Matthew Granade Discusses Quantitative Investment (Podcast)

long time. That was eventually converted into a family office which was Point seventy two that reopened to outside investors last year in UM and Grenade has been working there for a good couple of years. Previously he was at

Bridgewater with Ray Dalio. You'll hear all about that during our conversation, but more importantly, you'll hear about the intersection between man and machine, between the way models can be used to not only manage assets, but improve the entire process, along with a variety of big data and other approaches UH that are really quite fascinating if you are at all interested in quantitative investing, machine learning, hedge funds, UH, the state of investing today and what anybody who is

pursuing alpha must do to stay current, then you're gonna find this to be an absolutely fascinating conversation. So, with no further ado, here is my conversation with Point seventy two's Matthew Grenade. My extra special guest this week is Matthew Grenade. He is the chief market intelligence officer at Point seventy two. That is Stevie Cohen's new hedge fund, which employs about out people and manages about thirteen billion dollars.

Point seventy two asset management was converted into a hedge fund in and last year it reopened to external investors. Matthew comes to us by way of Bridgewater Associates Domino Data Lab, and he got his both undergraduate and graduate NBA at Harvard Business School, where at undergraduate he was the president of the Harvard Crimson. Matthew Grenade, Welcome to Bloomberg. Thank you for having me. So let's start. Let's start

with the most unusual thing on your um resume. You're president of the Harvard Crimson, not exactly a hotbed of future hedge funds officers. How did that come about? What was that experience? Like, well, a couple of things, I mean in terms of that experience. Running in newspaper is one of the most amazing things in the world, and I got to run a small one at Harvard. But I, you know, I think it's just an incredible job because you're in the middle of so much information, You're helping

shape the debate, you're investigating things, so many interesting people. Um, Harvard's an awesome place to do that at. And so there are there are a few jobs that I've loved as much as I loved that one. It was incredible how you get that job. There's sort of a couple of things. One, there's a bit of a path um so,

it generally is a newsperson, a reporter. Um. So I was a reporter for my first couple of years, and then I was the head of the central what's called the central Administration beat that covers the president of the university. That's also kind of a traditional stepping stone. Then there's a process called the Turkey Shoot. Um. The Turkey Shoot runs for about a month leading up to Thanksgiving, where

they picked the next president. There's all sorts of sort of arcane rules, but probably the most interesting is that every outgoing member of the paper gets to vote, and if more than three disagree, you're blackballed, and so you resually hold in uh sort of and you know, in sort of in veto mode for as long as that goes. And so the deliberations generally run about teen hours straight exactly, and then and then there's a big party and whatever

sort of once the sort of unlocking happens. But I had I think six or seven opponents for the job, and uh, you know, you have to you know, a little politics, a little politics, a little message, a little of this, um, and that's that's how it works. But it was an amazing opportunity. So that's an unusual background as a journalist and someone who's publishing the paper to really being a data scientist for a financial services shop.

How did that career path unwind? Well, they're probably more similar than you think, because I mean a lot of it comes down to information, collecting information, using information, UM. And so you know, I've always been someone who likes to know what's going on, um, you know, what's going on in the world. I like to sort of be ahead of other people and knowing things, and so that's

the that's the similarity. But the you know, but the career arc was um, I went from from college to Mackenzie, was a business analyist there, uh, and then went to business school like you mentioned, and then ended up at Bridgewater. Um and which is also a fascinating place. Is a fascinating place. So I was there for six years, um and Uh, it's a phenomenal place to work. I'm a big fan of Ray Dalio. I find his philosophy just totally intriguing. I think Bridgewater kind of gets a bad rap.

People have called it a cult and have criticized the radical transparency. You survived there for six years. Can't be all bad, right, how to be pretty good? No, it's not all bad at all. In fact, I think it's you know one uh, you know as investors go, um, you know they're they're as good as it gets um, and you know, just phenomenal at it. And look, I think the differences of the culture there get overstated, um, meaning the radical transparency and meaning like how different it

is from you know. Look, I mean like you know, I would say everywhere I've ever worked McKenzie point seventy two, Domino, Bridgewater, Um, you know, they've all been ambitious people who are trying to get to the right answer. Who wanted to do great things. Um. And you know, like at core, like that's that's a lot of what Bridgewater is about. And you know, Ray and the team, they're are very thoughtful about ways to um, you know, just sort of apply

certain ideas. Um. You know, like you want to. You always want to make sure you're getting the best opinions right, And so they're very explicit about you know, who should you listen to about things? But you know, I see, I see Steve asked that question all the time, you know, like why am I listening to you? You know, I should be listening to this person instead. Um. And so I think Bridgewater is great at sort of scaling it.

