Rob Arnott, Research Affiliates
Profiting from Index Deletions
Rob Arnott has been in the money management business for almost 50 years. He launched Research Affiliates in 2002 while simultaneously running First Quadrant, eventually giving Research Affiliates his full attention when the business demanded it. The firm's business model is unusual: they don't run money directly. They create strategies and license them to firms like Schwab, Invesco, ProShares, and PIMCO. Through those partnerships, Research Affiliates indirectly runs $158 billion. They're best known for Fundamental Index, which weights stocks by the size of their business rather than their market cap, accounting for about $75 billion in ETFs alone. Schwab's FNDX versus iShares' IWD is a real-world test case, and Arnott says the value added has been "relentless."
On this episode of Behind the Ticker, Rob talks with Brad about NIXT, the Research Affiliates Deletions ETF. The idea is simple and counterintuitive: buy the stocks that just got kicked out of major indexes, because they've been systematically sold at their worst prices.
The Upside of Getting Dumped
When a stock gets deleted from the S&P 500 or Russell 1000, it gets hammered. Index funds have to sell. The stock is depressed, out of favor, and everybody who was forced to own it is now forced to sell it. Arnott's research shows that deletions outperform the market by an average of 7% per year for the first couple of years, tapering to about 3% after five years, for a total of roughly 2,800 basis points of cumulative outperformance over the five years after deletion.
He used a vivid analogy: "We've all had the experience of getting dumped. You can either wallow in self-pity for life, or you can pull your socks up, take some learnings, and move on. Companies that get dumped from the index face the same choice." Most of them pull their socks up. The white paper is titled "NIXT: The Upside of Getting Dumped."
Bayesian Methods, Not Data Mining
Arnott was emphatic about methodology. Research Affiliates built their own databases and analytic tools. They are "obsessive" about not data mining, which he says the quant community is addicted to. "If you build models that maximize historical back-test performance, all you're doing is maximizing historical back-test performance." Instead, they use a Bayesian method: start with a theory, use data to test it, but never use data to tweak and improve the back-test. "We don't go wherever the data leads us." The result is strategies that work in live markets, not just in backtests.
Brad connected this to what he's seeing with AI strategies: back-tests that J-curve beautifully but will never work in production because of curve fitting and optimization. Arnott agreed that strongness is the key, and his 20-year live track record on the Fundamental Index provides evidence that the philosophy works.
Index Construction: Filtering Value Traps
NIXT doesn't just buy every deletion. First, it constructs its own top-500 and top-1000 by market cap (avoiding licensing costs from S&P and Russell). Companies that fall more than 10% out of those ranges hit the deletions list. The team then filters on quality metrics: not just profitability (which Arnott considers too narrow) but debt-to-equity ratios, coverage ratios, and multiple measures. The worst 20% on quality get filtered out as potential value traps. The remaining deletions are bought and held for five years. If a stock gets re-added to a major index during that period, it exits the NIXT portfolio.
The data goes back to 1991 and shows consistent outperformance. Arnott emphasized the critical distinction: "We use data to test an idea, not to create the idea." The theory came first (forced selling creates mispricing), then the data confirmed it.
Value at Historic Discounts
Arnott placed NIXT in the broader context of value investing. Since 2007, value has underperformed growth by roughly 3,000 basis points. The relative cheapness of value versus growth, measured by price-to-book, hit a 9-to-1 ratio in 2020 (meaning the market was pricing growth companies to outgrow value companies ninefold). It's currently at 8.5 to 1. "Value is incredibly cheap," Arnott said, "which is something that I find very exciting." NIXT is very much a deep-value strategy, and Dave Nadig called it a "completion strategy" that fills in what indexes no longer hold by buying companies that have proven they can run a big business. The companies in the portfolio were recently in the S&P 500 or Russell 1000. They're just temporarily out of favor.
Key Takeaways
- Stocks deleted from major indexes outperform the market by an average of 7% annually for two years, tapering to 3% after five years, totaling about 2,800 basis points of cumulative outperformance.
- NIXT filters out the worst 20% on quality metrics (debt-to-equity, coverage ratios, profitability) to avoid value traps before buying deletions with a five-year holding period.
