Episode #93: “There Is No Strategy That Outperformed the Stock Market Every Single Year”
Guest: John Reese. John is the founder of Validea.com, the author of The Guru Investor: How to Beat the Market Using History’s Best Investment Strategies, and is also a regular columnist for RealMoney.com, Forbes.com, NASDAQ.com, and Canada’s Globe and Mail newspaper. For over 19 years, John has been conducting extensive research into quantitative investment strategies. The goal of this research was to find strategies that consistently outperform the market. He combined this search with his computer science and artificial intelligence background to develop the multi-factor analysis models that are used by Validea Capital Management today.
Date Recorded: 1/30/18
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Summary: In Episode 93, we welcome entrepreneur, author, and quant investor, John Reese.
We start with John’s background. When John was a child, his father was a subscriber to Value Line, and John related to the charts and numbers. Later, this love of numbers took him to MIT, where he researched how to take the wisdom from books and turn it into computer programs. Years later, when he sold his company to GE Capital, John needed to learn how to invest the proceeds. Yet, he wasn’t sure which investment guru to follow in doing this. He decided to study a handful of gurus, and was disappointed to find that there was no repeatability and sustainability of outperformance over multiple time periods.
However, John then came across Peter Lynch’s One Up On Wall Street. In the book, Lynch had provided enough detail about his strategy that John was able to translate it into a computer program designed to pick the stocks that Lynch might have chosen. The results were solid. John then moved on to Ben Graham, eventually codifying 12 different guru strategies. He then put his research up on a website, which eventually morphed into Validea.
Meb asks about the challenges of this – namely, many managers have a qualitative component to their stock selection as well quantitative. How did John account for this?
John tells us this was very challenging. He had to re-read the various books multiple times, determining whether the printed word actually matched what the guru did in the market, versus his actions revealing more information or biases. Meb asks about filtering the incredibly long list of potential gurus to follow, and John tells us the list actually wasn’t too long. Most gurus didn’t have a sufficiently-long track record of performance, or they didn’t describe their strategies in sufficient details as to be able to be codified.
Meb then asks how John determines when a period of underperformance reveals a manager has lost his touch, versus the manager’s style is simply out of favor.
John tells us that he first looks at the length of time in which the strategy worked. If it was long enough, he tends to believe that, at some point, the strategy will come back into favor. He goes on to tell us that in all of his research, he found that there was not one strategy that outperformed the market every single year. They were these periods of going-out-of-favor that paved the way for the outperformance that occurred when the style came back into favor.
The guys then jump into an actual example of how John’s guru quant strategies work, using Buffett. Be sure to listen to this part for all the details.
Moving on from Buffett, Meb asks if there are any common attributes to the models that tend to do the best – any broad takeaways.
John tells us that, over time, the more successful strategies tend to have a value orientation, some kind of debt criteria, and they’re all profitable.
Meb asks – “Okay, gun to your head, which strategy has outperformed?” I’m going to make you listen to find out John’s answer, but odds are you’ll be surprised.
Next, the guys turn to factors, with Meb asking if there are any combination of factors that John tends to prefer. John says he likes momentum and mean reversion. This leads into a conversation on timing factors.
As usual, there’s far more in this episode: practical guidelines for listeners looking to follow along… portfolio construction in today’s challenging environment… what John would have done differently if he could start over again on Day 1… a roboadvisor for income investors… and of course, John’s most memorable trade.
This one happened the day after Black Monday. What are the details? Find out in Episode 93.
Links from the Episode:
- 1:36 – Introduction and the arc that led John into investing
- 2:43 – ValueLine
- 3:54 – Barron’s
- 3:55 – Fortune
- 3:55 – BusinessWeek
- 3:56 – Money
- 3:56 – SmartMoney (now MarketWatch)
- 5:07 – One Up On Wall Street: How To Use What You Already Know To Make Money In The Market – Lynch
- 5:58 – The Intelligent Investor: The Definitive Book on Value Investing. A Book of Practical Counsel – Graham
- 7:02 – John’s process for his stock screen and what he learned the first 10 years
- 10:09 – How John determined what books were worth studying
- 12:27 – How John made his final selection of investment strategies
- 13:14 – What tools John utilized in his research
- 15:57 – How John responded to a period of underperformance from a manager
- 18:48 – A general overview of one of John’s models (Buffett)
- 24:10 – The operational logistics of how people could follow this screen
- 24:23 – Validea
- 25:14 – AAII: American Association of Individual Investors
- 27:27 – The Guru Investor: How to Beat the Market Using History’s Best Investment Strategies – Reese
- 27:55 – Sponsor: Personal Capital
- 28:52 – Common attributes to the models that tend to be the best
- 30:59 – Any particular model that has been a positive outlier
- 31:20 – Foolish 8 Strategy
- 31:55 – What factors are particularly useful? What combinations are effective?
- 32:25 – What Works on Wall Street: The Classic Guide to the Best-Performing Investment Strategies of All Time – O’Shaughnessy
- 35:27 – Timing factors that come in and out of favor
- 37:03 – Practical takeaways for instituting John’s strategies on your own
- 39:03 – Thoughts on portfolio construction
- 40:55 – Do the screens work in international and developing markets
- 41:55 – Anything that John would do differently in the beginning knowing what he knows now
- 43:56 – What has John excited today
- 48:10 – Any tech trends that are piquing John’s interest
- 50:02 – John’s most memorable investment/trade
- 52:15 – Connecting with John – Validea, @guruinvestor
- 53:06 – Sponsor: Personal Capital
Transcript of Episode 93:
Welcome Message: Welcome to “The Meb Faber Show,” where the focus is on helping you grow and preserve your wealth. Join us as we discuss the craft of investing and uncover new and profitable ideas all to help you grow wealthier and wiser. Better investing starts here.
