Jon McAuliffe on Innovation and Statistical Methods

This is Master's in Business with Barry Ridholds on Bloomberg Radio.

This week on the podcast Strap Yourself In, I have another extra special guest. John mccauliffe is co founder and chief investment officer at the Volleyon Group. They're a five billion dollar hedge funds and one of the earliest shops to ever use machine learning as it applies to training and investment management decisions. It is a full systematic approach to using computer horsepower and database and machine learning and their own predictive engine to make investments and trades, and

Thanks, Barry. I'm really happy to be here.

So let's talk a little bit about your academic background. First, you start out undergrad computer science and applied mathematics at Harvard before you go on to get a PhD from California Berkeley. What led to a career in data analysis? How early did you know that's what you wanted to do?

Well, it was a winding path. Actually, I was very interested in international relations and foreign languages when I was finishing high school. In fact, I spent the last year of high school as an exchange student in Germany. And so when I got to college, I was expecting to major in government and go on to maybe work in the foreign service something like that.

Really, so this is a big shift from your original expectations.

Yeah, it took about one semester for me to realize that none of the questions that were being asked in my classes had definitive and correct answers.

Did that frustrate you?

It did frustrate me. Yeah, And so I stayed home over winter I stayed Excuse me, I didn't go home. I stayed at college over winter break to try to sort out what the heck I was going to do, because I could see that it wasn't My plan was in disarray. And I'd always been interested in computers, had played around with computers, never done anything very serious, but I thought I might as well give it a shot, and so in the spring semester I took my first

It doesn't, does not compile exactly. So that's really quite quite fascinating. So what led you from Berkeley to d sure that they're one of the first quand shops. How did you get there? What sort of research did Yeah?

I actually I spent time at d Shot in between my undergrad and my PhD program, So it was after Harvard that I went to that show.

Did that light an interest in using machine learning and computers applied to finance or what was that experience like?

Yeah, it made me really interested in and excited about using statistical thinking and data analysis to sort of understand then amics of securities prices. Machine learning did not play really a role at that time, I think, not at d SHAW, but you know, probably nowhere it was too immature a feel in the nineties. But I had already been curious and interested in using these kinds of statistical tools in trading and in investing when I was finishing college and then at d SHAW. You know, I had

Of us still one of the top performing hedge funds, one of the earliest quant hedge funds, A great a great place to absolutely cut your teeth at. So was it Harvard d SHAW and then Berkeley?

Yeah, that's right?

And then from Berkeley? How did you end up at Amazon?

I guess I should correct myself. There was a year at Amazon after d Eshaw, but before Berkeley.

And am I reading this correctly? The recommendation engine that Amazon uses you helped develop?

I would say I worked on it. I would you know it existed, It was in place when I got there, and sort of the things that are familiar about the recommendation engine had already been built by my manager and his colleagues. But I worked I did research on improvements and different ways of forming recommendations. It was funny because at the time, the entire database of purchase history for all of Amazon fit in one twenty gigabyte file on a disc, so I could just load it on my computer and run.

Now I don't think we could do that anymore. We could not, so, thank goodness is Amica Zone cloud services, so you could put what is it, twenty five years and hundreds of billions of dollars of transactions. So my assumption is products like that are highly iterative. The first version is all right, it does a half decent job, and then it gets better, and then it starts to

Well, that's a great question, because the two are inextricably linked. The way that you make algorithms great is by making them more powerful, more expressive, able to describe lots of different kinds of patterns and relationships. But those kinds of approaches need huge amounts of data in order to correctly sort out what's signal and what's noise. The more expressive.

There was a pretty infamous New York Times article a couple of years ago about targets using their own recommender system and sending out maternity things to people. A dad gets his young teenage daughters what is this and goes in to yell at them, and turns out she was pregnant and they had pieced it together. How far of a leap is it from these systems to much more sophisticated machine learning and even large language models.