- Research Affiliates indirectly manages $158 billion through licensing partnerships. The Fundamental Index alone accounts for $75 billion in ETFs.
- Arnott uses Bayesian methods exclusively: theory first, data to test. Never data mining. His critique: the quant community is addicted to maximizing backtests, which produces strategies that are "rubbish" in live markets.
- Value is at historic discounts versus growth (8.5-to-1 price-to-book ratio). NIXT is a deep-value "completion strategy" buying companies recently big enough for major indexes but temporarily out of favor.
Listen to the full conversation on Spotify, Apple Podcasts, or YouTube.
Full Transcript
4,528 wordsMachine transcribed from Brad Roth's conversation with Rob Arnott, Research Affiliates, with speakers identified automatically. Timestamps link to that moment on YouTube. Lightly cleaned, otherwise unedited.
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Welcome to Behind the Ticker. Today we have on Rob Arnault. He is the founder of Research Affiliates, and we are very pleased to have him. He's got over 50 years of investment management experience, particularly pioneering qualitative or quantitative, excuse me, investment strategies, and really coming up with index methodology for a ton of different strategies you are probably very familiar with. But today we're talking in detail about the NIXT, or N-I-X-T, which is the Research Affiliates Deletion ETF. But I'll let him do a better job explaining it than I will.
So without further ado, please welcome Mr. Rob Arnault.
Hey, Rob. Welcome to the show. It's a privilege to be on it.
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Thank you. So before we get started, why don't you give everybody a bit about your background and how you ended up founding Research Affiliates? Sure.
Well, I've been in the money management business almost 50 years. I launched Research Affiliates back in 2002. I'd long thought I ought to build an asset management firm for me, not just for other companies. Finally got around to doing it in 2002. I was running first quadrant at the time. I ran both of them in parallel for two years. And then the business at Research Affiliates began to demand my full attention. So I moved on. And it's been a wonderful ride. The focus has been innovation.
Our business model is not to run money, but to create strategies and run them through others. So Schwab, Invesco, a host of other companies around the world license our ideas and use them for their own strategy. So we indirectly run $158 billion. And it's fun creating ideas that resonate with the market to the tune of $158 billion.
Yeah, I can imagine. So before we get into all the specifics and all of the things that Research Affiliates does, I always like to ask, any hobbies, things like you like to do when you're not working or sitting behind the desk?
Everyone needs a hobby. I collect the fastest of their era motorcycles. I don't ride them anymore. I got a wake-up call a couple of years ago when I had an accident that I should never have had. It's just sheer poor reflexes and poor instinct. But I still collect them. And I love vintage wine. I'm going to a Chateau Angeles wine dinner this evening. And I love travel.
Yeah. Well, I've always wanted a motorcycle, but my wife is vehemently against it. And probably best given that I like to go fast on things and I don't own one.
Speed doesn't kill you. Other drivers do. Yeah. Well, my rule of thumb is don't just pretend that other drivers can't see you.
Pretend that other drivers are out to get you. Yeah. That's good advice. So let's talk broadly about Research Affiliates. You kind of brushed over. You got a lot of different strategies. You help a lot of different people with strategy development and ideas. So can you kind of dive into Research Affiliates just a little bit more? What do you guys, what types of strategies are you producing? How are they being used? I'm sure SMAs, mutual funds, ETFs alike. So can you just walk me through the firm a little bit deeper?
Sure. Absolutely. We're best known for our work in global multi-asset strategies, global tactical asset allocation. We run the PIMCO all asset product suite. And for developing quantitative equity strategies, notably fundamental index. Fundamental index is over 100 of our 156 billion. And the notion with fundamental index is quite simply, why should we choose stocks for our index based on how popular and expensive they are, which is what happens with cap weighting?
