Yet Another Value Podcast - Rules based investing with Methodical Investment's David Kaiser

Episode Date: February 8, 2026

In this episode of Yet Another Value Podcast, host Andrew Walker speaks with David Kaiser, founder of Methodical Investments, a rules-based quantitative investment firm. David shares his journey from qualitative research to systematic value investing, explaining how structure, discipline, and data inform his approach. The conversation explores maintaining consistency amid evolving markets, the limits of AI, how to avoid pitfalls like melting ice cubes and governance traps, and why being different might still deliver alpha. They cover profitability screens, sector exposure, rule creation, and the timeless tension between sticking to principles and adapting to change._____________________________________________________________[00:00:00] Introduction and host's gym mishap[00:03:40] David explains Methodical’s core model[00:04:21] From qualitative to rule-based process[00:06:13] Rules vs. adaptability tension[00:09:46] Quality plus discount over pure cheap[00:12:12] Profitability and portfolio construction[00:14:18] Metrics used: net income adjusted[00:16:14] Avoiding cyclicals and false cheapness[00:18:13] Sector tilts: discretionary, energy, financials[00:19:35] Competitive edge: consistency and patience[00:20:25] Value investing's long underperformance[00:22:09] Governance traps and data screens[00:25:24] Backtest: profitable companies outperform[00:26:26] Annual rebalance and risk control[00:29:08] Quarterly profit reviews to exit losers[00:31:06] Avoiding data errors and outliers[00:34:28] Addressing off-balance sheet risks[00:37:43] Building rules: testing, common sense[00:40:06] Rule relevance and market evolution[00:42:24] Sector constraints: no biotech, limit financials[00:44:43] Avoiding melting ice cubes stocks[00:48:26] AI as risk and potential edge[00:51:26] Fringe alpha in a crowded field[00:53:26] Backtesting across multiple market cycles[00:55:11] Where to find David and MethodicalLinks:Yet Another Value Blog - https://www.yetanothervalueblog.com See our legal disclaimer here: https://www.yetanothervalueblog.com/p/legal-and-disclaimerProduction and editing by The Podcast Consultant - https://thepodcastconsultant.com/

Boarding for flight 246 to Toronto is delayed 50 minutes. Ugh, what? Sounds like Ojo time. Play Ojo? Great idea. Feel the fun with all the latest slots in live casino games and with no wagering requirements. What you win is yours to keep groovy. Hey, I won! Feel the fun!

The meeting will begin when passenger Fisher is done celebrating. 19 plus Ontario only. Please play responsibly concerned by your gambling or that if someone close, you call 1-866-3-3-3-0 or visit comex-ex-entario.ca. All right, hello and welcome to the yet. another value podcast. I'm your host, Andrew Walker. We've got an interesting one for you today. You know what? I'm a little off because I went to the gym for lunch and the waterline broke. So now I'm just, I'm in my head. I'm just a nasty, nasty person because there was no shower or not. I'm an unshowered host right now. But we've got a really interesting one for you today. It's David Kaiser from Methodical Investments. David runs a quantitative slash rules-based firm.

And I think it's going to come through into conversation. It may be a very important. for a really interesting backdrop, it makes for a really interesting discussion when you're saying, hey, you're coming up with all of these rules. You know, a lot of the things I've talked about on the podcast, how are these rules getting impacted by AI? How do you think about when I'm doing something that's rule-based? Like, if it's rule-space, can computers copy it?

How do you think about evolving or not evolving evolving with the times, as you know, as I'll say in the podcast? Ben Graham, if you read the intelligent investor, he's telling you to buy things for two-thirds of networking capital. Well, guess what? you haven't bought anything but Chinese frauds in the past 40 years if that was the only thing you were buying. So how do you think about maintaining a rules and a value-based in almost a religion, but, you know, evolving with the times or not evolving. So I think it's a fun conversation. It's going to be,

you know, I try to start off with if you are a fundamental focus investor, what's one thing you can learn from rules based on. But I think it's going to really make you think about investing and sticking to principles and everything. So we're going to get there in a second. But first, I'm going to stop rambling and go to a word from our sponsors. This podcast is sponsored by AlphaSense. One of the hardest parts of investing is seeing what's shifting before everyone else. For decades, only the largest hedge funds could afford extensive channel research programs to spot inflection points before earnings and stay ahead of consensus.

Meanwhile, smaller funds had been forced to cobble together ad hoc channel intelligence or reliance their reports from sell-side shops. But channel checks are no longer a luxury. They're becoming table stakes for the industry. The challenge has always been scale, speed, and consistency. That's where Alpha Sense comes in. Alpha Sense is redefining channel research. Instead of static point in time research,

Alpha Sense channel checks delivers a continuously refreshed view of demand, pricing, and competitive dynamics, powered by interviews with real operators, suppliers, distributors, and channel partners across the value chain. Thousands of consistent channel conversations every month deliver clean, comparable signals, helping investors spot inflection points weeks before they show up in earnings or consensus assessment.

