Jack Forehand – Practical Quant, Applied Value, Momentum, And Guru Replication – ValueWalk Premium
Jack Forehand

Jack Forehand – Practical Quant, Applied Value, Momentum, And Guru Replication

In this episode of The Acquirer’s Podcast Tobias chats with Jack Forehand. He is a Partner at Validea Capital Management and responsible for the firm’s overall operations and portfolio management. During the interview Jack provided some great insights into:

Q3 2019 hedge fund letters, conferences and more

  • How To Recreate A Buffett Style Portfolio
  • Is The Under-Performance Of Value Investing A Permanent State Of Affairs?
  • Big Data Leads To More Value Traps
  • Value Investing Is A Bet Against Tech
  • What Is The Academic Argument For Value?
  • Investors Should Consider A Twin Momentum Strategy: Fundamental Trends Matter
  • The Mechanics Of Value
  • A Catalyst For Change: Glamour Stocks Being Unable To Continually Meet Expectations
  • Investors Can Generate Significant Returns Using Partha Mohanram’s G-Score
  • You Can’t Completely Avoid Value Traps
  • Sailing, Like Investing, Means Spending Most Of Your Time Going Against The Tide
  • What Is The Best Strategy To Hedge Value?

You can find out more about Tobias’ podcast here – The Acquirers Podcast. You can also listen to the podcast on your favorite podcast platforms here:

Full Transcript

Tobias Carlisle: If you’re ready, let’s get underway.

Jack Forehand: Yeah, go ahead.

Tobias Carlisle: Hi, I’m Tobias Carlisle. This is The Acquirers Podcast. My special guest today is Jack Forehand. He’s a partner at Validea. We’re going to talk to him about value investing, about whether it’s broken, whether it’s still alive, what may have caused the death of value investing. We’re going to talk to him right after this.

Speaker 3: Tobias Carlisle is the founder and principal of Acquirers Funds. For regulatory reasons, he will not discuss any of the Acquirers Funds on this podcast. All opinions expressed by podcast participants are solely their own and do not reflect the opinions of Acquirers Funds or affiliates. For more information, visit acquirersfunds.com.

Tobias Carlisle: Hi, Jack. How are you?

Jack Forehand: Good. How are you?

Tobias Carlisle: Very well, thanks. Thanks so much for joining me here today. You’re a partner at Validea. Can you just tell me a little bit about what Validea is?

Jack Forehand: Sure. Validea is… our goal of Validea is to follow strategies that beat the market over time, quantitative strategies. And so what we do is we try to find books, academic papers, anything that has a longterm record of beating the market, and we try to quantify it, program the models and then allow our subscribers to follow them. Our models fall into pretty much two different camps. One is people who have actually beat the market over time. People like Warren Buffet, Ben Graham, Peter Lynch, all those people who have either written a book themselves or had a book written about them, in the case of Buffett, that outlined a strategy that at least had enough quantitative elements that we could capture it.

Jack Forehand: The other part of what we do is the academic work. So if an academic has studied value or momentum or something over a long period of time, they’ve developed a strategy that we think was well tested and it has a longterm track record of beating the market, we’ll program those as well. So we have the practitioner side of it, and then we have the academic side of it. And it all, we have about 45 models we follow right now.

Tobias Carlisle: When you’re looking at someone’s say like Buffet. So Buffett hasn’t written a book but he’s written pretty extensively in letters, and then there are these quantitative analysis of him. There’s Joel Greenblatt, who I often talk about his magic formula, which has the two parts to it, but then there’s also AQR has that study where they looked at the factor influences and so they say, “Oh, he’s livid 1.7 times by virtue of the float, and he tracks the quality factor.” How do you guys go about recreating what Buffett’s doing?

Jack Forehand: Well, in our case we use the book Buffetology, which was written by Mary Buffet, his ex daughter-in-law, and inside there, there actually was a pretty step by step guide to how Buffett might look at a stock. And so it’s important to note though, we’re not trying to mirror what Buffett holds. What we’re saying is that that quantitative strategy on its own stands alone very well. It performs very well, but we’re not trying to look at Buffett’s portfolio and say, “Well, our strategy is not working if we’re not owning the same stuff Buffet’s owning.” We’re just trying to say, “If you follow that strategy in a disciplined emotion freeway, you can do well.” And that strategy has things like 10 years of consistent earnings growth, 10 years of high ROE, 10 years of high return on capital. So it ends up being, if you take a step back and look at it from a factor perspective, it ends up being a quality type strategy. It has an element of value in it, but it tends to track quality pretty well.

Tobias Carlisle: And how has that done over the last decade because it’s been a pretty rough run for value?

Jack Forehand: It has. It’s done a lot better than our value strategies just because of the quality element I was talking about. I know you looked at… at one point in your book, you looked at Greenblatt’s strategy and said, “If you carve the quality out of there, you get a better return.” And we’ve seen a similar thing, but in the past decade, that has not been true. In the past decade, anything to get away from deep value has been a really good thing. And so Buffett gets away from deep value with the quality element, whereas stuff like based on Graham and people like that has done much more poorly just because it is tracking more of a deep value type approach.

Tobias Carlisle: You guys also have an ETF, how do you construct the ETF?

Jack Forehand: Yes, so the ETF is based on blending the strategies together. So what we try to do is, we didn’t want to reinvent the wheel when we built these strategies. We wanted to follow strategies that had worked over time, where we think we can add value is if we can combine them together. So if we can take different value strategies and create more of a value composite or if we can blend a value strategy with a quality strategy, with a low vol strategy, with a momentum strategy, that’s where we think we can add value and that’s what our ETF does.

Jack Forehand: The other element of what our ETF does, which is why it’s heavily in value right now, is we are slight believers in factor timing. We do believe when something is very out of favor, you can slowly and methodically rotate towards it and add some excess return over time. We know we’ll never get the timing of it right, but we think you can add some excess return over time, and so our ETF is actually very much a value strategy right now just because we think all the longterm metrics indicate that value is very attractive.

Tobias Carlisle: That sounds a little bit like Cliff Asness’s sin a little, were you at all influenced by that one?

Jack Forehand: Right. Yeah, actually, I wrote an article at one point that I called Sin Less Than a Little, because Cliff is talking about market timing with sin a little, and I wanted to… I said with factors you should probably sin even less than that, because factor timing is really, really hard to do and emotions become a big part of it because you’re going to be early, you’re going to look bad for awhile, and before you get the rewards from doing it. And so which we do it, but we do it very slowly. We do it very methodically. We don’t believe in binary market timing. We’re never going to say, “Today’s the day to go all in on value.” But we do think if you slowly add to something that’s out of favor, you can enhance your returns over time.