Disclaimer: Meb Faber is the co-founder and Chief Investment Officer at Cambria Investment Management. Due to industry regulations, he will not discuss any of Cambria’s funds on this podcast. All opinions expressed by podcast participants are solely their own opinions and do not reflect the opinion of Cambria Investment Management or its affiliates. For more information, visit cambriainvestments.com.
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Meb: Welcome, podcast listeners. Today, we have an amazing show with you with one of the most respected quants out there. He’s been doing research for over two decades, combining quantitative analysis with computer science, a little AI background thrown in, to create various multifactor analytical models. He’s also a frequent speaker, writer, columnist. He’s tjhe founder of Validiea Capital Management. Welcome, John Reese.
John: Thank you very much, Meb.
Meb: So I’m calling you from warm sunny California. It’s snowy, cold in New York, but let’s take a step back in time. I know your investment history, have known you for awhile, but some of the podcast listeners may not. So let’s go back to kinda, I would say start at the origins of what brought you to starting an investment business because you didn’t, much like myself, being from an engineering background, you didn’t start out in the investment world. So maybe talk a little bit about the arc that set you upon setting up Validea.
John: Well, there were two or three little things that led up to this and a big thing. One that goes back to when I was still in elementary school, my father subscribed to Value Line. So every single week, he had this great magazine or issue of 100 stocks with a lot of different quantitative data that piqued my interest in the stock market and stayed with me for a number of years. I didn’t act on it at that time, but it got my interest. And I was highly could related to both the charts and the numbers.
Then I went to MIT to study electronic computers. And while I was there, I worked for the MIT Artificial Intelligence Laboratory. And my project was basically how to take wisdom and information from books and translate the ideas into computer programs. So that computer programs could actually implement the knowledge that was in books. That also was the second piece that would come to be useful many, many years later.
Then the third piece is after getting my MBA and going out in the business world, I started my own company, grew that over nine years, sold it GE Capital. And then, I was looking how to invest my own money and this time for real. And at that point, I embarked upon a research project. I was reading maybe 50 different periodicals and publications at the time, “Barrons,” “Fortune,” “Business Week,” “Money Smart,” “Money,” newsletters, etc. And I was asking myself the question, “What one person should I listen to? Whose strategy should I essentially follow?”
So I started to write down each of the recommendations that I came across them from each of these sources, and kept statistical track, and then started to go back for five years’ worth of data to see who I could essentially follow. And after all that research, I came to the surprising and disappointing conclusion was that whoever I found that did the best, over any period of time that I selected, whether it’s one year or a shorter period of time like three months or a week, whatever it was, there was no repeatability, no consistency in what would happen in the following or subsequent period of time. Therefore, I was unable to identify one key source of who indeed should I follow for the best recommendations.
So along this time, I was also starting to read some of the investing books, many great investing books, when I came across one in particular that really stood out to me, which was Peter Lynch’s “One Up on Wall Street.” So here we have somebody who already had a legendary track record, who wrote about how he went picking stocks that made him successful. And, more importantly, and here’s where it goes back to the MIT artificial intelligence days, but the wisdom that he expressed in his book was a suspicious detail and included sufficient quantitative information factors that I was able to translate that into a computer program that basically picks stocks according my best interpretation of the way Peter Lynch did. He picked stocks.
So as I tested this out, actually the results were very strong, such that I was very, very encouraged to actually do that, and I started with a second book, which was Benjamin Graham’s “The Intelligent Investor.”
Meb: Two good first choices, by the way.
John: Thank you. They’ve been excellent books, classic books. Everybody should absolutely read them. It will make them positively better investors. And I continued on, ultimately over the following few years. I codified 12 different gurus, and created a…ended at website, this was in the days actually just before 2000, when the internet was actually just starting to take off and I was sharing my research on the website, and allowed people tools with which they could evaluate stocks. And basically, x-ray the stocks from the best interpretation with Warren Buffet, with Peter Lynch, with Benjamin Graham, what would they think of that stock. And more explicitly, they would say exactly what. If they liked the stocks you could basically see it. If these models didn’t like a stock, it would explain about why as well, so people could basically learn from that. So that’s how I basically got started in the stock market, investing, and research business.
Meb: You know, so there’s so much to unwrap there. And it’s so interesting because it’s a lot of similarities and parallels to, you know, my career and things that we’ve done here. So one of the challenges from being a quant, you know, is that some of these managers, like a Peter Lynch, or Warren Buffet may have some, sort of, messy qualitative sort of assessments. Was there any time, kind of, as you were looking at these, where you saw some gray areas or was it actually pretty easy to quantify them? Did It matter at all? Maybe talk a little bit about the process of the first decade of doing this, kinda what you learned putting these together. Because I think a lot of people out there love stock screening, particularly there’s some, I imagine, wisdom you can impart on that process and what you learned.
John: Well, you ask a very good question. It actually was a very challenging process. Most books I actually had to read minimum of five times to really understand the detail of what was being said. In addition, I had to carefully look at each example explained by the author of a particular stock that he picked out and why he picked that. And say, “Did this actually correspond with what he said or does it reveal extra information? Does it reveal some sort of preference? Does it reveal a certain threshold of bias? Are there companies with certain debt to equity ratios that he would allow or no allow? Or, is he saying he doesn’t even look at that particular factor?”