The best part is that these proprietary channel checks integrate directly into AlphaCensus Research platform, which is trusted by 75% of the world's top hedge funds with access to over 500 million premium sources. From company filings and broker research to news, trade journals, and more than 240,000 expert call transcripts,

that context turns raw signal into conviction. The first to see wins, the rest follow. Check it out for yourself at AlphaSense.com slash yavp. That's alpha dash sense.com slash yavp. All right, hello, and welcome to you got another value podcast. I'm your host, Andrew Walker. With me today, I'm happy to have

on from Methodical Investments. David Kaiser, David, how's it going? Good. Thank you for having me, Andrew. Hey, really excited to talk today. Before we get started, start this podcast the same way. I start every podcast. Quick reminder, we'll remind everyone. Nothing on this podcast is investing advice. You can see a full disclaimer at the end of the podcast. David, super excited to have you on maybe a little bit of a different chat today. But, you know, you run methodical investments. it's more of a quantitative slash rules-driven firm. And I'm always fascinated by rules and quants in markets and stuff. So actually, before I get there, why don't you just give a quick overview of methodical

and then we can kind of dive into the conversation? Yeah, so we're fundamentally driven data-focused and rules-based value investors. I guess that's the best way to put it. Let's, we'll dive and say in a second, but, you know, when I have most of the people on this podcast come and pitch single stop. individual, qualitative focus. I guess, let's just start off with a headliner. You know, if somebody's listening to this, be like, oh, we've got a quack guy on,

I want individual stocks. What's just like one thing that, you know, fundamental, qualitative investors, concentrated, fundamental investors could take away from rules-driven quantitative models that kind of would just improve their investing overall? Yeah, so, and also I'll start with, you know, my background is subjective qualitative research, right? Individual company research. So that's where I come from. Where were you before methodical? I was at Robody and Company. Cool. Actually, I'm still at Robody and Company. I know, I know. I just, you know,

just full disclosure. Bob's a friend, popular podcast guest. You've got to get their Robody name when you can. Yeah, no. And I wouldn't be where I am now, even though this deviates somewhat without Bob. And so, you know, learn so much about, you know, fundamental, qualitative, what drives company growth, you know, what to look for, what causes stocks to go up, what are the important drivers, right? And so, you know, I really got focused on, to digress a little bit, on, you know, what those drivers are, how to exploit them in more of a rules-based arena, right? So do what you're comfortable with, stick with what you like. And I like structure, I like organization, I like process, and that's kind of how I got here.

So one of the things I think you can take away is qualitative and that subjective way of investing absolutely has a place can be excellent. To have rules, to have process can give you comfort both in how it's performed over time and also in an A to B, right? So if X happens, you're doing Y. It's not about trying to figure out with each individual company, with each portfolio, however you look at it, what the next step is. And I think that's a big difference with rules-based and quantitative investing,

is that level of comfort and knowing and being able to communicate if you have clients, what happens if, right? So I think this is going to be a recurring theme through the Pakistan. Let me just riff off that for a second. So you said rule space, you know, if X happens, then Y happens. And one of my worries just overall as an investor, you know, for the past 30 years, all investors have been worried like, hey, when is passive going to replace salt? When our computer is going to replace salt?

One of my worries is the things that computers and AI are the best at is following rules, right? If X happens, do Y. It's replacing humans in all fields all across the board. And this is even before AI and software. Like if you have a rule that can be followed eventually a computer, will automate that. And when you say having this rules-based and having like, if X happens, then why, the first thing I worry about, both from a quantitative, qualitative, whatever perspective, is like, why isn't that something that's just, if it's not already being done by AI,

that's getting done by AI in, you know, six months, two years, whenever you want, but why isn't this something that, like, turns us into dinosaurs eventually? You know, I don't have an answer as to how it won't turn us into a dinosaur. I think, And my understanding, and you certainly have a better understanding of AI, I think, than I do in this arena. It's learning. It's adapting, right? And one of the things with having rules and being consistent, and certainly the way I look at things, is that history repeats itself. So it's not about adapting all the time. It's more about being patient and understanding that there are times when what you're doing may not be, create the effect you want, but that it will, and to have that patience and to have

that fortitude, whatever you want to call it, Hutzpah, right, to say, all right, I'm not adapting in terms of trying to figure out what's working today. I'm working on what has worked over, over time, look at value investing for 90 years and that sort of thing. And obviously the different techniques and that sort of thing. But we're looking at, you know, methodological. we're looking to exploit those inefficiencies. And actually, I would argue that to some extent, AI and the inputs probably feed into more opportunities and more inequity in stock pricing. One thing you said there, right, you said not adapting.

I think you used that term three times. I've written and talked recently about adapting, right? Like, when you say not adapting, I could see two schools of thoughts, right? I think of the markets as a competitive game. And in every sport I followed, the people who don't adapt, right? In the 2010s, there were the people who say, oh, the three-point ball. If you based a team around the three-point ball, you could never win the championship. Those people are out of the league now, right?

The NBA is all about three-point ball. If you rewound it, you know, if you went 50 years back in the NFL and you said, hey, we're going to run a pass-based offense, they say, offense are running base, right? And those people are out of the league. So on the one hand, I hear not adapt and I say, hey, are you, Phil Jackson, hey, how were those threes holding up in the 2010s? And you're about to get around the league.

Or on the other hand, you know, there is, there's the Lindy effect, right? Like, hey, there are these overarching principles and every, all the time, people say, hey, I can get away from these principles. And the cycle, the pendulum always swings back to them and buying things at a discount. That's kind of like the Lindy thing. So, like, how do I marry those two in my mind where I hear, don't adapt, don't adapt, don't adapt, don't have to say, markets are really damn competitive. Well, one more I would throw.

Like, if you go back and read the intelligent investors. Right? Ben Graham's whole thing was buy stuff for two-thirds of working capital. And that's great. But those are generally gone. And the things that are still in the market, they're so stressed or so likely to be frauds, like there's no off-up. Like what Warren Buffett did was he adapted that approach, right? He said, hey, let's make an intrinsic value. And it doesn't have to be a hard asset. So when I hear not adapt, how do I know it's like Lindy versus the guy who refuses to shoot three-pointers? Yeah. So, okay, two things. One in reverse order. yeah, it's not just about discount, right? As Buffett changed from what Graham did, it's about quality too, right? It's that balance. And Graham kind of was a quant in my mind because he was very strict on criteria, right? Now, his criteria was so strict in terms of what you would buy a company at that he would have no opportunities today, as you say, right?

