think Bridgewater kind of gets a bad rap.
5:15
Speaker 1: People have called it a cult and have criticized the
5:18
Speaker 1: radical transparency. You survived there for six years. Can't be
5:22
Speaker 1: all bad, right, how to be pretty good? No, it's
5:24
Speaker 1: not all bad at all. In fact, I think it's
5:26
Speaker 1: you know one uh, you know as investors go, um,
5:30
Speaker 1: you know they're they're as good as it gets um,
5:32
Speaker 1: and you know, just phenomenal at it. And look, I
5:35
Speaker 1: think the differences of the culture there get overstated, um,
5:40
Speaker 1: meaning the radical transparency and meaning like how different it
5:44
Speaker 1: is from you know. Look, I mean like you know,
5:45
Speaker 1: I would say everywhere I've ever worked McKenzie point seventy two, Domino, Bridgewater, Um,
5:50
Speaker 1: you know, they've all been ambitious people who are trying
5:52
Speaker 1: to get to the right answer. Who wanted to do
5:54
Speaker 1: great things. Um. And you know, like at core, like
5:57
Speaker 1: that's that's a lot of what Bridgewater is about. And
6:00
Speaker 1: you know, Ray and the team, they're are very thoughtful
6:02
Speaker 1: about ways to um, you know, just sort of apply
6:06
Speaker 1: certain ideas. Um. You know, like you want to. You
6:08
Speaker 1: always want to make sure you're getting the best opinions right,
6:10
Speaker 1: And so they're very explicit about you know, who should
6:12
Speaker 1: you listen to about things? But you know, I see,
6:15
Speaker 1: I see Steve asked that question all the time, you know,
6:17
Speaker 1: like why am I listening to you? You know, I
6:18
Speaker 1: should be listening to this person instead. Um. And so
6:21
Speaker 1: I think Bridgewater is great at sort of scaling it.
6:23
Speaker 1: But but um, but I think that the ideas are
6:26
Speaker 1: are not not quite as radical as the media would
6:29
Speaker 1: want you to believe. And then the transparency, Um, it's
6:32
Speaker 1: just great. I mean I love the idea. Yeah, I
6:34
Speaker 1: mean I was. I always just saying like it's a
6:35
Speaker 1: very clean place to live. And the reason it's a
6:37
Speaker 1: very clean place to live at Bridgewater is you just
6:39
Speaker 1: don't say things behind people's back. You just say things
6:42
Speaker 1: to their face. Um, and you're just He writes about
6:46
Speaker 1: that in his first book in a chapter where he
6:49
Speaker 1: describes raise people problem. I mean, most founders and chairman
6:55
Speaker 1: don't spend the chapter describing the wrong people person. That's
6:59
Speaker 1: fairly trying its parent. Yeah, I mean I think that's
7:01
Speaker 1: fairly transparent. And and that's just how you're expected to operate,
7:05
Speaker 1: you know. I mean, if you're gonna say something about Ray,
7:08
Speaker 1: you say it to him. And um and I have
7:11
Speaker 1: many stories of of of saying things to Ray that
7:13
Speaker 1: I think people would find not horrifying. There's they were
7:18
Speaker 1: me being honest and him and I trying to sort
7:20
Speaker 1: of work out differences. But you know, the only rule
7:22
Speaker 1: was just don't say it behind his back, and and
7:24
Speaker 1: that's you know, it's it's interesting that that's considered so radical,
7:27
Speaker 1: you know what I mean, It's not it's not that radical.
7:29
Speaker 1: So now let's let's take this phote. You'll end up
7:32
Speaker 1: at at point seventy two. Your title is Chief Market
7:36
Speaker 1: Intelligence Officer. I've never even seen c M I O
7:41
Speaker 1: as a abbreviation. What does a c M I O do?
7:45
Speaker 1: That title was the title I had when I got there. Um,
7:48
Speaker 1: and I was really focused at that point on proprietary research.
7:51
Speaker 1: And so what we mean by that is how do
7:53
Speaker 1: we take UM data sets or surveys or web scraping
7:57
Speaker 1: or sort of all the different things you can do,
8:00
Speaker 1: UM and make that useful to our portfolio managers and analysts. UM.
8:03
Speaker 1: Since then, my job has evolved to include a couple
8:06
Speaker 1: other things. So I also oversee our central book at
8:09
Speaker 1: this point, UM, which is our sort of a systematic
8:11
Speaker 1: best ideas book we have and also receive venture capital.
8:14
Speaker 1: And we just haven't really changed the title. Quite fascinating.
8:18
Speaker 1: Let's talk a little bit about big data and machine
8:21
Speaker 1: learning and artificial intelligence. Help me make a little sense
8:25
Speaker 1: about those buzzwords which have come into vogue for a while.
8:31
Speaker 1: But but your shop has been using these things for
8:34
Speaker 1: for quite a while. UM. What's the state of the industry, uh,
8:39
Speaker 1: in terms of machine learning and big data and artificial intelligence? Well,
8:44
Speaker 1: I think the you know, the thing to sort of
8:46
Speaker 1: contextualize all those terms, UM. And you know, I agree
8:50
Speaker 1: with you, they're they're very buzzy. UM. But but the
8:52
Speaker 1: way I like to think about it as being model
8:53
Speaker 1: what I call model driven UM. And so you can
8:55
Speaker 1: talk about model driven businesses or model driven processes, and
8:58
Speaker 1: really the idea of a model is it takes in data.
9:01
Speaker 1: It could be big data, it could be not big data. UM.
9:04
Speaker 1: It runs a certain set of logic on that UM
9:07
Speaker 1: and then it produces a prediction of some variety UM.
9:10
Speaker 1: And you know, basically it tries to close the loop
9:13
Speaker 1: around that data so that you know, you're constantly improving
9:17
Speaker 1: the logic or the algorithms. And so Netflix is a
9:19
Speaker 1: model driven business intensents a model driven business UM. And
9:22
Speaker 1: obviously finance and and you know the hedge funds we're
9:25
Speaker 1: talking about there, you know, they're they're they're very model driven.
9:27
Speaker 1: What I would you know, what I would say is that,
9:29
Speaker 1: you know, the state of the industry, uh, in that
9:32
Speaker 1: regard is that UM, you know, these techniques are highly
9:35
Speaker 1: highly relevant to kind of almost everything we're doing, you know,
9:38
Speaker 1: whether it be extracting signal from data sets or you know,
9:41
Speaker 1: all the way up to making trading decisions. Uh. And
9:44
Speaker 1: so you know, we're investing, you know, like a lot
9:46
Speaker 1: of hedge funds were investing a lot in you know,
9:48
Speaker 1: people with the data science capabilities and with the machine
9:52
Speaker 1: learning capabilities as well. So ron course Ferry you famously said,
9:57
Speaker 1: torture the data long enough and it will confess to
9:59
Speaker 1: whatever you want. How do you avoid running into that
10:02
Speaker 1: problem of when you're building models and putting a ton
10:06
Speaker 1: of different quantitative information into it, how do you avoid
10:09
Speaker 1: that bad outcome of Hey, if we back test this
10:13
Speaker 1: enough and we make these tweaks, we could get this
10:15
Speaker 1: to say whatever we want. Yes, I think there's I
10:18
Speaker 1: think there's a couple of different ways you do that.
10:19
Speaker 1: I mean one is UM. You know, you want to
10:22
Speaker 1: have a fundamental intuition of some variety around what you're doing,
10:26
Speaker 1: you know, I mean, you're not just sort of running
10:27
Speaker 1: everything through a machine and some some people do, but
10:30
Speaker 1: but not not. That's not how I like to do it.
10:32
Speaker 1: You're not just sort of running everything through and sort
10:34
Speaker 1: of seeing, you know, seeing what fits, because to your point,
10:36
Speaker 1: something will fit UM, and it may be a real
10:38
Speaker 1: thing or it maybe you know, a very short lived
10:40
Speaker 1: thing UM. And then you know, you have to have
10:42
Speaker 1: a lot of discipline in terms of looking at your UM.
