YouSaid · the spoken record

Michael Recce

lines on the record: 62
first: 2018-06-12
most recent: 2018-06-12
sittings or episodes: 1
sources: podcast

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“I do have exactly the kindest thing, and it's a bit of a story. So when I was growing up in a working class background and I had started in school because my older brother was in the remedial thread, they naturally put me in the remedial thread. So the first few years of my education, I was actually in the remedial thread. So you were in these large classrooms and they couldn't even try to teach you. So I would read lots of books. And I was reading this book by George Gamoff called One, Two Three Infinity. And I was trying to approximate pi using circumscribed and inscribed polygons of increasing side. And I had worked something out and I went and asked my math teacher and he lifted me out of the remedial program and put me in the GIFID program that day. And then I arrived in the GIFID program and there are all these kids in the gifted program who had been in there for their whole career and they were a little bit sort of comfortable. And I was just like, wow, someone wants to teach you something. And it really got me accelerated in terms of, I mean, there's someone teaching me something. And the more I work, the more I got taught. And, you know, I'm on where I am today because”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, we've heard a lot of ground. I mean, it's been great fun. I appreciate being here. Now, I think I'm going to go and talk to university students. And one of the things I tell to university students is that they shouldn't leave university unless they can code. I don't care what they're studying. We're entering a new world where we're going to see AI everywhere. And coding is like the most powerful thing being able to make stuff, being able to make stuff with just you and a computer. And it's a very useful skill across all industries. And so I think that there's a, we'll Gibson quote, which I think I used with you before, but which I love, which is the future is here already. It's just not uniformly distributed. And the key thing is, the thing which excites me the most is it's not very often where you get to see the future before it happens and you get to sort of position yourself, your surfboard, or whatever, to try to take advantage of the wave. And I think that this is clearly an early stage of something which is going to permeate through Wall Street and change the whole industry. And it's just exciting to be able to see it before it actually...”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Company does. And so when is that Y combinator company going to grow and essentially disrupt using the new cloud-based technology and replace you name it, you name the industry, you name it. One of the things we're interested in is doing something like take every single sector. I don't care restaurants. How could they could be using AI? So which names in that sector are hiring, posting jobs and hiring people that most look like the future. Just as an example of a signal of who's going to win because leveraging either the computing power or the machine learning or the data science stuff, you name it, whether it's menu optimization, inventory optimization, everything you can imagine, which is going to be leveraging a lot of this machine intelligence stuff. And so I think it's going to permeate through the industry. And we'll almost have, when we get a little further down this path, almost an index by sector of who the leader is and rank the sectors by which sectors are actually changing most quickly to the future.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“I certainly think that Wall Street is in the top half of the distribution, and maybe it's in the third quarter rather than the fourth quarter. There's lots of industries. I mean, I saw this great presentation, which showed how you build a house 150, 200 years ago with wood framing and how you build it today with wood framing and how factories looked 15, 200 years ago and how factories look with robots now. Clearly, the construction industry is a relatively late adapter to how to use automation and so on and so forth and some technology. And so there are going to be lots of industries which are still really quite backward in how they use data science and technology. The incentive system in Wall Street actually will keep the firms actually nearly leading, but they still become conservative because certain types of investing, they don't want to change their process because if it works, certainly don't change it. And so there's a resistance to change. I think obviously the internet companies are in the forefront. And I think one of the risks is that there's a white combinator company that does almost everything in bricks and mortar.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“It's actually largely what we talked about today. I mean, I talk about lots of examples of, probably get in a little more detail, which I can, about examples of the way that internal teams have leveraged the data and the way it could be leveraged in a quant process in a little bit more detail. But essentially compare and contrast the way that the world was before without the data and the way the world is with the data. Again, it resonates. I mean, the clients get it, and the clients are very enthusiastic about seeing the firm take up this path.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“I see this happening. And because the thing is that sometimes the investing firm is going to be conservative with a small C, and they're going to want to keep doing what they were doing because it's a classic disruptions, classic Clay Christensen. You just keep trying to do what you're doing and the disruptor comes in and does something completely orthogonal to it. And so I think that part of this dynamics is going to be driven by clients who are trying to find the places where they're really being smart about how they're leveraging data.