So on top that, I would usually see if there was any other publications by or about that particular legendary investor that would also help me to decipher what he was saying, if it applied in any way to a specific industry, or excluded particular industries. So many times, that there were these revisions for it. So I’ve talked to, you know, a number of people have tried to do this themself after reading the books, and they usually find it a very difficult exercise to do. Now a few people who have actually done it they’ve created screens and published it, and I’m usually shocked to find in many cases, how different their screens were from my careful reading of the exact same book and material.
And then on top of that, you also have various industrious legendary authors who come up with revised books and methodology. And one of the things that was very interesting, because I also implemented their revised methodology, in almost every case, their original methodology remains superior to their new revised methodology.
Meb: I love it. And that makes total sense. You know, a lot of people revise them based on either what they expect the future to happen or revise them based on very recent performance. And we’ve seen this a number of times as well, where the actual messing around with a very simple algorithm causes more problems than good. So one or two more questions on this general process and we can kinda dig into maybe some of the specific models and stuff. How did you kind of think about filtering you know, this list? Because it almost seems to me like there’s an infinite amount of book writers, and academics, and famous fund managers, you know, was it something that you thought about in your head and say, “I’m only gonna look at value guys,” or “I’m only gonna look at guys or gals that are…” What was the kind of the criteria? Because I feel like this could be like an endless process of coming up with like 500 possible portfolios.
John: Well, actually no. It wasn’t an endless process. One of first key factors is, is there a long-term track record. Somebody who has a one or three-year track record just does not count. I think numerous other papers have shown, one year and three-year track records, don’t provide any predictability toward the future. So the longer the track record, the better. That was the first key thing.
Then the second key thing is that most of the books actually do not have the clarity with which you can extract the rules. For instance, a strategy that says you want to buy a company managed by excellent quality managers, that is not sufficient to implement. How do you define an excellent quality manager?
Meb: Yeah, I was gonna say, you ask 10 quants what quality means. And it’s funny, because we were doing this a couple of years ago, and looked at like, 10 of the most famous quants. And they literally had 40 different factors that they put under the category of quality. And they were all totally, for the most part, many very different. So I can totally sympathise with that.
John: That is so true. And even if they are talking about, well, you might wanna buy companies with low P/E ratios. How low is low? What earnings do you use in the price to earnings ratio? Over what period of time is the earnings? So there are a number of different criteria.
Meb: And then on top of that, like, we’ll probably get into the weeds and some screens, and we can kind of walk through one maybe to think about it. But listeners, you know, a lot of people say something like that, but it’s whether do you screen for say the bottom decile? So the bottom 10% of stocks? Well, if you’re doing that out of 2,000, you know, of 200 stocks. And so are you picking, then doing another screen to pick a consistent portfolio, yadda, yadda. Okay. So your, one, screen was it needs to be potable. Second, it needs to be a long enough track record to kinda ascertain, it’s not just short-term noise. How, then, do you kinda come down to this final list of final number? Is that something you’re consistently updating? Is it, kind of, you’re one in one out? What’s the process there?
John: Over time, usually it’s not the legendary investors anymore, but, particularly, in the most recent decade, it’s now been various kinds of factor investing and academic papers that have established long track records and usually have been through some peer review process and have been commented on by lots of other quantitative investors. That’s usually the key criteria these days.
Meb: And by the way, you know, from someone who’s been doing this a long time, you know, what sort of tools had you utilised? I mean, I imagine 20-plus years ago, you know, there weren’t as many options as there are today. Today, there’s probably a dozen great, even retail or individual stock screeners on the institutional side. You have the Bloombergs and FactSet. Was this something you had to build by hand? How did you kinda go about doing this in particularly the early days?
John: First of all, the very first model with Peter Lynch. I actually started with an Excel spreadsheet. And had to therefore, retrieve the data by hand a number of data points. It actually took about two hours to evaluate a particular stock. Then I was able to, over the years, subscribe to institutional databases. At one time, it was Multex, and then over the years it became Thompson Reuters, as an example, where you were able to get virtually complete fundamental data sets across the entire U.S. stock market.
And I was able to then, obviously using programming techniques draw and do database analysis of the particular stocks. So that became a tremendous helpful. So the screens actually were implemented not as a Bloomberg head screener, but in code using artificial intelligence techniques that actually go about thinking and making the statement the same way that the guru said that he went about selecting stocks. So we want a company that is in the top 10% of market cap, or maybe those that have a debt equity ratio of greater than two under certain circumstances.
And usually there are interesting variations of this. And something we’ll probably get into later on, but those who did use, let’s say, a price to earnings ratio, and interesting enough, less than half the gurus actually did even though that’s the most popular ratio, had some sort of proprietary twist on it that made it not a standard vanilla type of screening criteria. Peter Lynch as an example, would not apply price to earnings criteria for companies with less than a billion dollars in sales, just as an example.
Meb: You know, it’s interesting because the media, I think, does a very poor job of this, often where, you know, they’ll [inaudible 00:15:31] price to earnings ratio and is it trailing 12 months, is it forward, is it 10-year CAPE ratio, is it…? I mean, there’s literally 1000 different variants. And depending on how you talk about it, it gives totally different historical readings, and often different takeaways. And so, I think, you mentioned in time in a world of probably 500 or thousands of factors, you know, the specifics can actually make a big difference.