So what I'm talking about non-adapting is maybe an approach and the rules, not that there's no evolution in the portfolio. I don't know if that makes sense. So in other words, we don't buy the same exposure, the same P.E.'s, everything like that, you know, like metrics like that every year. We're using the same techniques, and we're getting different results, not just in terms of performance, in terms of the complexion of the portfolio. And so I think you're two different things, right? One is adapting the rules over time. And the other is, does the portfolio evolve even with the rules?

The rules of the NBA haven't changed, right? To give your example, the NBA, the rules haven't changed. It's the way that teams have approached it and offenses have evolved, right? And that's the thing. And is, again, more physical, less physical, is shooting more, you know, there's all sorts of factors, right? But the structure, the court's the same size, the rims are the same height, and, you know, those type of things, rules haven't changed in that regard.

I think the three-point line came, you know, 23 feet and stuff like that. You are correct. When you said the rules are like, well, the three-point line came. But in, what, 40 years, right, it's been consistent. We have mentioned, not the NBA rules, but we have mentioned the rules that methodical follows. for long time. And I, you know, in my mind, when you say it, I've got an idea of what they are, but I could be completely wrong. And for my listeners, like, why don't we talk about, what are some of the rules that are getting followed here? Okay. So, rules in terms of structure of

portfolio construction, right? And also rules in terms of risk management and holding, a holding period, things like that, right? So what are some of our core tenants? We're looking for a balance of quality and discount, right? Probably not that we're discounting and quality, right? So we consistently apply the same metrics, and the way we do that is we take a step back and we look at the portfolio as a whole, okay? So one of the rules is pretty much everything is done on a portfolio level in terms of of decision-making, so to speak.

Two, we only hold profitable companies, and I think that's an important thing. Because we rely on metrics, and we look at the complexion of the portfolio and how it compares to our benchmark, right? And things like that. Other benchmarks, the market, whatever you want to look at, we're looking for a portfolio that looks better.

It's more discounted, has better metrics, right? So if we're doing that, we need to look at it on the whole, and by doing profitable companies only, we are allowing for reliable information. So if you look, just as an example, if you look at the Russell 3,000 doesn't matter, and it says the PE is 18, just being arbitrary. The PE is 18, but there's a percentage of those companies that aren't profitable, that doesn't go into that, right? So it's 18, but they're also unprofitable companies. So one of the rules on the tenants is to own profitable companies, right?

So that, A, they tend to outperform over time. And since we're not picking individual companies in the same way as a qualitative, subjective, concentrated investor would, we are wanting that data to be as reliable as possible. So I think I'm going on low off here. No, no, that's great. I actually had a question on data. But let me start at the smaller profitable level, and then we can zoom out today. You said we only buy profitable companies. Correct.

What is a profitable company? Is it gap net income profitable? Is it adding back one-time items? Is it EBITDA? How are you getting at them? No, no. We use net income and we exclude one-time items. Okay.

Yeah. So let's go to data, and then we can come back that. One of the things, when you say rules-based, and I understand rules-based versus quantitative are different based on which thing, but there's some similar. One of the things like with screeners, I always have this issue. And I think I email this to you and listen to my news, like Gotham ran the little book that beats the market. And they had, hey, you sort by your quality metric, gives your return on invested capital.

And then your valuation is EV2. I think they use EBIT. And you kind of blend the two. And that's how you get your blend of quality and value. And then they would have this huge asterisk that said, hey, we have to exclude biotech socks. because so many of the biotech trade for way below cash, they're too cheap, we have to exclude them. With yours, when you've got, hey, we look for profitable companies. Like the first thing, the two things that jumped off to me is, A, the biotech stock exception, right?

And then be a mining company, right? It feels like if you're saying, hey, we need to buy profitable companies, then you're going to get a lot of mining companies when mining is really effing hot, right? Gold is 5,000, and guess what, gold miners are profitable and they're thrown in there. And history suggests, like, the wrong time to buy the gold miners is when gold is 5,000 and they're trading for five times for starting because it's a super cyclical high. So I'd love to ask you, you know, let's start with the cyclical and then we go back to the BITEC. How do you handle that cyclical process, right? Because it does seem like the portfolio could tilt really heavily into cyclicals at the top of the market.

If you're just like, hey, we only buy profitable companies cheaply, that's going to push a lot of cyclicals in there. Yeah, it's a good point. So first of all, when you talk about rules, we own quite a few companies, and we don't have a concentrated portfolio in terms of equity, individual names. So we will cluster in sectors, and we will invest more heavily in certain sectors than others, right, based on exactly what you're talking about, the clustering, right? But we're not just looking at two metrics. We have a variety of metrics that go into the selection process, and it is really looking to a more holistic view, right? It's not just focusing on A and B.

It looks to create a portfolio in aggregate that has better valuation metrics and better quality metrics like return on equity, right? We want that higher. We want lower P, lower price to book. We want lower enterprise ID, eBeta, things like that, right? So to do that, you're not just taking two metrics, although I'd be lying if I said that, you know, Gotham wasn't, and what, you know, Greenblatt does is wasn't a impetus for kind of my thinking about this, right? But I think it has to be simple, but also more complex than just two metrics.