It's the answer, it turns out, is that it's a question of scale. That wasn't at all obvious before GPT three and chat GPT, But it just turned out that when you have, for example, GPT is built from a database of sentences in English, it's got a trillion words in it that database, and when you take a trillion words and you use it to fit a model that has one hundred and seventy five billion parameters. There is apparently a kind of transition where things become, you know,

Right. It's eerie is in terms of how sophisticated it is, but it's also kind of surprising in terms of I guess what the programs like to call hallucinations. I guess if you're using the Internet as your base model, Hey, there's one or two things on the Internet that are wrong, so of course that's going to show up in something like chap GPT.

Yeah, you know. Underlyingly, there's this tool GPT three that's really the engine that powers jed GPT, and that tool it has one goal. It's a simple goal. You show at the beginning of a sentence, and it predicts the next word in the sentence, and that's all it is trained to do. I mean, it really is actually that simple.

It's a dumb program that looks smart.

If you like. But the thing about predicting the next word in a sentence is whether you know the sequence of words that's being output, is leading to something that is true or false, is irrelevant. The only thing that it is trained to do is make highly accurate predictions of next words.

So when I said it's really very sophisticated, it just for what we tend to call this artificial intelligence. But I've read a number of people said, hey, this really is an AI. This is something a little more rudimentary.

Yeah, I think, you know, a critic would say that artificial intelligence is a complete misnomer. There's sort of nothing remotely intelligent in the colloquial sense about these systems. And then a common defense in AI research is that artificial intelligence is a moving target. As soon as you build a system that does something quasi magical that was the old yardstick of intelligence, then the goalposts get moved by the people who are supplying the evaluations. And I guess

But it's complex and sophisticated.

It certainly is it has one hundred and seventy five billion parameters. That doesn't fit your definition of complex you know what would?

Yeah, that works for me. So your in your career line, where is aphametrics and what was that recommendation engine? Like?

Yeah, So that was work I did as a summer research intern during my PhD. And that work was about what's called the problem is called genotype calling. So genotype calling, I'll explain, Barry, do you have an identical twin? I do not, Okay, So I can safely say your genome is unique in the world. There's no one else who has exactly your genome. On the other hand, if you were to lay your genome in mind alongside each other lined up, they would be ninety nine point nine percent identical.

And this used to be a very expensive, very complex problem to solve that. We've spent billions of dollars figuring out now a lot faster, a lot cheaper.

A lot faster. In fact, even the technology I worked on in two thousand and five two thousand and four is multiple generations old and not really what's used anymore.

So let's talk about what you did at the efficient frontier. Explain what real time click prediction rules are and how it works for a keyword search.

Sure, the revenue engine that drives Google is search keyword ads, right, So every time you do a search, at the top you see ad ad AD, And so how do those ads get there? Well, actually it's surprising maybe if you don't know about it, but every single time you type in a search term on Google and hit return, a very fast auction takes place, and a whole bunch of companies running software bid electronically to place their ads at

A rough outline. So the first three sponsored results on a Google page, go through that auction process, and I think at this point everybody knows what page rank is for for the rest of that that's right, And that seemed to be Google secret sauce early on.

Right, Well, you know, to talk about the the ad placement. So the people who are supplying the ad, who are participating in the auctions, they have a problem, which is how much to bid, right, And so how would you decide how much to bid? Well, you want to know basically the probability that somebody is going to click on your ad, and then you would multiply that by how much money you make eventually if they click. And that's kind of an expectation of how much money you'll make.

So I was going to say, this sounds like it's a very sophisticated application of computer science probability and statistics. And if you do it right, you make money, and if you do it wrong, your ad budget is a money loser.

That's right.

Huh. So tell us a little bit about your doctorate, what you wrote about for your PhD at Berkeley.