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Before the buy side, because essentially the information, if it's not coming directly through them, it's going to come through someone else and they're going to get disintermediated. And so I think the risk of being disintermediated in the sell side and missing the boat is much, much worse than in some of the buy side shops. And certainly, and we are already seeing it, the buy side companies that are winners are also going to change because the ones who are most quickly to adapt the data process are actually going to be, and the thing is, interestingly, people get this. I do lots of client presentations also in my firm. And one of the recent presentations, one of the investors said, which of the investing teams use the data the most? Literally, it's going to become a force from that direction because people are going to say, I get this. I get how this is an advantage. And let me go and hunt around and listen to the story of who's actually leveraging this type of information and the success of the firms might be driven by the clients getting up to speed and figuring out, look,”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“A couple of thoughts here. One is when I joined the Internet Advertising Space in 2009, all web pages had static ads, like a newspaper. Everyone saw the same ads who went to the page. In 2014, we crossed the point where the majority of ads you see are dynamic, which involves thousands of companies participating in auctions in a tenth of a second and dynamically assigning the ad to your page. Huge change in technology, huge change in the companies involved in that six years. And that's for ads, which costs as much as a grain of rice. And so on one hand, you might think, well, look, there's much, much more opportunity in finance to do this right. And so, you know, is it going to be six years and the whole thing's going to change? But I think that there are people who are successfully investing with other strategies. And I think it might, I've been debating to myself whether it's going to be faster or slower than that, but I don't know, 10 years would be a long time for this change to occur. The other thing is, I think that people like the sell side might be affected.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“I have to say that when I worked at 0.72, I was very encouraged by the enthusiasm the firm had for actually getting involved in data. And I think that other firms are much less convicted about how powerful the data is going to be. And so I think that's impressive. Obviously, Two Sigma is in WorldQuant are examples of firms that have been investing in lots of different data sources for a while. I know there are several firms that have recently been working on building large teams. And I interview lots of folks and I see where they end up. Certainly Citadel Millennium are doing that sort of thing. When I first interviewed with lots of those firms, I think lots of the firms were actually still in relatively early stages. And I don't know what's happened in the intervening few years, but presumably they're moving along. But I think it's going to, I still think it's very early days.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Read out of the data instead of reading into the data. And there are many of these sort of moments of epiphany where you figure out that's what's happening.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Tell you, and I'll give you an example of that from way before I was in industry when I was in university, people would come with questions and we work on projects for industry. So Sainsbury's supermarket in the UK had come, I think it was Sainsbury, it might have been Tesco. They came with a data set and they wanted to print coupons. And they said, look, I want you to look at what people buy and determine their life stage, you know, single, married, all the way to retired and lifestyle, which they meant by wealth. And then figure that out, and then we'll figure out what's coupon to print. And it turned out that we did that for them, but the two parameters which were most predictive of their shopping were different. The first parameter was, were they immigrated to the UK from? This was in London. And the second parameter was how long they'd been in the country. And those two parameters actually were more predictive of their shopping than the other parameters. from what your top down point of view was and you want to”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think the moment I was talking about with the honeymoon period versus the organic growth rate of lots of stores, that's another one. Again, I think there are lots of examples where people haven't realized how easy it is to get certain data, and they've literally walk by data that's in the world and ignored it, and they're using a less useful source of information when the other sources are available. And I have to be careful about it. Some of them, because I want to protect people. But to get back to your question, which I like, in fact, I ask that all the time in my interviews also is I say, well, look, if you're in data science, there should be examples of things that were surprises where you started with one thesis, and once you figured it out, you have a completely different point of view. And I usually ask people in an interview to tell me about one of those moments, because if you just do engineering and you build something, you can sort of think it as a semi-mindless process because you're just building this thing and you started with a design and you just finish. But data science is not like that. You basically start with some hypothesis and you go in and you look at what the data.