Michael Mills | Mathematician meets Economist

What happens when a mathematician shares a room with an economist at college? The mathematician doesn't come from an investment background but he'd like to make some money and the economist has a dream to launch a hedge fund. It may not have been quite as simple as that, but I enjoyed hearing the story of Michael Mills and his co-founder Nicholas B. Spoors building the Infinitary Fund from humble beginnings at college.

We also had an interesting chat about the common characteristics of wealthy countries. There's usually a lot of intense collaboration between the government sector, the private sector, and academic institutions.

Michael Mills graduated from the University of Massachusetts with a BA in Economics and Political Science. Michael guides the overall strategy of growth at the fund. He recently consulted for an equity research firm and currently works for the FP&A team at a national retail company. Michael has been studying economics for nearly a decade and is currently studying for his MA in Economics.

Nicolas Spoors graduated from the University of Massachusetts with a BA in Mathematics. He has worked as an Adjunct Instructor of Finite Mathematics at KVCC and in the Boston area as a Data Analyst. Nicolas developed the proprietary model that the fund uses for investment selection, sizing and portfolio balance.

Infinitary Fund is a quantitative investment fund based out of New York deploying capital by utilizing cutting-edge mathematics and insights that are driven by proprietary mathematical theory. Specializing in the large-cap equities space with a buy-and-hold method.

Michael has many thoughts on the macro-economic environment and believes that low interest rates has contributed to inefficient allocations of money.

TRANSCRIPT FOLLOWS AFTER THIS BRIEF MESSAGE

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EPISODE TRANSCRIPT

Phil (34s):
Hi and welcome back to Stocks for Beginners. I'm Phil Muscatello. Many people think of Wall Street as the preserve of enormous investment banks and hedge funds, the wolves of Wall Street and the big shorters. So it's always good to hear from smaller players who are only dealing in millions, not billions. Hello, Michael.

Michael (50s):
Hi Phil. How are you doing?

Phil (52s):
Good. Michael Mills is the principal and CEO of Infinitary Fund; a limited partnership based in New York that provides investment advice in a pooled investment vehicle designed for long-term investors. So Michael, tell us about your background and where you've come from to this point.

Michael (1m 7s):
Yeah. So my background is that I grew up in the suburbs north of Boston in the United States. I studied economics and political science at UMass Lowell, which is a local university there. And then during my junior year, a friend and I decided to begin working on an investment advisory firm together, which eventually culminated in us launching Infinitary Fund in 2019. My career background includes private investigations, equity consulting, financial planning and analysis and now currently I'm running the fund, working at Bob's Discount Furniture in their financial planning and analysis department and living in New York with my wife.

Phil (1m 46s):
So private investigations, tell us about that.

Michael (1m 48s):
Yeah. So the company that I worked at was based out of White Plains, New York. And they basically specialized in providing due diligence investigations for private financial organizations. So an investment bank or a hedge fund, or a high net worth individual might come to us and say that they need us to investigate a company or a person that they want to do business with. And we would basically scour all public resources and collect as much information we can on that entity or person and provide it back to them. Usually people were looking out for reputational risks, so they didn't want to work with or invest in anyone or any company that would have a lot of like sexual harassment or discrimination lawsuits or people who said politically sensitive things on Twitter, that kind of thing.

Phil (2m 41s):
So you're kind of a financial Colombo ,were you?

Michael (2m 44s):
I guess you can say that

Phil (2m 47s):
While you were studying, you came across and met your current partner, your current business co-founder, Nicolas B Spoors. Tell us about that meeting, what it was that piqued your interest in investing and starting this fund.

Michael (3m 2s):
Yeah, so I entered university around 2013 and I would say I met Nick probably around 2014, 2015. And we became friends, it was through like an indirect meeting through a mutual friend group that we had, we shared. And I guess you could say there wasn't much interest, I guess you could say, in our friendship to begin with. But then eventually around 2016, he ended up being a roommate for a few months. And, you know, we found that we shared a lot of passions within economics and he was extremely passionate in math and shared his projects that he'd been working on. And eventually he invited me to help work with him on his investment process that would eventually become the investment process for Infinitary Fund.

Michael (3m 51s):
So it was almost accidental, in a sense. But, you know, once we found out what our mutual interests were, we just clicked very well together and have been working together pretty much since 2016.

Phil (4m 2s):
I suppose, sharing a room would lead to some longer conversations.