Yeah, so we're back to genomes. Actually, this was around the time when I was in my first year of my PhD program, is when the human genome was published in Nature. So it was kind of really the beginning of the explosion of work on kind of high throughput, large scale genetics research. And one really important question after you've sequenced a genome is well, what are all the bits of it doing. You can look at a string

Huh, really really interesting. You mentioned our DNA is ninety nine point ninety nine. I don't know how many places to the right of the decimal point you would want to go, but very similar. How how similar or different are we from let's say, a chimpanzee. I've always questioned, there's an urban legend that they're practically the same. It always seems like it's overstated two percent. So you and I have a point one percent different me and the average chimp. It's two point zero percent.

That's exactly right. Yeah, so chimps are essentially our closest non human primate relatives.

Really really quite fascinating. So let's talk a little bit about the firm. You guys were one of the earliest pioneers of machine learning research. Explain a little bit what the firm does.

Sure, so, we run trading strategies investment strategies that are fully automated, so we call them fully systematic, and that means that we have software systems that run every day during market hours, and they take in information about the

So fully automated. Is there supervision or it's kind of running on its own with a couple of checks.

There's lots of human diagnostic supervision, right, So there are people who are watching screens full of instrumentation and telemetry about what the systems are doing. But those people are not taking any actions, right unless there's a problem, right, and then they do.

So let's talk a little bit about how machines learn to identify signals. I'm assuming you start with the giant database that is the history of stock prices, volume movement, etc. And then bring in a lot of additional things to bear. What's the process like developing a particular trading strategy.

Yeah, so, as you're saying, we begin with a very large historical data set of prices and volumes, market data that kind, but importantly all kinds of other information about securities, financial statement data, textual data, analyst data.

So it's everything from prices fundamental everything from learnings to revenue to sales, etc. I'm assuming the change and the delta of the change is going to be very significant in that. What about macroeconomic what some people call noise, but one would imagine some signal in everything from inflation to interest rates to GDPs firm spending. Are those inputs worthwhile or how do you think about those?

So we don't hold portfolios that are exposed to those things. So it's really a business decision on our part. We are working with institutional investors who already have as much exposure as they want to things like the market or to well recognized econometric risk factors like value, and so they don't need our help to be exposed to those things. They are very well equipped to handle that part of their investment process. What we're trying to provide is the

That's interesting. So can I assume that you're applying your machine learning methodology across different asset classes or is it strictly equities? Oh?

No, We apply it to UH to equities, to credit, to corporate bonds, and we trade futures contracts, and in the fullness of time, we hope that we will be trading kind of every security in the world.

So, so currently stocks, bonds, When you say futures, I assume commodities, all.

Kinds of futures contract.

It's really really interesting. So it could be anything from interest rate swaps to commodities to the full gamut. So, so how different is this approach from what other quant shops do that really focus on equities.

I think it's kind of the same question as asking, well, what do we mean when we say we use machine learning or that you know we are our principles are our machine learning principles, and so how does that make us different than the kind of standard approach in quantitative trading? And the answer to the question really comes back to this idea we mentioned a little while ago of how powerful the tools are that you're using to form predictions. Right, So in our business, the thing that we build is

So I'm glad you brought up the concept of how easy it is or how frequently you can fool an algorithm or a complex rule, because sometimes the results are just random. And it reminds me of the issue of back testing. No one ever shows you a bad back test. How do you deal with the issue of overfitting and back testing that just is geared towards what already happened and not what might happen in the future.

Yeah, that is you know, if you like the million dollar question in statistical prediction, Okay, and it might you might find it surprising that relatively straightforward ideas go a long way here. And so let me let me just describe a little scenario of how you deal you can deal with this. All right, we agree, we have this big historical data set, right, One thing you could do is just start analyzing the heck out of that data set and find a complicated prediction rule. But you're you've

Really really intriguing. So you guys, have you've built one of the largest specialized machine learning research and development teams in finance. How do you assndle a team like that and how do you get the brain trust to do the sort of work that's applicable to managing assets.

Well, the short answer is, we spend a huge amount of energy on recruiting and uh, you know, identifying the sort of premier people in the field of machine learning, kind of both academic and practitioners, and we exhibit a

So let's talk about some of the fundamental principles Volnon is built on. You reference a prediction based approach from a paper Leo Briman wrote called two Cultures. Yeah, tell us a little bit about what two cultures actually is.