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I mean, I think the one I described about looking at the so if you think about that Pareto distribution at Walmart, it's a great example because the other thing is everything's Pareto. It's like the stores, which stores are doing well. It's not just which customers. It's every aspect of the business. And they're all power law. And so I think that is actually, it just speaks very strongly to why you want to have one level more granular in your information, which is really what the data does. The data just gives you one level more detail. Again, elaborate your spreadsheet with that extra level of detail and populate it with the data. and then work with the discretionary person to figure out what it means in terms of valuation.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“With the right discretionary people who can think about that process in enough detail to sort of drill down and say, what does it really look like? What would it look like? And where does the data come from that actually allows me to see that?”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“The outcome variables I'm interested in is literally who's going to win in the marketplace. And I think that what happens in the marketplace is at each scale, I mean, having been at startup companies, different scales look completely differently. There's a great book called Crossing the Chasm. Great book. And so that process of actually understanding who's breaking out and who's winning at what scale and how you measure it, which parameter you use for determining who's winning at what scale, that's really where the predictive power is going to come in. And again, and if they're winning in a microcosm, they could easily spread it across other geographies and so on and so forth. And really, it's understanding this gets me back to a discussion I've often had with discretionary folks. I say, why do you like the company? They say, great management. So what does that look like? Does it look like better cost efficiency? Does it look like new products? Does it look like new geographies, different types of people being hired? If you tell me what it looks like, I can go get data and we can see it. But if it just sounds like good management, I don't know how to go there. And so that's why it's building a bridge process.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“That's right, and be able to use the tools to answer it quickly. And that's what happens with any progress in science. If I give you a higher resolution microscope, it doesn't tell you the answers. It just allows you to do experiments faster and experiments with higher resolution. And so that's really what these methods are doing. And what people who are adopting the new methods should see is that they should have a higher cadence of being able to ask a question, work faster, work faster, and break things. And it's exactly that thought process around build lots of things. Try them. But build the things and work on them that you believe in, not just spraying and praying.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Enough to find out why it didn't work. And so any method where you're just spray and prey against everything is a waste of time.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“And lots of people in the machine learning world are doing the same thing. There's a company that literally just uses computing power to try every machine learning method on your data and you pick the one that works best. That's the problem of data mining and it's just laziness in terms of thinking. Now, if you have an idea for a signal or a way that you think something that should improve the data, there's a very important thing that happens. When I used to try to find money laundering, you're finding outliers in transactions. They don't transact like normal people. But they're also in that same distribution once you've separated out those outliers. You also get all these horrible bank errors. And so if you just look at the performance of the whole thing, you're combining the bank errors with the really good ones. You might throw it away, say, oh, it didn't work. But if you can partition, maybe your sigma increased, and because you actually have both of these two things groups being found by your outlier detection. If you can partition away the ones that are bad from the ones that are good, you actually have a phenomenal signal. And so you should start with something you have conviction that should work. And they should stick with it, not analysis paralysis, but stick with it.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Touched on that a little bit, but we'll come back to it. But one of the things I would point out is that I was reading a review of Udea Pearl's new book. And so he's one of the developers of this area of Bayesian inference. So correlation is not causality, but there is now a whole area of statistical analysis, which actually measures the causal influence. And so the data mining thing, which is talked about all the time, there are straightforward mathematical methods which can help you avoid the risk of just finding spurious correlations. And the other thing I would just say about the spurious correlation stuff is that”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“And let's say I make 50 to 100 BIPs more than the index. But if you're doing this entirely with a Zillow driven process, maybe it only costs 10 bits to run. And so the question is, what happens in that world? And so I think that, again, if you, it's very hard to make an AI that's as good as the best valuation guy in property or otherwise. But making something which is automatic, which actually makes things a lot easier to manage assets. And then you might end up with a pendulum swing of the money that moved to passive back to active management because now you have this AI-driven active process which has relatively low vol, essentially follows the index, but just tracks a bit above it because it's finding the negative ones in the process, finding the negative ones, because that's the easier problem to solve based upon this higher degree of granularity that the data's providing and that you're getting through the machine learning process.