ETF Expert Corner

Wall Street Veteran Marc Chaikin Explains Indexes Behind Two IndexIQ ETFs

February 6th, 2018 by ETF Store Staff

Marc Chaikin, a 50-year Wall Street veteran and Founder of Chaikin Analytics, explains the methodology behind the IQ Chaikin U.S. Large Cap ETF (CLRG) and IQ Chaikin U.S. Small Cap ETF (CSML).



Transcript

You can listen to our interview with Marc Chaikin by using the above media player or enjoy a full transcription of the interview below.

Nate Geraci: Our guest today is Marc Chaikin, Founder and CEO of Chaikin Analytics. Marc has some 50 years of experience as a Wall Street analyst, a stock broker, and an options expert, and his firm, Chaikin Analytics, has built a unique methodology that powers two IndexIQ ETFs: the IQ Chaikin US Large Cap ETF, ticker symbol CLRG, and the IQ Chaikin US Small Cap ETF, ticker symbol CSML. Both of these ETFs launched last year, the large cap ETF in December, and it has already crossed over $100 million invested in it. And then the small cap ETF launched last May, it has over $400 million dollars invested in it. So both very successful new ETFs. Marc is joining us via phone today from Philadelphia. Marc, our pleasure to have you on the program.

Marc Chaikin: Nate, it's great to be with you, and you just played one of my favorite Rolling Stones songs. Is that in honor of what's going on in the market? Or is that a theme song?

Nate Geraci: Hey, that's both in honor of what's going on in the markets and hopefully for New England Patriots fans as well. I'd love to hear what's going on on the ground in Philadelphia following the Super Bowl.

Marc Chaikin: Thursday is going to be wild. Our offices are right on the Super Bowl parade route, so we'll be able to see it with an unimpeded view from our 18th story office.

Nate Geraci: Well, congratulations. Marc, first just tell us a little bit about Chaikin Analytics and perhaps how you ended up partnering with IndexIQ.

Marc Chaikin: I started Chaikin Analytics with my wife in 2009 after the financial collapse of '08. My goal was to go way beyond technical analysis, which is what I was known for, and build a predictive model based on what I learned in 35 years of working with successful buy side, active mutual fund and hedge fund managers. We're now 26 people in Philadelphia. In 2010, after a year's worth of research, we locked down our model that's called the Chaikin Power Gauge Rating. This is a multi-factor model that looks at value, growth, technical and sentiment factors, 20 factors in all. The weights were locked down in 2010 in September. All the factors are the same, so we're not chasing alpha by tinkering with the model. We made it available both to individual investors and to advisors, beginning in 2011. And then in 2014, NASDAQ, after doing a serious due diligence on the Power Gauge Rating, said, "We'd like you to create three NASDAQ Chaikin indices based on our large cap 300, our small cap 1500, and our dividend achievers index." Those sub-indices went live on April 1st of 2015. They're rebalanced annually, and they've significantly outperformed their benchmarks since their launch.

Nate Geraci: Marc, so there are currently two IndexIQ ETFs using the indexes you built, again, the IQ Chaikin US Large Cap ETF, and the IQ Chaikin US Small Cap ETF. Let's start with the index for the large cap ETF. Walk us through the detailed methodology here.

Marc Chaikin: This is meant to be a top down, rules-based, disciplined methodology to create the index. We start with the NASDAQ 300, which are the 300 largest stocks in the S&P 500. We then say that if they have a very positive rating in our model, meaning they're in the top decile based on the Power Gauge Rating using the Russell 3000 as the core universe, they automatically make it into the sub-index. Now, NASDAQ put just two constraints on us: A) because they rebalance their indices annually, the NASDAQ Chaikin indices that underlie the large cap ETF are rebalanced annually. And B) they wanted at least 15% of the constituents, so in the case of the large cap, at least 45 stocks from the 300 to flow down. In order to satisfy that 15% requirement - and by the way, this year there are 53 stocks in the index, which will be reconstituted on April 1st going forward - in order to get to that 15% threshold, we do a value screen. We screen on price-to-book, looking for the cheapest stocks in the NASDAQ 300. And then we require that the Power Gauge Rating be bullish. So a key point here is if a stock has a bearish rating, in our multi-factor model, it can't make it into the index. That immediately puts the wind at your back because the model has proven itself as effective in identifying stocks that are likely to underperform, as well as stocks that are likely to outperform. So this year, we ended up with 53 names, and we then equally weight the index. And I think that's very critical because once you've gone through this rules-based, disciplined methodology, if you then play God and say, "This stock should be twice the weight of that stock," I believe that you're defeating the whole purpose of this rules-based, objective approach.