Yeah. So this this paper was written about twenty years ago. Leo Briman was one of the great probabilists and statisticians of his generation. Berkeley professor need I say, and you know, Leo had been a practitioner in statistical consulting actually for quite some time. I'm in between a U. C. L. A tenured job and returning to academia at Berkeley, and he learned a lot in that time about actually solving prediction problems and instead of hypothetically solving them in sort

The difference between practical use versus academic theory if you like.

Yeah, And so he identified two schools of thought about solving prediction problems, right, and one school is sort of model based. The idea is there's some stuff you're going to get to observe stock characteristics. Let's say there's a thing you wish you knew future price change, let's say, and there's a box in nature that turns those inputs into the output, right. And in the model based school of thought, you try to open that box, reason about how it must work, make theories. In our case, these

And are those statistics still valid today or have we shifted quite a bit?

We shifted quite a bit, And different different arenas of prediction problems have different mixes these days. But even in finance, I would say it's it's probably more like fifty to.

Fifty really that much?

Yeah, I think you know, And if you the logical extreme is natural language modeling, which was done for decades and decades in the model based approach, where you kind of reasoned about linguistic characteristics of how people kind of do dialogue and those models had some parameters and you fit them with data, and then instead you have, as we said, a database of a trillion words and a tool with one hundred and seventy five billion parameters, and you run that and there is no hope of completely

Huh, that's really quite fascinating. So let's talk a little bit about artificial intelligence and large language models. You follow d Shaw playing in e commerce and biotech. It seems like this approach to using statistics, probability, and computer science is applicable to so many different fields.

It is. Yeah, I think you're talking about prediction problems ultimately, So in recommender systems, you can think of the question as being well, if I had to predict what thing I could show a person that would you be most likely to change their behavior and cause them to buy it. It's a kind of prediction problem that motivates recommendations. In biotechnology.

And we've seen AI get applied to pharmaceutical development in ways that people just never really could have imagined just a few short years ago. Is there a field that AI and large language models are not going to touch or is this just the future of everything.

The kinds of fields where you would expect uptake to be slow are where it is hard to assemble large data sets of systematically gathered data. And so any field where it's relatively easy to at large scale, let's say, produce the kinds of the same kinds of informations that experts are using to make their decisions, you should expect that field to be impacted by these tools if it hasn't been already.

So you're kind of answering my next question, which is what led you back to investment management. But it seems if there's any field that just generates endless amounts of data.

It's the markets, that's true. And I had already been really interested in the problems of systematic investment strategies from my time working in d SHAW, and so my co founder Michael Kratanav and I, you know, we were both in the Bay Area in the two thousand and four. He was there because of a firm that he had founded, and I was there finishing my PhD. And we started to talk about the idea of using contemporary machine learning methods to build strategies that would be, you know, really

That there was a void. Nothing nothing in the market hates more than a vacuum and intellectual approach. So so you mentioned the diversity of various data sources. What what don't you consider, like how how far off of price and volume do you go in the net you're casting for inputs into into your systems.

Well, I think we're prepared as a you know, as a as a research principle, we're prepared to consider any data that has some bearing on price formation, like some some plausible bearing on how prices are formed. Now, of course we're you know, we're a relatively small group of people with a lot of ideas and uh, and so we have to prioritize so you know, in the event we end up pursuing data that you know makes a lot of sense, you know, we don't we don't try.

I mean, can you go as far as politics or the weather, like how far off of prices can you can you look?

So, you know, an example would be the weather. You're for most securities, you're not going to be very interested in the weather, but for commodities future as you might be, so that you know, that's the kind of reasoning you would apply.

Right, really really interesting. So let's talk about some of the strategies. You guys are running short and mid horizon US equities, European equities, Asian equities, mid horizon US credit, and then cross assets. So I might to assume all of these are machine learning based, and how similar different is each approach to each of those asset classes.