Tobias Carlisle: Well, that really cleverly answered the next question I was going to ask you, which was I think that your ETF is a little bit value biased and I wanted to know why, but I think you’ve probably covered that pretty closely for me. We’ve had your partner, Justin Carbonneau, on previously, but I wanted to get you on because you’ve written several blog posts that I found particularly interesting. And one of the things that you’ve done that I really like is asking whether, well, value has been underperforming, but asking whether this is a permanent state of affairs. And I thought you did a really good job, but do you want to take us through? I’ve got some of the headings that you used here. So one of the first ones that you highlighted, the world is different. What does that mean and what does that mean for value?

Jack Forehand: So I took this from my Ben Hunt, who’s one of my favorite people to follow on Twitter, and he’s talked about this. What the federal reserve did in the wake of the financial crisis, has changed things in many ways. Interest rates have been depressed for a really long period of time. They’re doing everything they can to stimulate the economy. What if that is a change compared to the past, and what does that mean for value? Interest rates being depressed, the evidence is mixed on whether that is a bad thing for value thought in theory it definitely would be because more of the value of a growth company is in the future and more of the value of value company is in the present.

Jack Forehand: So when rates are low, it’s better for growth with that low discount rate, but I think the evidence, the O’Shaughnessy and people like that have looked at this, and the evidence as to whether that matters is mixed. But what Ben’s point was that this is more of a random world now. You have to take what’s all these base rates and everything we were using in the past and you have to throw them out and you have to say, “Well, if this is the new world, we can’t rely on all that pre GFC data to say value is coming back.” It’s more of a random thing. And I think he was arguing more for using all the factors together and not necessarily saying something like value is going to come back because it struggled. So that is what I was getting at there, is the world may be a little bit different than the one we’ve built our data based on.

Tobias Carlisle: How do you feel about that argument?

Jack Forehand: I actually think it’s one of the better ones. Although I interviewed Jim O’Shaughnessy for our blog and he took the opposite side of that and said, “There’s been many, many times people say the world is different and none of those times was the world actually different.” And so it’s one thing to say this quantitative easing is going to go on forever and no matter what happens, they’re going to continue doing it, but we could have a change in politics, we could have a change in anything. People on the fed, and that could change. Down the road, there may not be quantitative easing. So I think it’s a good argument, but I don’t think it kills all the data that we use to support value because we’ve got a hundred plus years of data to support value, and I think that overrides the argument.

Tobias Carlisle: Embedded in your argument for why it might not persist is this idea that it actually is right that low interest rates do in fact stimulate the glamour stocks more and that tends to leave the value stocks behind you. Do you feel that mechanism is correct? That’s the way it works?

Jack Forehand: I don’t know. I think in theory it sounds perfect, but people like O’Shaughnessy Asset Management have looked at it and they’ve said there’s no correlation there. It’s interesting the, I don’t know if you follow the website factorresearch.com.

Tobias Carlisle: I do, yeah.

Jack Forehand: They just did something that said, it’s not the level of interest rates, it’s more whether the yield curve is flattening or steepening. And maybe that’s the case. Maybe in a case where the yield curve is flattening, that’s bad for value, and in the case where the yield curve is steepening, it’s better and value has a lot of financial stocks. So that could certainly make sense that that may be is a better argument, and that seems to be more backed up from the data than just whether rates are high or low.

Tobias Carlisle: One of the things that I find most interesting about many of these arguments is that the performance always leads the theory. So, for example, there are lots and lots of articles now about how price-to-book has broken down and it’s no longer a very good factor, but there were no articles like that before price-to-book broke down as a factor. So I always wonder whether are we just fitting a narrative to the data that we see? And if it goes back to working again, do people say, “Oh, well, that stuff went away?”

Jack Forehand: I think that’s right. You’re not seeing any articles right now about how low volatility is broken because low volatility has been killing it and if low volatility ever becomes broken, it’s going to be after low volatility has struggled for a really long time and then we’re going to take the time to look at it and say, “Is there something wrong with this?” So, yeah, the thing with price-to-book is the arguments make sense. Intangible assets are an issue for price-to-book. Some estimates are intangible assets are something like 80%, 85% of total assets at this point, and a price-to-book calculation doesn’t even consider them.

Jack Forehand: And a point you made, I interviewed you for our blog, and a appoint you made to me is all these buybacks have created these negative equity situations. So that’s also distorting price-to-book. So I think when you look at price-to-book, I think the conclusion to draw is, price-to-book probably has the worst performance of any of the value factors, but it nonetheless still has an excess return. It’s not something that is producing a return less than the market over time, but it is probably the worst of the various factors if you look at the long term data.

Tobias Carlisle: One of the interesting things, and I’ve… you can get from the Fama French from Ken French is website that they’ve got. He posts all of the free data series and one of the data series that he has is price-to-book value data broken into all of the deciles and terciles and anything you might want to look at. And that has worked pretty well over the full data set except for two very pronounced periods, and one of them is right at the very start of the data set and it starts in the 1920s. I can’t remember whether it’s pre or post the great crash, but it definitely had this huge underperformance that looks very similar to the huge underperformance that it’s having now, and it’s been a mainstay strategy because there’s of recommended that your book value shouldn’t change that much on a quarter to quarter basis, whereas earnings can be all over the place. Do you think that if it comes back again many of those arguments become invalid like it did work for nearly a hundred years?

Jack Forehand: Yeah, no, I think the arguments are valid, but I think it also doesn’t mean it doesn’t work. And Cory Hofstede had a really good piece on this. And to say statistically that this price-to-book is not working, it’s something like we would need 80 years of data and it’s the same thing on the way back. If it starts working for a decade, so does that tell us that it now works again or does that tell us that it just had a good decade? You could argue since price-to-book is probably been the worst factor during this value downturn, that it could, even if it has no excess return, the next decade, it could produce outperformance and it still is not a valuable factor.

Jack Forehand: It could just be a mean reversion thing because it’s been the worst during this particular downturn and these assets heavy type companies could start under performing again and it could really tell us nothing about as longterm performance. So I think we do use price-to-book in part. We use really a composite approach that has a lot of it because the strategies we follow all use different metrics, but I don’t think it’s dead. We do use it to an extent, but I do think it’s probably the worst of the factors.