Jason Lank: Marc, this is Jason Lank. Welcome to the show. While it's a 20 factor model, they seem to fall under four basic categories, value, technical, growth, and sentiment. Could you walk us through each of those four and just, for the listener, describe the basic concept behind each of those?

Marc Chaikin: Yeah, I'm happy to, Jason. And good to be with you. I worked with institutional investors for 35 years, teaching them how to use technical analysis. In order to do my job well, to sell my product, I had to learn what they were looking at every day. And we had clients that ran the gamut from value investors in the Philadelphia, mid-Atlantic region, like Delaware Management, dividend-oriented Miller Anderson, sort of value-oriented, to very growth-oriented managers up in Boston and New York. Common theme was even though they had different styles and time horizons, they were successful active fund managers. I heard you talking earlier about active mutual funds. Well these were the people running the successful active mutual funds at Fidelity, T. Rowe Price, Legg Mason, Invesco and so forth. So when I tried to make some sense out of what they were looking at in creating the Power Gauge Rating, I divided things into these four primary factors. So the value metrics are 35% of the model. And the two most important factors within the value component, free cash flow to market cap and price to sales ratio. So if that sounds like Warren Buffet or Rob Arnott, who runs RAFI four factor model, it should, because that's what they look at. In fact, if you've been watching CNBC over the last couple of days, what have they been talking about? Companies with good balance sheets. So free cash flow to market cap, very important metric. In the growth factors, we're looking at some traditional factors, but what we really cherish is earnings consistency. Because companies that report consistent earnings, as opposed to the ones that jump up and down, sort of trying to get your attention with big earnings increases, are really the factor that matters. And then we look at earnings surprises, how a company does relative to Wall Street estimates, under the theory that earnings surprises come in bunches. I learned this when I was at Drexel Burnham in the mid '80s from George Douglas, who did the original research in earnings surprise and earnings estimate revisions. So earnings surprises persist for quarter, after quarter, after quarter, until finally the trend is over. So we've got sort of the bedrock value metrics, 35% of the model, and then we've got some more sensitive factors, but ones that persist over time. The technicals are only 15% of the model. And then we think the sentiment factors are our secret sauce, because these factors are not included in your typical quantitative model that's been built by a Nobel Prize winner or an academic. These are the factors that drive Wall Street stock prices. What I like to say is that the model works because it's based on how Wall Street works. So in sentiment, we have short interest. High short interest is potentially negative for the stock because short sellers do great research. We look at insider buying. And insider buying is a great tell, particularly in the small cap space, but also in large caps. And then finally, we look at industry group relative strength. So the sentiment factors are sort of non-conventional models. A quant that's recently got his PhD from MIT probably isn't focusing on this type of metric or factor when he's building a model. I think that's what differentiates us, Jason, from the other quant models that are out there.

Jason Lank: A follow up to that, each of the four main overall categories has an assigned percentage. How did you come about that? Was that a regression analysis? Was that just someone's best idea? Why value at 35 instead of 30 or 40?

Marc Chaikin: I did what's known as an empirical back test project for over a year. So no regression analyses. In the same sense that the factors were called out looking at what successful active managers look at, I basically iterated it as if each factor was a standalone factor and looked at what the predictive value of that was. More of an IC coefficient than a regression analysis and the weights sort of fell out of that. So value metrics like price to sales and free cash flow to market cap turn out to be your best predictors of future price performance. But because you don't want to fall into a value trap, you want to combine them with the growth and sentiment factors, then a little bit of technicals, just to sort of sand the edges of the model. So I'll go back to what I said in the beginning of this segment, this is a model that works because it's based on how Wall Street works. And we actually think, Jason, that we're bridging the gap between active and passive management. We're looking at the factors that successful active managers look at every day, and then put them into an index that's equally weighted, and then into an ETF wrapper that encapsulates all the tax efficiencies and the liquidity that we've come to love about ETFs. So equal weighting is a key. That's a sort of strategic beta approach that we like. And the fact that we're annually rebalancing it is also very important. So we're not as affected by these fluctuations that we've seen in the market over the last few days. Don't have to worry about that because the index will take care of that on April 1st.