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Let me connect the dots. Let me connect the dots. So, one of the graphs I've seen recently, and I forget which of the research shops that came out of was plotted the number of years that stocks that have been in the S&P 500. Yep. Yeah, that's right. And essentially, it's going mostly monotonically down. And it used to be 40, 50 years, and now it's down to like 12 years or something. And then trillions of dollars a year are moving to passive still. So essentially, you're buying things which are less likely, lower probability to be winners because the temperature, if you want, the churn in the S&P is actually increasing with this decrease in time. So if I can use something like my calculation, which I talked about with Walmart, to determine who we're going to be the losers, or if I use any of the process we're talking about of predicting who the losers are, then essentially I can still be market weighted. I can still essentially have an index like thing. People say, well, is this smart beta? It's not smart beta. But to have an index-like thing where I underweight some names and overweight other names.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“In terms of trying to figure out the nature and way that the business is actually developing. One of the things I would say is, so I think there's obviously lots of different trading strategies. I didn't really answer that question fully you asked earlier that we're going to use data. And obviously trading on events is certainly one of them. But I think another one of them is what's the end point of this company? Is this a small company that's going to become a big company? And that's going to have to do with how they're expanding geography-wise, how they're expanding the nature of the way they're interacting with their customers. And I think the data is really going to help you pick the long-term winners. One of the things I've been saying recently is that I think that the asset management world where I live now might actually be become the leader in this space as opposed to the hedge fund world. And partially because they're less siloed in terms of the intellectual property and partially because it's a different game, which the game you're trying to play is an incremental advantage over a large amount of assets instead of actually trying to be the absolute highest return for a teeny fraction of the investment.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Think the sustainable advantage is going to be the death process of the data to get information, and then the extent to which you use multiple overlapping data sources to get confirmatory views to actually develop a stronger point of view. And so again, it's residue. So it has to be incorrect in lots of ways. And so you have to detect where it's incorrect. The more that you bring in lots of different sources of signal. And the other thing is that everyone's also looking at top line. What about costs? Looking at job postings or other stuff. Job postings are a big component of OpEx. They're also a leading indicator. How many people get hired based upon background checks from people who are doing background checks? What the cost of the commodities are or the raw materials? What's the cost of chicken going to a KFC, right? Or to a buffalo wild wings. And so a lot of the early stages, just looking at the revenue side of the picture, but that's certainly not the only thing which drives the business. The other thing I would say is that there's lots of advantage.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“That I want to find all house numbers in France and they'll pass out or something. So I would encourage this person at Citadel to start engaging in how to use those technology tools and move away from the ones that their comfort zone in order to be able to process larger data sets.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“And particularly if you're doing it with Lambda processing on AWS or some, you know, there's just some opportunities in computing a few hundred million rows is a hard problem with old technology. And there's an example which I often cite from new technology. Because in the bricks and mortar technology, the first job of the technology team is to make the trains run on time. The second job is to keep it secure. Distant third job is to stay up with technology. In the internet space, if you're not up with technology, you're dead. So superimpose that on a rapidly changing technology. And the technology in the internet companies is much, much farther ahead. And I can give you some very concrete examples of that. But, you know, an example is a blog from a French AI person at Google, and he was talking about finding the house numbers in France. So whereas the house number in the city, where is it in the countryside? How do I deal with lighting and occlusion and so on to try to extract the house number, runs a job overnight, finds from Google Street View, all house numbers in France? You tell that to an IT guy in a traditional bricks and mortar.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, first of all, I would say that I still think that the folks who have both experience as a discretionary investor and have math and computer science statistics background are relatively rare. In this new form of data investing, they're going to demand a premium and they're going to be really hard to find. And if you want a job today, reach out to me. But basically, those people are going to be relatively hard to find. And so you're already in a fortunate position. The thing is, I think they have to break out of us, if they're still using spreadsheets, they need to break out of it. Yeah, let's talk about the nitty-gritty here. And they're basically, you know, I describe it as like there's a one-to-one correspondence between, let's say, a Jupyter notebook running Python and a spreadsheet. Because in the spreadsheet, you're looking at the data and the formulas are hidden. In the Jupyter notebook, you're looking at the formulas and the data is hidden. But basically, it's the same one-to-one correspondence between what's happening in the processing. And basically, when the data gets so big that you can't look at it at all, in fact, you're much better off looking at the formulas if you want to optimize them. And so it's people who say, well, gee, I can't prove.