Tobias Carlisle: One of the reasons I think that low volatility could start underperforming, well, low volatility is particularly interesting because it seems to fly in the face of efficient market hypothesis, which is that higher volatility should generate higher returns, but it seems that the inverse is the case. But one of the reasons why it might stop working, and this is one of the things that you addressed in your paper, is simply that too many people are doing it, but you were talking about it in relation to value. So why is value going to stop working because… or why do you think that too many people have been doing it? Why has it stopped working?

Jack Forehand: I think it’s one of the weakest arguments I have, but you could argue there’s many, many value ETFs. Obviously what was in the academic research has become utilized in the real world a lot more. So there are many, many people throwing a lot of money at value, but if that was a valid argument, you would think that spreads would be widening. If too many people are [inaudible 00:13:59], spreads will be narrowing. So if too many people were following value, value would get more expensive relative to growth, you’d see narrow spreads, but we’re actually seeing the opposite of that. Spreads are widening out. So I don’t think there’s really much evidence to say that too many people are following value. I guess it’s a longterm concern, but I think in the near term, that’s probably the weakest of the arguments I came up with.

Tobias Carlisle: It’s probably more efficient now than it was when Buffett started up. But even when Buffett started out, he had to dig through the sort of positions that he was putting on in his hedge fund with tiny, tiny little positions where you had to go and read through pages of documents and dig them up, even he was working pretty hard back then to find those opportunities. I think it’s a reasonable argument because I saw, I covered this on my blog, Greenbackd in 2009, Goldman Sachs Asset Management said there’s this massive crowding into price-to-book, the quant strategy is going to struggle. And so though they were right about that, they have been writing about that for this next decade, it’s a tough one because for the reasons that you identify, the spreads are still pretty wide. I don’t talk to a lot of people who are in the finance industry who aren’t value because you have a great deal of respect for value in your life.

Jack Forehand: You’re right.

Tobias Carlisle: Because they look at the last 30 years, they say, “I worked for five of those last 30 years, I’ll take whatever is not that. I’ll take the thing that works for 25 out of 30 years.”

Jack Forehand: That’s right. If you carve out that 2000 to 2007 period, it’s been a really, really long period of underperformance for value. The outperformance was just so huge over that 2000 and 2007 period, that it made up for a lot of the other stuff that was going on.

Tobias Carlisle: One of the other arguments in that paper that you have is that the capital that is following value is more permanent. And this came from the anonymous Twitter account, modest war, the pseudonymous Twitter account, modest proposal.

Jack Forehand: Right. I took that from what are Patrick O’Shaughnessy’s podcast with him. And the theory is those of us that are value investors, what we want is for everybody else who’s a value investor to bail. That that allows the fact that they work over time. If it worked all the time, that it would stop working. So there’s some evidence. And he didn’t really have much evidence to it, but there is some belief that because staying with value after 1999 worked, that more people are staying with value now and they’re less likely to bail. And I don’t really know. Flow data is very inaccurate. I’m not really sure how you would figure that out, but there is an argument I’ve heard made that if you want people bailing, if people are not baling, then that’s bad for us as value investors because it’s going to lead to the fact you’re not working as well as it has historically.

Tobias Carlisle: That’s something that you and I have discussed in the past that flow data, it’s really hard to come by, and when there’s the flow data that I see is really mixed, they assumed it all says it flows to a blend and the blend might include value and momentum, which is not very helpful.

Jack Forehand: Right, and you have somebody multifactor funds and people using different implementations of value. There’s so many funds with value in their name that really don’t use what would be an academic definition of value. It’s very hard to figure out how much money is flowing the value.

Tobias Carlisle: The next point you make, which I think is a an interesting one, big data leads to more value traps, which are things that are genuinely, even though they might screen as undervalued, they really are just bad companies.

Jack Forehand: Right, so the example I use in the pieces, I was using the example of Walmart, which may not be a value stock right now, but it’s just for example purposes. If Walmart is really cheap in my value strategy based on past earnings likes Walmart, well, what happens with this new data, hedge funds have drones up over Walmart and they see that the parking lots are empty, or they have all the credit card data and they see that people are spending less money at Walmart, what if there’s data out there that’s not reflected in past fundamentals that may make those past fundamentals less valuable?

Jack Forehand: And that was the argument I was making is there might be stuff out there now that people who use historical fundamental statements in the past that was not available, that maybe changes how valuable historical, fundamental statements are now. It’s another tough one to try to figure out whether it’s true or not. It’s a tough one to prove, but I think it is true that there’s more data now and people are doing everything they can to get that data, and maybe that makes the piece of data we use, which is historical fundamental data, less valuable.

Tobias Carlisle: I sometimes wonder whether that arms race to create more data and better AI and machine learning to analyze it if it all just cancels itself out. Buffett gives that example of people going to the, you go to some parade and everybody stands on their toes to get a better view, which eliminates the effort of everybody else. Does all of that big data cancel each other out because everybody gets that hedge pretty quickly and so then you just go back to historical financial statements?

Jack Forehand: That’s right. If there is a valuable data set out there, whoever owns it is going to sell it to as many people as they can possibly sell it to. And so whatever hedge is associated with that is going to go away pretty quickly as all the other hedge funds pile on and try to use it. So that’s probably a good argument against what I’m saying, but I still want to recognize that data is out there and people are using it and maybe it devalues the data we’re using.

Tobias Carlisle: And the final argument that you made is that value is a bet against tick, which that certainly seems to be the problem. I would say that that’s the main problem over the last 10 years. It hasn’t held a lot of these, the tech names that seem to have made all the money.

Jack Forehand: That’s right. And it’s been a bet on financials and its bet on energy, and it’s been a bet against tech, and those are all have not been good things. And even if you run a sector neutral value strategy, which a lot of people do, you don’t tend to be buying. If I had forced myself to have an allocation to tech, I’m not going to be buying Google and Netflix. I’m probably going to be buying Seagate and Western Digital and Micron Technology and companies like that, so even if I’m doing it sector neutral, I still technically have a bet against the high growth tech that’s driving the market.

Jack Forehand: But the flip side of that argument is, if you looked at it in 1990, in the 1990s value, it was a bet against tech. And then it became, the fact that it was a bet against tech became a really, really good thing. So it’s part of the nature of it, but because these tech companies are making more money now, they’re doing much better than the tech companies in the late ’90s where you do have to worry about if they continue to dominate the world, if they continue to grow at the rates they’re growing. If the fact that value strategies aren’t investing in them, is that going to be a drag on the strategies going forward?