Nate Geraci: Our guest today is Marc Chaikin, Founder and CEO of Chaikin Analytics. He's behind the indexes for two IndexIQ ETFs. Marc, the other ETF is the IQ Chaikin US Small Cap ETF, again ticker symbol CSML. Which, by the way, as I mentioned earlier, this ETF launched in May of last year and it has already crossed over $400 million invested in it. Highly successful launch. But Marc, is the index methodology the same here as with the large cap version, just a different universe of stocks?

Marc Chaikin: It's very similar. There's one change that I'll highlight. I might point out that the large cap is up over 200 million in assets as of about ten o'clock today, so advisors who have been looking at the small cap ETF, seeing the good performance, have started to embrace the large cap. The difference in the index creation methodology is instead of screening for price to book to get up to that 15% flow down, we're screening on price to sales. So in the small cap space, price to sales is more important than price to book. So to get to what this year was 233 stocks, equally weighted on April 1st of 2017, we allowed everything into the index that was in the top decile based on the Power Gauge Rating. That’s 75% of the way there. To get the rest of the way there, we screened based on the lowest quintile of price to sales, meaning you're buying a dollar of revenue as cheaply as possible in that 1500 stock universe. And by the way, the NASDAQ 1500 has a .991 correlation to the Russell 2000. So whereas many of your listeners haven't heard NASDAQ 1500, they have heard of the IWM or the Russell 2000. And once we've screened for price to sales - again, must have a bullish Chaikin Power Gauge Rating to make it into the index - this screens out a lot of those stocks in the Russell 2000 or the NASDAQ 1500 that have no earnings. So I don't know if you've talked about this on the show, but a third of the stocks in the Russell 2000 earn no money. They can't benefit from the tax cuts and they tend to have a difficult time getting a bullish rating in our multi-factor model.

Nate Geraci: So Marc, if we were to boil this down for our listeners, where do these multi-factor strategies fit in a portfolio? Do you view these as core holdings, as replacements to market cap weighted strategies?

Marc Chaikin: I think they're an excellent replacement for market weight strategies and we've all heard on the recent upswing in the market how buying a market cap, a large cap index is really making a momentum bet on six or seven very successful large cap stocks like Amazon and Apple, Google and so forth. This is sort of an antidote to that. So we think that this is a core holding, but that it can be used to replace ... It's really a blend. So it's small cap and large cap blend. So blend of growth and value. And we believe that this is a good candidate to replace these market weighted indexes, which have underlying momentum risk or factor bet risk. In other words, what's done well usually has done well because it falls into a bucket of high momentum, high growth, or what have you. We think this is an excellent replacement for market cap weighted indices.

Nate Geraci: As investors consider these strategies, just given the sheer number of factor-based ETFs that have come to market, I think it can be difficult to differentiate between these strategies, and really in some cases, just understand the strategies all together. Are there a few tips you might offer to investors to help them sift through these ETFs? What should they be looking for?

Marc Chaikin: I think the underlying model or factor approach has to pass the smell test. So free cash flow to market cap makes sense. If a company is generating free cash flow, they're in a healthy situation. If you pay too much for a dollar of revenue, that's price to sales ratio, you're on a high wire without a safety net, so better to buy stocks with low price to sales. So I think that the approach that people are evaluating has to make sense in the real world. It can't be some theoretical model built by newly minted PhDs. That's what got us in trouble in 2008 with the mortgage backed security collapse. Also got us in trouble in 1998 when Long Term Capital Management, which was depending on models that were built by Nobel Prize winners, blew up. This is a model built by a 50 year Wall Street professional, looking at what successful investors look at. If the factors make sense, and of course, the performance - and we've got a four year live index track record that bears out the approach - if the approach makes sense and the performance is there, then I think investors can be comfortable committing capital to a particular vehicle.