10:45
Speaker 1: You know it's called out a sample, uh sorry, basically
10:48
Speaker 1: in sample, out of sample and live UM. And what
10:50
Speaker 1: that basically means is where are you allowing yourself to
10:53
Speaker 1: to fit the parameters where you're sort of just looking
10:55
Speaker 1: at the results but still in a in a backwards
10:57
Speaker 1: looking way, and when are you sort of really trying
11:00
Speaker 1: it out? And you know, we have very strict rules
11:02
Speaker 1: about how we segment those different things before we start,
11:05
Speaker 1: you know, using you know, putting money against a certain strategy,
11:09
Speaker 1: so and out of sample, just to put a little
11:11
Speaker 1: flesh on that. If you're testing on a large cap us, hey,
11:15
Speaker 1: let's see how the status in the past. Let's see
11:17
Speaker 1: how it does overseas, not just the area you're looking
11:20
Speaker 1: forward to see if it's really something to the model.
11:22
Speaker 1: Is that a fair descriptor yeah. So let's say you
11:24
Speaker 1: were using um, you know, credit card data to trade Chipotle,
11:27
Speaker 1: you know, or something like that. Um. You know, what
11:30
Speaker 1: you would do is you would sort of, you know,
11:31
Speaker 1: you build some rules, um, and you would sort of
11:33
Speaker 1: fit those rules to some sub some set of data
11:36
Speaker 1: some time period, you know, three or four years. Then
11:38
Speaker 1: you would stop fitting the rules and you would sort
11:40
Speaker 1: of look at the next three or four years and
11:41
Speaker 1: sort of see it, does that those two match. Do
11:43
Speaker 1: they look the same or is the behavior very different?
11:46
Speaker 1: And then you would and then you would basically start
11:48
Speaker 1: running the model live from today and then see again
11:51
Speaker 1: if those match the other two periods and so you're
11:53
Speaker 1: looking sort of for a consistency across that and if
11:55
Speaker 1: you're not seeing that, then that's a good sign that
11:57
Speaker 1: you're overfitting it. It's also you know, because going back
12:00
Speaker 1: to my original point, you know, you want to think
12:01
Speaker 1: about whether or not there's a real intuition there. You know,
12:04
Speaker 1: I mean, should credit card and chipotle a make sense together? Right?
12:07
Speaker 1: It probably does because a lot of people use a
12:08
Speaker 1: credit card at chipole. But you know, if you were
12:10
Speaker 1: using uh, you know, credit card to trade ge you know,
12:13
Speaker 1: you might you might start scratching your head about what
12:15
Speaker 1: you're doing. Right, might just be a random correlation as
12:18
Speaker 1: opposed to a real causal relationship. So so let's talk
12:21
Speaker 1: about some of these unusual UM data sources. I know,
12:27
Speaker 1: alternative satellite data is all the rage these days. People
12:31
Speaker 1: are looking at parking lots, how filled they are. They're
12:33
Speaker 1: looking at how deep transport ships are sitting, uh in
12:38
Speaker 1: the water, how far below the waterline they might actually be.
12:41
Speaker 1: How esoteric can we get with these alternative types of data? Well,
12:47
Speaker 1: I think you can. I think you can get quite esoteric.
12:50
Speaker 1: I mean I think satellite, um you know, satellite has
12:52
Speaker 1: been around for a while and to your point. I mean,
12:54
Speaker 1: it's it's very widely used. Um. You know, you know
12:58
Speaker 1: what we think much more about now is um, you know,
13:01
Speaker 1: sort of much more specific data sets. UM. You know
13:04
Speaker 1: kind of that that give you, you you know, a read
13:06
Speaker 1: into a limited number of tickers, often via some sort
13:10
Speaker 1: of payment system or something like that. UM. And uh,
13:14
Speaker 1: you know I think that we're I think we're just
13:16
Speaker 1: you know, we're probably in the third inning of something
13:19
Speaker 1: or something like that in the in the in the
13:21
Speaker 1: data movement in investing. That's that's quite fascinating. So let's
13:26
Speaker 1: talk a little bit about complexity. You know, we could
13:29
Speaker 1: go back a hundred years and just look at Graham
13:31
Speaker 1: and Dodd simple p ratio and more expensive stocks over
13:36
Speaker 1: time perform less well and have lower expected turns than
13:40
Speaker 1: less expensive stocks. Are we running the risk of making
13:44
Speaker 1: things too complex? At at what point does complexity get
13:49
Speaker 1: outweighed by its own internal complications? Well, I think, um,
13:53
Speaker 1: you know, I think this goes back to the point
13:55
Speaker 1: I was making about, you know, about an intuition. Um.
13:59
Speaker 1: And you know, at the end of the day a
14:02
Speaker 1: point of two. You know, we are we are fundamental investors,
14:05
Speaker 1: you know, we believe that Uh, that you know that
14:07
Speaker 1: companies ultimately, you know, trade on how they're doing as
14:11
Speaker 1: a business and the kind of cash flows they're going
14:13
Speaker 1: to produce UM and you know, everything we do, I mean,
14:16
Speaker 1: we will use very sophisticated data science to predict a
14:20
Speaker 1: revenue stream or something like that, but we're at core
14:23
Speaker 1: trying to do something fairly simple. You know, we're trying
14:25
Speaker 1: to understand what the revenues are, what the costs are,
14:28
Speaker 1: you know, what the growth profile of the earnings are UM,
14:31
Speaker 1: and you know, we never sort of lose that grounding
14:34
Speaker 1: UM and so you know, look, there's a lot of
14:35
Speaker 1: ways to make money in the markets UM, and I'm
14:38
Speaker 1: only I'm not an expert in a lot of them.
14:40
Speaker 1: I'm only familiar with some of them. But but for us,
14:43
Speaker 1: I think that grounding back to pretty simple principles U
14:48
Speaker 1: is very important and not something that we lose track of.
14:50
Speaker 1: It's interesting that you I think of you guys as
14:53
Speaker 1: a quant shop, but you keep referring to intuition. What's
14:57
Speaker 1: the intersection like between man machine? Is it really UM
15:03
Speaker 1: technology aiding human decision making or is it mostly hey,
15:07
Speaker 1: let's go and make the decisions and we'll just see
15:10
Speaker 1: what happens, so it points in me too. We do UM,
15:13
Speaker 1: we do UM. We do a mix of of three things.
15:15
Speaker 1: We have a very large discretionary business that's global long
15:19
Speaker 1: short equity you know, people driven its portfolio managers and
15:23
Speaker 1: analysts UM looking at some subset of the of stock
15:26
Speaker 1: universe UM, meeting with management teams, looking at data sets UH,
15:31
Speaker 1: and then making decisions in a in a fairly discretionary fashion. UM.
15:35
Speaker 1: We also have a systematic business that's running on algorithms UM.
15:39
Speaker 1: And then we have a people plus machine business, which
15:41
Speaker 1: is the one that I oversee, which is the you
15:44
Speaker 1: know what what what you call the central book earlier UM,
15:47
Speaker 1: where what we're doing there is we're looking at UM
15:50
Speaker 1: what the behavior of all the people is as one
15:52
Speaker 1: of the important inputs UM. But we're also looking at
15:55
Speaker 1: the data sets and we're running algorithms to essentially helped
15:58
Speaker 1: make decisions out of that. So one way of thinking
16:01
Speaker 1: about it is that historically Steve had a best ideas
16:04
Speaker 1: book that he he ran as a discretionary investor, and
16:07
Speaker 1: over time we've built that up into a systematic best
16:09
Speaker 1: ideas book UM. But but a lot of the input
16:13
Speaker 1: of that is from discretionary investors and so UM. So
16:16
Speaker 1: you know, one of the kind of key questions we're
16:18
Speaker 1: always asking is what are the people best at and
16:20
Speaker 1: what are the machines best at? And you know, our view,
16:23
Speaker 1: UM is that you know, in terms of of really
16:26
Speaker 1: being able to interpret fairly nuanced and complicated situations inside
16:30
Speaker 1: a specific company, that people are still um, really really good. UM.
16:35
Speaker 1: You know, there's other things that machines do very very well.
16:38
Speaker 1: But you know, if you're going to meet with the
16:39
Speaker 1: management team and interpret a large set of data that
16:42
Speaker 1: that has a lot of sort of nuanced and specifics
16:44
Speaker 1: to it, UM, the people still beat the machines at that.