So do names get in that maybe shouldn't or they're cyclical and that sort of thing? Can it happen? Absolutely. Do we mitigate it by kind of spreading out the risk in terms of companies? yes, and we rebalance. So by rebalancing consistently, if we're wrong, we're wrong for a relatively short period of time. And again, there's that balance of quality and discount. It's not just about discount. What sectors right now are just like, like I'm sure over time, different sectors pop up, right? Anybody's running a value screener to different sectors. What sectors are consistently popping up right now? What sectors is the rules and the factors leaning overweight right now because I think it's really interesting to see what sectors are getting discounted.

Yeah, so right now we're heavy consumer discretionary, which is, it's such a broad sector, right? There's a lot of different things in there. But right now we're long, we're heavy consumer discretionary. We have some energy, not as much as we've had in the past, but this year we're pretty heavy in energy, financials is up there. I think those are the biggest. I think industrials is up there too. I'm going off the top of my head.

No, no, no, that's fine. Let me get. So one question I like to ask every podcast guest, right? And again, normally it's individual stocks, but the market is a competitive place. What are you seeing that the market's missing? Let me just ask you, like, the market, especially when you're talking about applying a rules-based model, which a rules-based or quantitative models, which, you know, that is computer-based.

You hire one computer programmer and they can go run, you know, Renaissance runs with 30 people and can manage $40 billion and they've got the best computers in the world and they can generate 50% alpha forever. You're talking to rules, so it can handle a lot more money. It's a lot more scalable. What are you seeing that it's kind of your competitive edge that allows you to compete in the market against, you know, just A, the market in general, but B, all these quant models and quad-specific firms that are trying to compete here? Yeah, I think. going back to what I talked about, which is my background, right? I didn't come at it from a scientific view. I came at it from a fundamental qualitative view, right? That's my background.

And I think that gives me a little bit of a unique perspective. In terms of competing, I go back to, and I feel like I'm being a dead horse, but the consistency and not constantly or even frequently changing your approach, and having that patience, and having the willingness to have the confidence that the market will give you opportunity, and you'll be able to profit from that opportunity. Okay, so you've got a rules-based system, and it's heavily value-based, right? It means value.

Oh, very much so, yes. The past, let's call it 10, but I think it's been more like 15 years, growth has stomped value, right? Absolutely. And, you know, you'll hear a lot of people. And it's not just quants, right? You read the Ironhorn letter. And he'll say broken market, passive lows, all this sort of stuff.

But I guess at one point, you said the confidence to stick to the rules. At what point do you look and say, hey, instead of, what's the Simpsons thing? It's like, are the kid, is it me? And then he said, no, it must be the kids, right? It can't be me. And what did you look in the mirror and you say, oh, it's not the market. It's me. Like, how do you deviate, like, kind of, hey, I'm insane.

The definition of doing insanity is doing the same thing over and over again. 15 years growth being valued versus no, I'm sticking two rules. So, you know, it's a great question. I don't have a clear answer, right? I'm stubborn in terms of process, right? And what is my breaking point, if that's what you're asking? don't know. I'm certainly not there yet. And I think historical data and, you know, talk about data supports the unsustainability of what's happening now, that people are paying unreasonable

prices for quality at this point. And so I have more confidence today than I probably did three years ago, just because of where things have progressed in terms of valuation, in terms of concentration in terms of, you know, how much people are betting on the future and not paying attention to what's going on today as much, in my opinion. We are taping February 3rd, 2006. This is like a borderblind Black Monday for payments and software. I'm curious, have payments and software started popping up in your models recently? No, not really.

We have very low exposure to like IT and things like that. And also we rebalance in January after tax loss selling. There's an added bump in terms of cheap things getting cheaper, right? Discounting companies getting cheaper. We're discounted. So what's happened over the past month doesn't really affect so much what we're doing. Let me ask corporate governance. Corporate governance has been a big focus for me.

And a lot of the stocks I know that are the absolutely. cheapest are that way because of corporate governance, right? So you've got a great business, great asset value, all this sort of stuff, and the CEO just seems determined to light the money on fire through dumb acquisitions or pocket everything for himself. And one place I could see a rules-based model really failing is that accounting for, you know, it's very difficult to read a 10K or 10Q or proxy and say, oh, this CEO is going to take everything for himself. But it's pretty easy if you're an investor and you like read two conference calls, say, oh my God. I'm swoon with sharks here. How do you account for corporate governance when you're

running this? And how do you not just end up in, you know, 15 different hold code controlled companies that look very cheap. And the CEO is going to pay themselves $50 million per year for all time. And shareholders will, you know, take what they can get. Yeah. So, So we don't have a specific fail-safe for corporate governance. You talked about really cheap, right? And one of the things we do, and I guess we're going around a little bit with the rules, is we're not buying the cheapest, right? We look at the data we're moving outliers.

The things that are really cheap in whatever metric you want to talk about, right, P.E. Price the book, et cetera, they probably are for a reason. And then also that combination of metrics is important, right? It's not just that it's the absolute or even one of the cheapest P.E companies, right? That's not enough. So in terms of corporate governance, it's not something that we apply. And I think theoretically, right, and in practice, we're applying kind of log numbers, right? Even if you take the market and you were to only buy profitable companies over a period of time, you would outperform, right? So we're not What you're talking about, I think, is more if you had a 15, 20% position in a company.

It'd be really important to know that. And it's not that it's not important to me. It's that how do you consistently screen out companies that don't meet criteria that you're comfortable with? And also, how does that combat what's worked over time? And that's the balance, right? You said if you, in the middle there, you said if you only bought companies that were profitable, you would outperform the market over time. And they, we mentioned you're referring to gap profitability. Yeah.