And why should we weight stocks that way? Why not weight them by how big they are in the consumer marketplace, how big their business is? And if you do that, you have an anchor for contra trading against the markets, constantly changing opinions. Price soars. Fundamentals don't. We'll say thanks for the nice high price. We'll trim it. Price tanks. Fundamentals don't. We'll say thanks for the nice deep discount. We'll top it up. And sure, this is a better company with better prospects, and this is a worse company with headwinds, but it's already in the price. So it doesn't help you. And out of that, we have approximately $75 billion in ETFs at Invesco, ProShares, Schwab.
Schwab is the biggest relationship, and PIMCO. And that money is all tied to variants of Fundamental Index.
Got it. So when you talk about, I read a little bit about your background. So when you're sourcing and looking for these types of opportunities, given the fact that you have a mathematics as well as a computer background, is a lot of this quantitative in nature that you're running screens, or have you developed internal proprietary things that help you make these types of decisions?
Oh, we built our own databases, our own data sourcing. We've built analytic tools that help us parse through the data and gauge what works and what doesn't. One of the things that we are obsessive about is to not data mine, because the quant community is addicted to data mining. And people think that if you are a quantitative investor, you're stripping emotion out of the decision process, which you are. And you aren't chasing performance, which is bunk. Because if you build models that maximize historical backtest performance, all you're doing is maximizing historical backtest performance.
And so we are very strict about hewing to a Bayesian method where you start with a theory, you use the data to test the theory, and you don't use the data to tweak and improve the backtest. That's easy, and it leads to products that are rubbish. And so we use our common sense to think, if we wanted to make this strategy better, what might we do to make it better? And then we use the data to test that. So we don't go wherever the data leads us. And the result is our strategies have had a tremendous record. If you look at Schwab's FNDX fundamental index relative to iShares Russell 1000 value, IWD, the value added is relentless.
The fundamental index has a stark value tilt, but it has a rebalancing alpha, which a value cap-weighted value index doesn't have.
Yeah, two things off of that, right, is when you're developing quantitative strategies, robustness is key. But I'm starting to see, just in my line of work, specifically around a lot of these AI strategies, exactly what you've just talked about is a backtest that J-curves. And you know in production that that's never going to be the case. And there's a lot of curve-fitting and over-optimization going on. And we know over time that you might have a short window of outperformance, but that's likely not to be the case, specifically for someone who's had a long track record like you guys.
Yeah. We've had the fundamental index is now 20 years old. And its performance relative to the market hinges on how value is doing. So if value is doing well, we have a tailwind. If value is struggling, we have a headwind. But we beat value by enough that our 20-year results are ahead of the broad market, even though value is 3,000 basis points behind the mark. Yeah.
Value's been tough. That's fine. Value's been tough for anybody invested in values over the last, geez, 36 months, it seems.
Well, you can go back 15 years. It's really since 2007 is when value peaked relative to growth. And summer of 2020 is when it hit bottom. But it's been bottom bouncing since then. Right now, it's not too far off of the 2020 lows in terms of the relative cheapness of value relative to growth. If you look at price-to-book value, value was one-third as expensive as growth in 2007. It was one-ninth as expensive as growth in 2020. Nine-to-one ratio. That means the market was saying these companies are going to grow nine-fold relative to these companies in the decades ahead.
That's a tall order. And right now, we're eight-and-a-half to one. So value is incredibly cheap, which is something that I find very exciting.
Yeah. I actually just had Catherine Legraw. She's from GMO. They launched GMO-V, which is their deep value ETF. She's very excited about that prospect. And somebody like myself who runs a lot of equal-wage strategies that include value, I would love to see a resurgence of value. Yeah. Yeah.
And of course, our new Nix index is very much a deep value index. And it's a beautiful example of using common sense to develop a theory and using data to test the theory. So one of the things that we've noticed with fundamental index is it wins because it has a rebalancing discipline. And it wins because it doesn't add stocks just because they're frothy and expensive. Right. Which is a really key factor. If you look at cap-weighted indexes for every Tesla or NVIDIA that gets added to an index, there's a dozen or more companies that are hot bubble stocks popular, beloved, and then go on to crater.