But but um, but I think that the ideas are are not not quite as radical as the media would want you to believe. And then the transparency, Um, it's just great. I mean I love the idea. Yeah, I mean I was. I always just saying like it's a very clean place to live. And the reason it's a very clean place to live at Bridgewater is you just don't say things behind people's back. You just say things to their face. Um, and you're just He writes about that in his first book in a chapter where he

describes raise people problem. I mean, most founders and chairman don't spend the chapter describing the wrong people person. That's fairly trying its parent. Yeah, I mean I think that's fairly transparent. And and that's just how you're expected to operate, you know. I mean, if you're gonna say something about Ray, you say it to him. And um and I have many stories of of of saying things to Ray that

I think people would find not horrifying. There's they were me being honest and him and I trying to sort of work out differences. But you know, the only rule was just don't say it behind his back, and and that's you know, it's it's interesting that that's considered so radical, you know what I mean, It's not it's not that radical. So now let's let's take this phote. You'll end up at at point seventy two. Your title is Chief Market Intelligence Officer. I've never even seen c M I O

as a abbreviation. What does a c M I O do? That title was the title I had when I got there. Um, and I was really focused at that point on proprietary research. And so what we mean by that is how do we take UM data sets or surveys or web scraping or sort of all the different things you can do, UM and make that useful to our portfolio managers and analysts. UM. Since then, my job has evolved to include a couple

other things. So I also oversee our central book at this point, UM, which is our sort of a systematic best ideas book we have and also receive venture capital. And we just haven't really changed the title. Quite fascinating. Let's talk a little bit about big data and machine learning and artificial intelligence. Help me make a little sense about those buzzwords which have come into vogue for a while. But but your shop has been using these things for

for quite a while. UM. What's the state of the industry, uh, in terms of machine learning and big data and artificial intelligence? Well, I think the you know, the thing to sort of contextualize all those terms, UM. And you know, I agree with you, they're they're very buzzy. UM. But but the way I like to think about it as being model what I call model driven UM. And so you can talk about model driven businesses or model driven processes, and really the idea of a model is it takes in data.

It could be big data, it could be not big data. UM. It runs a certain set of logic on that UM and then it produces a prediction of some variety UM. And you know, basically it tries to close the loop around that data so that you know, you're constantly improving the logic or the algorithms. And so Netflix is a

model driven business intensents a model driven business UM. And obviously finance and and you know the hedge funds we're talking about there, you know, they're they're they're very model driven. What I would you know, what I would say is that, you know, the state of the industry, uh, in that regard is that UM, you know, these techniques are highly highly relevant to kind of almost everything we're doing, you know, whether it be extracting signal from data sets or you know,

all the way up to making trading decisions. Uh. And so you know, we're investing, you know, like a lot of hedge funds were investing a lot in you know, people with the data science capabilities and with the machine learning capabilities as well. So ron course Ferry you famously said, torture the data long enough and it will confess to

whatever you want. How do you avoid running into that problem of when you're building models and putting a ton of different quantitative information into it, how do you avoid that bad outcome of Hey, if we back test this enough and we make these tweaks, we could get this to say whatever we want. Yes, I think there's I think there's a couple of different ways you do that.

I mean one is UM. You know, you want to have a fundamental intuition of some variety around what you're doing, you know, I mean, you're not just sort of running everything through a machine and some some people do, but but not not. That's not how I like to do it. You're not just sort of running everything through and sort of seeing, you know, seeing what fits, because to your point, something will fit UM, and it may be a real thing or it maybe you know, a very short lived

thing UM. And then you know, you have to have a lot of discipline in terms of looking at your UM. You know it's called out a sample, uh sorry, basically in sample, out of sample and live UM. And what that basically means is where are you allowing yourself to to fit the parameters where you're sort of just looking at the results but still in a in a backwards looking way, and when are you sort of really trying

it out? And you know, we have very strict rules about how we segment those different things before we start, you know, using you know, putting money against a certain strategy, so and out of sample, just to put a little flesh on that. If you're testing on a large cap us, hey, let's see how the status in the past. Let's see how it does overseas, not just the area you're looking forward to see if it's really something to the model.

Is that a fair descriptor yeah. So let's say you were using um, you know, credit card data to trade Chipotle, you know, or something like that. Um. You know, what you would do is you would sort of, you know, you build some rules, um, and you would sort of fit those rules to some sub some set of data some time period, you know, three or four years. Then you would stop fitting the rules and you would sort of look at the next three or four years and

sort of see it, does that those two match. Do they look the same or is the behavior very different? And then you would and then you would basically start running the model live from today and then see again if those match the other two periods and so you're looking sort of for a consistency across that and if you're not seeing that, then that's a good sign that you're overfitting it. It's also you know, because going back to my original point, you know, you want to think

about whether or not there's a real intuition there. You know, I mean, should credit card and chipotle a make sense together? Right? It probably does because a lot of people use a credit card at chipole. But you know, if you were using uh, you know, credit card to trade ge you know, you might you might start scratching your head about what you're doing. Right, might just be a random correlation as opposed to a real causal relationship. So so let's talk

about some of these unusual UM data sources. I know, alternative satellite data is all the rage these days. People are looking at parking lots, how filled they are. They're looking at how deep transport ships are sitting, uh in the water, how far below the waterline they might actually be. How esoteric can we get with these alternative types of data? Well, I think you can. I think you can get quite esoteric. I mean I think satellite, um you know, satellite has

been around for a while and to your point. I mean, it's it's very widely used. Um. You know, you know what we think much more about now is um, you know, sort of much more specific data sets. UM. You know kind of that that give you, you you know, a read into a limited number of tickers, often via some sort

of payment system or something like that. UM. And uh, you know I think that we're I think we're just you know, we're probably in the third inning of something or something like that in the in the in the data movement in investing. That's that's quite fascinating. So let's talk a little bit about complexity. You know, we could go back a hundred years and just look at Graham and Dodd simple p ratio and more expensive stocks over time perform less well and have lower expected turns than

less expensive stocks. Are we running the risk of making things too complex? At at what point does complexity get outweighed by its own internal complications? Well, I think, um, you know, I think this goes back to the point I was making about, you know, about an intuition. Um. And you know, at the end of the day a

point of two. You know, we are we are fundamental investors, you know, we believe that Uh, that you know that companies ultimately, you know, trade on how they're doing as a business and the kind of cash flows they're going to produce UM and you know, everything we do, I mean, we will use very sophisticated data science to predict a revenue stream or something like that, but we're at core

trying to do something fairly simple. You know, we're trying to understand what the revenues are, what the costs are, you know, what the growth profile of the earnings are UM, and you know, we never sort of lose that grounding UM and so you know, look, there's a lot of ways to make money in the markets UM, and I'm only I'm not an expert in a lot of them.