16:47
Speaker 1: And so we have a you know, we have several
16:48
Speaker 1: hundred people that do that. Do you see that edge
16:52
Speaker 1: of humans over machines continuing indefinitely or at at some
16:57
Speaker 1: point in the future, will smart um computers and artificial
17:02
Speaker 1: intelligence be able to do that also well? And definitely
17:06
Speaker 1: is a very long time. So I'm gonna I'm not
17:09
Speaker 1: gonna I'm not gonna comment on indefinitely. What I will
17:12
Speaker 1: say is that our our thesis is a firm right
17:15
Speaker 1: now over the next call it you know, seven to
17:19
Speaker 1: ten years, is that UM, is that it is people
17:23
Speaker 1: plus machines UM, and that the people are very good
17:26
Speaker 1: at the nuanced situation, at the idea generation, at the
17:30
Speaker 1: interpreting the thin data at the synthesis UM. And that
17:34
Speaker 1: the machines are very good at conducting, UH, correcting for
17:38
Speaker 1: behavioral bias at portfolio construction, at trade execution. And you know,
17:42
Speaker 1: what we're trying to do is figure out how you
17:43
Speaker 1: marry those two up in a really smart way UM.
17:46
Speaker 1: And that that is essentially the you know, the next
17:48
Speaker 1: wave of hedge fund but UM. But you know, like
17:51
Speaker 1: where where we are ten or fifteen years in terms
17:53
Speaker 1: of what people can do versus machines, I don't think
17:56
Speaker 1: I can comment on that quite quite fascinating. Let's talk
18:00
Speaker 1: about the venture capital work you guys do. UM. What
18:05
Speaker 1: makes you different from traditional vcs? Well, I think a
18:10
Speaker 1: couple of things make us UM different than traditional vcs,
18:13
Speaker 1: But probably the most important is we we are extremely
18:18
Speaker 1: expertise focused in how we are designed, so UM, we
18:22
Speaker 1: have no generalists. UM. We have certain practice areas. Right now,
18:25
Speaker 1: we have three different three or four different practice areas UM,
18:28
Speaker 1: all of which are led by people who have worked
18:31
Speaker 1: in that space and invested in that space for quite
18:33
Speaker 1: some time, and kind of one of the standards I
18:36
Speaker 1: use is, you know, when when when portfolio companies are
18:41
Speaker 1: meeting with the investors on our team, do they believe
18:44
Speaker 1: that the person they're sitting across from is the one
18:47
Speaker 1: of the world's leading experts on the area that they're
18:49
Speaker 1: working in. UM. So that that's one difference. I think
18:52
Speaker 1: that the other different side point too is we're extremely
18:54
Speaker 1: outbound in how we operate. So one of our challenges was,
18:58
Speaker 1: you know, we don't have a we don't have a
18:59
Speaker 1: brand end NVC you know the way a sequoia does
19:02
Speaker 1: or something like that. And so, you know, one of
19:04
Speaker 1: the biggest concerns you gotta have in venture investing is
19:06
Speaker 1: adverse selection UH. And you probably don't want to be
19:09
Speaker 1: taking what's coming through the door. UM. So you know,
19:12
Speaker 1: what we focus on is um themes that we think
19:15
Speaker 1: are gonna be big money makers, where we think real
19:19
Speaker 1: change is happening, where technology is is um uh is
19:23
Speaker 1: driving really important impact UH. And then we go try
19:26
Speaker 1: to find the companies that we want to invest in
19:27
Speaker 1: and knock on their door proactively, look extremely proactive. Almost
19:32
Speaker 1: almost all of it is an outbound motion like ninety
19:36
Speaker 1: eight percent of it UM and then UM, and so
19:39
Speaker 1: that it would be the two big differences. I'd also
19:41
Speaker 1: say that UM, you know, you know, probably as firms go,
19:45
Speaker 1: our diligence is more intense than a lot of venture firms.
19:48
Speaker 1: I think that comes from Steve Um. You know Steve
19:51
Speaker 1: uh Um. Steve's one of Steve's sayings is do the
19:54
Speaker 1: work um. And you know, when we go into an
19:57
Speaker 1: investment committee to talk about something, uh, there's kind of
20:00
Speaker 1: only one answer, which is I did the work um.
20:03
Speaker 1: Otherwise the meaning's gonna end very soon. And so we
20:05
Speaker 1: we hold a pretty high bar in terms of the
20:07
Speaker 1: amount of research we're gonna do when we're looking into
20:09
Speaker 1: a company. So there would be the three things I
20:10
Speaker 1: point to. So, once you decide to make an investment
20:13
Speaker 1: in a startup or an existing company, how actively involved
20:18
Speaker 1: um with the corporate management are you? Are you guys
20:22
Speaker 1: they're giving them advice assistance? Or is it more of
20:25
Speaker 1: an arms length here's some money, now, now go do
20:29
Speaker 1: something great. It varies, but I would say we're fairly active.
20:34
Speaker 1: And the reason we end up being active is goes
20:36
Speaker 1: back to this expertise thing that I was describing, which
20:40
Speaker 1: is that, Um, you know, because the team is made
20:42
Speaker 1: up of people who are very deep experts, it tends
20:45
Speaker 1: to be that the entrepreneurs want them on the boards
20:48
Speaker 1: because you know, they're they're they're very useful and sort
20:51
Speaker 1: of sorting through the strategic questions and knowing where the
20:54
Speaker 1: business should go. Um. You know. It's interesting because when
20:56
Speaker 1: we started out, I was actually, uh pretty really sucked
21:00
Speaker 1: tot to take board seats because I actually, you know,
21:02
Speaker 1: I think it can be a bit of a distraction
21:03
Speaker 1: from doing the next investment. UM. But it turned out
21:07
Speaker 1: it was an important ask from a lot of our entrepreneurs.
21:09
Speaker 1: So we do end up taking a lot of board seats,
21:10
Speaker 1: which means we're pretty involved. And we talked earlier about
21:15
Speaker 1: the quantitative approach UM point seventy two. Often employees, how
21:20
Speaker 1: much big data do you bring to bear when trying
21:23
Speaker 1: to make a decision about either an area to invest
21:27
Speaker 1: in or a specific company. Very little, very little, very little. Uh.
21:32
Speaker 1: You know. Part part of it is the areas we're
21:34
Speaker 1: investing in. I mean, we're generally investing in enterprise companies
21:37
Speaker 1: uh in their early stage, and so you know, lots
21:39
Speaker 1: of times they'll have three or four customers UM, and
21:42
Speaker 1: there isn't a whole lot to sort of, you know,
21:43
Speaker 1: torture the data for UM. Doesn't mean we don't do research.
21:47
Speaker 1: We do a tremendous amount of research, but it tends
21:49
Speaker 1: to be more interviews with people and UM, you know,
21:52
Speaker 1: you know, customer follow ups with customers and probing on
21:56
Speaker 1: how you know how a certain product works UM or
22:00
Speaker 1: market sizing exercises or things like that UM. But we've
22:04
Speaker 1: not brought a lot of the of the of the
22:05
Speaker 1: big data to bear on on venture UM though I
22:09
Speaker 1: do think you know, in the consumer space there could
22:11
Speaker 1: be opportunities for that UM, and that that might be
22:13
Speaker 1: something we explore down the road. So this might be
22:15
Speaker 1: a little bit of a weird question. But how challenging
22:19
Speaker 1: is it two manage two distinct businesses with two very
22:25
Speaker 1: different approaches. One is so quantitative and data intensive, the
22:30
Speaker 1: other seems to be a little more intuitive and subjective.
22:33
Speaker 1: Do you find any sort of when you switch hats?
22:36
Speaker 1: Is that a little bit different to get into that
22:40
Speaker 1: a little bit challenging to get into that different headspace?