Yeah, they're all machine learning based. The kind of principles that I've described of using as much complexity as you need to maximize predictive accuracy, et cetera. Those principles underlie all the systems. But of course it's trading. Trading corporate bonds is very different from trading equities, and so the implementations reflect that reality.

Huh. So let's talk a little bit about the four step process that you bring to the systematic approach, and this is off of your site, so it's it's data prediction, engine, portfolio construction, and execution. Yeah, I'm assuming that is heavily computer and machine learning based. At each step along the way. Is that is that fair?

I think that's fair. I mean to different degrees. The data gathering that's you know, that's a that's largely a software and kind of operations and infrastructure job.

Do you guys have to spend a lot of time cleaning up that data and making sure that because you hear between CRISP and s and P and Bloomberg, sometimes you'll pull something up and they're just all off a little bit from each other because they all bring a very different approach to data assembly. How do you make sure everything is consistent and there's no errors or errants inputs throughout.

Yeah, through a lot of effort. Essentially, there there we have. You know, we have an entire group of people who focus on data operations, both for gathering a historical data and for the management of the ongoing live data feeds. There's no way around that. I mean, that's just work that you have to that you have to do.

You just have to brute force your way through that. And then the prediction engine. Sounds like that's the single most important part of the machine learning process if I'm understanding you correctly, that that's where all the meat of the technology is.

Yeah, I understand the sentiment. I mean, it's worth emphasizing that you do not get to a successful systematic strategy without all the ingredients. You have to have clean data because of the garbage in garbage out. You have to have accurate predictions. But you know, predictions don't automatically translate into returns for investors. Those predictions are kind of the power that drives the portfolio holding part of the system.

So let's talk about that portfolio construction. Given that you have a prediction engine that and good data going into it, so you're fairly confident as to the output. How do you then take that output and say, here's how I'm going to build a portfolio based on what this generates.

Yeah, so there are three big ingredients in the portfolio construction. The predictions what is usually called a risk model in this business, which means some understanding of how volatile prices are across all the securities you're trading, how correlated they are, how you know if they have a if they have a big movement, how big that movement will be. That's

So execution becomes really important. If you're not executing well, you are moving prices away from your profit.

That's right, and it is you know, probably the single thing that undoes quantitative hedge funds most often is that they misunderstand how much they're moving prices. They get too big, they start trading too much, and they sort of blowed themselves up.

It's funny that you say that, because as you were describing that, the first name that popped into my head was long term capital managements trading these really thinly traded obscure fixed income products, and everything they bought they sent higher because there just wasn't any volume in it. And when they needed liquiditly there was none to be had. And you know that plus no risk management one hundred x leverage equals a kaboom.

They made a number of mistakes the book. The book is good. So when genius fail in, oh absolutely love that fantastically fascinating.

So when you're reading a book like that, somewhere in the back of your head are you thinking, hey, this is like a what not to do when you're setting up a machine learning fund. How influential is something like.

Well one hundred percent? I mean, look, I think the most important adage I've ever heard in my professional life is good judgment comes from experience. Experience comes from bad judgment. So the extent to which you can get good judgment from other people's experience, that is that that is like a free tuition. And so we talk a lot about all the all the mistakes that that that other people have made. And you know, we do not congratulate ourselves

Yeah, right, it's they just didn't have a guide book on what not to do, which which you guys.

Do We don't. No, I don't think we do. I mean apart from that, apart from reading about right, But everybody is undone by a failure that they they didn't they did, they didn't think of ever didn't know about yet. And we're extremely cognizant of that.

Huh. That has to be somewhat humbling to come being on the lookout for that blind spot that could disrupt everything.

Yes, yeah, humility is the key ingredient in running in running these systems.

Really quite amazing. So let's talk a little bit about how academically focused volling On is. You guys have a pretty deep R and D team internally, you teach at Berkeley. What does it mean for a Hedge fund to be academically focused?