Okay. So what time period, like what is the basis for that? So for saying that. Okay. So if you look at S&P 600 over the past 30, 3rd. 30 semi-years, right? It's noticeably outperformed the Russell 2000. And the main difference between the two benchmarks is profitability, this profitability requirement for the SB 6. So, and there are other studies, I can't think of off the top of my head right now, but

generally speaking, if you're buying an index and you're buying profitable companies, comparatively, you will have performed in the last 30 years, so I've seen. You mentioned kind of on the sides of the discussion, rebalancing. You know, and you mainly mentioned it, I think, as December and referring to a little bit of tax loss, harvesting. I'll just throw in, no one's a tax advisor here, you know, don't take tax advice, all that sort of stuff. No, no, no. You mentioned rebouncing. How do you think about rebalancing when you're running this rules-based model?

You mean in terms of, like, what the reasoning is to rebalance when I do before in terms of, No, more, not the reasoning. I think everybody can understand the reasoning, right? You buy a company that's treated in five times fee, it's a wind field, it's trading it 25 times FEE, probably time to, if you're running a rules-based model, probably, right, but I'm more meant to, in terms of the timing of when you rebound, because, you know, again, if I was doing, if you and I were running a quantitative book,

you know, 3,000 stocks in the, in the Russell, 3,000, whatever, we're going to be long, 1,500, short, 500, net neutral, like, that's going to, and we're going to do it on quantitative value moment. that's going to rebalance basically not just every day, every minute, right? I'm guessing you're not rebalancing every minute. And how frequently are you rebalancing? And what's the thought process behind that? Yeah.

So we do a big rebalance once a year, and that isn't in January. So the, first of all, you mentioned one reason why, which is the stock goes from 5P to 25P, right? and it's because it's increased in price, right? Well, what if it does that because it qualitatively falls apart? You know, example you gave earlier about, you know, earnings fall apart, that kind of thing. So that's another reason, right? So I think it's a balance and the reason we do it at the time frame we do between giving things time to be recognized and kind of handing off like a value stock to a growth stock and also keeping things.

fresh or inexpensive discounted enough that there's a margin of safety and that there's some downside protection and not just upside. So when we looked at this, we looked at more frequent rebalancing, and it doesn't give companies enough time to kind of come to fruition. You're running a rules-focused quantitative model, right? Why isn't the right answer once a month? And if company X reports a bad quarter and it's no longer profitable or, you know, their stock trades up, why isn't it better to just kick and adjust more frequently because you're running a rule space model? And you said once a year, why is the answer not once a decade then?

Why is it not once a day? Right. So it's, first of all, we've looked at this over time, right? It's not an arbitrary number. Second of all, as I mentioned, all profitable companies, and we do review that more frequently. That's a quarterly review. We make sure that the companies are profitable in the portfolio. So if a company goes from profitable or not profitable, it's no longer in the portfolio.

And another risk tenant is if the portfolio gravitates to being more expensive than the benchmark for whatever reason, then we're going to be in significantly higher cash. That's not a normal. Why would that happen? That would happen because of either the portfolio is up noticeably, deterioration in fundamentals or some combination. Okay. Or the portfolio holds up and the market falls apart, right?

And there are multiple scenarios when that would happen. They're very unlikely, right? Especially since the discount we look for and the quality metrics we look for, we look for substantial differentiation from our benchmark where we fish. So the time period, like I said, is about not holding the leash so tight, but also being true to value. And you mentioned being value versus something else in quant, like momentum and that sort of thing. We want to consistently be a value book. And so, yeah, we rebalance.

but we rebalanced frequently enough that the portfolio isn't kind of running away. And what I mean by that is if we held for three years, there's probably a high probability that things have changed enough in the book that our valuation is not advantageous. We don't have that leverage that we do giving it, let's say, a year. Let me go back to data. I think I ask this, but I just want to make sure I'm clear on it, right? If I do a Yahoo finance screen, one of the tough things I can,

find with, you know, Yahoo Finance screen, I'm going to sort. I'm going to say, hey, show me the, let's just say the cheapest companies on a price earnings basis. And the first 30 is going to show me are unusable, right? The first 10, the first 10 had a one-time gain. Okay, you can edit that up. The next 10 are obvious frauds, you know, Chinese reverse merger frauds or something. And then the next 10 is a data error on Yahoo's price. Like maybe the company did a reverse split and the stock hasn't adjusted for that yet, or I guess it's more likely they did a split, and it's showing, hey, this company earned $100 per share,

and it's actually no, it should be 10. The stock did a split, but it chose 100, so you see that. How do you guys, you're doing it, or rules-based investing across basically all the larger U.S. companies, how do you guys do data integrity? So data integrity, we rely on CAP IQ. and testing over time, the validity of their data and the reliability of their data. Now, interesting, you said Yahoo, IQ feeds into Yahoo, I believe.

One of the things is to have some checks and balances in the way we look at the data. We don't just verbatim take. And you mentioned like mergers and acquisitions, right? That's something we look at. And if a company is involved, then it's been announced. It's not something we'll buy. So, like, there are things like that that we look at. And, you know, again, can there be an error in data?

Yes, of course. But that's another reason why, you know, we spread out the portfolio and we're not, if there's a bad Apple that gets in because of bad data, and certainly if it's with profitability, it's not going to be held long. And it should not have a significant impact on the portfolio. Think about, you know, this has been clear. cleared up a little bit with, I can't remember when it was, like eight years ago bringing operating leases on the balance sheet. But, you know, one of the issues I remember

got some used to run into was retailers looked unbelievable because they used operating leases that was all off the balance sheet. There are other companies that, you know, sometimes they do it. Or look at Facebook right now. One thing I've got a post I'll have to do at some point, but like, you know, Facebook working these complex JV structures that are like, honestly, not honestly reminiscent of Enron, right? But working these complex JV structures to keep, to keep their data centers, these huge data center buildouts off their balance.