22:44
Speaker 1: I wouldn't say so. I think the similarity between both
22:47
Speaker 1: of them is that in both cases. You know, we're
22:50
Speaker 1: very process driven. UM. You know in in the process
22:53
Speaker 1: looks different in each case. But UH, you know, I'm
22:56
Speaker 1: I'm a very big believer and I think this comes
22:58
Speaker 1: from my my Bridgewater training UM in sort of process
23:01
Speaker 1: over outcomes. UH. And you know you have to you know,
23:05
Speaker 1: you have to think ahead of time about how you're
23:06
Speaker 1: going to approach a problem and why that's going to
23:08
Speaker 1: give you an advantage in uh in your approach um
23:12
Speaker 1: and on on on both sides of the business UM
23:14
Speaker 1: that I'm involved with. You know, that's how we how
23:17
Speaker 1: we come at it. Uh. And you know when we
23:19
Speaker 1: have very elaborate uh sort of you know, predesigned sort
23:23
Speaker 1: of ways that we're going to develop algorithms, and we
23:25
Speaker 1: have very uh clear ways that we're gonna make investment
23:28
Speaker 1: decisions on the venture side. UM. And so for me
23:31
Speaker 1: as a as a manager of both of those areas,
23:33
Speaker 1: that's mainly what I'm trying to do is make sure
23:35
Speaker 1: that process is really solid um and UH. And and
23:38
Speaker 1: that's that's the similarity. How how significant uh portion of
23:43
Speaker 1: the point seventy two book are the venture sides. So
23:47
Speaker 1: the venture investments are all Steve's personal investments. UM, so
23:51
Speaker 1: there's not point they're not well, I mean we use
23:53
Speaker 1: it's points a ventures, we use the brand, but it's
23:56
Speaker 1: not it's not in the fund. Uh, it's it's Steve's
23:58
Speaker 1: personal money. UM. And it's you know, it's it's not
24:01
Speaker 1: it's not super large. I mean it's a it's a
24:03
Speaker 1: couple hundred million. So now I have to ask the
24:05
Speaker 1: obvious question, if it's Steve's personal money, is there a
24:09
Speaker 1: different UM thought process in terms of an exit. How
24:13
Speaker 1: does that pressure or how does that structure affect how
24:18
Speaker 1: you approach it or is it just a continuum across everything.
24:22
Speaker 1: And his philosophy is the same whether it's public or
24:26
Speaker 1: private investments. I think his philosophy is very similar across both.
24:30
Speaker 1: You know, he is he is an I r R
24:32
Speaker 1: focused investor UM. And you know he has a hedge
24:35
Speaker 1: fund that does well and produces a good return every year. UM.
24:38
Speaker 1: And you know he expects us to be the same,
24:41
Speaker 1: to bring the same discipline to the private investments. And
24:44
Speaker 1: so you know, we think about I r rs, We
24:46
Speaker 1: think about exits, we think when we can get cash
24:48
Speaker 1: back out, we think about applying leverage. We you know,
24:50
Speaker 1: we think about all these different things UM. But but
24:52
Speaker 1: it all comes back to you know, producing a you know,
24:55
Speaker 1: a good rate of return UM and that's that's that's
24:59
Speaker 1: how he thinks of about the world. Quite fascinating. So
25:02
Speaker 1: you mentioned traditional UM forms of fundamental analysis. What what
25:08
Speaker 1: metrics do you find important? Lots of people have talked
25:11
Speaker 1: about price the book, and then it seems to have
25:12
Speaker 1: fallen a little bit out of favor. Other people are
25:15
Speaker 1: looking at various forms of valuation. What's the most important
25:19
Speaker 1: fundamental approaches that that point seventy two is considering. It's
25:24
Speaker 1: just very so widely. I mean, you know, we're trading
25:27
Speaker 1: you know, in the U S we're trading almost eight
25:29
Speaker 1: hundred names, and we also trade in Asia and Europe,
25:31
Speaker 1: and so, uh, you know, there's I can't give sort
25:35
Speaker 1: of a one size fits all answer to that question
25:37
Speaker 1: because there's there's so many different sort of subsegments. So
25:40
Speaker 1: following up on that, you have written that investing changes
25:45
Speaker 1: over time and it's the role of the portfolio manager
25:48
Speaker 1: to adapt to those changes. How have you seen recent
25:52
Speaker 1: changes in the marketplace and what sort of adaptations do
25:55
Speaker 1: people have to make? Well, I think, you know, I
25:57
Speaker 1: think it's some of the things we're talking about them.
25:59
Speaker 1: I think the UM you know, the explosion of big
26:02
Speaker 1: data or what we call alternative data UM is you know,
26:07
Speaker 1: a big impact. Uh. You know, it used to be
26:10
Speaker 1: that most of the investing was a conversation between the investor,
26:13
Speaker 1: the company and the cell side UM. And now you
26:16
Speaker 1: know you have UM. You know, just you know, whether
26:20
Speaker 1: it be credit card or geolocation or email receipts or
26:22
Speaker 1: all these different satellite like you were talking about UM.
26:25
Speaker 1: You know, all these different things that you know that
26:27
Speaker 1: you can you can bring to bear. So I think
26:29
Speaker 1: that's a really important trend. I think the other important trend,
26:31
Speaker 1: like we're talking about earlier, is is people plus machine.
26:33
Speaker 1: You know, what what are machines good at versus what
26:36
Speaker 1: are people good at? UM? You know, machines uh, quite
26:39
Speaker 1: good at UM at repetitive math and complicated math, and
26:44
Speaker 1: UM you know have a lot to bring to bear
26:46
Speaker 1: in terms of portfolio construction and trading and and and
26:49
Speaker 1: those sorts of areas UM. So those are probably the
26:52
Speaker 1: two most important trends that that we're seeing and thinking about.
26:56
Speaker 1: Quite interesting. So you you talked earlier about the pursuit
27:00
Speaker 1: of alpha for a lot of the hedge fund industry,
27:04
Speaker 1: this has been a rough decade. Alpha has been hard
27:07
Speaker 1: to come by. Lots and lots of other hedge funds
27:10
Speaker 1: have had a hard time meaning their benchmark. Two questions
27:16
Speaker 1: that come from that, what's behind alpha's um elusiveness these days?
27:23
Speaker 1: And what must elusive alpha? You haven't thought of that previously?
27:27
Speaker 1: And what do active managers have to do to stay
27:31
Speaker 1: relevant and at the top of their game? Yeah, well, Steve,
27:34
Speaker 1: Steve always jokes that he'd just like to go back
27:36
Speaker 1: to the nineties, Um, you know, when it was easy,
27:39
Speaker 1: when it was when it was a lot easier. And
27:41
Speaker 1: uh and look, I mean, you know, success straws competition,
27:45
Speaker 1: that's just capitalism. And I think that um, you know,
27:49
Speaker 1: you know, I think there's not a whole lot of
27:51
Speaker 1: mystery to why it's harder. I think it's harder mainly
27:53
Speaker 1: because a lot more people are doing it. Um, you know,
27:56
Speaker 1: there's there's certain i'd say, sort of boogemen in the market,
27:59
Speaker 1: you know, like et F flows and things like that
28:01
Speaker 1: that people also talk about. But but I think the
28:04
Speaker 1: core thing that makes Alpha harder is just, you know,
28:06
Speaker 1: the scale at which it all takes place today. Um,
28:09
Speaker 1: and you know, I think in terms of of of
28:11
Speaker 1: maintaining an advantage. UM. You know, I uh, I remember
28:16
Speaker 1: the very first time I met Steve, I asked him
28:18
Speaker 1: the question of how he had been able to sustain
28:20
Speaker 1: his fund for so long UM and he's at such
28:24
Speaker 1: a high level, and he said, well, because I've rebuilt
28:26
Speaker 1: it four or five times UM. And you know, and
28:29
Speaker 1: and you know, the point he made is that this
28:32
Speaker 1: is just a constantly changing game that's always attracting competitors.
28:36
Speaker 1: And if you think that whatever success you have today
28:38
Speaker 1: is going to be true tomorrow, you are really naive UM.
28:42
Speaker 1: And so you know, there's it's part of what I
28:44
Speaker 1: like so much about working with him, And there's just
28:46
Speaker 1: a restless energy to him because he knows that that's
28:49
Speaker 1: what's required to continue to survive. And so that's how
28:52
Speaker 1: we approach the firm we have UM. You know, always
28:55
Speaker 1: you know, tons of new initiatives and experiments going on,
28:57
Speaker 1: and things will succeed or you know, and things will
28:59
Speaker 1: fail and will kill them, and things that will succeed
29:01
Speaker 1: will scale UM. But that but you know, I think
29:04
Speaker 1: his his view, and I agree with it, is that
29:07
Speaker 1: it's that you know, it's that activity that's how you
29:09
Speaker 1: maintain an advantage um, because the business you know, in
29:13
Speaker 1: three or four years isn't gonna look anything like it does,
29:15
Speaker 1: you know, three or four years prior to now. So
29:18
Speaker 1: Michael Mobison calls that the paradox of skill, that the
29:22
Speaker 1: success of the hedge fund industry and other sectors of
29:25
Speaker 1: finance have attracted so many intelligent, talented people that the
29:30
Speaker 1: easy money has gone away and it's becomes so much harder. Well,
29:33
Speaker 1: that's what makes it fun, right, I mean, that's what
29:35
Speaker 1: makes it. It's the you know, it's the competitive drive
29:37
Speaker 1: and the and the knowing that the bar is always
29:39
Speaker 1: going up. Um. You know, it's that challenge that I
29:42
Speaker 1: think draws a lot of people to the industry to
29:44
Speaker 1: to say the very least. So look around at some
29:48
Speaker 1: of the other hedge funds out there, like the Show
29:51
Speaker 1: or Citadel or Renaissance Technologies, and they were pretty early
29:55
Speaker 1: onto the high frequency trading and other computer driven UH approaches.