Michael (4m 6s):
Oh yeah

Phil (4m 7s):
What were these conversations about because he wasn't from an investing background?

Michael (4m 12s):
No, he has a mathematical background. So at the time he was in graduate school for mathematics at the same university. And so he doesn't come from an investment background, but his sense basically was that, you know, if he was going to go into a mathematics background, he wanted to apply it to something that would also be able to generate income for him. So that was the primary idea that made him start working on investment philosophies. And then that eventually culminated into his idea to launch this fund. We talked a lot about a lot of things. Honestly, our interests were broad between not only me and Nick, but also the third roommate that we were living with at the time.

Michael (5m 0s):
We'd talk about anything ranging from just general philosophy to political science, geopolitics, economics, math, you know, really depends, but we had a lot of interests and it wasn't difficult to spend lots of time talking about something along those subjects,

Phil (5m 18s):
Which particular aspect of mathematics was Nick able to apply to investing philosophy?

Michael (5m 25s):
So I guess you could say set theory if people are familiar with that,

Phil (5m 33s):
Isn't that Venn diagrams, Venn diagrams, a pout of set theory.

Michael (5m 36s):
Yeah. That's like an example of way to conceptualize it. But set theory is essentially the way that most of mathematics has been built upon. So if you think of a simple expression, Y equals MX plus B, that Y is essentially a set of the components that make it up. So MX plus B is packaged together into one set, which equals Y right. So set theories, essentially a foundation again for will a lot of what mathematics is. But what Nick has discovered is that there have been some errors in the current foundations of mathematics and in set theory.

Michael (6m 16s):
So what that basically means is that he's deviated from Orthodox mathematics and has reconstructed set theory based off of his own research, which is how he was able to devise the insights that we now use for the fund.

Phil (6m 30s):
Well, let's get back to you. And you mentioned in your topics of discussion amongst your roommates economics, and that's your major area of interest. And I was just interested to hear about your ideas about the common characteristics of wealthy countries.

Michael (6m 43s):
Yeah. I mean, there are certainly no shortage of contrasts in the development trends of fining and combinations, but there are certainly some shared characteristics to varying degrees. But one main thing that you generally see among high-income countries, examples being the United States, Germany, Japan, France, is that there's usually a lot of intense collaboration at one point or another, between government sector, private sector, academic institutions. So what that usually looks like practically speaking is a large amount of money and attention is usually diverted towards a particular industry or aspect of the economy.

Michael (7m 25s):
And just the fact that if any success is found in these areas, usually that breeds a lot of compounding growth within those areas. You can think of just the way that when any area becomes economically viable, you usually see a lot of compound growth over time. You know, you think of New York City or Silicon Valley, you know, those places didn't start off in the sense that they were planned to be what they are today. They started off, they became successful when that success basically compounded over time until they are now the economic powerhouses that they are today. Does that make sense?

Phil (8m 3s):
And some of the common characteristics, I mean, we had a previous discussion where you mentioned that a lot of government investment this arm I'm assuming is the kind of collaboration that you're talking about can lead to some of these greater benefits leading forward.

Michael (8m 17s):
Yes, absolutely. So I can give you two specific examples of what I mean, just to paint a better picture. So if you think about the United States, a lot of growth that it experienced in the late 18 hundreds and also the early 19 hundreds came about from agricultural productivity increases. That was a process that started in the 1860s with public policy acts from the government to basically create foundations for state universities, to pursue research in mechanical and agricultural arts, because that was moderately successful at the time more policies were enacted to give those areas more money.

Michael (8m 58s):
Eventually by 1940, almost 40% of federal funds were directed towards essentially agricultural research. And basically from 1940 onwards, that's when the sector started reaping huge dividends in agricultural productivity. And that also created indirectly. And this is what I mean by success, breeds success. These consistently funded areas over time basically created a situation where now you have chemical companies that sprouted up like Monsanto and DuPont. And those had indirectly created innovations in the chemical sector as a result of all this research activity and just indirect sector, so to speak.

Michael (9m 39s):
So another example would be if you looked at Japan and what happened after isolation ended in 1854. So the government of Japan set out to basically change a lot of different aspects of the economy. So they can be competitive, not only because they were threatened militarily, but also because they saw a lot of innovations that were taking place abroad that they wanted to take advantage of. So what happened is they not only imported a lot of technology to reverse engineer it from countries, such as Britain, but they also imported professors from these countries. So the university of Tokyo, for example, essentially has a huge legacy in engineering, but a lot of the early parts of that, like a started with a lot of imported British professors coming into the area.