I'm only familiar with some of them. But but for us, I think that grounding back to pretty simple principles U is very important and not something that we lose track of. It's interesting that you I think of you guys as a quant shop, but you keep referring to intuition. What's the intersection like between man machine? Is it really UM technology aiding human decision making or is it mostly hey, let's go and make the decisions and we'll just see what happens, so it points in me too. We do UM,

we do UM. We do a mix of of three things. We have a very large discretionary business that's global long short equity you know, people driven its portfolio managers and analysts UM looking at some subset of the of stock universe UM, meeting with management teams, looking at data sets UH, and then making decisions in a in a fairly discretionary fashion. UM. We also have a systematic business that's running on algorithms UM.

And then we have a people plus machine business, which is the one that I oversee, which is the you know what what what you call the central book earlier UM, where what we're doing there is we're looking at UM what the behavior of all the people is as one of the important inputs UM. But we're also looking at the data sets and we're running algorithms to essentially helped

make decisions out of that. So one way of thinking about it is that historically Steve had a best ideas book that he he ran as a discretionary investor, and over time we've built that up into a systematic best ideas book UM. But but a lot of the input of that is from discretionary investors and so UM. So you know, one of the kind of key questions we're always asking is what are the people best at and

what are the machines best at? And you know, our view, UM is that you know, in terms of of really being able to interpret fairly nuanced and complicated situations inside a specific company, that people are still um, really really good. UM. You know, there's other things that machines do very very well.

But you know, if you're going to meet with the management team and interpret a large set of data that that has a lot of sort of nuanced and specifics to it, UM, the people still beat the machines at that. And so we have a you know, we have several hundred people that do that. Do you see that edge of humans over machines continuing indefinitely or at at some point in the future, will smart um computers and artificial intelligence be able to do that also well? And definitely

is a very long time. So I'm gonna I'm not gonna I'm not gonna comment on indefinitely. What I will say is that our our thesis is a firm right now over the next call it you know, seven to ten years, is that UM, is that it is people plus machines UM, and that the people are very good at the nuanced situation, at the idea generation, at the interpreting the thin data at the synthesis UM. And that the machines are very good at conducting, UH, correcting for

behavioral bias at portfolio construction, at trade execution. And you know, what we're trying to do is figure out how you marry those two up in a really smart way UM. And that that is essentially the you know, the next wave of hedge fund but UM. But you know, like where where we are ten or fifteen years in terms of what people can do versus machines, I don't think I can comment on that quite quite fascinating. Let's talk about the venture capital work you guys do. UM. What

makes you different from traditional vcs? Well, I think a couple of things make us UM different than traditional vcs, But probably the most important is we we are extremely expertise focused in how we are designed, so UM, we

have no generalists. UM. We have certain practice areas. Right now, we have three different three or four different practice areas UM, all of which are led by people who have worked in that space and invested in that space for quite some time, and kind of one of the standards I use is, you know, when when when portfolio companies are meeting with the investors on our team, do they believe that the person they're sitting across from is the one

of the world's leading experts on the area that they're working in. UM. So that that's one difference. I think that the other different side point too is we're extremely outbound in how we operate. So one of our challenges was, you know, we don't have a we don't have a brand end NVC you know the way a sequoia does or something like that. And so, you know, one of the biggest concerns you gotta have in venture investing is adverse selection UH. And you probably don't want to be

taking what's coming through the door. UM. So you know, what we focus on is um themes that we think are gonna be big money makers, where we think real change is happening, where technology is is um uh is driving really important impact UH. And then we go try to find the companies that we want to invest in and knock on their door proactively, look extremely proactive. Almost almost all of it is an outbound motion like ninety eight percent of it UM and then UM, and so

that it would be the two big differences. I'd also say that UM, you know, you know, probably as firms go, our diligence is more intense than a lot of venture firms. I think that comes from Steve Um. You know Steve uh Um. Steve's one of Steve's sayings is do the work um. And you know, when we go into an investment committee to talk about something, uh, there's kind of only one answer, which is I did the work um.