Jason Lank: Marc, Jason again. I want to ask you really kind of a thought experiment. I'm always curious when we visit with people about some of the ideas that maybe have been researched but haven't come to market. This particular model is a long only. You're looking for the best of the best that meet all the criteria. Does it work on the short side? Can I short the worst of the worst? And if that side of the equation works too, when does the Chaikin long short ETF come out?

Marc Chaikin: That's a very good question, Jason. Yes, it does work on the short side. The Power Gauge has a history over the six years that it's been live, seven years now, of identifying stocks that have significant risks. Stocks like Chipotle at 700, or the auto parts stocks in April last year, or Under Armour, which has had a bearish rating for 18 months. So we're actually running a pilot hedge fund in Switzerland. It's a publicly traded vehicle. And the Power Gauge Rating sort of boosts the long side, but we're looking at long short strategies. The question is what vehicle to put them in. Do you put them into a ‘40s act fund? Do you put them into a hedge fund? A little more difficult to do it in an ETF because you need to actively manage, in my view, your short positions because of the volatility that we're now starting to see again. So yes, the Power Gauge Rating is probably, if you said to me, "Marc, would you rather use it on the long side or the short side?" I'd rather use it on the short side because for an individual, if you know what stocks to avoid and leave out of your portfolio, or you have a vehicle that leverages that knowledge, you're ahead of the game. Someone once said, "Anybody can make money in a bull market just by throwing a dart at the newspaper." My response in 2015 was that's true unless your dart happened to land on an energy stock. Yeah, in 2014 and '15, the Chaikin Power Gauge Rating was bearish on the whole energy complex, from large cap all the way down to small cap fracking stocks. Therefore, on April 1st of 2015, when the annual rebalance was constituted, not one energy stock was in the large cap index, and almost no energy stocks were in the small cap. So that was because the Power Gauge Rating was not just not bullish, it happened to be bearish on all of those stocks in the energy complex. That's changing now, by the way. Power Gauge Rating has turned bullish over the past four or five months on a lot of the refining stocks and also some of the major integrated oil stocks, as well as some of the fracking stocks.

Nate Geraci: Marc, we have about two minutes left here, before we let you go, I would be interested in hearing your views on active management. Obviously, these IndexIQ strategies are rules-based, but as you mentioned earlier, they're really a hybrid between active and passive. How do you view active management just in terms of its ability to add value?

Marc Chaikin: Well, one of the sort of interesting facts that is not publicly sort of zeroed in on is the fact that active managers historically have done well in a period of rising interest rates. So we've been in a period of falling interest rates now for 10 years since the financial collapse. In theory, active managers should do better. Now, in the small cap space, the problem is that successful active managers run into a capacity issue and close down, so that the good managers aren't available for new investment. I think that going forward, what you were talking about earlier, active managers charge higher fees, there's the liquidity issue, and I think that this trend away from actively managed mutual funds, A, because of the fees, and B, because most of them have underperformed their benchmarks, will continue. So I think that while there's a place for active ETFs, they haven't yet gained traction because of the transparency issue and some of the best active managers don't want the world knowing what they're doing. So I think this trend toward passive, or what we're doing, which bridges the gap between active and passive, will continue for the next five to ten years.

Nate Geraci: Well, Marc, on that note, we'll have to leave it there. We greatly appreciate you joining us on the program today, and certainly hope to visit again down the road. Thank you.

Marc Chaikin: Nate, Jason, thank you very much. Appreciate you having me on.

Nate Geraci: That was Marc Chaikin, Founder and CEO of Chaikin Analytics. And I'm going to give you two websites here. First, you can learn more about the two IndexIQ ETFs by visiting indexiq.com. And then you can learn more about Chaikin Analytics by visiting chaikinanalytics.com. They have a really interesting website. As a matter of fact, they won last year's Benzinga Global Fintech award for best trading idea platform or tool. So certainly worth checking that site out as well. Again, that's chaikinanalytics.com.