Tobias Carlisle: I think that’s an interesting argument. That comparison between the late 1990s where they were basically they had to come up with the unusual metrics like eyeballs and clicks and things like that to justify that valuations were as they say, the ones now they have much more in the case in the way of revenues. They still don’t have a lot in the way of profits though, and not a lot of that-

Jack Forehand: No.

Tobias Carlisle: Revenue is falling to the bottom line, so there’s a lot of money that’s been invested in those tech companies. And I sometimes think, so use WeWork as an example, there’s no reason, there’s nothing particularly technological about WeWork. It’s still ultimately leasing space and then chopping it up and subleasing it to somebody else, which is, that’s not a new strategy, but it does if you look at they’ve grown very quickly, but it’s just because they’ve jammed a whole lot of capital into it. And I think that some of the tech companies have done the same thing. If you just jam an enormous amount of capital into something, you’re going to see growth. You don’t necessarily see profits and you don’t necessarily see return on equity. I don’t know.

Jack Forehand: Yeah, and WeWork is a perfect example of what you were just talking about. If you want to talk about making up metrics, the whole community adjusted EBITDA, but they came up, which is a perfect example of the same type of thing you were seeing in the ’90s, but the one thing that I will say is different is if you look at the top end of the technology, if you look at Google, Google is a profitable company. Amazon is a company that if they chose to be profitable, could be a profitable company. So there is more of that going on that some of this underperformance of value has been a function of fundamentals. Has been a function of some of these companies, these tech companies have done better than the tech companies in the late ’90s. That doesn’t mean that evaluations aren’t inflated or they’re not crazy or anything like that, it just means that those companies have performed better than the internet type companies in the late ’90s have performed.

Tobias Carlisle: But Google and other companies like Apple have been value companies on occasion over the last decade. Google spin on a couple of times if you backed out the cash. It got pretty cheap and true of Apple as well, which I’ve written about. I don’t expect tech companies or any company to be a value all the time, but I think if they become value stocks on occasion, that’s proof that value is still working.

Jack Forehand: Yeah, and we’ve actually seen with our Warren Buffett model, Apple comes in and out fairly regularly depending on the valuation. When the valuation gets a little high it doesn’t, but it’s been in there three or four times over the past decade. So it definitely has been a value stock at times.

Tobias Carlisle: So one of my favorite posts of yours where you looked at all of those things that all the problems with value and then you summed it up and you were… one of the ideas in it was that you said that the academic argument for value still holds. So what is the academic argument for value?

Jack Forehand: Well, they’re really… it’s really a two part argument. One part is that value is riskier, and you’d expect if you take on additional risks, you’re going to get additional return. And with what we’re going through right now, it’s hard to argue that value is not risky. That value is not riskier than the market. We’ve gone through a long draw down here, we’ve gone through a long period of poor performance and I think it’s hard to argue value does not continue to be riskier than the market as a whole. The other one is the behavioral argument, which is that if you buy a basket of value stocks, in general, the market is going to overestimate the problems with those stocks.

Jack Forehand: That doesn’t mean that a lot of those companies don’t have problems. They all have problems. It doesn’t mean that some of them don’t have worse problems than the market has estimated because some do, but on average, their problems are not as bad as the market has said. And so there is a behavioral element to it as well, and if you put both of those together, the both of those still seem to hold. I don’t think there’s really much evidence that either one of those is different now. So I think that should give all of us that invest in values and faith that maybe things might get better here at some point in the future, although who knows when that’s going to be.

Tobias Carlisle: Do you have a preference for either of those two arguments? The risk-based one or the behavioral-based one?

Jack Forehand: No, I really don’t. I’m not great on the academic stuff, so I don’t. I like the behavioral argument because behavioral anomalies tend to persist forever, whereas the risk-based ones are a little bit different. So I do like the fact that if there’s a behavioral thing going on, if people are always are going to behave or behave badly, then that’s probably a tailwind of value, and I do think that’s a good thing.

Tobias Carlisle: The risk-based one I think is interesting because I’ve understood as the individual names are riskier, but I think that that has morphed over the last few years into value itself is risky because it has these long stretches of underperformance. And so it’s the tracking error that introduces the risk into value. Have you noticed that change or am I stating the problem incorrectly?

Jack Forehand: No, I think you have. I think you’re seeing people make both arguments now. You’re seeing them make the argument that the individual names are riskier, but also the risk of value is reflected in these really long drawn out periods and that something, if it has those long drawn out periods, it has to be riskier than what it’s tracking in the S&P 500.

Tobias Carlisle: So one of the other blog posts that you wrote that I think is great where you’re discussing the mechanics of value. So can you take us through the mechanics of value?

Jack Forehand: Yeah, what I was trying to figure out there is if you’re going to develop a value strategy or really any factor strategy, what are the major questions you have to answer? So you may have them in front of you. I don’t, but the first question is what is your universe you’re going to invest in? And that’s a major question because a lot of people will just invest in the large caps S&P 500 type companies. You can broaden it out and invest in maybe, if you want to have some liquidity requirements, probably 2,700, 3,000 companies, if you want to include the small and mid caps in there. And so what you do there is a really big part of what your strategy is because one of the things that happens is all of us that invest get judged against the S&P 500, but the S&P 500 is a market cap weighted index of 500 companies, the 500 largest companies.

Jack Forehand: If you think about that compared to a 3,000 stock index that’s equally weighted, which is basically what most factor value type investors are doing, there’s going to be massive deviations in return before you even start paring down that index by using value or momentum or anything. And so it’s really important to understand if you’re using that all stock universe, you’re going to have a lot of tracking here. You’re going to be very different than the S&P 500. And for us, that’s what we want to do. We think there’s more alpha in those small names than there are in the big names, but if what you’re concerned about is tracking error and you’re concerned about not getting too different from your benchmark and not having these long periods of struggle, then you probably want to use something more like an S&P 500 universe.

Tobias Carlisle: But you could then implement that strategy. You could implement value in an S&P 500 universe and you could say, “We will find the decile. We’ll find the 10% that’s cheapest on whatever our particular measure might be in it.” That’s 50 stocks. That’s a pretty good portfolio, But it’s not equal while it’s not market amended.