30:00
Speaker 1: Is that anything that is UH in point seventy two's
30:04
Speaker 1: field of interest or is that something that hey, let
30:07
Speaker 1: the computer driven guys do that. You have your own
30:11
Speaker 1: specific skill set, So we don't do any high frequency trading. UM,
30:15
Speaker 1: we do a fair bit of computer driven trading in
30:18
Speaker 1: our systematic unit, and then in some of the units
30:21
Speaker 1: I oversee their systematic as well, so driven by computers. Um.
30:24
Speaker 1: But uh, but but nothing that would constitute high frequency um. Uh.
30:29
Speaker 1: You know, it's certainly an area where a bunch of
30:33
Speaker 1: people made a bunch of money, but it wasn't something
30:35
Speaker 1: that we did. One of the things I didn't ask
30:38
Speaker 1: you earlier but is relevant here is the Domino Data Lab.
30:43
Speaker 1: What was the thinking behind that? And how have you
30:47
Speaker 1: used that experience at Bridgewater and at point seventy two? Yes,
30:53
Speaker 1: So the thinking behind that was really sort of two
30:55
Speaker 1: big ideas. One was that we were moving to a
30:58
Speaker 1: model driven world, um, where you know, we're algorithms that
31:03
Speaker 1: were trained, fed and trained data that made predictions or
31:07
Speaker 1: decisions for businesses. That that was going to be a
31:10
Speaker 1: very important um thing that took place, and so you know,
31:14
Speaker 1: you see the rise of Netflix and Amazon and all
31:16
Speaker 1: these things that I would call model driven businesses. Uh.
31:19
Speaker 1: And then the second sort of big idea was that,
31:23
Speaker 1: um that as that happened, the people who did that work,
31:26
Speaker 1: the data scientist needed a system of record. So salespeople
31:30
Speaker 1: work in salesforce HR people work in work day. There
31:33
Speaker 1: was not an equivalent for data science, and so we
31:35
Speaker 1: were building and in our building, uh, the system of
31:38
Speaker 1: record for UM for data scientists and and those were
31:43
Speaker 1: those were really the two big big ideas behind it.
31:45
Speaker 1: And whatever happens to Domino Data LAMB. Does it still exist?
31:48
Speaker 1: It still exists doing great UM you know, just you know,
31:51
Speaker 1: continues to grow leaps and bounds. I'm on the board. UM.
31:54
Speaker 1: It's still an independent company, still an independent company backed
31:57
Speaker 1: by Sequoia and COT primarily UM and some others actually
32:02
Speaker 1: including Bloomberg, Beta, UM and uh, you know, and it's uh,
32:06
Speaker 1: it's it's been. It's been very successful. And probably one
32:08
Speaker 1: of the most interesting things about it is just the
32:11
Speaker 1: diversity of industries now that are representing the client base.
32:14
Speaker 1: You know, it started out a lot of finance firms,
32:16
Speaker 1: insurance firms were interested, but now we have everything from
32:19
Speaker 1: retailers to grocery stores, to auto makers to pharmaceutical makers.
32:24
Speaker 1: Because you know, basically the thesis we were betting on
32:27
Speaker 1: was that the world was going to become model driven.
32:30
Speaker 1: And this is a tool set. This is a tool
32:32
Speaker 1: set to help track how effectively you're deploying your model.
32:36
Speaker 1: It's a it's a tool set that um you know, basically,
32:40
Speaker 1: data scientists build their models using the languages and tools
32:43
Speaker 1: they want in Domino, and then Domino revisions those things.
32:47
Speaker 1: Means they keep track of the data and the code
32:48
Speaker 1: and the results, and then you can also publish out
32:51
Speaker 1: so you can run models from that, and so it's
32:53
Speaker 1: sort of the your central repository, your system of record
32:57
Speaker 1: for models. Quite interesting, and I keep coming back to
33:01
Speaker 1: the idea of of man and machine. When you're evaluating talent,
33:06
Speaker 1: be it a startup management team or a a potential
33:13
Speaker 1: higher or a portfolio manager, how much of that is
33:17
Speaker 1: data driven and how much of that is your own
33:19
Speaker 1: human intuition? Well, in in people processes, you know, look,
33:24
Speaker 1: I think there's still a lot of human intuition into it. Uh.
33:29
Speaker 1: We we do try to be as rigorous and as
33:31
Speaker 1: systematic as possible. And what I mean by that is,
33:34
Speaker 1: you know, we we try to start with the job
33:36
Speaker 1: and the outcomes we expect. And as you think about
33:39
Speaker 1: those outcomes, what capabilities are required? And you think about
33:42
Speaker 1: those capabilities, you know, what's the best way to evaluate
33:44
Speaker 1: those capabilities? I personally don't like interviews. I don't think
33:47
Speaker 1: they're particularly useful. UM. I think that work samples and
33:50
Speaker 1: projects and these sort of and more testing and those
33:53
Speaker 1: sorts of things are very valuable. UM. But you know,
33:55
Speaker 1: obviously there's also you know, you do need to meet
33:57
Speaker 1: the people. And that's that's a part of it. Um
33:59
Speaker 1: by it for us, the hiring process or the evaluation
34:02
Speaker 1: process of people adventure, UM, you know, just has a
34:05
Speaker 1: certain methodical nous to it. That's that's very important, quite
34:09
Speaker 1: quite fascinating. We have been speaking with Matthew Grenade. He
34:13
Speaker 1: is the chief market intelligence officer at Point seventy two. UH.
34:17
Speaker 1: If you enjoy this conversation, we'll be sure and come
34:20
Speaker 1: back for the podcast extras, where we keep the tape
34:23
Speaker 1: rolling and continue discussing all things quant and hedge fund investing.
34:28
Speaker 1: We love your comments, feedback and suggestions. You can write
34:32
Speaker 1: to us at m IB podcast at Bloomberg dot net.
34:36
Speaker 1: Be sure and check out my daily column at Bloomberg
34:39
Speaker 1: dot com slash opinion. You can follow me on Twitter
34:42
Speaker 1: at rid Holts. I'm Barry Ridholts. You're listening to Masterson Business.
34:47
Speaker 1: I'm Bloomberg rad Ye. Welcome to the podcast. Matthew thank
35:04
Speaker 1: you so much for doing this. UM. I've been looking
35:06
Speaker 1: forward to this conversation. I have followed Stevie Cohen's career
35:12
Speaker 1: from AFAR for since the nineties, and I find him
35:15
Speaker 1: to be an absolutely intriguing individual, both as a investor
35:22
Speaker 1: and an art collector, and a person who has managed
35:26
Speaker 1: to um thrive despite a lot of really fascinating challenges. So, UM,
35:33
Speaker 1: when we first made contact with your office, I was
35:36
Speaker 1: really excited about this. UM, so thank you for doing this.
35:41
Speaker 1: One of the things we did not get to talk
35:43
Speaker 1: about during the broadcast portion was the OpEd that you
35:47
Speaker 1: and Steve wrote in the Wall Street Journal. And UM,
35:51
Speaker 1: it's not software is eating the world, it's models will
35:56
Speaker 1: run the world. Tell us a little bit about that.
35:59
Speaker 1: So so Mark Andresen wrote a piece several years ago
36:03
Speaker 1: and called software is Eating the World, and it's basically
36:05
Speaker 1: the idea that software is going to change every business. UM,
36:09
Speaker 1: and Steve and I were thinking about, you know, kind
36:12
Speaker 1: of what's the equivalent today, because I think that was
36:14
Speaker 1: written almost seven or eight years ago. Uh. And you know,
36:17
Speaker 1: the thing that we zero in on was this idea
36:20
Speaker 1: that that really models we're going to change the change
36:23
Speaker 1: the business landscape and you know, you know the idea
36:26
Speaker 1: of a model um, you know, think about Netflix, right.