Otherwise the meaning's gonna end very soon. And so we we hold a pretty high bar in terms of the amount of research we're gonna do when we're looking into a company. So there would be the three things I point to. So, once you decide to make an investment in a startup or an existing company, how actively involved um with the corporate management are you? Are you guys they're giving them advice assistance? Or is it more of an arms length here's some money, now, now go do

something great. It varies, but I would say we're fairly active. And the reason we end up being active is goes back to this expertise thing that I was describing, which is that, Um, you know, because the team is made up of people who are very deep experts, it tends to be that the entrepreneurs want them on the boards because you know, they're they're they're very useful and sort of sorting through the strategic questions and knowing where the

business should go. Um. You know. It's interesting because when we started out, I was actually, uh pretty really sucked tot to take board seats because I actually, you know, I think it can be a bit of a distraction from doing the next investment. UM. But it turned out it was an important ask from a lot of our entrepreneurs. So we do end up taking a lot of board seats, which means we're pretty involved. And we talked earlier about

the quantitative approach UM point seventy two. Often employees, how much big data do you bring to bear when trying to make a decision about either an area to invest in or a specific company. Very little, very little, very little. Uh. You know. Part part of it is the areas we're investing in. I mean, we're generally investing in enterprise companies uh in their early stage, and so you know, lots of times they'll have three or four customers UM, and there isn't a whole lot to sort of, you know,

torture the data for UM. Doesn't mean we don't do research. We do a tremendous amount of research, but it tends to be more interviews with people and UM, you know, you know, customer follow ups with customers and probing on how you know how a certain product works UM or

market sizing exercises or things like that UM. But we've not brought a lot of the of the of the big data to bear on on venture UM though I do think you know, in the consumer space there could be opportunities for that UM, and that that might be something we explore down the road. So this might be a little bit of a weird question. But how challenging is it two manage two distinct businesses with two very

different approaches. One is so quantitative and data intensive, the other seems to be a little more intuitive and subjective. Do you find any sort of when you switch hats? Is that a little bit different to get into that a little bit challenging to get into that different headspace? I wouldn't say so. I think the similarity between both of them is that in both cases. You know, we're very process driven. UM. You know in in the process

looks different in each case. But UH, you know, I'm I'm a very big believer and I think this comes from my my Bridgewater training UM in sort of process over outcomes. UH. And you know you have to you know, you have to think ahead of time about how you're going to approach a problem and why that's going to give you an advantage in uh in your approach um and on on on both sides of the business UM that I'm involved with. You know, that's how we how

we come at it. Uh. And you know when we have very elaborate uh sort of you know, predesigned sort of ways that we're going to develop algorithms, and we have very uh clear ways that we're gonna make investment decisions on the venture side. UM. And so for me as a as a manager of both of those areas, that's mainly what I'm trying to do is make sure that process is really solid um and UH. And and that's that's the similarity. How how significant uh portion of

the point seventy two book are the venture sides. So the venture investments are all Steve's personal investments. UM, so there's not point they're not well, I mean we use it's points a ventures, we use the brand, but it's not it's not in the fund. Uh, it's it's Steve's personal money. UM. And it's you know, it's it's not it's not super large. I mean it's a it's a couple hundred million. So now I have to ask the obvious question, if it's Steve's personal money, is there a

different UM thought process in terms of an exit. How does that pressure or how does that structure affect how you approach it or is it just a continuum across everything. And his philosophy is the same whether it's public or private investments. I think his philosophy is very similar across both. You know, he is he is an I r R focused investor UM. And you know he has a hedge fund that does well and produces a good return every year. UM. And you know he expects us to be the same,

to bring the same discipline to the private investments. And so you know, we think about I r rs, We think about exits, we think when we can get cash back out, we think about applying leverage. We you know, we think about all these different things UM. But but it all comes back to you know, producing a you know, a good rate of return UM and that's that's that's how he thinks of about the world. Quite fascinating. So you mentioned traditional UM forms of fundamental analysis. What what

metrics do you find important? Lots of people have talked about price the book, and then it seems to have fallen a little bit out of favor. Other people are looking at various forms of valuation. What's the most important fundamental approaches that that point seventy two is considering. It's

just very so widely. I mean, you know, we're trading you know, in the U S we're trading almost eight hundred names, and we also trade in Asia and Europe, and so, uh, you know, there's I can't give sort of a one size fits all answer to that question because there's there's so many different sort of subsegments. So following up on that, you have written that investing changes over time and it's the role of the portfolio manager

to adapt to those changes. How have you seen recent changes in the marketplace and what sort of adaptations do people have to make? Well, I think, you know, I think it's some of the things we're talking about them. I think the UM you know, the explosion of big data or what we call alternative data UM is you know, a big impact. Uh. You know, it used to be that most of the investing was a conversation between the investor, the company and the cell side UM. And now you

know you have UM. You know, just you know, whether it be credit card or geolocation or email receipts or all these different satellite like you were talking about UM. You know, all these different things that you know that you can you can bring to bear. So I think that's a really important trend. I think the other important trend, like we're talking about earlier, is is people plus machine. You know, what what are machines good at versus what

are people good at? UM? You know, machines uh, quite good at UM at repetitive math and complicated math, and UM you know have a lot to bring to bear in terms of portfolio construction and trading and and and those sorts of areas UM. So those are probably the two most important trends that that we're seeing and thinking about. Quite interesting. So you you talked earlier about the pursuit of alpha for a lot of the hedge fund industry, this has been a rough decade. Alpha has been hard

to come by. Lots and lots of other hedge funds have had a hard time meaning their benchmark. Two questions that come from that, what's behind alpha's um elusiveness these days? And what must elusive alpha? You haven't thought of that previously? And what do active managers have to do to stay relevant and at the top of their game? Yeah, well, Steve, Steve always jokes that he'd just like to go back to the nineties, Um, you know, when it was easy,

when it was when it was a lot easier. And uh and look, I mean, you know, success straws competition, that's just capitalism. And I think that um, you know, you know, I think there's not a whole lot of mystery to why it's harder. I think it's harder mainly because a lot more people are doing it. Um, you know, there's there's certain i'd say, sort of boogemen in the market, you know, like et F flows and things like that