Jack Forehand: Correct. Yeah, I know. Absolutely. I have no problem whatsoever with doing that. I just think the evidence shows that if you include the small and mid cap stocks, you get more alpha. If you look at it as an example, like if you and I are going out to dinner and you give me a choice of three restaurants or you give me a choice of 300 restaurants, I’m more likely to find the type of restaurant I want in the 300. And that’s the same way. If you give me 500 stocks and say, “Find all these factors you’re looking for,” or you giving me 3,000 stocks and say, “Find all these factors you’re looking for,” I’m more likely to find the factors I’m looking for in the 3,000. And so that’s why we do that. There’s obviously issues in small caps. It’s a little tougher to execute. You have more costs transactionally, but we think the additional alpha you get down there is worth it. So we do it and we’re willing to live with a tracking error for doing that.

Tobias Carlisle: It’s interesting, isn’t it? That you use that bigger universe in a equal way and you’ve already taken a big step away from the S&P 500, and you should, and over time, that portfolio should do better. This is before you even put in the value too, but you will have long periods of time where you’re underperforming. I’m guessing it’s probably underperformed over the last two, five, 10 years.

Jack Forehand: Oh, yeah, by a wide margin. And you’re right. Anything, the research shows, anything you do to break the link with price, enhances your returns. So basically anything. You can weigh it by the letter of the alphabet, you can equal weight, you can do anything, but if you think about it, with the academic research, if you take the 500 largest companies market cap weighted and you compare that to an equal weight of all the companies, the equal weight of all the companies is going to do better over time because it’s breaking that link with price.

Tobias Carlisle: One of the ideas that you covered in there that I like is avoiding value traps. So how do we avoid value traps?

Jack Forehand: Well, the first answer to that is you don’t because you can’t. You’re buying these cheap companies that many of them are cheap for a reason, so you can’t just completely avoid value traps. There’s no way to do it. But the way we like to look at it is, when we build quant strategies, we try to say, “If I or you or anybody is looking at it as a person, how would we try to find situations where the past fundamental data we’re using is not indicative of the future?” And that’s what we tried to do when we built our value trap system. And so one of the examples we use is, well, what if we’re using these past earnings to bail for our valuations, but analysts are estimating that earnings are going to plumb it in the future?

Jack Forehand: That’s an example of a situation where maybe those past earnings don’t tell me what’s going to happen in the future. Now, analysts estimates are notoriously bad. So I don’t want to pretend like we’re using… we’re not trying to count on analysts’ estimates being accurate. We’re trying to count on them being directionally accurate. So if, and I’ll give you an example of an energy company. If the price of oil just got cut in half, and a energy company made $2 a share last year, but they’re expected to lose money this year, that may be a company where we don’t want to trust that historical valuation. And so the analysts’ estimates are going to fall dramatically in a situation like that, and our quality, our negative quality system, might screen that company out based on that. So that’s one example of a… Go ahead.

Tobias Carlisle: No, no, no. I didn’t want to interrupt. Keep going.

Jack Forehand: So that’s one example of what we do. Another one would be, obviously debt magnifies everything. So if you have a cheap company, if they have a lot of problems, if they get into trouble, if they’ve got a lot of debt, they’re not going to make it. And so we want to screen out very, very high levels of debt. So that’s another example of something we screen for. Another one is we want to say, “We want to have a catchall for what if there’s something going on with the company that we don’t recognize in the data, but that the market recognizes it?” So we use, there’s two ways I think that can be done. One is really, really low relative strength, the other is high shortages. So in both of those cases, the market is saying there’s something major wrong with this company. We may want to exclude it. And I don’t know that there’s a fourth one in our value trapping, I don’t have it in front of me, but we do have one other one that we use as well.

Tobias Carlisle: I like those approaches because I think that a lot of value investing is trying to buy things that appear to be value traps in it. And my definition of a value trap I guess is more like just the business can’t recover. The business is declining all the time because there’s some problem with the business, but I like you still, in that instance, you still want to have if it’s got a healthy balance sheet, you could still go through all of that process that you’ve described, the healthy balance sheet, low short interest, reasonably good relative strength, and it could still be a value trap.

Tobias Carlisle: And that’s just something you have to live with. That’s the risk that you are taking on as a value investor. And the reason that you do it is because when the market gets those things wrong, they reprice a lot that you get that asymmetric trade where, if it is a value trap, it’s already priced as a value trap and it’s going to keep on going down as a value trap, and so you’re going to lose a little bit of money on it, but if the market’s wrong, all of a sudden that valuation is way too cheap, and that’s how you make that. And that’s, in my opinion, that’s how value works.

Jack Forehand: Right, and there’s always going to be an error rate inherent and value. You’re always going to have some of these companies that are workouts. So one of the points I made in the article is any effort to try to eliminate value traps, it’s not going to work because you’re going to basically take away what makes value work by trying to eliminate all the value traps. And so what we try to do is we only eliminate a very small portion. So we try to use negative quality. So we’re not looking for value coupled with great quality, great companies, we’re trying to only take the absolute worst at the bottom of our database. So we take those four factors we use, we sort all companies based on all four of them, and we just drop the bottom 5%. So these aren’t companies that have a little bit of debt, these are companies that have a lot of debt. They don’t have slightly low relative strength, they have terrible relative strength. We’re trying to find the extremes of these factors and just filter those out.

Tobias Carlisle: And do you find that filtering out that 5% leads to the rest of the universe doing better than it would otherwise do?

Jack Forehand: Yes, but just incrementally. This is not some panacea we found where we’ve saved value investing, it’s just a slight improvement. But that’s all we want. A lot of quant investing is trying to make these little changes and gain a slight improvement here and a slight improvement there, and that’s all we’re trying to do. The strategies work well without this, but it adds just a little bit of extra return over time, and so that’s why we implemented it.

Tobias Carlisle: One of the things that I love that Validea does and that you do is that you guys actually track the implementation of these various different strategies rather than just tracking a theoretical implementation of the strategy. So I wanted to know, and I asked you a month or so ago, what is the best strategy hedge to value? Because we’ve gone through this very, very long period of value not working, because one of the things I’m asking myself this question all the time too, if that is not working, what is working? And then I want to take that thing that is working and see how that’s looked over the full dataset. Because if it looks terrible over the full dataset, then I don’t want to go into it now. But if it has been a pretty good strategy but it’s working very well now, then that’s a good strategy hedge to value. So I asked you that, and would you remember what you told me?

Jack Forehand: Yeah. Well, we’ve looked at it in a lot of different ways. The first caveat is we’re not looking at this from an academic perspective. We’re running these focus 20 stock strategies and then we’re grouping them by factors and saying which ones are value or aren’t value.

Tobias Carlisle: Which is what I love.