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Has to happen at some point in time, either the growth stops or something else happens. So I think that I see this as one level deeper than the Thai level financial statement. If you can use the data to model the business at that level, then you get a lot more insight into the stage and health of that business.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“80 20 rule. And so imagine on the y axis, I have the year over year growth of that business. And there's the line of zero and there's negative and there's positive. The upper quadrant is positive growth. On the x-axis, I have what's happening to that Pareto distribution. Is it flattening or is it steepening? So if it steepening, then now 8% of Walmart's customers generate 50% of the revenue. And unfortunately, Walmart sometimes has been going in that direction, whereas Amazon's in the other quadrant of actually growing and spreading and flattening its distribution of where the money's coming from, which is a healthier place for business to be in. So now imagine a business through its history of birth thriving to death, then you can think of parameters like that, which you used to think of as a scalar, moving around these quadrants, which has a trajectory in those quadrants that corresponds to what stage of its life it's in. Because when you were desperately getting growth and you're getting growth only out of your loyal customers, you're painting yourself into a corner of a room. If you're not expanding your customer base and you're growing, it's just not going to be sustainable.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Of invested capital. Yeah, that's right. All of those types of things. Exactly. So things like, as I say, same store sales or could be whatever you have to be looking at. And I think the key thing is that from a machine learning point of view, if you classify things as if you use the data to sort of classify the business into its regimes. And the other thing that I think that's important is that a lot of this information is at a lower level of granularity. Again, going back to the products or the cohorts. Because think about it, one cohort of customers. That cohort of customers, maybe it's people in advertising talk about as a funnel. Maybe I don't know about it first. And then I gradually know about it. And then I want to find out more. Then maybe I'll become a customer, but then maybe I become a fanatic about that product. And then maybe I plateau at that rate of engagement for a while, and then I decide to move on. But let me give you a real example. So 9% of the best customers at Walmart generate 50% of the revenue. So why am I using a scalar to look at same-store sales? It's a Pareto distribution. They're all Pareto distribution.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“That it looks, and the better you can understand that partitioning, the better you can actually know when the change of thesis.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Here's one of the ways, important ways I think I have of thinking about this. Again, I think what happens is, and it's partially because of the tools we had, people reduce things to a scalar or single number like a temperature when that wasn't the right thing to do. So if we think about a business, people say, well, you know, what's the consensus number and what do I see? And is it different from the consensus number? Maybe there'll be some earnings approach. But I don't think that's the right way to think about it. I think the way to think about it is that there's a notional cliff or a couple notional cliffs. And right now the business is between those values. And if it falls below a certain year-over-year growth rate, it's a different business. And that's a cliff. If it goes above a year-over-year growth base, then that's another cliff. That's another business. And these are just like those partitions. And so what's really going to cause a change in your investment thesis is when the business actually crosses one of those cliffs. And so what you want to do is you want to partition what the business looks like when it's acting normal. And that, you know, there's going to be lots of sort of variation in the way.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Of this diagonal line is what was used to be called a threshold logic in it. You have inputs going into two weights weight one times the first input, weight two times the second input, and if it's greater than the threshold, it actually is output is the thing. But if it's equal to the threshold, it's the boundary. And so you can easily reduce that w1 times x plus w2 times y equals the threshold into the equation for a straight line, where y equals some combination of the weights times x plus t divided by one of the weights. And so then if you just change the two weights, you're literally changing the slope and the intercept of this line. And so the process of learning the partitioning between these two clusters is literally just changing the two weights in the threshold, which moves the partition function in two-dimensional space between these two clusters. And so that's how they work. Now all you need to do is scale it up to 10,000 dimensions. It's exactly the same math, but you're now moving this hyperplane in this space to construct a partitioning. Now, each neuron's one partitioning. If I want to keep all clusters,”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, rather than regression, let me start with a partition, simplest partition. So here's the way I've used it in my class, in directory class. I say, let's say you have a friend in a foreign country and you want to send them some fruit. You put some apples and oranges in a box. You send it off. And your friend receives them and he's a scientist too. But you didn't label them. And he says, oh, this is great. Thank you very much. But which ones are apples and which ones are oranges? So you say, well, you know, the ones that are more round, those are oranges. So he's a scientist. He gets it out. He measures the roundness of all these objects. And it turns out that some of the apples are quite round and some of the oranges are a little bit oblong. So the distributions overlap with each other. He says, you know, this doesn't really partition them for me. And you say, well, you know, the apples are more red than the oranges. And so now he goes and measures the redness of these things. And again, they overlap a little bit. But you say, well, just plot on one axis how round they are. And the other axis how red they are. And the higher the dimensional space, the more that the data separate. And so now there's a diagonal line that partitions this space. Now, you know, the simplest model, you think and think...”