Let me ask you one question on kinda the whole process of calling these managers, and then maybe we’ll walk through a specific one like Buffet, just to, kind of, give some examples. One of the things I struggle with, and I don’t know the answer to this, so if you don’t have an answer that’s totally fine, too. But, you know, often with strategies or managers in this case, you know, hopefully they have a long track record. But, you know, one of the things that’s a challenge to me so much, and this applies to anything, asset classes, managed futures, whatever it may be, is when to determine whether a period of underperformance is the manager’s lost his touch, it was never a good strategy in the first place, or is it simply out of favour and it’s a great buying opportunity? Do you have any general thoughts there or any suggestions?
John: Yes, that is definitely a hard one. Where I basically lean on that is what is the length of the track record, where this actually works? So if there was a strong period of time, I have a lot of reason to believe that at some point in time, it’ll come back in as a factor. One of the things that I found about all strategies, much to my dismay, and I’ll even call it disappointment, was that there was no strategy, not one single strategy from these gurus or some 250 other strategies that I’ve looked at over time, that outperformed the stock market or even have guaranteed positive years every single year.
And we’d all love to have that consistency, but all of the strategies come in and out of favour. And it is the fact that they go out of favour for sometimes long periods of time, that allow it to come back in favour. So I actually do believe when we’d, like, isolate either factors or particular guru strategies, that have worked for quite awhile, I have a fairly high degree of confidence that the performance may not come back in the full strength that it was originally, but will still return one or several years in the future.
Meb: We’ll come back to that in a minute when we get back into factors in a little bit because it’s a topic that a lot of people are certainly thinking about. I mean, if you look at a lot of managers, I mean, 2017 was kind of a graveyard for really famous hedge fund managers finally giving up. And so many top names shut down shop, had terrible returns after a long period of underperformance, and, you know, we’ve talked a lot about…and this may be a good segue into Buffet, a lot of his style has been out of favour for much of this cycle. A lot of the stocks he loved in the early 2000s had monster performance. And it’s funny because you always get the circular coming back to, you know, has he lost his touch which seems to happen about once a decade and then it comes roaring back.
Let’s use that as a lead in to dig a little deeper into some of your models and we can use Berkshire if there’s another one you’d prefer to use or Buffet, I’m happy to use that one. But why don’t you take one of your models and kinda walk us through a general overview of how it may work?
John: Okay. And by the way, Buffet is actually a good one. And overall, Buffet has used factors, and I’m speaking at a very high, general level, that could be considered quality and low risk in terms of the factors that have mostly been success for him. How that actually gets implemented when I do it is based upon a particular process that first starts to ask is this even a Buffet-type of company? Does it have a strong brand recognition whether it’s at regional or national? Does the company have the ability to pass on costs? Is the product line complex? There are only a fraction of companies on the market that meet those criteria. So first of all, that acts as an overall screening criteria.
Meb: Is that a qualitative screen or is that something you can actually put into, sort of, a quantitative factor?
John: That one turns out to be a qualitative screening. It has to be done by looking at each of the companies that are in the stock market and what is their brand recognition. Using Morningstar data, a lot of that corresponds now to actually having a moat. So in current times, you can use Morningstar’s criteria for whether this company has a wide moat, okay?
And here’s a more complex strategy than many of the gurus but one of the first things he looked at is the earnings predictability of the company over a 10-year period of time. And he is the only guru that actually goes back that far and wants to see at least the monotonically increasing earnings each and every year with one set of exceptions. He has bought companies with up to a 45% dip in total over that 10-year period of time. So that can be like he’ll take two 20% dips occasionally. That won’t turn him off. But overall, there has to be increasing earnings usually steadily increasing earnings over a full 10-year period of time. And there is a really, really important reason that he does that.
His methodology actually banks on making a estimate of what will the company be earning 10 years hence? And let’s face it. It’s very, very difficult to predict the company’s earnings even for the current year, let alone for 10 years. So one of things that he’s doing to put that in his favour is saying, steady earnings over the prior 10-year period of time for these types of companies is a very good predictor of what they’re likely to earn over the next decade. So that’s the first criteria.
The next thing is the return on equity. Again over a 10-year period of time, he wants to see that that’s greater than 15% a year for each of 10 years. He looks at the total capital. So not just the equity but he adds the depth and he wants to see that the return on the total capital is at least 12% per year over, again, a 10-year period of time. Then he looks to see the retained earnings of the company. He wants to know of the earnings that have been kept and not distributed in any kind of dividends or shareholder buy backs, have the company made a profit with that in excess of 15% per year. So utilisation of retained earnings now becomes criteria.
Assuming that and that the free cash flow of the company is greater than zero, he wants companies that contribute cash not pay cash to pay for investments in replacement of property and equipment. Assuming it passes all these criteria, now he applies the second set of criteria which is, is the price at the right point that I can buy it now I will make my target profit? So it’s not just good enough that it passes all those other kind of requirements and criterias, he will then look at projected earnings from the company. Projected return from the company over the following decade using two different methods. One projecting based upon the return on earnings that he found, and one projecting based on a EPF projection, and he’ll average the two. If both of those come out to be at least 12% to 15% per year, he says, “Yes, this is a good time for me to actually buy.” So those are the steps or the screening that he has.