What I would say probably is kind of evidence based rather than academically focused. Saying academically focused gives the impression that kind of papers would be the goal or the desired output, and that's not the case at all. We have, you know, a very specific applied problem that we are trying to solve.

Papers are a mean to an end.

Papers are you know, we don't write papers for external consumption. We do lots of writing internally, and that's to make sure that that you know, we're keeping track of our own kind of scientific process.

But you're fairly widely published in statistics and machine learning. Yes, what purpose does that serve other than a calling card for the fund as well as Hey, I have this idea and I want to see what the rest of my peers think of it. When when you put stuff out into the world, what sort of feedback or pushback do you get?

I guess I would have to say, I really I do that as kind of a double life of non financial research. So it's just something that I really enjoy. Principally, what it means is that I get to work with PhD students, and you know, we have really outstanding PhD students at Berkeley in statistics, and so it's an opportunity for me to do a kind of intellectual work that namely, you know, writing a paper laying out an argument for public consumption, et cetera that is kind of closed off as far as so.

Not adjacent to what you guys are doing at Volleyon generally. No, No, that's really interesting. So then I always assume that that was part of your process for developing new models to apply machine learning to new assets. Take us through the process. How do you go about saying, Hey, this is an asset class we don't have exposure to. Let's see how to apply what we already know to that specific area.

Yeah, we have it's a great question. So we're trying as much as possible to get the problem for a new asset class into a familiar setup, into you know, as standard a setup as we can, and so we know what these systems look like in the world of equity. And so if you're trying to do the same kind, if you're trying to build the same kind of system for corporate bonds, and you start off by saying, well, okay, i'd like I need to know, you know, closing prices

The old joke was trading by appointment only. Yeah, and that seems to be a bit of an issue. And there are so many more bond issues than there are equities. Absolutely, is this just a database challenge or how do you work?

No, it's a statistics problem, but it's it's a different kind of statistics problem. We're not in this case. We're not trying to yet. We're not yet trying to predict the future of any quantity. We're trying to say, I wish I knew what the fair value of this of this CSIP was. I can't see that exactly because there's no live order book that with a bid and an offer that's got lots of liquidity that lets me figure out the fair value. But I do know what.

At best, you have a recent price, maybe not even so recent.

I have lots of related information. I know you know this bond. Maybe this bond didn't trade today, but it traded a few times yesterday. I get to say I know where it traded. I'm in touch with bond dealers, so I know where they've quoted this bond, maybe only on one side over the last few days. I have some information about the company that issued this bond, et cetera. So I have lots of stuff that's related to the

Huh. That's really kind of interesting. So I'm curious as to how often you start working your way into one particular asset or a particular strategy for that asset and just suddenly realize, oh, this is wildly different than we previously expected, and suddenly you're down a rabbit hole to just wildly unexpected areas. It sounds like that isn't all then uncommon.

It is not uncommon at all.

Huh.

No, it's a nice you know, there's this kind of wishful thinking that all we have. You know, we figured it out in one asset class in the sense that we have a system that's kind of stable and performing reasonably well that we that we have a feel for, and now we want to take that system and somehow replicate it in a different situation. And while we're going to standardize the new situation to make it look like

So stocks are different than credit, are different than bonds, or different than commodities. They're all like starting fresh. Yeah, over what some of the more surprising things you've learned as you've applied machine learning to totally different asset classes.

Well, I think, you know, corporate bonds provide a lot of examples of this. I mean, the fact that you don't actually really know a good live price or a good live bid offers it seems seems you know, it's surprising. I mean, this is this fact has started to change.

Yeah, So you get so when in other words, if I'm looking at a bond mutual fund or even a bondytf that's trading during the day. That price is somebody's best approximation of the value of all the bonds inside. But really you don't know the nav, do you.

You're just kind of guessing, Barry, don't even get me started on bonditfs real because.

That it seems like that would be the first place that would show up. Hey, bondytf's sound like throughout the day they're gonna be mispriced a little bit or wildly mispriced.