And I'm not accusing them a fraud. I'm just saying like that literally is what Enron did as well, right? They wanted to keep the, they wanted to look asset lighter. You see in telecom right now, Verizon, T-Mobile, they're doing these complicated JVs to keep these fiber buildouts off their books. How do you look at like, and obviously I've picked a couple, Cherry picks a couple of samples, but I've now hit retailers, telecoma, there are several others. How do you look at companies that are maybe structuring things to be off-balance-y to make their

sales look asset-light, or just accounting rules make them look asset-light? Or how do you think about those types of issues? Yeah, that's a great question. And again, it's a combination. The way we combat that is a combination of not relying on any one metric, like price-to-book. and spreading things out. And there are going to be times when a company gets in the portfolio, either because there's a profitability error,

not in terms of necessarily the data, but it's a one-time item that wasn't scrubbed out. It's in price to book and off balance shoot, something like that. We're not immune, but it's not a frequent phenomenon either. So it's a great question. And I think one of the things that I'm thinking about when you're asking a lot of these questions is there's a lot of things to account for. But it's very difficult kind of to create like a perfect system. And one of the ways I think about this when I talk about like the holistic approach and like looking at value from different angles to create a portfolio with a certain complexion, I think one of the things that's important, you know, I'm Jewish.

And so on Yom Kippur, we talk about, you know, the word, I'm not remembering the Hebrew word at the moment, but it means missing the mark, right? And it means like you're human, you're fallible, you are going to make mistakes, and you want to do the best you can. It comes as close to hitting that mark every year and being the best person you can be. And I try to kind of apply that thinking to how I look at the point. portfolio, that I want a good portfolio. If I shoot for the bullseigne, I try to create a perfect portfolio, I'm going to miss things too. And like, even qualitatively, some of the names that have worked out in the past, if I look at them, I would have been like, I don't know, you know, and I would have had other thoughts. And by sticking to what works and the data,

putting my, pardon the expression, you know, faith in that over my own necessarily, necessarily my own expertise. And so, we're not, you're asking great questions. And I think a lot of those things, if I was doing qualitative research are all things you check off, right? They're all the boxes.

But when you're buying, I think, you know, let's say 50 to 80 companies in a portfolio, it's not that they're not important, but how do you account for all those things and then not exclude things that you would want in the portfolio, right? And I think that's where the complexity, the problem occurs with coming up for the rules. And that's something we're talking about, right?

Is how do you consistently find companies that have potential to pop and grow and make money for your portfolio. And how do you do that consistently? And I'm sorry. No, no, that's great. Well, a lot of it comes back to the rules, right? So how do you come up with the rules?

So, you know, the basis for all of this is things that the investors have looked at over time. Now, the rules, mostly in testing to see what works, but a lot of it has to do with, I don't want to say like common sense, but stuff that value investors would think about, right? Like, if my portfolio is upside down in the sense that it's not discounted, right? It doesn't create that discount that I want. That's not an exposure I want, right? things like that. So they're not out of the left field, I guess, is the best way to put it, right? And you talked about some of the data and some of the ways you look at the data and do I have unique data and that sort of thing. You know, I don't. It's how I look at it, right, that

differentiates. It's not that it's unique or, you know, I have some crazy rule like, you know, if I can't think of one. But, you know, if company A does X, Y, and Z and, you know, it's the third Sunday and the month falls on whatever. You know, I'm not, I'm not doing that. Well, let me, okay, so a lot of people are familiar with the Buffett indicator, right? Buffett used to say, hey, when the stock market's value trades for in excess of US GDP, it's over about it, right? And this was, you would hear this time and time again from investors.

And then, you know, if you follow that rule, if you follow that rule, you would literally, the only time in the past 15 years you would have been able to buy stocks was like the absolute depths of the global financial crisis, right? Yeah. And now, you know, I could, you could make two arguments. And I'm not saying the Buffet GDP indicator is a rule, but it was a very useful frame of, you can make two arguments. Hey, we need to stick to this rule, right?

We need to be cash and like cash is king. And one day we will get a shot. You know, one day there will be another. But I think another way of thinking would be like, hey, if you've got a buy signal that says one time in the past 30 years, it was you were good to buy. Like the buy signal is outdated. That's not a good buy signal. Why is the Buffet rule that stated? Well, in part, you know, the U.S. used to be the public companies were GM Ford and they were selling all their cars in the U.S.

So GDP was a good tracker. And now it's Apple, Facebook, you know, it's a global. But, you know, how do you think about the rules when I'm guessing some of the rules are, as you said, profitable, trading for low price to earnings, trading for. are we? I think you're probably, like, when you say those rules and we backtest them, was the back test 100 years? Like, how do we know, like, the next 10 years aren't different than the last 100 years? The rules from the last 100 years are the Buffett indicators, right? And, hey, I could imagine a world where when I say low-price earnings, good ROEs, that's probably going to push you a little bit heavier into banks a lot of the time, right?

Which tend to trade for low-price to book, good ROEs. That's historically been a great exposure. But banks have all the fintech risks, right? Like, how do I think about the rules evolving and sticking to them even? I think I've thrown a lot out there. I don't know how to quite bring it, but I'm sure people can understand where I'm going based on the buffing on that. Okay, so first of all, you know, we are looking for opportunity in every market. So we're looking for relative opportunity. We don't have an I should be clear about this when we talk about rules and we want discount of P and that sort of that sort of thing.