I think I missed one other very important criteria that he has. And that is a debt criteria. He would like to see the debt of the company be able to be repay from five years worth of earnings and if they can repay them with just two years worth of earnings that would be absolutely wonderful. And, you know, as you know, that’s very different. Most people have, like, a debt to equity criteria he doesn’t. He actually does it in terms of how many years of earnings does it take to actually repay that debt.
Meb: I love it. It’s so nuanced. Then how does this actually play out? Do you update it once a quarter, once a year? Is it spitting out a certain amount of stocks? Is there a minimum market cap? What’s kinda some of the operational logistics of how one might follow this?
John: Well, two things. At Validea, I publish a list that is basically updated daily of what are top either 10 or 20 stocks that meet the screening criteria. And actually, foreign portfolios out of those that are either updated monthly, quarterly, or annually depending on how much somebody actually likes to trade. So I actually provide all those variations that each of the individual investor can choose based upon their own, you know, investing preferences.
When I actually then go the next step, which is managing client money, I then apply some additional criteria in terms of liquidity and minimum market cap size to ensure that the companies can be actually traded in volume.
Meb: That’s important because a lot of people, not to criticize the AAII, which is the American Association of Individual Investors, would love, love that organization, their writing a lot of great people there, but a lot of their screens, for example, may kick out a variable amount of stocks. You may have 3 one month, you may have 20 the next, and in some cases, you end up with companies that are, if I remember correctly, under $100 million in assets that trade really on appointment. So you gotta be careful with your screens, you know, one that there’s some consistency, listeners, or two, that you can actually trade them at it be something that’s representative of an actual, possible, real world outcome.
One more question while we’re on Berkshire and maybe we’ll think about some other models. You know, a lot of people I’m sure, listening would say, “Hey, instead of buying Buffet’s screen, why wouldn’t I just buy Berkshire stock?” Is that something that you’ve ever looked at comparing? Is it similar? Is it different? You know, because he’s got a lot of private operations of you know, investments that aren’t public. What’s your thoughts there?
John: I have two thoughts. One, I think people should hold a small portion of Berkshire, but two, it is not the same thing. And the size of Berkshire, they’re very, very limited in the number of investments they can make. Virtually, you know, maybe there’s 300 or 500 companies that they could possibly buy or invest in that would essentially move the needle on such a company holdings that big. So because there are so many other companies that are available that would not be of interest to the real life Berkshire Hathaway, people can invest in things that even Berkshire can’t invest in right now, but following the original strategy that made Warren Buffet successful.
Meb: Yeah, I mean, I think that echoes a lot of what Buffet has actually said, you know, where he said, “Look at Buffet at 40 versus age 80,” you know, when he had a lot less capital to deploy could do a lot more things. And says it actually quite a bit, where if he was managing $100 million, he could be a lot more opportunistic and that’s simply a mathematical, you know, reasoning because he’s severely limited with his breadth. Like you mentioned, he only has a couple of hundred stocks in his universe, whereas you or I could probably operate a world of 2,000. So it’s reduced by say 75%.
That’s interesting. So by the way, listeners, John has a great book called “The Guru Investor.” I think it came out by the way, like, literally near…didn’t it come out in 2009?
John: Yes, it did.
Meb: Okay. So great timing for the performance of the stocks in the book, and maybe not the best timing on selling a bunch of copies when no one wanted to buy stocks because you, I think, were about a month away from the market bottom. But it’s a great book. It outlines all of these screens. We’ll post a link to the show notes, and you guys can go read all about a lot of these other ones because there are some pretty famous, interesting screens in there.
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Meb: As we talk about, kinda, the models and you’ve built these, are there any kinda common attributes to the models that tend to do best? You know, is it certain types of strategies you, kind of, gravitate towards, you know, in general? Is there any kinda broad takeaways? Or do you advocate more of like a fund to fund methodology where you should invest in two, three, four, five of these guys to get a diversified portfolio?
John: Well, overall, for investment reasons, one should invest in several of the guru strategies, ideally that are not correlated Because strategies come in and out of favour and you really need to allow for that. So somebody should really pick several of their favourite strategies to invest in. But at any particular time, what are some of the more successful strategies? It’s interesting but they tend to be more value oriented, is one of the criteria.
Second, even though each of the strategies are quite different on their own, and rarely do you see any two gurus using the same factor, one of the general factors that most of the gurus use is some type of debt criteria. And that is, whether they use it as Buffet does, actually he was the only one who looked at debt in terms of how many years does it take to pay back. Other people look at debt to equity, total debt, long term debt, various kinds of criteria like that. But usually there was a debt criteria low debt or no debt being much better. That was part of the success factors. And it makes a difference when the U.S. economy goes into a recession.
Second thing that interestingly comes up, is that each new strategy wants to see the company as profitable. They actually don’t like and pretty much some companies whose most recent year or perhaps even sometime over the past five years, have been unprofitable. And I though that was an interesting strategy because there’s so many concepts, facts, and stories where a company is currently unprofitable, but these gurus for the most part, don’t like them.
Meb: Interesting. And so going to your head, just because I know the listeners will ask this question. Is there any particular model that has been backward looking, rear-view mirror, outsized performer? Or the best, sort of, screen historically? Is there one that kinda sticks out?
John: Yes. Actually, surprisingly. Lesser known but it turns out “The Foolish 8 Strategy” by the Gardner brothers, but the book goes back about 20 years at this particular point. But it’s a fairly complex strategy that looks for high quality stocks. It has both value factors and momentum in them, and that has actually outperformed more consistently year to year, and returned the higher performance than another of the other strategies.