Right. And so the net effect is you're adding frothy stocks. A few of them go on to great success. Most don't. And so they pull you down. And with fundamental index, you're choosing stocks based on how big their business is. You're waiting until the NVIDIA is big enough to be a successful business. And you're not dropping a Dillard's department store just because it's having a terrible couple of years. And the result is you have lower turnover. Right. And the turnover is more sensible and more effective. Well, one side of that is adding frothy stocks is bad for you.
Another side of it is dropping temporarily on love stocks is harmful to your wealth. And so the whole NIXT concept is built on a foundation of, well, why do we drop stocks at exactly the wrong time when they're deeply out of favor and dirt cheap? And so that's been a wonderful find to put that strategy together.
Well, you've walked me right into what we really are here to talk about today, which is the NIXT ETF, which is the Research Affiliates Deletions ETF. NIXT, nice ticker, by the way. I don't know if you came up with that or the guys over at ETF Architect did. But you guys specifically created the index. ETF Architect launched the ETF. So really what inspired the creation of the NIXT index and what market inefficiencies is it really seeking to exploit?
It's seeking to exploit deep value. It's seeking to exploit the fact that stocks get flobbered down harder when they get dropped from an index and pushed to even deeper value. It exploits the fact that in order to get into an index, a company has to have had some measure of meaningful success. So it's cherry picking the deep value names for those who have shown that they can be successful. And I like to joke that companies that get kicked out of indexes, some of them go on to achieve great failure.
But you only need a few to snap back and show their metal. A wonderful example is Lumen. Lumen Technologies was kicked out of the S&P in March of 23 and kicked out of the Russell Index in June of 2023. And this past year, it was up over 500%. Why was it up over 500%? Because Meta cut a deal with them to help Meta get up the learning curve on Lumen's category of AI.
So Lumen has a niche within AI where they're the leading expert. And Meta cut a deal with them to help Meta get up that curve. And so stock's up 500%. It's almost certain to be re-added to the Russell Index in June. And that happens a lot. I mentioned Dillard's earlier. Wonderfully fun little company. It's a small department store chain. It's been a member of the Russell 1000 four times in the last 30 years. It's been kicked out four times. So business is good. Stock rises. It gets onto the index.
Business hits a downturn. Stock craters. It gets kicked out. Buy high. Sell low. Buy high. Sell low. Buy high. It's four times. Last time it was kicked out was 2017. How's it done since then? It's up over 500%. And if my arithmetic is correct, it's going to be added to the Russell 1000 a fifth time in June of this year. Well, that's why buy high and sell low again and again when the size of the business was moving up and down a little bit and the size of the market cap was plunging and crashing. Why not just say, OK, it's a big enough business to matter.
We're adding it to our index. And, oh, it's out of favor. It's cheap. Business is still pretty big. It's struggling, but it's pretty big. Let's not kick it out yet. And so Dillard's been in nonstop that whole time.
So it's interesting when you have. So let's talk about the index construction process on both sides for addition and deletion. So how are stocks selected after they are removed from a major index? But if you if you just look at this, Dillard's example that you just laid out for us, is it going to be removed from your index when it's added back? Or do you let that positive news run for a little bit? Can you just talk about the addition and deletion?
The way we construct the index is straightforward. Firstly, we have to be respectful of S&P and Russell's intellectual property. So they would want to charge a not inconsequential sum for us to use their specific deletions. So what we've done is to create a top 500 by market cap and a top 1000 by market cap and companies that fall more than 10 percent out of that range. We view those as deletions. Now, it turns out that deletions from a mechanically constructed 500 and 1000 works just as well as deletions from the S&P and Russell.
So it's not that S&P and Russell are doing anything wrong. Right. It's that the mere process of adding a stock just because its market cap is soared, deleting a stock just because its market cap is tanked, is that process in and of itself is the problem. And so what we do is when a stock falls out of the top 550 or out of the top 1100, it's on our deletions list. Now, what we've noticed is that deletions outperform the market by an average of 7% per year the first couple of years, tapering off to about 3% after five years.
Total of 28%, 2,800 basis points of outperformance over the five years after deletion. That's marvelous. So you buy the deletion, but we don't want value traps. So the first thing we do with a deletion is filter it on quality metrics. There's many quality metrics. The academic community is obsessed with profitability as the sole criterion. That's kind of narrow. What about debt equity ratios? What about debt coverage ratios? We use multiple measures. Filter out the 20% worst, the ones that are likely to be value traps, and then buy those stocks and hold them for five years.