We don't have a cutoff, you know, like five times earnings, I'm just being arbitrary, right? So we're taking what the market gives us at any given point in time. Like when we're in balance in January, it's what are the opportunities today, right? So that's number one. You mentioned financials and earlier you mentioned biotech. Biotech is not something we invested. There's too much variability in earnings, right? You can have a drug that hit and it's going to go away next in two months type of thing.

right? It's going to go generic or whatever it is. So, so that's, that's one. And then financials is another one where we limit exposure. And we do that because exactly what you said, when you're looking for low P or low price to book and high return equity and things, you can get a lot of those. And those are not necessarily the companies that are going to drive performance over time, right? So we limit exposure. We don't eliminate exposure to financials, but we do limit it for exactly that reason. That's two areas of the market, right? So I guess you just said, hey, we don't do biotech. And I'm with you, right? Like, biotech is really effing hard. And you've got drug cliffs and, you know, up and down, coin flips, safety approvals. Like, safety approvals are the one where it's like,

hey, you can have everything right. And then you've got, sorry, not safety approval, safety issues. You've got everything right. You've done all the analysis. And out nowhere it could be, hey, this drug caused liver failure. And like, how are you going to catch that as, forget individual investor, as a big investor? Like, You just don't know. And I understand, hey, that's a risk, but, you know, those are truly out of left field. And you do that, the drug goes zero. But you said, hey, we don't do biotech.

And we systematically limit our exposure to financials. And I'm sure part of that is, hey, these things are going to drug to do bump. And, hey, like, financials are one of those funny industries where, you know, Lehman Brothers looked really cheap on Friday in September of 2008. And then on Monday, it was zero. So you've got those risks too. But that's two, like, pretty big things where you're kind of stepping in and imposing limits and systematically, how do you think about that where you are God imposing limits and

overriding these rules versus, hey, maybe if the system says we should have 15 banks and 24 biotex right now, maybe we should be leading to that. Right. So you talked about, and let me go back on a second. We don't have a hard limit on what we limit exposure to financials. It's when we're creating the portfolio, we look at it with financials and then we do a second run actually eliminating financials. Okay.

So, and then therefore there's a lot of redundancy, and I'm not getting in the whole process of putting the portfolio together, but we're open to financials on the first run. On the second run, we're limiting it. We tend to have exactly like we just talked about high exposure to financials more so than would help us in terms of performance over time. And that's the answer, right? So, like, what I'm doing, I don't think, is the, the most complex thing in the world.

It's kind of how I look at it and the consistency in which I look at it. But again, we're using the same metrics everyone else is, right? But it's, yeah, it's in terms of like, sorry, I'm also sure I thought again. That's great. One more question. This has been like at the heart of it. We've touched on some things, but it's just one that keeps hopping up. Melting ice keeps, right?

These are investors' least favorite things. The one that I think of right now is until 2016, when Disney comes out and says, hey, we're losing ESPN subs, if I remember correctly. Linear cable channels are, as many people said, they're probably the best business in the world. I kind of disagree with them in some, but it's, hey, you know, ESPN, especially ESPN, right? You've got scale because of that, you can afford to pay for the NFL. No one else can. You get the ads. If a cable channel tries to kick you, all their subs are going to.

turn or they're going to be so angry. You've got great pricing power. You've got this huge network effect, huge scale benefits, all this sort of stuff. Until 2016, it's the best business ever. Very low-cap acts, right? After 2016, it's death, right? Go pull up the chart and forget, you know, the tiny media companies like AMC, you can look at them. All the regional sports networks are, you know, in 2016, they're great. By 2020, they're all going bankrupt, left and right. look at the chart of Disney. So media companies, right, are the shining example of melting ice cubes,

and they're near and near to my heart. And they're also one that, you know, values-based, rules-based models tended to love, right? Again, capital light, after 2016, a lot of them start trading really damn cheap, and they're just cheap, cheap, cheap, cheap, cheap, cheap, down, down, down, down, down, down. We can probably think of other examples of melting ice cubes, but how do you avoid getting a portfolio

that because you're using trailing numbers, right? How do you avoid having a portfolio that looks great on trailing numbers and is just buying left and right, melting ice cube, melting ice cube, and I understand some of that is, hey, multi-nice cubes tend to be probably overly discounted, but I would just point to the media example and say, hey, you know, if you did these over the past five years,

you're no longer in business because it's involved the price of the AMC networks. So, you know, we tend to have fairly frequent turnover. think for, I don't know I'm going to compare it, but we generally have fairly substantial turn-up when we do a rebalance. So the idea of a company that is not executing the way it should in terms of quality metrics as opposed to just discounted metrics, staying in the portfolio over a long period of time is unlikely, right? And also the combination of metrics we look out, right? So there can absolutely be falling ice cubes, you see, melting ice cubes, excuse me, it can happen.

But, you know, also, we talked about the year rebalancing, right? But it's not, the focus is in a year. It's just kind of in the grander scheme. That's how we rebalance. So the idea. that, again, we try to create like a perfect mechanism for one year, it's not kind of how we're looking at it. We're looking at consistent, I don't know, maybe to use a sports analogy, baseball, we're consistent looking for fastballs, right? If we get a curve, we're going to hold off. Right. So the, yeah, the, the, the long-term play is to create alpha over a period of time, right? So, Again, because of the way we spread out exposure in terms of companies, because of the metrics we use, is that something that frequently happens that we get a plethora of melting ice cubes now?

