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On November 14, Litman Gregory's Chad Perbeck, CIMA®, moderated a discussion on how to evaluate and implement multifactor equity investing for clients. We were pleased to host panelists Adrienne Ross, CFA, Vice President at AQR, and Jeremy Schwartz, CFA, Executive Vice President, Global Head of Research at WisdomTree. Both of their firms are leaders in the space, and they shared their insight as well as participated in an open Q&A.
Here is a replay of the discussion:
Hello, everyone. Thank you for joining us today. This is Chad Perbeck, Senior Research Consultant at Litman Gregory.
This webinar really seeks to leverage our 360 Views Program, in which we seek to aggregate valuable research content from our research-alliance members, centered around typically a focused theme. Then we distribute as a value-add to subscribers of Advisor Intelligence through webinars and through transcriptions.
Really, the overarching goal is to address pressing topics that are impacting our business, and to share our experiences and those of our partners, in an effort to better serve our clients.
Lately we've been receiving questions from our Advisor Intelligence subscribers and followers of our research -- even over the past few years -- about factor-investing, in general. From a broad viewpoint, multifactor equity investing seeks index-outperformance with potentially less tracking-error, and you could say more consistent alpha than the strategies of a traditional fundamental discretionary active manager.
It may be a suitable fit for investors who believe in a more systematized, quantitative approach with defined decision rules around it.
Really, I think there's been a proliferation of these strategies over the last few years. Each has a different methodology and varied results. As a lot of academic research, data and analytical tools have become more robust and more widely available, I think we're starting to see -- I guess you could call it -- a "Quantimental" trend. More of a blend of quantitative-systematic strategies with fundamental research and human input and oversight on top of the machine-based learning.
I don't know -- maybe George Orwell the author of "1984," was a little bit early, like a lot of value managers usually are. He was pretty prescient. But that's as little bit of a digression.
Today, advisors are seeking out multifactor strategies, I think, in an effort to combine the best of active and passive, at the lowest-cost possible for their clients. Our panelists today are going to share their research and efforts on this quest, and educate us about the tradeoffs to consider when implementing these strategies for our end clients.
Here's just a snapshot of our agenda for today.
I'm briefly going to introduce each speaker, and then pass the mic to them for some prepared remarks. Each presenter will probably have about 15 minutes.
After each presenter has shared his view, we're going to open up the Go-To-Webinar control panel for a virtual q-and-a session, while the attendees will be prompted to submit their questions via panel.
Even if you think of questions as we're going through, and the presenters are speaking, feel free to submit them at that time. I'll see them come in. I'll aggregate them. We can address them at the end, time-permitting.
If we get overloaded with questions, that's a great thing. If we don't get to cover them in the allotted time, the panelists and I will certainly follow up with you afterwards.
Now I'm going to go ahead and introduce our first panelist from our friends over at AQR. Adrienne Ross is Vice President of Global Stock Selection at AQR, where she writes white papers and conducts investment research.
She's also involved in the design of multi-asset portfolios, and engages clients on portfolio-construction, risk-allocation and capturing alternative sources of returns.
Adrienne has published research on regional economic factors in the Journal of Economic Geography, and on the website of the Federal Reserve Bank of New York. Prior to joining AQR, Adrienne was a senior account associate at PIMCO, where she began her career as a researcher at a macroeconomic think-tank in Canada.
Adrienne earned a BA in economics and mathematics from the University of Toronto, and an MA in quantitative finance from Columbia University.
Welcome, Adrienne. Thanks for being with us today. Please take it away!
Adrienne: Great! Thank you so much! Thank you everyone for joining us today.
I have some slides that I plan to run through as it relates to our thoughts and our experience running factor-based strategies. Of course we're happy to take any of your questions as relates to these concepts.
Just quickly, to take a step back -- to try to understand where AQR fits in this broader landscape –
Just trying to get to the next slide, here. We are having some technical difficulties over here. There we go.
Just a little bit of context in terms of AQR and where we fit.
As a brief update, AQR is a quantitative, systematic manager that has been running factor-based strategies since 1998. As markets have evolved, and I'll talk a little bit about how things have changed -- and we've certainly touched on the proliferation of factors, more recently -- we have been trading on these investment ideas since Day 1 of our existence, which goes all the way back to 1998.
We have launched and run stand-alone what we call "style-premia" strategies, which are effectively factor-based strategies where we're trying to outperform cap-weighted benchmark in the case of a traditional offering. Or outperform cash in the case of a long-short offering, based on some well-known factors.
These have been more-recent launches. You'll see that the inception dates are -- call it -- 2009 and 2012. They've effectively taken a lot of the ideas that we have that there's a wealth of evidence in terms of why they should drive market returns and ideas that we've been trading on for about 20 years and culled out the most well-known measures and most well-known styles or factors. Effectively, packaged them together in a strategy that offers a lot of transparency and evidence in terms of what exactly we're looking at.
While we have a shorter-term track record running what we're calling "style," or "single-style," these ideas and these principles do go back to our inception since 1998.