Meb: Interesting. But it makes sense, you know, anything that’s particularly geared towards the smaller world often so many different reasons. Institutions can’t play in the sandbox, there’s less efficient market usually, has some interesting potential. So we started to talk about some factors and we’ve already mentioned about a dozen of them, but maybe we’ll kinda switch gears slightly and stop to talk about factors. And there’s a lot of traditional building blocks. Value, momentum, you both mentioned. And I think a lot about this because I’ll be listening often to a lot of managers, where they’ll be talking in Barron’s or somewhere and they’ll mention a factor as important in their process.
And then I’ll go look at say, Jim O’Shaughnessy’s classic bible “What Works On Wall Street,” and it turns that factors are really, kind of, irrelevant in many cases on screening for stocks or not that useful. But a lot of the kinda qualitative people will think that it’s a very important part of their universe. Is there any, sort of, building blocks you think are particularly awesome? You know, where if I say, “John, you gotta construct a multifactored portfolio of stuff that you like.” And then the part B to that question is, is there any particular combinations that you gravitate towards as well?
John: Well, it’s an interesting combination about momentum and mean reversion. But figure out which stocks in advance each of those two factors will essentially apply to. I’ve been very interested recently actually in the variations of momentum that have come up. What has caught my attention recently is idiosyncratic momentum, where you actually take out the traditional French/Fama factors and see what’s left in terms of the company stocks that are outperforming, and particularly identifying the companies they were outperforming slowly or gradually over a period of time as compared to suddenly all at once. I think there’s a lot of potential there for that factor. So it’s momentum, but it’s now a unique perspective on momentum.
Another one that’s caught my attention, again, with momentum, is, you know, traditionally momentum and academics it’s kind of month 2 through 12 months ago, ignoring the most recent month. One study that I read that I thought was very interesting and has some potential, says the momentum period is actually from month 7 through 12 months ago. So I think there’s another factor there that deserves more exploration.
Meb: Man, there’s a million layers to the onion, that’s for sure.
John: And the value factor, I think, also is very important. Even though that is really underperformed since about 2009, to me, that is even more indication or stronger. It’s like a spring being pushed down and it’s going to highly recoil. So, again, if a gun were put to my head, I would strongly want value as a very big important factor there. And I would not have small cap as criteria. That I’m no longer sure if that exists or is gone forever or it’s been arbitraged away, but that’s been gone for a long time now.
Meb: Or exists sometimes in January, yeah. It’s a tough one. One of the things we always tell listeners about factors, is I say, and really about any investment approach, is I say, you know a lot of people say if you’re using value, focus on the benefits of buying the cheap stuff. And that’s all well and good, but it’s also about avoiding the most expensive. So as you think about, yes, you’re picking the cheap stuff, but let’s also think about as if you’re wiping away the, you know, cream off of coffee, or sludge on the top of something where you’re just cleaning off the bad stuff, too, which can be equally important.
Where do you weigh in on, you know, this has been a debate for the last couple of years between some of the big quant shops on factors that come in and out of favour and the ability to time them? Like, people fall on, sort of, different sides of the seesaw. You know, because some factors, like you mentioned, go through years if not decades of underperformance and then revert. And part of that, possibly, has to do to flows going in. So the example we often get is dividends over this cycle because everyone’s looking for yield or potentially moving into momentum at times or value. Is that something you guys have ever thought about adjusting? It’s another hard question, but you guys have any thoughts on that?
John: Yes. It is. And we’re still believers that when things go widely out of favour that we want to tilt toward that. I mean, not make 100% bet on it because we don’t know exactly when is it going to actually revert, but we do feel that there are very strong mean reversions in factors when they come in and out of favour. And that we can buy some extra performance when we emphasize a little bit more strategies that are greatly underperformed in the recent year or recent years. I know, you know, as this is done on paper, on this then has not come up with a conclusion or says it’s very, very difficult to do, I’m not gonna dispute that, but my gut feeling is, it’s still an important part of investing and still should be done.
Meb: I like to think it’s possible. I haven’t found any magic implications or ways to do it, but I’d like to think it’s possible. So, okay. So for the listener who’s listening in and he says, “All right. I’m either gonna hire John to do this or I’m gonna do it on my own.” Are there any practical takeaways? You mentioned using multiple managers. Is there some general suggestions you could give to listeners, say look, this is the amount of stocks you should target, this is how often you should rebalance, this is the way you can think about doing all these sort of things.
And one of the reasons I ask this is because I remember back to an old comment from Joel Greenblatt where he used to have a screener on his website, and he said that a lot of the people that took the screener and picked their own stocks did actually worse than just the screener itself. You know, you’ve been doing this for a couple of decades. Any takeaways for, sort of, the individual investor that’s maybe a do-it-yourselfer?
John: I certainly believe in the choice for people to do it, and I think it helps that they go through the learning curve when they do it themselves. But I think if anything, what we’ve learned is for about 98% of the people who think they can manage their own portfolio, where they get into problems is when that portfolio is either dramatically under performing the S&P 500, or we have a recession and the market keeps going down by 20%, 30%, 40%. Quite a few of the people who are actively following their portfolio get scared and abandon the market, usually at the worst point.