that people also talk about. But but I think the core thing that makes Alpha harder is just, you know, the scale at which it all takes place today. Um, and you know, I think in terms of of of maintaining an advantage. UM. You know, I uh, I remember the very first time I met Steve, I asked him the question of how he had been able to sustain his fund for so long UM and he's at such a high level, and he said, well, because I've rebuilt

it four or five times UM. And you know, and and you know, the point he made is that this is just a constantly changing game that's always attracting competitors. And if you think that whatever success you have today is going to be true tomorrow, you are really naive UM. And so you know, there's it's part of what I like so much about working with him, And there's just a restless energy to him because he knows that that's what's required to continue to survive. And so that's how

we approach the firm we have UM. You know, always you know, tons of new initiatives and experiments going on, and things will succeed or you know, and things will fail and will kill them, and things that will succeed will scale UM. But that but you know, I think his his view, and I agree with it, is that it's that you know, it's that activity that's how you maintain an advantage um, because the business you know, in three or four years isn't gonna look anything like it does,

you know, three or four years prior to now. So Michael Mobison calls that the paradox of skill, that the success of the hedge fund industry and other sectors of finance have attracted so many intelligent, talented people that the easy money has gone away and it's becomes so much harder. Well, that's what makes it fun, right, I mean, that's what makes it. It's the you know, it's the competitive drive and the and the knowing that the bar is always

going up. Um. You know, it's that challenge that I think draws a lot of people to the industry to to say the very least. So look around at some of the other hedge funds out there, like the Show or Citadel or Renaissance Technologies, and they were pretty early onto the high frequency trading and other computer driven UH approaches. Is that anything that is UH in point seventy two's field of interest or is that something that hey, let the computer driven guys do that. You have your own

specific skill set, So we don't do any high frequency trading. UM, we do a fair bit of computer driven trading in our systematic unit, and then in some of the units I oversee their systematic as well, so driven by computers. Um. But uh, but but nothing that would constitute high frequency um. Uh. You know, it's certainly an area where a bunch of people made a bunch of money, but it wasn't something

that we did. One of the things I didn't ask you earlier but is relevant here is the Domino Data Lab. What was the thinking behind that? And how have you used that experience at Bridgewater and at point seventy two? Yes, So the thinking behind that was really sort of two big ideas. One was that we were moving to a model driven world, um, where you know, we're algorithms that were trained, fed and trained data that made predictions or

decisions for businesses. That that was going to be a very important um thing that took place, and so you know, you see the rise of Netflix and Amazon and all these things that I would call model driven businesses. Uh. And then the second sort of big idea was that, um that as that happened, the people who did that work, the data scientist needed a system of record. So salespeople

work in salesforce HR people work in work day. There was not an equivalent for data science, and so we were building and in our building, uh, the system of record for UM for data scientists and and those were those were really the two big big ideas behind it. And whatever happens to Domino Data LAMB. Does it still exist? It still exists doing great UM you know, just you know,

continues to grow leaps and bounds. I'm on the board. UM. It's still an independent company, still an independent company backed by Sequoia and COT primarily UM and some others actually including Bloomberg, Beta, UM and uh, you know, and it's uh, it's it's been. It's been very successful. And probably one of the most interesting things about it is just the diversity of industries now that are representing the client base.

You know, it started out a lot of finance firms, insurance firms were interested, but now we have everything from retailers to grocery stores, to auto makers to pharmaceutical makers. Because you know, basically the thesis we were betting on was that the world was going to become model driven. And this is a tool set. This is a tool set to help track how effectively you're deploying your model.

It's a it's a tool set that um you know, basically, data scientists build their models using the languages and tools they want in Domino, and then Domino revisions those things. Means they keep track of the data and the code and the results, and then you can also publish out so you can run models from that, and so it's sort of the your central repository, your system of record for models. Quite interesting, and I keep coming back to

the idea of of man and machine. When you're evaluating talent, be it a startup management team or a a potential higher or a portfolio manager, how much of that is data driven and how much of that is your own human intuition? Well, in in people processes, you know, look, I think there's still a lot of human intuition into it. Uh. We we do try to be as rigorous and as systematic as possible. And what I mean by that is, you know, we we try to start with the job

and the outcomes we expect. And as you think about those outcomes, what capabilities are required? And you think about those capabilities, you know, what's the best way to evaluate those capabilities? I personally don't like interviews. I don't think they're particularly useful. UM. I think that work samples and projects and these sort of and more testing and those sorts of things are very valuable. UM. But you know, obviously there's also you know, you do need to meet

the people. And that's that's a part of it. Um by it for us, the hiring process or the evaluation process of people adventure, UM, you know, just has a certain methodical nous to it. That's that's very important, quite quite fascinating. We have been speaking with Matthew Grenade. He

is the chief market intelligence officer at Point seventy two. UH. If you enjoy this conversation, we'll be sure and come back for the podcast extras, where we keep the tape rolling and continue discussing all things quant and hedge fund investing. We love your comments, feedback and suggestions. You can write to us at m IB podcast at Bloomberg dot net. Be sure and check out my daily column at Bloomberg dot com slash opinion. You can follow me on Twitter

at rid Holts. I'm Barry Ridholts. You're listening to Masterson Business. I'm Bloomberg rad Ye. Welcome to the podcast. Matthew thank you so much for doing this. UM. I've been looking forward to this conversation. I have followed Stevie Cohen's career from AFAR for since the nineties, and I find him to be an absolutely intriguing individual, both as a investor and an art collector, and a person who has managed

to um thrive despite a lot of really fascinating challenges. So, UM, when we first made contact with your office, I was really excited about this. UM, so thank you for doing this. One of the things we did not get to talk about during the broadcast portion was the OpEd that you and Steve wrote in the Wall Street Journal. And UM, it's not software is eating the world, it's models will run the world. Tell us a little bit about that.