Michael (10m 25s):
So, and an unintended consequence of this was a lot of graduates from these places that they set up, went on to found major manufacturing companies in Japan. Now that wasn't the necessarily intending goal of what they were doing when they set up these academic institutions, but that is just an indirect result. So that's kind of what I mean by compounding growth. A lot of attention and money goes into an area it's successful and over time it just continues to stack and people consolidate even more and companies grow up around these spaces and eventually you get this well oiled machine, so to speak.

Phil (11m 5s):
So in a broader sense, many of the companies that we invest in, in the stock market are the direct by-product of these forces then.

Michael (11m 13s):
Yeah, well direct or indirect. So they certainly reaped the benefits of these forces, MIT. For example, we all know how much innovation comes out of that university. And also how many people come out of that universities that start companies. A lot of the money they received, for example, in the 1940s, came from the government like office of technology programs that came from the US government. So again, it's all an interlocking force that works together. And again, it's not necessarily intentional, although there could be certain aspects of it that are intentional, but it's just the fact that again, that you're having all these people working together towards some sort of common goal.

Phil (11m 57s):
So your thoughts about economics and economic policy and economic theory, does that inform any of the investing philosophy at Infinitary?

Michael (12m 12s):
We certainly look out for what's going on in the economy and within politics as well, but we use a quantitative process driven by an algorithm, which isn't necessarily informed by economics. It's more informed by the underlying mathematics that Nick had generated when he was doing the initial research. So I guess you could say, we go deeper than economics is again, economics is more surface level when it comes to using mathematics to solve a problem.

Phil (12m 46s):
So many hedge funds do use algorithms to base their investments on, is that the case? Is that how many hedge funds work?

Michael (12m 55s):
It depends on the hedge fund. There are quant funds, which yes, they will systematize via an algorithm.

Phil (13m 1s):
Yep. What is a quant fund? What does that mean, quant fund?

Michael (13m 5s):
So it's a broad term. If you think of a common way that people can decide to pick a stock, again this isn't advice, but they may, might look at a situation and say, look at Home Depot, for example. And they will say, okay, there's a hurricane coming a major hurricane. And when that happens, usually Home Depot stock rises because a lot of materials are needed to rebuild the area that was devastated. So one might say that's a qualitative assessment, essentially. So you look at that and you say, okay, I'm going to buy the stock because of that. So that's a qualitative assessment, whereas a, basically a quantitative process means that you're using an algorithm to derive your process, to run your process, as opposed to a quality to measure like choosing to buy Home Depot because there's a hurricane coming and you want to hedge the fact that people are going to buy materials to rebuild.

Phil (13m 57s):
So the mathematics behind your fund are they based on things like price movements and volume and the kind of things that you see as parts of technical analysis,

Michael (14m 6s):
It looks like technical analysis in a sense, but it's not technical analysis. It's essentially a new form of data analytics that we've invented. Basically, all we need to do is input price, data, but the, what it's doing with the price data is fundamentally different. As far as we can tell from what anyone else is doing. So that's part of what gives us the edge, but also just the underlying mathematics that it's using and machine learning techniques that it's using are also like gives us its power.

Phil (14m 35s):
And so this analysis is this applied to the whole of the stock market, or just particular parts that you're only interested in.

Michael (14m 42s):
So we focus mainly on large cap stocks. So primarily the S&P 500. So we collect price data every day on all the stocks within that set. There's that word set that we're targeting. And then the algorithm is essentially able to generate new patterns that we don't predetermine. And then we choose our stocks from there, from its recommendations.

Phil (15m 8s):
Algorithms can be very simple things as well. They're not necessarily going to be incredibly complex, but many investors do set up a process where they want to have a checklist before they're going to jump in and buy a particular stock. Is that analogous to what you're doing?

Michael (15m 26s):
Do you mean the final product or do you mean it as a part of the process

Phil (15m 31s):
Is part of the process

Michael (15m 32s):
In a sense sort of, so the algorithm in oversimplification produces a list that we should invest in of stocks, but it doesn't automatically execute the trades. And we do that to kind of have that final check on the process. So we look at the companies and, you know, we make sure there's nothing immediately obvious going on because obviously an algorithm is faulty. He may not necessarily have all the information. So, you know, we checked to make sure that there isn't anything major going on in the company that may not have detected, but it doesn't take much. It's a very short check. There really isn't much that we have to rely on for that.