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“In the space to partition the good answers from the bad answers. So let me try to make that more clear. So let's say you've never drinking a bad glass of milk. All of a sudden you go to the fridge, you pour yourself a glass of milk. It tastes horrible. So what do you do immediately? You look at the date. You look at the texture. You smell it in everything. And so what you're doing is you're adjusting the features in your brain to try to adjust the partition functions so the class of glasses of milk that you drink is now more restricted by moving these partition planes in this feature space. And so that's what I mean by feature space. Now what deep learning does is it automatically in a self-organizing way learns what the best space is in order to actually optimize the partitions. So the classic example is you have a spiral of data inside another spiral of data. Well, you can't construct planes to partition it. But if you map it into a space where there are actually two separate Gaussian clusters, you can easily partition it. So if you select the parameters correctly, you end up in a space where you can partition the data.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“A team who actually was influential in him leading to the decision. And so you could think of that as an organizational description of what back propagation is. And the only rest of the detail you have to worry about is, you know, what are the rules for correcting the weights? And so the bigger the weight, the bigger the change, the bigger the, there's a whole bunch of also things which make heuristic sense about how much you change the weights when someone's right and when they're wrong in this process. Okay, well, so that's backpropagation. 1986 neural networks. So what's the problem with that? The problem is that you don't know what decisions the organization's making. But what if you could actually frame all the questions in the right way and make sure they're framed correctly before you do this? And you can think of that as automated parameter selection. So in the old days when you're trying to solve a problem, you would have to figure out, well, what are the features in the world? What are the parameters? What are the factors that I want to use into my network to try to train? And it might be that that set of factors just doesn't partition well in the space because ultimately what the model is doing is trying to use plug”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“CEO, and they don't know each other, but they're going to work together. And so you try to make decisions, and so the CEO is going to take everyone's vote. And so because they don't know everyone, he starts with everyone, one person, one vote. And so you all make a decision about whether we should do X or Y. And then at the end of the day, you made it a good decision or a bad decision. So unconsciously, in the brain of the CEO, what he's saying is, oh my God, you know, Michael, when he says yes, he's wrong. So he's wrong. So I'm going to decrease his weight. So next time in the vote, I'm not going to tell him necessarily, but his weight is going to be a little bit less in this class of decisions. And maybe this other guy's Patrick's weight is going to be slightly higher in these class of decisions. So these weights gradually change based upon trying to make better decisions for the firm. Now, Michael has a team, and Patrick has a team. And so when Michael gets something wrong, he's not going to take full responsibility. He's going to back to his team and say, which of you idiots actually told me this decision? And so he then backpropagates the correction to his weight to the person on”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Think mostly most of the time it's ill post because it really you're saying, what's the mapping of these variables to that variable? And if you can't solve it, the thing is that we can identify a cat in a YouTube video and we can't pick the price. So I don't know there isn't a solution that I know. And so to assume that there is a mapping from these input variables to those output variables is already sort of misleading. But is the commerce that I observe in the world a predictive of the growth of a business? Of course it is. I mean, that's the discretionary guys do, right? So basically, if you're using the machine learning to predict something that you know is a solvable problem, but you want to do more accurately, you want to hire a resolution microscope. Why not use the machine learning for that? It's something that we know is a solvable problem and we just want to do it better. So yeah, I mean, I'm happy to discuss machine learning. I actually have a little, I used to teach machine learning at lots of different levels. And so I have a sort of a business level description of how machine learning works, which I can try on you and see if it helps. Let's do it. So let's say that you're starting a company and you have five or six people. Let's call it six people and they're all starting a company.