So so Mark Andresen wrote a piece several years ago and called software is Eating the World, and it's basically the idea that software is going to change every business. UM, and Steve and I were thinking about, you know, kind of what's the equivalent today, because I think that was

written almost seven or eight years ago. Uh. And you know, the thing that we zero in on was this idea that that really models we're going to change the change the business landscape and you know, you know the idea of a model um, you know, think about Netflix, right.

So I think Netflix is a great model driven business where you know, eight percent of the content consumption there comes from their recommendation engine, right, and so basically what they're what they're trying to do is they're trying to build the best recommend or possible. There you know, you're signing up, they're taking in data about you there, you know your zip code, and but then they watch everything you do. They watch you know how you um, you

know what shows do you jump on right away? Which shows do you finish? Which shows do you not? And that lets them recommend better and better content for you. And then basically at the core of their business is this engine that's that's you know, holding or basically recommending content for you, um that you're going to enjoy more and more. And now they're using that same data in

that same approach to build content as well. Um. So I think we think about that as a model driven business and it's a it's a really sort of powerful mode because once you get the loop going where you're collecting the data and seeing the outcomes that you're driven, you're driving you can make the model better and better. Um. And so you know we in the in the out ed what we talk about is uh, some public and

some private companies. Um that Uh you know that that our model driven and and and some of the implications of this trend um and um and so Yeah, it was a fun piece to write. Yeah, and it's still available if you anybody wants to go see it. Models will run the world. It's in the Wall Street Journal. UM. So when you see something like and Reeson's peace, Uh, software is in in the world. I want to say that he's half right. Software had started to eat the world.

But we run into problems all the time. That software, it only gets you half the way there. And and the entire infrastructure of everything from the hardware to the network too, everything else that's involved has to work seamlessly. Doesn't quite feel like we're in the future yet. How do you am I overstating that or how do you how do you perceive the world where you know, a robot butler doesn't take you to work each day, but

it's not too far off in the future. I can't remember who said it, but somebody said, uh, you know, the future is here. It's just unevenly distributed, you know. William Gibson, Yeah, I think there's I think there's a lot of truth to that, you know. I mean when you're in uh, you know, San Francisco and you you know, you see the self driving cars that you know, Cruise and and Google and others are making. Um, you know,

then that that feels that feels very in the future. Uh. And then you know, like you said, you look at some other industries and you sort of scratch your head about you know, why can't I get a good cell signal in Manhattan? It's exactly why point why can I maintain the still signal on the train back to back to Connecticut? Um? But um so I certainly, I certainly agree that it's it's unevenly distributed. But but you know, there's also a tremendous amount of very exciting things happening.

Um and uh And and look, that's what makes the venture investing so much fun, you know, is seeing all that and being involved in that world, having that view of upcoming technologies. How does it affect the way you look at the world of existing public companies. That's a

great question. I look, I think, um, uh, you know, it makes you um much more skeptical about their advantages and about the durability of their um of their moats quote unquote right uh and um, you know, you look at how fast the change has happened in retail and how and how deep and dramatic some of that took place, um, you know, and you go back and you look at some of these companies and all the moats they were talking about and the customer loyalty, and then you know,

um and so you know, one of the things we try to do at at points of ME two is we we try to sort of cross pollinate some of the big thematic learnings um from the venture work in

with them, in with the public market investors. We had a dinner a few months ago on robotics UM, and we had through four CEOs of robotics companies, and we had our industrial a couple of our industrials pms, and our healthcare pms, you know, and it's essentially a discussion, you know, exactly along the lines you said of you know, how is how is robotics going to And obviously there's gonna be a bunch of private companies that get created, but it's also going to really change, you know, in

those two areas. You know a lot of companies as well. How often do you guys have dinners like that? It sounds like that's an intriguing evening. We do them about once a month. We're doing one tonight actually um and uh um, you know it's what's the topic tonight tonight? Topics actually talent evaluation. So Angela Duckworth is going to join. Um wrote a book, Grita. Have you gotten to Have you read that yet? I have read gret And how

do you like it? I think it's great. It's been at the top of a number of people's lists for for quite a while. Yeah, I have a you know, I think it's a it's an interesting way to sort of think about, you know, why people are successful. Also, as a parent, it's something you know, you you you think a lot about, uh, you know, what can you actually teach your kids? And you know how and and you know, probably at the top of my list of

things I realized my children to have and to learn. Um. And so we have we have rules now about sticking with things and stuff like that, largely because of her books. So that's that's quite fascinating. UM, I could talk about this stuff forever, but I know only have you for a finite amount of time, and I wanted to get to my favorite questions. UM so let me jump right into this, So feel free to answer these as longer

as short as you want. These are pretty straightforward, um, but they usually are a little uh insightful into who you are. Tell us the first car you ever owned, you're making model? It was a Volvo S forty two thousand, sort of, the two door with the hatchback. Is that the one I'm I'm thinking of it? Four door? It was? It was it was a new model year. Um, so, yeah, it was a four door. It was blue. What's the

most important thing people don't know about Matthew Grenade? Um. People are usually surprised to learn that I'm from the South, Um, you know, having gone to Harvard twice and worked at hedge funds and things like that, and uh, and my family has been from the South from for a very long time. Um. You have the slightest wisp of an accent, but not a heavy the slightest whisp. And then you know, and and and I think, uh, you know, certainly affects my my manners and that kind of thing. So are

you a courtly southern gentleman? Is that I wouldn't go that far. But but but but my my my mom raised me right, she would say, so, So, tell us about some of your early mentors. Who are the people who helped guide your career. Yeah so, um uh so Bo Jones, who was publisher of the Washington Post. Um he had been a president of the Crimson as well.