36:28
Speaker 1: So I think Netflix is a great model driven business
36:31
Speaker 1: where you know, eight percent of the content consumption there
36:34
Speaker 1: comes from their recommendation engine, right, and so basically what
36:37
Speaker 1: they're what they're trying to do is they're trying to
36:38
Speaker 1: build the best recommend or possible. There you know, you're
36:41
Speaker 1: signing up, they're taking in data about you there, you
36:44
Speaker 1: know your zip code, and but then they watch everything
36:46
Speaker 1: you do. They watch you know how you um, you
36:49
Speaker 1: know what shows do you jump on right away? Which
36:51
Speaker 1: shows do you finish? Which shows do you not? And
36:53
Speaker 1: that lets them recommend better and better content for you.
36:55
Speaker 1: And then basically at the core of their business is
36:58
Speaker 1: this engine that's that's you know, holding or basically recommending
37:02
Speaker 1: content for you, um that you're going to enjoy more
37:04
Speaker 1: and more. And now they're using that same data in
37:06
Speaker 1: that same approach to build content as well. Um. So
37:09
Speaker 1: I think we think about that as a model driven
37:10
Speaker 1: business and it's a it's a really sort of powerful
37:13
Speaker 1: mode because once you get the loop going where you're
37:15
Speaker 1: collecting the data and seeing the outcomes that you're driven,
37:18
Speaker 1: you're driving you can make the model better and better. Um.
37:21
Speaker 1: And so you know we in the in the out
37:23
Speaker 1: ed what we talk about is uh, some public and
37:25
Speaker 1: some private companies. Um that Uh you know that that
37:29
Speaker 1: our model driven and and and some of the implications
37:32
Speaker 1: of this trend um and um and so Yeah, it
37:35
Speaker 1: was a fun piece to write. Yeah, and it's still
37:38
Speaker 1: available if you anybody wants to go see it. Models
37:40
Speaker 1: will run the world. It's in the Wall Street Journal. UM.
37:44
Speaker 1: So when you see something like and Reeson's peace, Uh,
37:48
Speaker 1: software is in in the world. I want to say
37:51
Speaker 1: that he's half right. Software had started to eat the world.
37:55
Speaker 1: But we run into problems all the time. That software,
38:00
Speaker 1: it only gets you half the way there. And and
38:02
Speaker 1: the entire infrastructure of everything from the hardware to the
38:07
Speaker 1: network too, everything else that's involved has to work seamlessly.
38:12
Speaker 1: Doesn't quite feel like we're in the future yet. How
38:16
Speaker 1: do you am I overstating that or how do you
38:20
Speaker 1: how do you perceive the world where you know, a
38:23
Speaker 1: robot butler doesn't take you to work each day, but
38:26
Speaker 1: it's not too far off in the future. I can't
38:28
Speaker 1: remember who said it, but somebody said, uh, you know,
38:31
Speaker 1: the future is here. It's just unevenly distributed, you know.
38:33
Speaker 1: William Gibson, Yeah, I think there's I think there's a
38:36
Speaker 1: lot of truth to that, you know. I mean when
38:38
Speaker 1: you're in uh, you know, San Francisco and you you know,
38:41
Speaker 1: you see the self driving cars that you know, Cruise
38:44
Speaker 1: and and Google and others are making. Um, you know,
38:48
Speaker 1: then that that feels that feels very in the future. Uh.
38:52
Speaker 1: And then you know, like you said, you look at
38:54
Speaker 1: some other industries and you sort of scratch your head
38:56
Speaker 1: about you know, why can't I get a good cell
38:58
Speaker 1: signal in Manhattan? It's exactly why point why can I
39:01
Speaker 1: maintain the still signal on the train back to back
39:03
Speaker 1: to Connecticut? Um? But um so I certainly, I certainly
39:07
Speaker 1: agree that it's it's unevenly distributed. But but you know,
39:11
Speaker 1: there's also a tremendous amount of very exciting things happening.
39:14
Speaker 1: Um and uh And and look, that's what makes the
39:17
Speaker 1: venture investing so much fun, you know, is seeing all
39:19
Speaker 1: that and being involved in that world, having that view
39:24
Speaker 1: of upcoming technologies. How does it affect the way you
39:27
Speaker 1: look at the world of existing public companies. That's a
39:31
Speaker 1: great question. I look, I think, um, uh, you know,
39:35
Speaker 1: it makes you um much more skeptical about their advantages
39:40
Speaker 1: and about the durability of their um of their moats
39:45
Speaker 1: quote unquote right uh and um, you know, you look
39:48
Speaker 1: at how fast the change has happened in retail and
39:51
Speaker 1: how and how deep and dramatic some of that took place, um,
39:55
Speaker 1: you know, and you go back and you look at
39:56
Speaker 1: some of these companies and all the moats they were
39:57
Speaker 1: talking about and the customer loyalty, and then you know,
40:00
Speaker 1: um and so you know, one of the things we
40:02
Speaker 1: try to do at at points of ME two is
40:04
Speaker 1: we we try to sort of cross pollinate some of
40:06
Speaker 1: the big thematic learnings um from the venture work in
40:10
Speaker 1: with them, in with the public market investors. We had
40:12
Speaker 1: a dinner a few months ago on robotics UM, and
40:15
Speaker 1: we had through four CEOs of robotics companies, and we
40:17
Speaker 1: had our industrial a couple of our industrials pms, and
40:20
Speaker 1: our healthcare pms, you know, and it's essentially a discussion,
40:22
Speaker 1: you know, exactly along the lines you said of you know,
40:25
Speaker 1: how is how is robotics going to And obviously there's
40:27
Speaker 1: gonna be a bunch of private companies that get created,
40:29
Speaker 1: but it's also going to really change, you know, in
40:32
Speaker 1: those two areas. You know a lot of companies as well.
40:35
Speaker 1: How often do you guys have dinners like that? It
40:37
Speaker 1: sounds like that's an intriguing evening. We do them about
40:40
Speaker 1: once a month. We're doing one tonight actually um and
40:44
Speaker 1: uh um, you know it's what's the topic tonight tonight?
40:47
Speaker 1: Topics actually talent evaluation. So Angela Duckworth is going to join.
40:52
Speaker 1: Um wrote a book, Grita. Have you gotten to Have
40:56
Speaker 1: you read that yet? I have read gret And how
40:58
Speaker 1: do you like it? I think it's great. It's been
41:00
Speaker 1: at the top of a number of people's lists for
41:03
Speaker 1: for quite a while. Yeah, I have a you know,
41:06
Speaker 1: I think it's a it's an interesting way to sort
41:09
Speaker 1: of think about, you know, why people are successful. Also,
41:12
Speaker 1: as a parent, it's something you know, you you you
41:15
Speaker 1: think a lot about, uh, you know, what can you
41:17
Speaker 1: actually teach your kids? And you know how and and
41:20
Speaker 1: you know, probably at the top of my list of
41:22
Speaker 1: things I realized my children to have and to learn. Um.
41:26
Speaker 1: And so we have we have rules now about sticking
41:28
Speaker 1: with things and stuff like that, largely because of her books.
41:31
Speaker 1: So that's that's quite fascinating. UM, I could talk about
41:35
Speaker 1: this stuff forever, but I know only have you for
41:38
Speaker 1: a finite amount of time, and I wanted to get
41:41
Speaker 1: to my favorite questions. UM so let me jump right
41:45
Speaker 1: into this, So feel free to answer these as longer
41:48
Speaker 1: as short as you want. These are pretty straightforward, um,
41:52
Speaker 1: but they usually are a little uh insightful into who
41:56
Speaker 1: you are. Tell us the first car you ever owned,
41:59
Speaker 1: you're making model? It was a Volvo S forty two thousand,
42:04
Speaker 1: sort of, the two door with the hatchback. Is that
42:07
Speaker 1: the one I'm I'm thinking of it? Four door? It was?
42:10
Speaker 1: It was it was a new model year. Um, so, yeah,
42:13
Speaker 1: it was a four door. It was blue. What's the
42:15
Speaker 1: most important thing people don't know about Matthew Grenade? Um.