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“To regress it to the mean based upon a two year stack and bring it down the assumption for the other two months instead of just assuming the other two months are also going to be good. And so that's because the growth of a piece of a business is a very stationary thing. And so if you think about the good applications of machine learning, there has to be cats in YouTube. It has to be something which relatively doesn't change over time in order to truly get the benefit of machine learning. And so if you're using machine learning to build models of a stationary thing, which is the growth of a business, a relatively stationary, you're much better off than if you're trying to use models to try to...”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Is because what inspires one person is a great essay is not what inspires someone else. And I think this applies to companies too, because when someone has a great belief in a company, where a company is going to be and what they're going to develop into, that space of possibilities is very large. But the things which actually are problematic in a business in terms of loss of market share or what's happening with the customer base or so on and so forth, what's happening to the financials, those things are actually a smaller space. And so if you're trying to use something to learn it, you're better off. And the other thing I would just bring into this conversation, and I think it's really important, maybe we should talk about, we could talk about it more later on, is this idea of where do you apply machine learning? Because again, if you're trying to predict price action, it's non-stationary. It's all over the place. It's driven by all sorts of mood and regime and so on and so forth. But the growth of a business and a segment of a business is like a rock. It's very solid and very stationary. And in fact, if say Home Depot has a really good month in the first month of the quarter, a discretionary guy is most likely.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, you had spoken in your talk about a belief you had that shorting or knowing when a company was going to fail was actually an easier thing than actually knowing when it was going to succeed. And so I had some observations and data that had led to similar conclusions I found you afterwards, and that's how we started talking. And this data was actually, it come from building automated systems, and I was helping a company that was building automated systems to score and evaluate college entrance applications, including the essay. And it turns out when you're evaluating the essay, and you're using experts to as a ground truth to sort of come up with what are good essays and what are bad essays, it's much easier to actually have an accurate prediction for the lower half of the distribution because the type of mistakes which occur in essays people generally agree on. And so the machine learning system can be trained to actually detect how bad you are from the mode of the distribution on down to worse. But it turns out that it's very, very hard to predict the other side of the distribution. And you can think of that.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Way of thinking about it if I have a clean signal on a business. Let's just say if I actually can see a running signal of the health of this business, then I can create a much larger position because my risk is actually much smaller, right? So I'm not waiting for whispers to tell me if my investment is going to have a problem. I just literally can see the commerce going on in this business.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“And the other thing, the only thing I would add to that is that what you want to do is short sprints. It's a traditional agile process. And so what you would have is people self-organizing into groups with ideas. And there always are more ideas than there are possible. Having the whole team decide which ideas are best, run them for a short period of time, find out what works, everything has to be proved. And so it has to be small enough chunk that you can prove the idea works. And then you do it again. You keep cycling through that process and stimulating innovation in the team. And people love this. This is a great work environment for people and they get to think about ideas for how to move things forward because the classically it's a boil the ocean problem. There's so much data. There's so many problems to work on. And there's a challenge to figuring out what are the best sets of ways to leverage data into information to support an investing process.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Where to dig, then it'll be great. If they don't guess right where to dig, then it'll be a problem. But my personal view is that in a brand new field, you're better off actually hiring very smart people and letting them work on different aspects of the problem that they think of in order to try to figure it out because it's an unknown area.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“One of the ways I have of describing quant shops is I think it's interesting if you look at across industry. So when I was in the electronics industry, the next processor from Intel was the next processor, and I described it as like strip mining because everyone's working on the same project. Everyone knows what the target is. The whole company's working together. If you contrast that to the drug discovery in a pharmaceutical business, which I just is more like prospecting, you have lots of individual scientists. They all have their own lab. They don't need to talk to each other. And if one of them finds gold, then the whole company wins. And I think that it's in a brand new field where you don't know the answer, or if you have 20 prospectors, 20 people working on something, you're better off letting them prospect than telling them all what to do. And I think it's what causes classical disruption is that someone has an idea top down and tells everyone what to do. And they all go and work on that. But if it's wrong, all the eggs are in one basket. And so I think that there are a bunch of firms that are still trying this. If you want, strip mining approach to trying to solve the problem, and they guess right.