Last question. A rules-based model, a more quantitative model, it seems like there's lots of opportunity for AI in the research process, the fundamental. But how are you thinking about and using AI, not in terms of competition or as a risk to the underlying companies, but just in terms of assisting or helping with the portfolio construction, with the rules, with the back testing, whatever it is? Yeah. So at this point, we're not really using AI. I think AI is a great tool, as you said, like subjectively. And if you're gleaning through 10 years of 10Ks and trying to, you know, find a trend or things that are, talked about, written about consistently, things like that.

I think it's really important. Is it something that we thought about? Yeah, and it's something that we could implement potentially. But again, it goes back to we believe in the opportunities that we're finding and will continue to find. And so we're not looking to evolve. And I think that's really where AI helps, right? AI helps you evolve a process. And I think that if everyone else is using AI and they evolve, I have the risk of being a dinosaur,

but I also have the risk of really being differentiated and sticking with something that will continue to work. No, it's, you know, AI is something I've thought a lot about. Yeah, yeah. Yeah, yeah. I'll see a bunch of your podcasts. You know, AI is funny because when I would say, hey, you know, a lot of people would email me and say, hey, AI, like when you talk about it as a risk to investor, you forget that investors can use it. And Buffett, of course, somebody sent it to me. Buffett, of course, had a great quote for this, you know, it's this standing on your tiptoes at the parade. Well, if you do it, everyone else does it.

So it's a counter. And I do think what you said is interesting. Someone else sent me, like, you know, a lot of times what happens is when these games get so optimized, right? So you think about the best example, it's a difficult one, but Daily Fantasy Sports, which people use, everyone started using optimizers, which would optimize like, hey, if you're in a competitive thing, it's going to optimize your bracket. Well, when everyone used it, the edge actually went to people who didn't use the optimizers, so they could build good portfolios. In this case, it's the only fantasy sports. They could build good teams, but that weren't optimized because, you know, if everyone buys Albert Pool hosts and Chase Utley, and every team has that.

Well, if you don't have one of them, you actually have a huge edge and huge variance. And yeah, it's just interesting because I guess where I'm coming with this is, hey, if everyone else is using AI, they're running into the tiptoes problem. And maybe there is alpha on the edges of an old systematic process. I'm not 100% sure, but that's kind of one of the things. Yeah, I'm not 100% sure either. But I do think that the opportunity exists, as you said, on the fringes now, right? Because if everyone is using AI, adapting, learning, trying to kind of keep up with the Joneses. And even like you talked about 15 years in the history of stock market, it's not a long period, right?

It's a substantial. It's a noticeable period, but it's not huge, right? And these things do tend to be cyclical in terms of what drives market performance. And right now, safety isn't where it's at. It's phoma, right? It's fear of missing out. And that, in my opinion, is what's pushing the market. And the idea that there will be a return to caring about,

where you are today and what your safety level is, right, relative to what you can potentially make, I think, you know, is, it is foreign to me the idea that that will not happen. Can I tell you when? No. But I banked on it, right? And I think that's the, you talk about differentiator and fringe. Yeah, I guess I'm on the fringe now, right?

value, being consistent in what I do and what's worked over time and does it work in the future? I don't know. But I believe it does and I'm betting on it, right? Last question. So I think a lot of this just, it comes out. It is, hey, I believe in value, right? I always have a religious type belief and value in these metrics. But we mentioned back testing itself a few times.

I am curious, when you think about back testing, and this was sparked by your saying 15 years is not a long time in the stock market, I agree. But when you think about back testing, how long do you think about back testing? How long do you think about bat testing and idea as trying to come up when you're looking, thinking about this? Ideally, you know, I would say, you know, several market cycles, right? So periods where when I say cycles, I don't just mean ups and downs in the market, but, you know, different techniques, different approaches, right, growth value, whatever you want to say, have excelled or worked over time. I think you'd have to look at,

if you said the past 15 years is more growth-oriented, you'd have to go back a lot further to times when value was more in favor, right? I guess the reason I ask is, again, I think about this in devolution. Like, if a lot of the, let's just hypothetically say, a lot of the value out performance comes from 1980 to 2000,

and a lot of the value out performance comes from 1940 to 1960, right? So I just use 20-year cycles. Well, 1940 to 1960, I'd tell you, get out of here, right? Like, yes, Buffett comes along in the ends, but you're buying completely different things. The market is much less efficient. There's lots of pink sheets. Like, it's just crazy out there, right? So if you're doing back-test and you're saying, hey, you know, I'm back-testing to 1940-19-50,

I say, I don't think that's relevant. If you're doing the back-test in the 80s to 2000s, well, now we're talking about a more modern market, but, you know, computers still aren't around. There's still a lot. I just wonder, like, when you're thinking about back- access, forgetting the market cycles, how are you thinking about how, like, the market evolution is just something to think about. I'm trying to remember the data was in the 90s when everyone had to report digital, right?

What was that? I think that's, I think SEC actors. Right. So I would think that would be a fairly good period to kind of be looking at because you have enough information and the data would be whole, so to speak. Cool. Okay. This is important.

David, where can people find you if they kind of want to learn? a little bit more. Oh, methodicalinvestments.com. And David at methodicalinvestments.com is my email. Feel free to reach out anytime. Perfect. David, guys, methodical investments. This has been great. Thanks so much for coming on. Thanks, Andrew. I really appreciate it. A quick disclaimer. Nothing on this podcast should be considered investment advice. Guests or the host may have positions in any of the stocks mentioned during this podcast. Please do your own work and consult a financial advisor. Thanks.