Well, the bond ETF there's a sense if you're a if you're a market purist, in which they can't be mispriced because there's their price is set by supplying demand in the ETF market, and that's a super liquid market, and so there may be a difference between the market price of the ETF and the under the nave of the underlying portfolio, except in many cases with bond ETF

So when I asked about what's surprising when you work you in on a rabbit hole. Hey, we don't know what the hell's in this bond ETF. Trust us, it's all good. That's a pretty surprise. And I'm only exaggerating a little bit, But that seems like that's kind of shocking.

It's it is surprising when you find out about it, but you quickly come to understand. If you trade single name bonds, as we do, you quickly come to understand why bond ETFs work that way.

I recall a couple of years ago there was a big Wall Street Journal article on the g l d E t F, and from that article I learned that GLD was formed because gold dealers had just excess gold piling up in their warehouses and they needed a way to move it. So that was kind of shocking about that ETF any other space that that led to a sort of big surprise as you worked your way into it.

Well, I think ETFs are a kind of a good source of these examples. So the volatility ETFs, the you know, the ETFs that are that are based on the VIX or that are short the vics. You may remember several years.

Ago I was gonna say the ones that haven't blown up.

Yeah right, there was this event called Valmageddon where.

That was ETF notes, wasn't it the Yeah.

Right, there are these essentially these investment products that were short VIX, and VIX went through a spike that caused them to have to liquidate, which was part I mean, the people who designed the sixteene traded note. They understood that this was a possibility, so they had a sort of uh descriptions in their in their contract for what it would mean. But yeah, always surprising to watch something suddenly go out of business.

We seem to get a thousand year flood every couple of years. Maybe we shouldn't be calling these things thousand year flood. That's right, that's a that's a big misnomer.

As statisticians, we tell people, you know, if you if you think that you've experienced a six sigma event, the problem is that you have underestimated sigma.

That that's really interesting. So so, given the gap in the world between computer science and an investment management, how long is it going to be before that narrows and we start seeing a whole lot more of the sort of work you're doing applied across the board to to the world of investment.

Well, I think it's happening. It's been happening for for quite a long time. I mean, for example, all of modern portfolio theory. Really, it kind of began in the fifties with you know, first of all, Markowitz and other people thinking about, you know, what it means to benefit from diversification, and the idea that you know, diversification is

I know, I only have you for a little while longer, So let's jump to our favorite questions that we ask all of our guests, starting with tell us what you're streaming these days? What are you either listening to or watching to keep yourself entertained.

I A few things I've been watching recently. The Bear, I don't know if you've heard So Great, So great, right, and I'm in Chicago, as I know, we were just from Yeah, so.

So and and there are parts of that show that are kind of a love letter to absolutely as you get deeper into the series, because it starts out kind of gritty and you're seeing the underside, and then as we progress, it really becomes like a lovely postcard. Such an amazing show.

So really really love that show. Was I was late to better call Saul that I'm finishing up. I think as good as as Breaking Bad, So I maybe when you haven't heard of there's a show called Mister in Between.

Which is mister Yeah.

It's not Hulu, it's from it's from Australia. It's about a guy who's, you know, a doting father living his life. He's also essentially a muscle man and hitman for for local criminals in his part of Australia. But it's half hour dark comedy.

Right, so not quite Barry and not quite Sopranos somewhere.

Yeah, that's exactly.

Yeah, sounds really interesting. Tell us about your early mentors who helped shape your career.

Well, Berry, I'd been lucky to have a lot of people who were you know, both really smart and talented and willing to you know, take the time to help me learn and understand things. So actually, my co founder, Michael Caratanov, he was kind of my first mentor in finance. He he had been a d SHAW for several years when I got there, and he he really taught me kind of the ins and outs of of market micro structure.

But not nine people that kind of matter, Yeah, exactly.

So it was just it was a real it was kind of a once in a lifetime privilege to get to spend time with someone of that intellectual caliber. And there were others too. I mean, I've been I've been very fortunate that.