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Thought of Whole Foods as actually good food but expensive. And so he triggers the honeymoon period back in all of his stores as if they just were brand new open to generate foot traffic as if it was a brand new store. And so by thinking about it in this mode, and that's why it really requires, if you want, a partnership between people who think like quants and people who actually think with valuation in order to think about how do you actually use the computer methods to, and ultimately you're going to be in a world where just like in chess and everything else, where the computer plus the person does better than either alone. If I give you Tim Cook's dashboard, you don't understand Apple as a business, what do you do with it? But if I give it to someone who's a discretionary investor who's been studying Apple for a bunch of years, I can guarantee it's a boon for them and they have a huge advantage long term in terms of how to predict what's happening at Apple.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Bedrooms, bathroom square fee. Yeah, The interesting thing is everything that sounds idiosyncratic really ends up not being. So, for example, let's say you have a chain of restaurants, and the chain of restaurants is growing very quickly, and you're looking at same-store sales. Well, some of those restaurants are brand new. So when something's brand new, store restaurant, it has this honeymoon period where everyone wants to go. That falls down to an organic growth rate. And so with the data, you can sort of time shift them so the opening date's the same date, and you can calculate what that function looks like. And so now then you can move it back and you can then figure out what it's going to look like in the future. You can look at cannibalization when you open a new store. How many people actually were going to the old store now going to the new one, so you can calculate the maximum density. You say, well, gee, is that specific to one restaurant? The answer is no, because all restaurants behave the same. And after you learn this kind of thing by talking to discretionary folks, then you can implement this across all restaurants, stores, anything that involves consumer. And one of the things I loved was when the Friday before the deal was finished with Amazon buying Whole Foods, Jeff Bezos said, we're going to lower the prices in all the Whole Foods.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Are growing at a relatively stable rate, but maybe iPhone X in China, or maybe what happens with iPads, or what happens with their physical stores. And so the focus on that one piece of the spreadsheet, but the other piece of the spreadsheet will essentially roll up to the amount of revenue generated by that item. So another example like that is, so let's say that you think that a loyalty program at someone like Starbucks is going to generate lots of new revenue for them. You can measure in the data what's the conversion rate to that loyalty program. And then once you find the conversion rate, you can say, well, when they convert, what is their change in spend? And you can then calculate on a future basis how much of a new revenue machine you get by doing X, maybe all day breakfast or whatever it happens to be that is a policy of a store. Maybe they roll it out in one geography first. And so you can use the data to figure out how much of a money machine is this in that geography and what's going to happen when it spreads to the other places.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, so let's say you take, let's take Apple. And if you were to plot a distribution where the y-axis is the number of transactions you see in the credit card data, and the x-axis is the dollar amount, what you would see in that distribution is lots of bumps. There'd be a bump that corresponds to iTunes purchases, bump that corresponds to Apple Music, a bump that corresponds to watches, handsets, iPads, computers. And so, and obviously some people are buying multiple things. Not all the data points are going to be nicely in these bumps. But if you broke the data into those bumps, you could create a dashboard that looks by geography, by cohort of when they bought their first iPhone because their first 18 months, they're going to be higher app spend than other times. Buy product by geography, by cohort. And essentially this is reconstructing Tim Cook's dashboard if you want. And so then what a valuation person would do is they'd say, okay, what's the year-over-year growth of this segment of the business or this segment plus this geography? And one of the things the discretionary guys do is they'll look at where the vault is. And so they might say, well, look, all the parts of Apple's business.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“Or they're actually processing that data sufficiently to really glean the information out of it. That's the trick. What information are you trying to get out of it? The data sets are very large. And again, the most common thing people are doing with the data is rolling it up to get a top line revenue number.”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source
“And now you can see this is all riding on a line. And we can see that because we've inferred through the mixture of all your spending of people in the credit card panel what their gender is. It wasn't something that was given at the beginning. So I think that if you take your data and you use it to build a detailed informational picture, then that hasn't been done. People have been finding the shiny pebbles on the surface of the sand. They haven't been actually taking the data and using it to build models. And then that model is actually a much longer lasting form of alpha. Because if I could give you Tim Cook's dashboard and you could see in live real-time, byproduct, by geography, by cohort of Tim”
2018-06-12 · Invest Like the Best · Michael Recce – Tim Cook’s Dashboard - [Invest Like the Best, EP.91] · IDENTIFIED FROM THE TRANSCRIPT · source