And furthermore, it’s usually several years before they get back in again. So they made two bad timing decisions for the majority of people who basically manage their money themselves. Those who are very cavalier about it and who don’t bother to open their financial statements, or check online every day, or listen to CNBC every day, they actually can do well. You know, they let their money sit for several years through strategies that they may have picked, they’ll be fine. But the most people, it’s those challenging times when the market drops or they’re underperforming that they lose confidence and switch at very inopportune times.
Meb: So if you were listening today and said, “Okay, I’m going to start implementing some of these models. We’ll do it in a low-cost brokerage or free brokerage account, we’ll be tax mindful. Maybe do it in a tax-exempt account.” But what about right now? A lot of concerns from individuals as well as a lot of institutions saying you know, we’re in for potentially a low-return environment. Maybe stocks are expensive, maybe foreign is cheap. What are your general thoughts on portfolio construction? Is it something you think about? Is there anything you guys would add to, you know, an equity only portfolio that you think may be potentially helpful?
John: Sure. You’ve actually raised a really big question and concern. And that is the U.S. stock market in particular is highly valued by most criteria, you know, including especially by the CAPE, although it’s not a short-term indicator. I am in agreement that the long term, let’s say the 10-year returns, are likely to be very underwhelming for new money invested at this point of time. And therefore, although I would not say abandon the stock market, I would say one, you know, international stocks should be a substantial portion of people’s portfolios right now, value stocks are really, really, really out of favour and have been for a long time. I think that is an area that should be emphasized for the long term. Commodities have also had a terrible run. Could be due to bounce.
And then also, the other thing for managing portfolios, and I know you’re a big believer in this, is trend following to help people get out of the market when the market has taken a very bad technical turn, get them back in the market. I think that’ll reduce the volatility and allow them to have much more successful returns over the long term.
Meb: And you mentioned international. Is that something you guys do? Screening international as well? Or think about and do you think it applies in developed and emerging markets similarly as it does in the U.S.? Is it too hard to find the data? What’s your general thoughts there?
John: We do it, but we use the ADRs that are listed on the U.S. stock markets. So within that, there are a few hundred ADRs, both emerging countries and developed countries, that it’s possible to gather a pretty good selection of international companies. Obviously not the bigger, the broadest selection, but I think you can be relatively confident in the data that occurs on there and there’s still quite a number of bargains that appear.
Meb: You know, in a world of opportunity, we talk about this so much where, you know, investors focus so much on their own shores. And it’s not just us in the U.S., but also our friends in Brazil, or Australia, or Italy, everyone seems to really think about their own world.
So John, for someone who’s been doing this for awhile and screening, and I know you guys offer an automated service, as you look back over doing this, are there anything that if you went back to say, day one, take a time machine, talk to younger John, say, you know, what are the things you may have done differently, or thought about, or maybe it’s nothing. But is there anything in general? You know, the battle-scarred older investor would maybe tell the younger version of you? Just after you sold your tech company and striking out in the world?
John: That’s a very, very interesting question. So I’m thinking about that on the fly. I haven’t been asked that before. I think, one, using a trend-following system overall would have been best. It’s only in recent years that I’ve come to the conclusion that that needs to be a part of people’s portfolio management systems.
Second, is diversify among strategies. Although I guess I, kind of, had that feeling relatively early on, somehow I was still seeking the holy grail. The one. Best one. And it was only after several years that I really realized there’s quite a few actually best strategies, but they do come in and out over time.
Meb: Yeah. It’s funny because as you think back, I talked about this a lot on the podcast before, is there’s so many lessons, there were fantastic lessons to learn. Like, you know, having a blowing up your account, or losing your money, or doing really stupid things. Preferably, listeners, when you have very little money, and you can learn those lessons early and not later. But you see so much kinda silliness going on in certain pockets of the market today, and, you know, I would never go back and say I shouldn’t have those lessons, I would certainly never do them again. You often learn more from mistakes than you do from the successes. So, all right. I’d love to keep you forever, but we got about three more questions and we’ve gotta start winding down.
What’s got you most excited today? So I know you’re a curious mind, you’re often spending a lot of time in research, and thinking about markets, and looking for Validea, what sort of research projects are you working on, what sort of new initiatives, anything else that’s in the skunkworks?
John: Well, one has to do with a robo advisor for income investors in particular. But usually it’s older people, close to or in retirement. They’re looking to get a set amount of money from their portfolios, and very few of the railroad portfolios that exist today are designed for that type of investor, who’s looking to get a much higher yield on his portfolio. And that’s challenging, particularly in today’s environment. So that has particularly excited, and it’s a point of differentiation. Then the second, the incorporation of more factors.
Meb: Yeah, you found any good ones lately? I feel, like, you know, the challenge there is that, you know, so many brilliant quants out there have so many access to the more traditional databases. And so the nuggets, in my mind, are often the things that…You mentioned even with momentum, all the different cousins of momentum and different flavours that may be superior to the very just brute force last 12 months. Are there any areas, you don’t have to name specific ones if you don’t want, but where you think there’s particularly high potential for there to be some mining in particular areas?
John: Well, I did mention the variations on momentum. The other area that I’m particularly interested in, actually, is closed-end funds right now that I’m doing a lot of personal research on. I believe it is possible to identify certain mean-reverting funds within certain categories or certain types of closed-end funds. And then to find them when they’re discounted, in particular, is it statistically large standard deviations where they normally trade.