Um he uh. I worked for him for a summer and uh, you know, one of the things, one of the things that a couple of things very interesting about working from one was, you know, he and and Don Graham um in the Graham family in general sort of really understood the ecosystem of their business well and and

sort of how all the parts interconnected. Um uh in the in sort of you know, the basically how the subscription revenue, um you know was important, but you didn't want to You wanted to make sure that you kept that price low enough. You have the advertisers, and they'd a very holistic way of thinking about the business. And then the second uh thing that I thought they you know,

they're very principal based leaders. Um. You know, a new a newsrooms of place, things can run quite a mock and and the Washington Post has the backs of their reporters and that was always interesting to watch. UM. Another would be Tom Barkin, who Um, Tom uh is now president of the Richmond Fed UM and on the FOMC at the moment, but he was a very senior partner at McKenzie UH and one of the people who I worked with the closest and most when I was there

right out of college. UM. And you know, I think the thing Tom taught me was the uh sort of seeing the essence of the of a problem. UM. You know, when you're when you're first out of school and and you can think of a two thousand analyzes to do, you know, let's do all these things. And Tom Tom was great at knowing what the what the right question was to ask and the and the right one to answer. So what investors influenced the way you look at markets

and your approach to deploying risk capital. Well, look, I mean it's it's really the two I've worked you know, closely with. It would be it would be Ray and Steve Um. And that's quite a pair of mentors his uh. You know, with Ray, I think UM sort of two big lessons. One is um um being systematic, being process driven that you know, you don't you don't look at outcomes, you look at how you got to those outcomes. Uh.

And then also being fundamental um. And you know, as we're talking about earlier, in the world of data science, you can torture the data to say anything, and so you really have to think about how the how the world actually works and why what you're finding matters. Um. And then with Steve UM, you know, it's it's the sort of tenacity to to really dig in and do the work, you know, which, as I mentioned, is one

of the things he he says over and over UM. Uh. You know, you you don't go talk to Steve about a name or a venture, investment, or a new strategy without having sort of turned it over a hundred different ways. Um. And you know his bar for just having you dig deep is very high. Um. And Uh, there's probably the lessons I've learned most from those guys. So we mentioned, um, grit tell us about some of your favorite books fiction, non fiction, FINANCEI related whatever. Yeah, So, UM, I mean

some of my favorite books of all times. Uh, let's see. So and just so you know, just so you know, the feedback I get on this question is consistently the most asked about question, and people say to me, I'm always looking for a well thought out suggestion for a book, and it's my favorite question you ask people because I've created a reading list off of that question, so it's not just a random Hey, what are you thinking about the books people recommend? Other people say, he seems like

an intelligent guy. I want to read the books that he likes to read. So I'm just I'm just annotating before you. So let's try to do three from fairly diverse areas. So uh, so you know more finance data science. E. I love super Forecasters, which you know is basically tetlock, which talks about how you, you know, essentially get good predictions. And he's spent his life studying how you get good predictions or someone in the markets. You know, it's it's

it's critical. Um, then let's go outside of investing in financing those sorts of things. One of my favorite sort of historical books is Wild Swans UM Wild swan Swands, which chronicles the life of three women in China and the twentieth century. UM. I think I think China is such an interesting story because it just you know, it's, it's,

it's there's been so much dramatic change. And you look at those three lives and uh you know, uh, you know, one of which is a fair bit of which has been on the cultural revolution, and you sort of think the world you're living in is the world you're living in, and it can just change so dramatically. I want to make sure I have the right book Wild Swans Three Daughters of China by Jung Chang. Is that it? That's it quite interesting? Uh. And then we'll go for a classic,

uh I Um, I love The Tempest by Shakespeare. Um, and uh you know it's where I me there's a lot of things goes on go on in that book, but uh, that's where you know he he wrote, you know what was past his prologue, UM, which I think is really true. The past is prologue could really be the slogan for anybody who creates models. So so that works out. That works out pretty well. Also, UM, tell us about a time you failed and what you learned from the experience. There's been a bunch, but uh, you know,

well I'll do this one. So um, Before we started Domino Data Labs, my co founders and either two of us, three of us total, all all of us from from Bridgewater. We started a previous business called Cerebro UH and Cerebro was in the talent evaluation space, and so I was trying to sort of figure out smarter ways to help

companies assess their talent. And we had some great clients UH in tech, mainly technology firms UH, and we mainly had leaders from the business lines, and so we would sort of do this work, they would love it, and then we would get past to the recruiting department and the project would just die. And we did this like over and over and over again. UM. And what we finally realized was realized a couple of things. One was that at a micro level, that the incentives between the

recruiters and the business people were very different. That the recruiters wanted to put people in seats and that the UH, and that the business people wanted to have great people