42:21
Speaker 1: People are usually surprised to learn that I'm from the South, Um,
42:24
Speaker 1: you know, having gone to Harvard twice and worked at
42:27
Speaker 1: hedge funds and things like that, and uh, and my
42:30
Speaker 1: family has been from the South from for a very
42:32
Speaker 1: long time. Um. You have the slightest wisp of an accent,
42:35
Speaker 1: but not a heavy the slightest whisp. And then you know,
42:39
Speaker 1: and and and I think, uh, you know, certainly affects
42:42
Speaker 1: my my manners and that kind of thing. So are
42:46
Speaker 1: you a courtly southern gentleman? Is that I wouldn't go
42:48
Speaker 1: that far. But but but but my my my mom
42:53
Speaker 1: raised me right, she would say, so, So, tell us
42:57
Speaker 1: about some of your early mentors. Who are the people
42:59
Speaker 1: who helped guide your career. Yeah so, um uh so
43:05
Speaker 1: Bo Jones, who was publisher of the Washington Post. Um
43:08
Speaker 1: he had been a president of the Crimson as well.
43:10
Speaker 1: Um he uh. I worked for him for a summer
43:14
Speaker 1: and uh, you know, one of the things, one of
43:17
Speaker 1: the things that a couple of things very interesting about
43:19
Speaker 1: working from one was, you know, he and and Don
43:21
Speaker 1: Graham um in the Graham family in general sort of
43:25
Speaker 1: really understood the ecosystem of their business well and and
43:29
Speaker 1: sort of how all the parts interconnected. Um uh in
43:33
Speaker 1: the in sort of you know, the basically how the
43:37
Speaker 1: subscription revenue, um you know was important, but you didn't
43:41
Speaker 1: want to You wanted to make sure that you kept
43:43
Speaker 1: that price low enough. You have the advertisers, and they'd
43:45
Speaker 1: a very holistic way of thinking about the business. And
43:48
Speaker 1: then the second uh thing that I thought they you know,
43:51
Speaker 1: they're very principal based leaders. Um. You know, a new
43:54
Speaker 1: a newsrooms of place, things can run quite a mock
43:56
Speaker 1: and and the Washington Post has the backs of their
43:59
Speaker 1: reporters and that was always interesting to watch. UM. Another
44:02
Speaker 1: would be Tom Barkin, who Um, Tom uh is now
44:06
Speaker 1: president of the Richmond Fed UM and on the FOMC
44:09
Speaker 1: at the moment, but he was a very senior partner
44:11
Speaker 1: at McKenzie UH and one of the people who I
44:15
Speaker 1: worked with the closest and most when I was there
44:18
Speaker 1: right out of college. UM. And you know, I think
44:20
Speaker 1: the thing Tom taught me was the uh sort of
44:23
Speaker 1: seeing the essence of the of a problem. UM. You know,
44:25
Speaker 1: when you're when you're first out of school and and
44:28
Speaker 1: you can think of a two thousand analyzes to do,
44:30
Speaker 1: you know, let's do all these things. And Tom Tom
44:32
Speaker 1: was great at knowing what the what the right question
44:35
Speaker 1: was to ask and the and the right one to answer.
44:38
Speaker 1: So what investors influenced the way you look at markets
44:41
Speaker 1: and your approach to deploying risk capital. Well, look, I
44:46
Speaker 1: mean it's it's really the two I've worked you know,
44:49
Speaker 1: closely with. It would be it would be Ray and
44:51
Speaker 1: Steve Um. And that's quite a pair of mentors his uh.
44:56
Speaker 1: You know, with Ray, I think UM sort of two
44:59
Speaker 1: big lessons. One is um um being systematic, being process
45:04
Speaker 1: driven that you know, you don't you don't look at outcomes,
45:07
Speaker 1: you look at how you got to those outcomes. Uh.
45:09
Speaker 1: And then also being fundamental um. And you know, as
45:12
Speaker 1: we're talking about earlier, in the world of data science,
45:14
Speaker 1: you can torture the data to say anything, and so
45:16
Speaker 1: you really have to think about how the how the
45:18
Speaker 1: world actually works and why what you're finding matters. Um.
45:21
Speaker 1: And then with Steve UM, you know, it's it's the
45:25
Speaker 1: sort of tenacity to to really dig in and do
45:28
Speaker 1: the work, you know, which, as I mentioned, is one
45:30
Speaker 1: of the things he he says over and over UM. Uh.
45:33
Speaker 1: You know, you you don't go talk to Steve about
45:36
Speaker 1: a name or a venture, investment, or a new strategy
45:39
Speaker 1: without having sort of turned it over a hundred different ways. Um.
45:42
Speaker 1: And you know his bar for just having you dig
45:45
Speaker 1: deep is very high. Um. And Uh, there's probably the
45:48
Speaker 1: lessons I've learned most from those guys. So we mentioned, um,
45:53
Speaker 1: grit tell us about some of your favorite books fiction,
45:57
Speaker 1: non fiction, FINANCEI related whatever. Yeah, So, UM, I mean
46:01
Speaker 1: some of my favorite books of all times. Uh, let's see.
46:05
Speaker 1: So and just so you know, just so you know,
46:08
Speaker 1: the feedback I get on this question is consistently the
46:14
Speaker 1: most asked about question, and people say to me, I'm
46:17
Speaker 1: always looking for a well thought out suggestion for a book,
46:21
Speaker 1: and it's my favorite question you ask people because I've
46:24
Speaker 1: created a reading list off of that question, so it's
46:27
Speaker 1: not just a random Hey, what are you thinking about
46:31
Speaker 1: the books people recommend? Other people say, he seems like
46:35
Speaker 1: an intelligent guy. I want to read the books that
46:37
Speaker 1: he likes to read. So I'm just I'm just annotating
46:41
Speaker 1: before you. So let's try to do three from fairly
46:43
Speaker 1: diverse areas. So uh, so you know more finance data science. E.
46:47
Speaker 1: I love super Forecasters, which you know is basically tetlock,
46:51
Speaker 1: which talks about how you, you know, essentially get good predictions.
46:54
Speaker 1: And he's spent his life studying how you get good
46:56
Speaker 1: predictions or someone in the markets. You know, it's it's
46:58
Speaker 1: it's critical. Um, then let's go outside of investing in
47:02
Speaker 1: financing those sorts of things. One of my favorite sort
47:05
Speaker 1: of historical books is Wild Swans UM Wild swan Swands,
47:09
Speaker 1: which chronicles the life of three women in China and
47:13
Speaker 1: the twentieth century. UM. I think I think China is
47:17
Speaker 1: such an interesting story because it just you know, it's, it's,
47:20
Speaker 1: it's there's been so much dramatic change. And you look
47:23
Speaker 1: at those three lives and uh you know, uh, you know,
47:28
Speaker 1: one of which is a fair bit of which has
47:30
Speaker 1: been on the cultural revolution, and you sort of think
47:32
Speaker 1: the world you're living in is the world you're living in,
47:34
Speaker 1: and it can just change so dramatically. I want to
47:36
Speaker 1: make sure I have the right book Wild Swans Three
47:39
Speaker 1: Daughters of China by Jung Chang. Is that it? That's
47:42
Speaker 1: it quite interesting? Uh. And then we'll go for a classic,
47:46
Speaker 1: uh I Um, I love The Tempest by Shakespeare. Um,
47:50
Speaker 1: and uh you know it's where I me there's a
47:52
Speaker 1: lot of things goes on go on in that book,
47:53
Speaker 1: but uh, that's where you know he he wrote, you
47:56
Speaker 1: know what was past his prologue, UM, which I think
47:59
Speaker 1: is really true. The past is prologue could really be
48:03
Speaker 1: the slogan for anybody who creates models. So so that
48:07
Speaker 1: works out. That works out pretty well. Also, UM, tell
48:10
Speaker 1: us about a time you failed and what you learned
48:13
Speaker 1: from the experience. There's been a bunch, but uh, you know,
48:20
Speaker 1: well I'll do this one. So um, Before we started
48:24
Speaker 1: Domino Data Labs, my co founders and either two of us,
48:28
Speaker 1: three of us total, all all of us from from Bridgewater.