Jack Forehand: But the results may be different than somebody who’s doing a true academic test of this. But I think the first thing is if you want to purely hedge value and you don’t care about your longterm return, the best hedge is anti value or glamour. So if you want to hedge the bottom 10% of stocks, a good thing to do might be buy the top 10% of stocks. The problem is buying the top 10% of stocks in valuation is just a horrible long term investment strategy. And so you’re going to get a hedge out of it, but you’re going to also bring down your longterm returns by doing it.

Jack Forehand: So that’s one that probably should be thrown out. It’s been a great one in the past decade, but it’s probably one that is not, as you said, is not supported by the longterm data. I think of the strategies that actually do work over the longterm, the best is clearly momentum. AQR had a paper on this where they looked at it, I think it was called Value and Momentum Everywhere or something like that. but yeah, momentum tends to be negatively correlated with value, but it also has a similar longterm excess return to value. So if you take two things that both have a longterm excess return and they behave differently, you’re getting a similar longterm return, but you’re getting it at less risk. So I think momentum is probably the best longterm hedge, but as we talked about earlier with the GFC, post GFC, all the data is messed up now.

Jack Forehand: So it’s hard to judge because if you were to look post GSC and say, “What is the best hedge to value?” Well, the best hedge to value is low volatility, but before that, low volatility is a horrible hedge to value. So what do you make of that? Do you go with the longterm data or do you say the world is different and now low vol is a great hedge for value? I don’t have the answer, but I think if you look, if you want to go to the longest period possible, the best is probably momentum.

Tobias Carlisle: And when you say that momentum is a good hedge, you’re talking long short or are you talking long only?

Jack Forehand: Yeah. Well, when the academics do it, they do at long short, when we do, we do it based on excess return. So we’re looking at the performance of the factor relative to the S&P 500 over time. And so those differences are best. In our data, those differences are best hedged by momentum. It gives you a smoother, excess return over time than some of the other factors that are available.

Tobias Carlisle: Do you implement any momentum strategies in the firm?

Jack Forehand: Yes, we have several of them.

Tobias Carlisle: And what-

Jack Forehand: We have one common.

Tobias Carlisle: Yeah, what flavors of momentum do you use?

Jack Forehand: Well, we have the traditional ones. We have canceling, sometimes we have a similar approach at least to cancel them. We have one called twin momentum, which is based on a paper by a guy called Dashan Huang, and it’s based on the theory that if you take price momentum and combine it with fundamental momentum, you get a greater excess return. So he started with price momentum and then he found seven fundamental variables, and he looked at the trend in those seven fundamental variables over time to develop a fundamental momentum measure and then combine those two together into a momentum strategy.

Tobias Carlisle: What are his fundamental… what does he like in the fundamental sense?

Jack Forehand: I don’t know all of them off the top of my head and some of them were earnings base, ROE, I think return on capital I think was in there. There were a bunch of what you would think of as the the obvious things to probably measure how a business is growing and the health of a business. And he just brought all those together and tried to get a longterm trend in fundamental momentum. What he saw when he-

Tobias Carlisle: And has it worked?

Jack Forehand: Yeah, it has, but the one thing I have to say as a caveat is we started that strategy in 2009, and 2009 has been a period where momentum has worked. Since 2009. So obviously, all of this, if I were to judge our value strategy since 2009, I would tell you that value is a terrible way to invest, and so we have to judge all of this in the context of the period we’ve been following it. So our strategies have just been like everybody else’s. The momentum ones have done great since 2009, the low vol ones have done great since 2009, the value ones have done the worst and the quality ones have been in between.

Tobias Carlisle: How does low volatility look over the full dataset?

Jack Forehand: We only have low volatility since 2009, in our… when we start running our models, we will then put them up on our website and track them. We try not to do too much in terms of, we don’t do a lot of academic testing and we try not to do too much in terms of back testing, although there is some of that in what we do. So I don’t know. I know low volatility certainly hasn’t been in the longterm, academic work hasn’t been as good as it has been recently, but there’s definitely a strong evidence to support low volatility in the academic work as well.

Tobias Carlisle: What is the, not the empirical evidence, but what’s the theory behind low volatility?

Jack Forehand: I have trouble with, to be honest, I have trouble explaining what volatility is. It’s the hardest one to explain. People say that investors chase risk and so they drive up the high beta stocks and then that makes the low volatility stocks a better value over time. I’ve heard a lot of different explanations, but it’s definitely the hardest one to explain because it shouldn’t… if we have an efficient market, it just should work. You shouldn’t be able to buy lower volatility stocks and get the same or better return in the market over time, but you do. So it’s definitely one of the hardest ones to explain.

Tobias Carlisle: When I pose this question to you, you came back with, there is this one paper that has done particularly well over the last decade and it’s by Partha Mohanram, I hope I’m pronouncing that correctly.

Jack Forehand: Yes, that’s correct. Partha Mohanram, I think is the way to pronounce it.

Tobias Carlisle: Can you exp… because I think it’s a little bit hilarious, but can you describe the strategy for us?

Jack Forehand: Sure. So he built off of a paper by Joseph Piotroski, I don’t know if you’ve heard of him-

Tobias Carlisle: Of course.

Jack Forehand: But Joseph Piotroski took the bottom 20% of the market in terms of price-to-book and said, “How can I identify the companies that do well among these cheap stocks?” And he came up with something called F-Score, which is I think eight fundamental variables that separated the winners from the losers in those cheap stocks. Well, Mohanram flipped that and he said, “All right, can I take the most expensive 20% of stocks and can I separate the winners from losers there?” And so he came up with something called G-Score, which is the same thing as F-Score. It’s different criteria, but the same theory that I want to try to find what are the fundamental variables that find these expensive stocks that actually tend to do well versus the expensive stocks that do what most expensive stocks do, which is do poorly.

Jack Forehand: And so that was the gist of the paper. It’s important to note that just like any academic paper, both of these papers are long short, so he was getting some of his value. He really wasn’t making a bet on expensive stocks, he was buying some expensive stocks in shorting other expensive stocks, the difference being those eight criteria. So it wasn’t a bet. He did get alpha on the long only part of it, and he did get alpha on the short part of it. More of it came from the short part, but it wasn’t necessarily a better and expensive stocks, but we run the long only part of it just because all of our portfolios are long only on our site and that has been the best performer over the past decade. Starting with the most expensive decile and then taking these additional eight criteria and applying them has outperformed everything else we do over the past decade.