If they're re-added to the index, then they're pulled out of ours. And so we've tested the idea. Once again, we use data to test an idea, not to create the idea. And going back to 1991, from 91 to 2023, we find that stocks deleted from S&P or Russell, held for five years, would have given you 74 times your money. Well, 74 times your money in 32 years is not bad. No. And what if you just put your money in the S&P?
You would have had a little under 30 times your money. That's wonderful, but it's not as good as 74 times your money. And so we decided this is fun. This is a niche strategy that's built off a foundation of everything we've learned about problems with index construction. And one of those problems, one of those Achilles heels, is kicking out stocks at the wrong time, just when they're facing their worst news and most deeply undervalued. On average, stocks kicked out are kicked out at half the market multiple. Stocks that are added are added at twice the market multiple. Well, neither of those makes a lot of sense.
And the niche side of it, the deletion side of it, is particularly fun and interesting to me now because small cap is out of favor and cheap. Value is out of favor and cheap. This is a deep value strategy. The largest holding in it is Lumen, which was a deep value strategy until Meta signed up with them.
So you're making a good case here. I'm going to ask one more question about the index as a whole, and I want to speak a little bit more broadly about some of the things you've seen in this index. So the fund rebalances annually, I'm sure you guys did the research. Why do you believe that frequency is optimal compared to maybe a more active quarterly type rebalance?
It turns out it doesn't much matter when you rebalance or how frequently. Now, just as a nuance, this is a strategy which owns 150 small cap value stocks, and the liquidity on those is not brilliant. I can confidently predict that if this ETF attracts a billion dollars in assets, we're going to start looking at moving to quarterly. Right. And so it just makes sense to be responsive to the issues we'd face, our implementers, ETF architect, would face in implementing the strategy as the strategy grows.
But at its current size, the ETF has attracted just under $40 million in its first few months. That's pretty cool. Yeah. Especially without marketing teams focused on wholesalers going around telling people about it. We don't have any wholesalers, nor does ETF architect. But for the strategy at its current size, the liquidity is not an issue. And we did test rebalancing quarterly. Same returns. Yeah. It deteriorates if you rebalance less frequently than annually.
But it deteriorates actually only modestly. The advantage of more frequent rebalancing is liquidity and capacity. You would roughly double the capacity by moving to quarterly. And we estimate the capacity of the strategy at about $5 billion. So if we wanted it to be $10 billion, we'd do it quarterly.
It will be nice if we ever have that problem. That's what I was going to say. I said that'll be a good problem to have. Got to grow a few hundredfold before we're there. Yeah. Well, you never know. Are there any particular sectors or industries where deleted stocks have shown a stronger mean reversion tendency? And I also want to ask that question a different way. Are there certain sectors that have been or industries that have been subject to more deletions? And do you have any kind of industry or sector constraints on the strategy? So you maybe don't get in one area a little bit too heavy.
The best way for us to constrain sectors is to hold the stock for five years because sectors come in and out of favor. What we've noticed is that the performance of deletions goes up when there are more deletions. So when the indexes are relatively stable from year to year, the value add is nice. When you get to a burst of deletions, S&P or a top 500 index would have been busy chasing the dot-com stocks in 99 and 2000 and kicking out basic industries and energy stocks.
And then two years later would have been doing the opposite. During bursts of heightened turnover, the performance goes through the roof. And so that's one of the things that's interesting to watch for. There was a burst of turnover in both Russell and S&P in 2009, global financial crisis. And there were industry concentrations, financial services, more were being kicked out than consumer discretionary. And consumer non-durables, I should say. Um, and so we don't constrain the sectors at all.
Um, but in one year, there may be much more of a particular sector getting kicked out. And in the next year, something totally different. And so by holding for five years, you get broad diversification. Uh, I actually started running this strategy in my personal account back in 2018, um, using actual S&P deletions and, uh, holding them for a year. And I quickly discovered, okay, sometimes I have, uh, eight stocks in the portfolio and sometimes I have 20, uh, it's not very diversified. It's not scalable. Um, but the performance was lovely.