in those seats. But then more deeply, what we learned was that we actually had no idea what we were doing UM, and that you know, that we were really trying to build a business in an area that we weren't experts in and that you you know that that is starting a business is just so so so hard in like a thousand different ways, and uh, you know, and so you have to you have to take advantages where you can. And so what we uh, you know what we've we started asking ourselves, so what do we

actually know about? And in those areas of what we actually know about, where are their actual problems? And that led us do Domino in the data science space. So so you come from the school of Ray Dalio's use failure as a learning experience to improve your next uh, your next attempt at whatever it is. Oh. Absolutely, so he told me a funny story about the inside of his uh of his book with with the failure cycle, and someone who will remain nameless said to him, Ray,

what sort of signature is that? They obviously hadn't read the book, but quite quite hilarious. Um, so tell us what you do for fun when you're out of the office. Would you do to kickback, relax, have a good time. Um. I like to cook um. And this is like going back to being from the South. So my my grandmother taught me to cook um and uh, and so my wife and I will throw parties and we'll cook, in particular fried chicken and things like that, and that's probably

what I enjoy. You work off a cookbookers at all grandma's recipes, it's usually a combination. Um. So I like to, you know, kind of mix in some more modern cooking with some of the more traditional recipes. So give us a few dishes. Uh. Well, you know, a traditional dinner party would be, um, you know, fried chicken with macaroni and cheese and biscuits and blueberry cobbler. But southern, real southern cooking. But I'll also do you know, like maybe

some molecular astronomy with like a watermelon drop or something. Um. So you gotta keep it, keep it modern. But um, did you see Nathan Revold's Get gast Row cookbook? I have all those it's supposed to be a fascinating Have you tried any of those dishes? So he has so he has his his five volumes, five or six volume set that's very intense and completely overwhelming. And then he has a home version, um, which I have done a couple of things out of the home version. Do they work?

They work? Um? But he's he's he's much more serious than I am. So he's he's he's very intense yet to say, to say the least. So what are you most excited about within the financial industry today? Well, I think you know the thing that the most interesting question right now is the people plus machine question. You know, what, what are the people good at? How do you the most out of them? How do you um uh, how do how do you think about those capabilities? And how

do you couple those with what machines are good at? Um? And I um, you know, I think that, Like I said, I think the next generation hedge fund is going to be a mixture of those two things and um. And that's a it's a really it's hard in a lot of ways, but it's a very exciting question. So a millennial or a recent college grad comes up to you and says they're interested in a career in either investing

or quant what sort of career advice would you give them? Well, I'm not sure it would be so specific to any field. I mean, I think, uh, I think the career advice I would give and I'm I'm not a huge fan of giving advice, but since I'm on the show and on the spot. Um. Look, I Number one would be, um, hm, set your goals as preposterously as you can set them. Um, you will continuously surprise yourself and what you can do. And UM I think uh, um you know so so

aim big and dream really big. Um. So that would be one I think. Second, Um, the is work hard. Um. The you know, no no one I've ever met, uh doesn't know, no one, no one who I've ever worked for, you know, Ray, Steve, these guys, none of them are slackers. Um. You know, I mean Steve starts every he starts the week on Sunday morning at um and you know, uh, and and that's when that's when the then he works all day Sunday and he works a fair bit today Saturday and so um, so you know, I think I

think it would be to set really almost preposterous goals. Uh, you know, be willing to work really really hard. And then I think the third would probably be, um, you know, love what you do. Um. I've also never really met someone who was successful who didn't really love what they did. Um. And I think you know, Steve Jobs had something that he said I think in the Stanford commincement speech. It's like if you haven't found, if you haven't found what

you love yet, just keep looking. UM. And I think that's I think it's right. I think all those things are true, good good advice. UM. And our final question, what is it that you know about the world of investing today? Did that you wish you knew twenty years or so when you were first getting out of college? Well, stay long, Microsoft right, that that was a good time to not not panic, right exactly, But I mean we're

as opposed to crystal ball, more processed. Absolutely. Look, I think the UM uh, you know, I think I think one of the most interesting things is just how different um,

different periods of time will feel and be UM. And this goes a little bit too, you know what has passed his prologue and using history and things like that, you know, I mean, UM, when you know I graduated in from college in two thousand, you know that was the just the bubble was peaking and UM and the tech bubble, and that sort of felt one very one certain way. And then you know, you get to two thousand eight and you're just in a very very different regime.

And I think, UM, I think the differences between these regimes and how what's gonna work in these regimes is quite different. Um. You know, you really have to kind of get your your head around that, um and and kind of really appreciate that quite quite fascinating. We have been speaking with Matthew Grenade. He is the chief market intelligence officer at Point seventy two, where he also oversees their main book as well as helping to manage their

venture capital business. If you enjoy this conversation, we'll be sure and look up an inch or down an inch on Apple iTunes, overcast at your Bloomberg dot com wherever final podcasts are sold and you can see any of the other let's call it two dred and forty or so past conversations we have had. We love your comments, feedback and suggestions right to us at m IB podcast at Bloomberg dot net. I would be remiss if I did not thank the crack staff that helps put together

this conversation each week. Medina Parwana is my producer slash audio engineer. Taylor Riggs is our booker. Attica val Broun is our project manager. Michael Batnick is my head of research. I'm Barry Riholts. You've been listening to Masters in Business on Bloomberg Radio