48:32
Speaker 1: We started a previous business called Cerebro UH and Cerebro
48:36
Speaker 1: was in the talent evaluation space, and so I was
48:38
Speaker 1: trying to sort of figure out smarter ways to help
48:41
Speaker 1: companies assess their talent. And we had some great clients
48:44
Speaker 1: UH in tech, mainly technology firms UH, and we mainly
48:48
Speaker 1: had leaders from the business lines, and so we would
48:52
Speaker 1: sort of do this work, they would love it, and
48:53
Speaker 1: then we would get past to the recruiting department and
48:57
Speaker 1: the project would just die. And we did this like
48:59
Speaker 1: over and over and over again. UM. And what we
49:01
Speaker 1: finally realized was realized a couple of things. One was
49:04
Speaker 1: that at a micro level, that the incentives between the
49:07
Speaker 1: recruiters and the business people were very different. That the
49:09
Speaker 1: recruiters wanted to put people in seats and that the UH,
49:14
Speaker 1: and that the business people wanted to have great people
49:16
Speaker 1: in those seats. But then more deeply, what we learned
49:18
Speaker 1: was that we actually had no idea what we were
49:20
Speaker 1: doing UM, and that you know, that we were really
49:22
Speaker 1: trying to build a business in an area that we
49:25
Speaker 1: weren't experts in and that you you know that that
49:28
Speaker 1: is starting a business is just so so so hard
49:31
Speaker 1: in like a thousand different ways, and uh, you know,
49:33
Speaker 1: and so you have to you have to take advantages
49:35
Speaker 1: where you can. And so what we uh, you know
49:38
Speaker 1: what we've we started asking ourselves, so what do we
49:40
Speaker 1: actually know about? And in those areas of what we
49:43
Speaker 1: actually know about, where are their actual problems? And that
49:45
Speaker 1: led us do Domino in the data science space. So
49:48
Speaker 1: so you come from the school of Ray Dalio's use
49:51
Speaker 1: failure as a learning experience to improve your next uh,
49:55
Speaker 1: your next attempt at whatever it is. Oh. Absolutely, so
49:59
Speaker 1: he told me a funny story about the inside of
50:03
Speaker 1: his uh of his book with with the failure cycle,
50:09
Speaker 1: and someone who will remain nameless said to him, Ray,
50:11
Speaker 1: what sort of signature is that? They obviously hadn't read
50:15
Speaker 1: the book, but quite quite hilarious. Um, so tell us
50:19
Speaker 1: what you do for fun when you're out of the office.
50:21
Speaker 1: Would you do to kickback, relax, have a good time. Um.
50:25
Speaker 1: I like to cook um. And this is like going
50:28
Speaker 1: back to being from the South. So my my grandmother
50:31
Speaker 1: taught me to cook um and uh, and so my
50:35
Speaker 1: wife and I will throw parties and we'll cook, in
50:37
Speaker 1: particular fried chicken and things like that, and that's probably
50:41
Speaker 1: what I enjoy. You work off a cookbookers at all
50:44
Speaker 1: grandma's recipes, it's usually a combination. Um. So I like to,
50:48
Speaker 1: you know, kind of mix in some more modern cooking
50:51
Speaker 1: with some of the more traditional recipes. So give us
50:54
Speaker 1: a few dishes. Uh. Well, you know, a traditional dinner
50:58
Speaker 1: party would be, um, you know, fried chicken with macaroni
51:02
Speaker 1: and cheese and biscuits and blueberry cobbler. But southern, real
51:06
Speaker 1: southern cooking. But I'll also do you know, like maybe
51:08
Speaker 1: some molecular astronomy with like a watermelon drop or something. Um.
51:13
Speaker 1: So you gotta keep it, keep it modern. But um,
51:16
Speaker 1: did you see Nathan Revold's Get gast Row cookbook? I
51:22
Speaker 1: have all those it's supposed to be a fascinating Have
51:25
Speaker 1: you tried any of those dishes? So he has so
51:27
Speaker 1: he has his his five volumes, five or six volume
51:30
Speaker 1: set that's very intense and completely overwhelming. And then he
51:33
Speaker 1: has a home version, um, which I have done a
51:36
Speaker 1: couple of things out of the home version. Do they work?
51:39
Speaker 1: They work? Um? But he's he's he's much more serious
51:42
Speaker 1: than I am. So he's he's he's very intense yet
51:45
Speaker 1: to say, to say the least. So what are you
51:47
Speaker 1: most excited about within the financial industry today? Well, I
51:52
Speaker 1: think you know the thing that the most interesting question
51:54
Speaker 1: right now is the people plus machine question. You know, what,
51:57
Speaker 1: what are the people good at? How do you the
52:00
Speaker 1: most out of them? How do you um uh, how
52:04
Speaker 1: do how do you think about those capabilities? And how
52:07
Speaker 1: do you couple those with what machines are good at? Um?
52:10
Speaker 1: And I um, you know, I think that, Like I said,
52:12
Speaker 1: I think the next generation hedge fund is going to
52:15
Speaker 1: be a mixture of those two things and um. And
52:18
Speaker 1: that's a it's a really it's hard in a lot
52:21
Speaker 1: of ways, but it's a very exciting question. So a
52:23
Speaker 1: millennial or a recent college grad comes up to you
52:26
Speaker 1: and says they're interested in a career in either investing
52:31
Speaker 1: or quant what sort of career advice would you give them? Well,
52:37
Speaker 1: I'm not sure it would be so specific to any field.
52:40
Speaker 1: I mean, I think, uh, I think the career advice
52:44
Speaker 1: I would give and I'm I'm not a huge fan
52:46
Speaker 1: of giving advice, but since I'm on the show and
52:48
Speaker 1: on the spot. Um. Look, I Number one would be, um, hm,
52:57
Speaker 1: set your goals as preposterously as you can set them. Um,
53:00
Speaker 1: you will continuously surprise yourself and what you can do.
53:03
Speaker 1: And UM I think uh, um you know so so
53:07
Speaker 1: aim big and dream really big. Um. So that would
53:11
Speaker 1: be one I think. Second, Um, the is work hard. Um.
53:17
Speaker 1: The you know, no no one I've ever met, uh
53:21
Speaker 1: doesn't know, no one, no one who I've ever worked for,
53:23
Speaker 1: you know, Ray, Steve, these guys, none of them are slackers. Um.
53:29
Speaker 1: You know, I mean Steve starts every he starts the
53:31
Speaker 1: week on Sunday morning at um and you know, uh,
53:35
Speaker 1: and and that's when that's when the then he works
53:37
Speaker 1: all day Sunday and he works a fair bit today
53:39
Speaker 1: Saturday and so um, so you know, I think I
53:42
Speaker 1: think it would be to set really almost preposterous goals. Uh,
53:46
Speaker 1: you know, be willing to work really really hard. And
53:48
Speaker 1: then I think the third would probably be, um, you know,
53:51
Speaker 1: love what you do. Um. I've also never really met
53:54
Speaker 1: someone who was successful who didn't really love what they did. Um.
53:57
Speaker 1: And I think you know, Steve Jobs had something that
53:59
Speaker 1: he said I think in the Stanford commincement speech. It's
54:02
Speaker 1: like if you haven't found, if you haven't found what
54:04
Speaker 1: you love yet, just keep looking. UM. And I think
54:07
Speaker 1: that's I think it's right. I think all those things
54:09
Speaker 1: are true, good good advice. UM. And our final question,
54:13
Speaker 1: what is it that you know about the world of
54:15
Speaker 1: investing today? Did that you wish you knew twenty years
54:19
Speaker 1: or so when you were first getting out of college? Well,
54:24
Speaker 1: stay long, Microsoft right, that that was a good time
54:28
Speaker 1: to not not panic, right exactly, But I mean we're
54:32
Speaker 1: as opposed to crystal ball, more processed. Absolutely. Look, I
54:35
Speaker 1: think the UM uh, you know, I think I think
54:38
Speaker 1: one of the most interesting things is just how different um,
54:42
Speaker 1: different periods of time will feel and be UM. And
54:45
Speaker 1: this goes a little bit too, you know what has
54:47
Speaker 1: passed his prologue and using history and things like that,
54:49
Speaker 1: you know, I mean, UM, when you know I graduated
54:52
Speaker 1: in from college in two thousand, you know that was
54:55
Speaker 1: the just the bubble was peaking and UM and the
54:58
Speaker 1: tech bubble, and that sort of felt one very one
55:01
Speaker 1: certain way. And then you know, you get to two
55:03
Speaker 1: thousand eight and you're just in a very very different regime.
55:07
Speaker 1: And I think, UM, I think the differences between these
55:09
Speaker 1: regimes and how what's gonna work in these regimes is
55:13
Speaker 1: quite different. Um. You know, you really have to kind
55:15
Speaker 1: of get your your head around that, um and and
55:18
Speaker 1: kind of really appreciate that quite quite fascinating. We have
55:23
Speaker 1: been speaking with Matthew Grenade. He is the chief market
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Speaker 1: intelligence officer at Point seventy two, where he also oversees
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Speaker 1: I'm Barry Riholts. You've been listening to Masters in Business
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