Uh, uh, I was having great fun with it and I realized that's when we went back and tested, okay, how long does the outperformance last? Five years, give or take, um, uh, it tapers off 7% ads, 6%, 5%, 4%, 3%, and who needs to stick around for two? Um, and, uh, by holding it five years, you quintuple the number of stocks in the index by using top 500 and top thousand deletions. Um, you triple the number relative to just say using S&P deletions. So you could create an ETF that strictly focused on S&P deletions, pay S&P the licensing fee for access to that, which would not be an inexpensive licensing fee.
Um, uh, and have a narrow, undiversified, highly volatile portfolio, or you can construct something that has 150 stocks in it. Right. And, uh, equal weights once a year. Lumen is now a 2% holding because equal weighting in June, uh, turns into a 2% position now. Right.
I, I was just looking at that. You, you, you, again, walked right into the next question. So at rebalance, you're going to do everything equal weight. You're not going to take an opinion. You're going to let things run. So at next rebalance, you're going to pull Lumen back down in line. You're just get everything back on the same level playing field.
Uh, we would pull it back down unless it's re-added to the top thousand, which it will be. So Lumen will turn out to be one of our short-term one-year holdings. Got it.
So when, uh, looking at an advisor, you do a lot of model portfolio work. So looking at an advisor that's got, a diversified rep is PM model that they're running fairly strategic. Where are you advising they use this strategy as a side card of their existing market cap weighted exposure or a, how do you think about that?
Dave Natig, uh, when I talked to him about this, he said, oh, this is just a completion strategy. It fills in what the indexes don't have anymore. And it does it in a clever way that, um, uh, buys the companies that are no longer in the index that have proven that they know how to run a big business. Um, and so one way to think about it is, is a completion strategy. I'd never advocate somebody putting 50% of their money into this. It's a niche strategy, but if you want small cap value, this is a really cool way to do it. And so if you're putting money into IWN Russell 2000 value, um, uh, uh, buying an ETF that's tied to the next index is a really interesting alternative.
Um, as an index provider, I'm not allowed to specifically recommend an ETF, but I can recommend the index. Yeah. Yeah.
Our, our, the compliance people will be listening. So hello to them. Well, Rob, this has been, uh, you've been a wealth of knowledge and I really appreciate your time. I think the strategy is really, really cool. I'm going to keep my eyeballs on it. It's going to, it's going to go on my watch list, but before I let you go, where can people learn about research affiliates, all the work that you do and get some more information on the next index?
Well, firstly, I'd, I'd strongly suggest, um, going to our website, research affiliates.com, or if you don't want to type that many letters, R-A-L-L-C.com, uh, they both take you to the same place. Um, we have, um, uh, we have an interactive tool that helps people look at long-term future returns for asset classes. It's called, uh, asset allocation interactive. Um, it's, it gets about a half million, um, new views every year. So it's gone viral by the standards of our industry. Right. Uh, the, um, next index is written up in there.
The white paper that describes the next index is in there. Um, uh, it's called next, the upside of getting dumped. And, uh, the point of the paper is, uh, uh, we've all had the experience of getting dumped, or at least most of us have probably not here, but, um, anyway, you, you can either, uh, wallow in self-pity for life, or you can pull your socks up and, uh, uh, take some learnings from the experience and move on. And a company that gets next can either wallow in self-pity and flounder and go on to great failure or can decide, okay, we need to get our act together.
And some of them do. And if they're dirt cheap, if they're half the valuation multiple of the market, then just doing an average job means the price doubles. Right. That's cool.
Well, also I was on your website as well. And, uh, there's, you, you publish a lot on there. So if you're looking for articles on it. Yeah. If you're looking for insights and things, uh, to stir the brain a little bit, I would definitely go and check the website. But again, Rob, thank you so much for your time. Thanks for being here with me today. Thanks for the invitation. This has been great fun. Thanks for having me today. Bye. Bye.
Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye. Bye.
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