Phil (16m 12s):
And how many stocks would you have in the fund at any particular moment?

Michael (16m 15s):
So it depends, but essentially what happens if someone were to give us an allocation, the algorithm doesn't know the amount of money that's being inputted, but it will almost always proportion the output by about five stocks in one bond ETF. So at this point I would say we have about 30 plus stocks and some bonds sprinkled in there at this point in our portfolio.

Phil (16m 41s):
And what's the churn like? How often would stocks be added and removed to portfolios? It actually sounds like you're doing it on individual allocations at any one time.

Michael (16m 52s):
Yeah. So obviously not everyone in the fund comes in at the same time. So what you end up with are essentially sub portfolio. So you have the entire portfolio, but then is if a batch of people come in that make up 30% of the value of the portfolio that 30% value will then begin to clock where after that portion has been in the portfolio for about a year, then we sell after the fact. So it's a one-year timeline horizon for any particular batch that we've bought at any particular time.

Phil (17m 27s):
Is there any reason why you only focus on the top 500 companies rather than the whole universe?

Michael (17m 31s):
Yes. Because S&P 500 is a space where you can still capture a large amount of growth, but you can also mitigate the risk to a large extent, because usually the, the adage in the industry goes that if you want high returns, you've got to take high risk. So the S&P 500 is a place where we've personally believe that you can balance the two where you can still get the high returns, but you don't have to get the high risk as well. Because a lot of these companies, they're not disappearing anytime soon, the likelihood that the entire portfolio would sink in any given time is so astronomically low because these companies have been around for years, if not decades, some, even over a hundred years.

Michael (18m 16s):
So they're solid companies to invest in, but also because these companies are so large, you could continually put money in without diluting the strategy. So say if you were investing in smaller companies, you would have only so much runway space before you would have to pick another stock. Because at that point you're dealing in hundreds of millions instead of tens of billions or even hundreds of billions for the companies that are in the S&P 500.

Phil (18m 43s):
Tell us about emotions, obviously having a process and an algorithm like this means that emotions aren't playing a huge part in the decisions that you're making in the fund. Is that a big help for yourselves and investors?

Michael (18m 57s):
Yeah, I believe so. I think obviously in a situation like the stock market, and as you personally put more money into it, I think it becomes more dangerous when you react emotionally. So if you think about like a market crash, people always sell on those because either they're afraid or they're trying to rebalance, or essentially just trying to keep ahead of the curve. And our philosophy is that if you put in a strategy and you put it in metrics that are able to withstand shocks to the system, then you should not react in those situations because that's usually where you lose.

Michael (19m 36s):
So for example, when the pandemic crash happened in early 2020, where even the crash that's happened as a result of the Russian Ukraine invasion, it's because we're long-term oriented. You know, we don't sell in those situations because our system is designed to handle the shocks. It can be very tempting because in some cases, you know, we're seeing the value go down, but almost always, if not every time, you know, we've come out on top, if we just stick to our guns. So if you have a strategy and it's workable, even in a system that experiences a shock, you want to stick to it.

Michael (20m 18s):
You don't want to try to beat the market so much by selling in the rebuy, because usually that never works flexibility. And an investment sense has been shown in academic research time and time again, that selling to not lose more money. And then rebuying at a later point, just doesn't work. You're better off almost always sticking it out because usually what happens is that you miss the bounce back and that's a lot of growth that you'll miss. If that happens,

Phil (20m 44s):
You just want to survive. Don't you,

Michael (20m 47s):
Even for us,

Phil (20m 47s):
No investors in general.

Michael (20m 48s):
Oh yes. Yes.

Phil (20m 50s):
It's all about survival, isn't it?

Michael (20m 53s):
Yeah. Yeah. And a lot of people, you know, they're chasing one or two or 3%, you know, and a lot of these big institutions, they're trying to save those margins, but we don't believe that's the most efficient way to go about it. So that's why we don't do that.

Phil (21m 8s):
You mentioned previously that bonds play a part in some of these portfolios or in this portfolio, is that part of the cushioning effect?

Michael (21m 16s):
Yes, but it's not by any means the majority of the cushioning effect. And I don't know if I would say that we even have so much a cushioning effect built in it's more of that. The algorithm has been fine tuned in such a way where it's able to pick the best stocks, even in a situation such as the market crash. So I wouldn't say that it's an all weather fund, but that's in a sense what it's doing. So if you think of, you know, the risks associated with the market crashed on any given year, now you have a bell curve. What is the likelihood of a market crash happening?