Tobias Carlisle: And has it outperformed the… so if you’re splitting that decile or you’re splitting that quintile or whatever it is, so you’re splitting the most expensive 20%. Has the ones that… does his criteria identify the ones that do better than the ones that don’t make it into the model?

Jack Forehand: Yeah, does he identify the stocks that do better versus the stocks that don’t do better.

Tobias Carlisle: If you’re just looking in that, so he’s cutting out the 80% of the cheapest stocks. So he’s just looking at the 20% most expensive.

Jack Forehand: Correct.

Tobias Carlisle: And then he is applying some other filter in that 20%. Do the stocks that he picks out of that 20%, do they do better than the rest of that 20% cohort?

Jack Forehand: Yes. Yeah, he definitely found that in the paper. And he used things like spending on R&D, spending on advertising return on assets, spending on capital expenditures. He had these criteria and tried to figure out what’s different about the companies that go on to continue doing well versus the companies that don’t continue to do well. And so he did find in the paper that he did get alpha by using those factors to separate the expensive stocks that might have more reason to be expensive versus the expensive stocks that are expensive maybe unjustifiably.

Tobias Carlisle: It’s kind of genius because if you then short that expensive, the rest of the stuff that’s in there, that expensive stuff, you know that that over time that’s basically going to not do very well, but you are going to have periods of time like the last decade where probably that short hurts you, but you hope that your longs do so much better over that period that it works, and it’s probably a pretty good hedge to value.

Jack Forehand: Yeah, that’s right. And the actual long only strategy also, in terms of excess return, is also a pretty good hedge to value, but with the caveat we talked about before, which is the first criteria, this strategy if you use the long only portion is buy expensive stocks and if you want to succeed over the long term, starting with buy expensive stocks is probably not the greatest idea.

Tobias Carlisle: I have to admit that when I saw… I tracked on that paper and I had a look at that and when I saw that the first cut was just buy the most expensive 20%, I didn’t get much further than that. That’s my own bias.

Jack Forehand: Well, for you and I, it’s basically the opposite of everything we believe. And you’re definitely a value investor and I tend to be a value investor as well, and, yeah, I would probably wouldn’t normally have read past that either, but I thought it was very interesting that he could separate, that he could find a way to separate the expensive stocks that were expensive for a reason from the other ones, so I wanted to. And also for us, the long only strategy, he did hold its own. It’s not as if his long only strategy underperformed, the top stocks with the highest G-Score did do pretty well as a long only portfolio, so we felt it was something that was worth capturing for our website.

Tobias Carlisle: Yeah, that’s… I like that. Just to change tack a little bit here and that’s a pun that’s wholly intended. You’re a sailor?

Jack Forehand: I am.

Tobias Carlisle: So tell us a little bit about your… what sort of sailing do you do?

Jack Forehand: I do sailboat racing. I have a 35 foot sailboat, and I race it on Long Island Sound off of Connecticut. In a lot of ways, I think I’m a glutton for punishment because I tend to go after these things in life that are very difficult to solve, these problems that are very difficult to figure out. And so investing is obviously a problem that is almost impossible to figure out. No matter how good you are at it, you can always be better because there’s always more variables going on than you could possibly account for. And sailing’s the same thing. You’re out there racing other boats, trying to sail directly into the wind, and the only way you can sail directly into the wind, because obviously a boat sails won’t fail when going directly into the wind is to take this back and forth approach where you do these 45 degree angles to try to get to this mark before all the other boats, and at the same time you’re doing that, you’re dealing with all these variables that are changing.

Jack Forehand: The wind speed is changing, the wind direction is changing, the currents are changing, the other boats are trying to mess with you and prevent you from getting to where you want to go, and so a lot like investing, it becomes a problem that you can’t solve. So sailing for me is a great way to get on the water, be outside, have fun with my friends, but also face a very strong challenge at the same time. Face something that’s very difficult to figure out. So it keeps my mind working at the same time.

Tobias Carlisle: A 35 foot boat. How many crew do you have on a boat that size?

Jack Forehand: When we do a serious racing, we’ll have seven or eight depending on how heavy the people are. There’s a weight limit that’s associated with it that we can’t exceed. So we do two types of racing. We do some serious racing on the weekends, and then we do a Wednesday night beer can type race, where everybody races, people might enjoy a beer during the race and then afterwards you go over to the bar and everybody hangs out. So that’s a much more laid back and that’s just based on who can get off work at the time to make it there in time to do it. So that racing the numbers can vary, but when we do serious racing it’s seven or eight people.

Tobias Carlisle: I would have thought that the best analogy between sailing or racing and investing is that you spend a lot of the time going against the tide and a lot of the time going against the wind.

Jack Forehand: Yeah, that’s right. You definitely have an uphill battle all the time. There’s no doubt in… Yeah, sailing into the wind is obviously the most challenging part of a sailboat race. Once you turn around your mark and then the wind’s behind you, it’s a much easier thing, but the force, forcing the boat through the wind and trying to figure out the optimal path to do it and sailing against currents, that can definitely be a challenge.

Tobias Carlisle: So just to bring it back to investing, we’ve over the last few weeks, maybe three weeks ago, there was this pretty violent turn in the markets where momentum stocks seem to break down pretty heavily in one day and then that continued on and value stocks had their best. And I think both the momentum stocks had their worst day in 10 years and value stocks had their best day in 10 years. So that’s going back to 2009, which is a pretty good time for value. So have we turned the corner? Do we now have a tailwind?

Jack Forehand: I wish. It hasn’t really held since then, and I still haven’t known. You may know better than me, but I still haven’t found somebody who can make a great argument as to exactly what happened on those two days where value turned like that. I don’t really know that there’s a great explanation as to what happened, and the problem with calling the turn is you never know when it is. There’s really no academic. There’s no longterm data that’s going to allow you to call the turn and the turn can go on for any period of time, and it still might not actually be the turn. So 2016, we had a year that looked like the turn.

Jack Forehand: After Trump’s election, value stocks, small stocks, did exceptionally well, but yet then it wasn’t the turn. And so the problem with calling the turn is even when we think it’s the turn, it may not be the turn. So, I don’t know that there’s any great way to do it. I think all you can do is just sit here through the pain of value investing and understand that when things do turn, they may turn in a big way and you have to be around for that in order to succeed.

Tobias Carlisle: It is incredibly hard to work out what happened that day, just because it was such a big move. It felt coordinated. So it felt like it was one big player deciding, I’ve had enough of momentum, I want to get some exposure to value.