That's quite a list to begin with. Let's talk about books. What are some of your favorites and what are you reading right now?

Uh? Well, I'm a I'm a big book reader, so I had a long list.

But probably by the way, this is everybody's favorite section of the podcast. People are always looking for good book recommendations, and if they like what you said earlier, they're gonna love love your book recommendations, so fire away.

So I'm a big fan of kind of modernist dystopian fiction. So a couple of examples of that would be the book Infinite Jest by David Foster Wallace, wind Up Bird Chronicle by Hirouki Murakami. Those are two of my all time favorite books. There's a I think much less well known but beautiful novel. It's a kind of academic coming of age novel called Stoner by John Williams. A really moving, just a tremendous book. Sort of more dystopia would be White Noise to Lilo and kind of the classics that

Huh, it's funny when you mentioned The Bear. I'm in the middle of reading a book that I would swear the writers of The Bear leaned on called Unreasonable Hospitality by somebody who worked for the Danny Meyer's Hospitality Group. Eleven Madison Park in Ramsey Tavern and all these famous New York haunts, and the scene in The Bear where they overhear a couple say, oh, we visited Chicago when you never had deep dish, So they send the guy

That does sound really interesting.

Yeah, really, you mentioned the Bear and it just popped into my head. Any of the books you want to mention that's that's a good list to start with.

Yeah. My other kind of big interest is science fiction, speculative fiction. Unsurprisingly right, Sorry, sorry, but so there are some classics that I think everybody should read. Ursula LeGuin loves just amazing. So The Dispossessed and The Left Hand of Darkness, those are just two of the best books I've ever read period.

Forget Left Handed Darkness stays with you for a long time.

Yeah right, yeah, really really amazing books. I'm rereading right now, Cryptonomicon Neil Stevenson. And one other thing I try to do is I have very big gaps in my reading. For example, I've never read Updyke, so I started reading The Rabbit.

Serious World of Corn. It's a garb and they're they're very much of an era.

Yeah, that's right.

What else give us more? Uh?

Wow? Okay, let's see George so.

He.

Oh wow, I think I think you'd love him. So He's his real strength is short fiction. He had He's written great novels too, but tenth of December this is his best collection of of fiction. And that this is more kind of modern dystopian, kind of comic dystopian stuff.

You keep coming back to dystopia, yeasinating.

I find, you know, it's uh, it's very different from my my day to day reality. So I think it's a you know, it's a great change of pace for me to be able to read this stuff. So, uh,

You know, it's just framing.

It's the framing and the perspective that are really that really kind of blow your mind. So it's a great it's a great book to read.

Huh, that's a hell of a list. You've given people a lot of things to start with, and now down to our last two questions, HM, what advice would you give to a recent college grad who is interested in a career in either investment management or machine learning.

Yeah? So, I mean I work in a very specialized sub domain of finance, So there are a lot of people who are going to be interested in investment in finance that I that I couldn't give any specific advice to. I have kind of general advice that I think is useful both for finance and even more broadly. This advice is really kind of top of Maslow's pyramid advice. If you know, if you're trying to kind of write your novel and pay the rent while you get it done,

That's a really intriguing answer. So final question, what do you know about the world of investing, machine learning, large language models, just the application of technology to the field of investing that you wish you knew twenty five years or so ago when you were really first ramping up.

I think one of the most important lessons that I learned, had to learn the hard way kind of going through and running these systems, was that it's kind of comes back to the point you made earlier about the primacy of prediction rules. And it may be true that the most important thing is the prediction quality, but there are lots of other very necessary, mandatory ingredients, and I would put kind of risk management at the top of that list.

John, This has been really, absolutely fascinating. I don't even know where to begin other than saying thank you for being so generous with your time and your expertise. We have been speaking with John mccauloff. He is the co founder and chief investment officer at the five billion dollar hedge fund Volleyon Group. If you enjoy this conversation, well, be sure and check out any of the previous five