Meb: Yeah, and so, listeners, closed-end funds, you know, if you aren’t familiar, are similar to an ETF except that they don’t have the arbitrage mechanism, so they can trade away from their net asset value. Most often, it’s usually plus minus 10%, some cases it gets plus minus 20. But there’s examples in many cases where that it’ll be plus minus 50. And one of my favourite case studies and why our markets aren’t necessarily perfectly efficient, is that a Cuba closed-end fund and I have no idea where it’s trading now, but that sucker has batted back and forth plus minus 50% in that asset value over time. Depending on who’s in power, what sort of travel restrictions there are, what’s going on.
But one of the things that you traditionally want, and you can agree with me or not, John, is that, you know, a lot of these funds will trade at a discount forever. So they’ll just go down to 10% discount and sit there and, you know, because they have a high fee. Others that oscillate around the net asset value seem to be more opportune for potential trading. Is that something you agree with, disagree with, don’t care?
John: No, I agree with it and that is one of the things that I’m trying to research is how you screen out those that are likely to sit there forever versus those that are likely to mean revert. And one of the other things I’m playing with is in combining that with the traditional price in terms of the variation. Particularly if you talk about, let’s just say a segment of closed-end funds that are bond funds, unlike, you know, equity funds they can’t, you know, run away one way or the other. So there may be some room there to interpret the price deviation as well as the discount to come up with the right formula.
Meb: We always think about closed-end funds. And closed-end funds traditionally have a very odd distribution mechanism when they kinda IPO, the initial investors get hit with this 8%, traditionally-ish. Yeah, and so, like, one of the best things you can do as investors is make sure your broker never ever puts you into a closed-end fund upon launch because they get paid per putting you into it and it’s a huge conflict of interest. In my mind, it’s strange that no competitor hasn’t existed to be able to launch closed-end funds. Maybe that’s just ETFs, I don’t know. But it’s always been a strange thing in my mind that the SEC would allow this massive front-end load, who knows?
Shift gears real quick. Two more questions. From someone who’s been a kinda life-long tech guy, you know, studied at some of the most prestigious tech school, have doing kinda quanty tech stuff for awhile. You know, the world’s changing pretty fast. All sorts of cool technology getting developed, you know, anything that as your roots are in tech, as you look forward. Is there anything in particular that’s got you most excited about technology? What may change in the next 10 years, any just general thoughts, comments?
John: I’m starting to think or look at, whether big data can actually help or, you know, put their hands around the financial projections in picking stocks. I’m not entirely convinced that they were because there’s so much variation, they’re big fat tails. But the potential there has me excited.
Meb: Interesting. Yeah. I struggle. We often call ourselves quant-lite, and so I, sort of, bat back and forth between the old Occam’s Razor, you know, like, what is the most simple way we can distill this process and keep it simple. But also there’s so many just, you know, shiny dangling trinkets and attractions and then so many other interesting ideas that suck me down rabbit holes for months and years on research, too, and new ideas. So I don’t have any particular fun takeaways. Other than the fact there’s no investment implications here, but I had bought, which is for sale by the way, I had bought a PlayStation Virtual Reality machine and you can see the just crazy potential of what’s to come in future years. Anyway, really, really fun.
But also on the flip side, there’s a lot of research coming out now that’s basically saying, you know, there’s a lot of social, emotional, and psychological issues with how connected we are, so anyway.
All right. So we gotta start to wind down. This is flown by. I can’t believe we’re at an hour already. We always ask our guests a last question which is, what is the most memorable investment or trade that comes to mind in your career. It can be good, it can be bad, it can be an investment, it could be a trade, anything that you think of. And you can name more than one if there’s a couple, but what’s…
John: Okay. I actually have two to share. One was basically in October 20th, 1987, the day after Black Monday. When I woke up to read the headlines that, you know, the Dow had dropped 23%. And I’m like everybody around me, I was really excited, and I placed a trade, although it was not easy to get through to my broker. I think it took three hours of trying. Buying Novell at that time. It’s a company that I was looking at, it was very experienced with, and that turned out to be a fantastic trade, both a great time to buy and had fantastic return. So that was one.
The second was there was a previous time that I was actually playing the options spread, on the January Effect, where the trade was buying the value lines 1700 arithmetic index future. And at the same time, shorting either the S&P 500 or the New York Stock Exchange Future. So it was sort of a small cap, long large cap short on that. And I was able to do that several years in a row successfully. Not with, you know, heart-stopping moments because there where points in time when the trade went against you by, oh, more than 5%. Of course, that wipes out your equity and you’re almost facing a margin call and then it magically reverses on that. But that proved to be a successful trade. And so finally, the value line index future was having so few shares basically sold on it that that became a very risky trade. There was, like, no liquidity left.
Meb: I love those sort of ideas. I’m gonna have to revisit. We used to write about something similar like that, man, probably 10 years ago. I’ll have to walk it forward and see how it does. John, it’s been a blast. A lot of fun. Where can investors find more information? The podcast listeners on you, your company, all that good stuff?
John: So great starting point for learning more about our system is my research site of validea.com, validea.com. So that actually, contains our free subscription research, as well as links to money management offerings. And you can also follow me on Twitter via @guruinvestor.
Meb: Awesome. We’ll add show note links to all those and post them online. John Reese, thanks so much for taking the time today.
John: Thanks, man.
Meb: Podcast listeners, it’s been a fun episode, we’ve talked about a lot. So I will definitely add all these show links to mebfaber.com/podcast. You can always find the archives there as well as we’d love for you to leave a review. Good, bad, whatever. Subscribe to the show on iTunes, Castro Overcast, thanks for listening friends, and good investing.
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