Jack Forehand: Yeah, it did. It had to be for that kind of move to happen. I think it was something like a 5 sigma event I think.

Tobias Carlisle: Right.

Jack Forehand: If you take the odds of that happening, the odds of that are incredibly low, so something went on, but it just the follow through really hasn’t been… Value hasn’t done terribly since then, but you certainly haven’t seen follow through like that since then. So I hope it was a turn.

Tobias Carlisle: It worked for a few weeks.

Jack Forehand: Yeah.

Tobias Carlisle: Yeah, it worked for a few weeks and then it has softened up a little bit last week. I don’t know this week. The only thing that I have noted, and it’s been intermittent, but the momentum stocks or the glamorous software as a service type stocks, a lot of them do seem to have broken down. So Netflix, as an example is, it’s done a round trip for the year, but it was up 44% at one stage for the year and now it’s off. So it’s flat for the year and I’ve seen that in a few. I just wonder if there’s some overall people who are just getting tired of the more expensive side of the market and it’s hard to see where they can go from here.

Jack Forehand: Yeah, and sometimes the expectations will just collapse under their own weight. And that a lot of times, investing is such a game of expectations and at some point, the expectations for those types of companies are going to get too high and they’re not going to, even though they’ll still be growing, they’re not going to meet those expectations. And that could be a big part of when this finally terms is when those companies consistently can’t meet the expectations that Wall Street has put on them, that’s when you might finally see a turn, and maybe, as you said, maybe we’re starting to see that a little bit with companies like Netflix. I hope so. It’s been a very long run here, so hopefully things are starting to turn in the other direction.

Tobias Carlisle: I wonder if it’s that a lot of the IPOs have been very disappointing. A lot of the IPOs have done all eye-popping amount and then we work coming right up to the threshold of listing at what seemed like an absurd valuation and then not being able to get it done and now the knives are out for the CEO there.

Jack Forehand: Yeah, that’s one way that this is very different than the late ’90s. You could pretty much get any garbage out as an IPO you wanted to in the late ’90s. It didn’t matter what the company. Tons of companies that should have never came public did, and now they seem, people seem to be looking at these companies and WeWork got destroyed. When they filed the S-1, they got completely destroyed because for justifiably itself, and they’re not going to be able to get WeWork out it looks like. And things like Uber and Lyft did not do well after their IPOs. So maybe that’s also a sign of a turn to some degree because they can’t get these tech IPOs out like they used to and maybe that’s a sign of sentiment turning against it.

Tobias Carlisle: The risk, I guess, is that it’s just that the air comes out of the really expensive stocks and you don’t see the value stocks getting much of a run because I still think value is much, much cheaper than the rest of the market which is not always the case surprisingly. In the mid 2000s, 2010, value I think was quite expensive. There was not much of a premium or there was not much of a discount to being in value. You will probably pay to be in the more growth the end of the market there. So I think one possibility is just that all of the steam comes out of the really expensive stuff but value still doesn’t do anything because it’s not cheap enough for any of the fundamental guy, the P firms or anybody to come in and do anything.

Jack Forehand: Yeah, that’s right. Value is cheap relatively, but it’s not. It’s somewhat cheap absolutely, but it’s not… it’s definitely not crazy cheap absolutely. So you’re right. It could be a rotation here. A lot of people tend to anchor on what happened recently, and so a lot of people expect this 2000 situation to happen again, where you get this huge bear market and value actually goes up during the bear market or at least small cap value does. And that’s probably not what’s going to happen because it’s usually what we expect to happen based on recent events is not what actually happens. So you could have a situation where both end up going down but growth just goes up, it goes down more. And so, and I know you’ve argued a lot of times for a long short implementation and that would be your best argument, for some sort of long short implementation here is that if everything goes down, you want to get that spread between value and growth rather than betting on value on its own.

Tobias Carlisle: That’s right. I think that’s the best argument at the moment for long short is that the expensive side is three or four times its long run mean whereas the cheap side is about 50% rich to it’s long run mean, which you could explain by having very low interest rates.

Jack Forehand: Yeah, so I think you’re right about that. I think that could be the exact situation that plays out here. We may not have the huge returns from value we hope, but we may have pretty good relative returns.

Tobias Carlisle: Have you noticed any of your strategies working particularly over these last few weeks, is it just the value strategies or is there anything else perked up?

Jack Forehand: Yeah, no, basically the deeper value it is, the better it’s worked over the past few weeks. The type of stuff that is done horribly in it’s whole value downturn has been the stuff that’s been doing the best. So I talked about that strategy based on Joseph Piotroski before, which is it’s only price-to-book is it valuation metric. That’s been one of the absolute best. So that low price-to-book asset heavy type company that had done horribly during the entire thing is, for our strategies, at least has led the way back. And the further down you go in value, the better you’ve done. The deeper the value, the better you’ve done.

Tobias Carlisle: I see people on Twitter describing it as a junk rally, which has my feelings.

Jack Forehand: Yeah, yeah. Well, I guess to some extent, all of these value companies do have issues. We’re not betting on them being the greatest companies, we’re not betting on them being the next Google, we’re betting on the fact that the market is overestimated on average these problems. And so I think owning junky companies is part of what we have to do to be a value investor.

Tobias Carlisle: Well, Jack, really appreciate the insights that you’ve given us today and the time that you’ve spent talking. If folks want to get in contact with you, what’s the best way of going about doing that?

Jack Forehand: Well, they can reach me personally on Twitter, @practicalquant is my handle. Our blog is blog.validea.com where the articles we discussed where they can find those. And then our capital management site is valideacapital.com.

Tobias Carlisle: And you’ve got an ETF out there. What’s the ticker on your ETF?

Jack Forehand: Yes, the ticker is VALX. It’s called The Validea Market Legends ETF.

Tobias Carlisle: And you’ve got some blend of the 45 strategies you hold about a hundred stocks-

Jack Forehand: Sure, we have-

Tobias Carlisle: Roughly equal one.

Jack Forehand: Yes, we have 80 stocks right now, equal weight, which is eight strategies we’re using right now 10 stocks from each of the strategies to come up with 80 stock portfolio.

Tobias Carlisle: And how often do you rebalance that portfolio?

Jack Forehand: We do it once a month.

Tobias Carlisle: So that’s lots of activity in there. So it’s probably a good ETF to take a look at. Jack Forehand, thank you very much.

Jack Forehand: Thank you for having me.

Tobias Carlisle: My pleasure.

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