Michael (21m 56s):
The tail ends, those small percentages of the two and a half percent would have traditionally is, is essentially what those small risks are in terms of a market crash. But when you plan for the entire bell curve, you're essentially able to get an output that is somewhat resilient in all situations.

Phil (22m 15s):
So we're recording on the 28th of April and the market's been through, of course, just, you know, it's a little bit of a more volatility and then suddenly a little bit less volatility at the moment, but in your view, what's the macro economic looking like this year 2022.

Michael (22m 32s):
Yeah. So I can mainly speak to what's going on in the United States. I have not as much knowledge as to what's going on in other major countries around the world, but as far as what's happening the United States, I think we're going to continue to see rising inflation. I think a lot of people think that, and that's primarily going to be driven by energy price increases. Essentially. I think it's been very palpable in the last year and a half, like where the price increases have been. You know, you go to McDonald's, it's very obvious to see that they've increased their prices by a large margin. So I think inflation is essentially where we're going to be looking at for this year.

Michael (23m 15s):
And I think a lot of people don't really expect it to cool off until 2023. Maybe even the tail end of that. I do wonder what's going to happen with the interest rates. I think in a sense it's almost too little too late because inflation is in a sense almost a runaway train. So you're seeing the federal reserve now reacting where they're starting to consider raising interest rates by half a percentage point at any given time. And they've talked about doing that maybe three or four times in 2022 alone, but this is probably something they should have done already before any of this had happened because now inflation is risen and it's continuing to rise and it's not getting any better.

Michael (23m 58s):
And I worry that it's probably going to be a similar situation as what happened in the 1980s, where we experienced high inflation and not large amount of growth.

Phil (24m 6s):
And that's going to be quite a break on the economy because one of the major forces that can act on markets is rising interest rates.

Michael (24m 14s):
Yeah, absolutely rising interest rates can in a sense, be good for an economy. Obviously it depends on where the business cycle is. But I think part of the issue is that for the last 10, 15 years, essentially since 2008, we've had a situation where we've had cheap debt. And there were a lot of checks in place to make sure that the people who are receiving debt should be receiving debt. So, you know, you think about what happened 2008 in the mortgage crisis. A lot of people who receive mortgages, who maybe, you know, they weren't the right people to receive a mortgage. You know, they weren't able to keep up with it. And I think a similar situation has arisen where now you see a lot of companies coming out of the stock exchange.

Michael (24m 55s):
They're not profitable. Some companies don't even generate revenue and yet they have raised billions of dollars. And I think that's partially as a result of the cheap debt situation. And those are just inefficient allocations of money. You know, if interest rates were higher, I would imagine money would have been allocated in companies that are a little bit stronger

Phil (25m 15s):
As always, we might say an unraveling and basically the market correcting itself in a sense.

Michael (25m 20s):
Well, yeah, that's essentially what happens is that when you have all these companies that have not only cheap debt that are not profitable, it don't generate revenue and they don't do well in a normal market situation. Why don't you start to raise the interest rates, everyone flips out. That's why you see the market reacts so aggressively when rates rise by 25 basis points. When in reality, that's not really a huge increase, you know?

Phil (25m 46s):
Yeah. And a lot of people don't think of a basis point. So that's a 0.25 of a percent. Is that correct? Yep. How long has Infinitary been around for and what's the performance been like?

Michael (25m 56s):
So Infinitary Fund launched in January, 2019, our performance has essentially been a CAGR of 20 plus percent, which is essentially the average return that we experienced at any year for 2019, we had about 55% after fees and 2020, it was about 4%, 2021, it was about 44%.

Phil (26m 17s):
How can listeners find out more about Infinitary Fund?

Michael (26m 21s):
Yeah, so we have two ways or two avenues where people can go, they can go to the Twitter, which is @InfinitaryFund handle, or they can go to LinkedIn, which is just Infinitary Fund, LP. And in both pages, we post insights about economics, math and sometimes our thoughts about the market as a whole.

Phil (26m 42s):
And of course we'll put links in the episode notes and the blog posts so that you can just click on it rather than having to try and write it down as we listen. Michael Mills. Thank you very much for joining me today.

Michael (26m 52s):
Thank you, Phil. Appreciate you having me on.

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