With that kind of context, I think what's important to keep in mind is the evolution of markets. We've talked a lot about how factors have become a lot more popular.
What it's shown on this page is a philosophical evolution of investment ideas. Where effectively it's getting at the crux of active-management. Over time, what was once considered alpha eventually becomes beta. Your definition of beta can evolve over time, as well.
At one point, everything was considered alpha. Once the Cap-M or market-framework came into the picture, market-risk was deemed as a risk factor that generated a source of returns. Then anything above that represented alpha.
Fast-forward to the last 10 or so years, there are a variety of styles or factors which have also come into popularity, from the perspective of systematic exposures that are something in-between a market-beta and a pure-alpha approach.
There is a sort of philosophical debate of what is alpha, and what is beta, and what falls in-between. As markets and managers and academics study these investment ideas, what had once been considered priority in terms of a truly unique and truly idiosyncratic source of returns -- that's what I'm going to refer to as "alpha" -- can fall into more of the traditional "beta," or "alternative-beta" category.
I would classify a factor or an investment style as an alternative form of beta. These ideas do rely on manager-skill from the perspective of how to put the ideas together. But they're not relying on an individual human or discretionary approach to pick individual securities.
Instead, we're relying on rules and a predetermined set of descriptors that tell us which securities we like versus dislike from the perspective of these factors.
I think we can all agree that there are some well-known styles or well-known factors that many people tend to be investing in. Especially more recently. We have a few of them that are certainly our favorites, and there are others beyond what we think about trying to capture.
There are some common characteristics in terms of these styles or investment ideas that we tend to look for. The first characteristic -- we're showing three styles or factors on this page.
I might argue that these are our favorite factors. There are others, and we can certainly debate which ones drive returns versus others. What we're trying to look for are characteristics in securities that tend to drive market returns.
We're focusing specifically on stocks. But a lot of these ideas can be applied across many different asset classes, too. That kind of application gives us a lot of confidence in these investment ideas.
That is, you can test these concepts in many different regions. Whether that's US largecap in stocks, or whether that's small-caps, emerging, developed -- or whether we're talking about fixed-income or commodities or currencies, for example. These ideas and these concepts are broadly applicable.
There is a very good intuition in terms of why they work and why they should continue to work.
I'll dive in a little bit more in terms of the specifics of these three styles, but I would say that these three styles are the hallmark or the core of a lot of our strategies and exposures that we'd argue that a lot of investors should have within their broader portfolios.
A little more detail in terms of value. Value relates to the tendency for cheap securities to outperform expensive securities over the long-term. There's been a huge wealth of academic evidence studying this investment idea. Specifically in the case of stocks, this might be more well-known as the Fama-French high-minus-low factor. That is effectively going to look at book-value of equities and relate that to a notion of price, to get a sense for whether a particular stock is cheap or expensive.
It effectively allows you to divide the investable universe or rank companies on this characteristic. That is trying to look at some notion of fundamentals, and relate that to price. Based on the fact that we know over the long-term, cheap securities tend to outperform expensive securities.
When it relates to why value works over time, some of the intuition helps support why we think this investment idea should not go away. Why despite the fact that a lot of recent money is pouring into these ideas does not necessarily mean that the prospects of getting paid to invest in cheap securities will be diminished.
Part of the reason why that is the case is because of the intuition of why it works. Across all the investment styles, we can argue whether or not there's a risk-based explanation or a behavioral-based explanation for value in the case of risk.
Some people may argue that there's compensation for risk and value companies are cheap for a reason. It's riskier to invest in these types of securities.
Or, on the flipside, there's also this behavioral-based aspect, which effectively says that there are some investor-biases that mean they don't always act optimally. From the perspective that market-participants tend to get pretty excited about the future growth prospects, they tend to prefer more growth-oriented kinds of companies, if you think of growth as the opposite of value.
They tend to pay more than probably what they should for these kinds of companies. That helps to explain why they're so expensive and may not manifest themselves in terms of actual subsequent stock price performance.
These reasons give us confidence that these things are not based on just some statistical chance that we're looking at these ideas. They work across many different capacities. There's a reason they work. We can elaborate on any one of these investment ideas.
That economic intuition is very important. We talk about a systematic approach to these ideas, but these are really just fundamental concepts.
They're fundamental ideas that a lot of stock-pickers and a lot of discretionary managers tend to look for. The difference is really just how you implement them and how you go about constructing the portfolio more from a rules-based approach.
Moving on from value to momentum is the tendency for securities that have recently performed well, to continue to perform well. The tendency for securities that have recently performed poorly to continue to perform poorly. It's a close-relative to trend-following. There are lots of reasons why this works, to begin with, as well.
I think the most compelling reason is the behavioral reason for markets to slowly adjust to new information. For prices to slowly adjust to new information.
There's a lot of evidence that shows when new information comes out on the fundamental value of a particular company, markets initially underreact to that new information. They eventually overreact, which explains the persistence of these kinds of trends and performances over a 12-month horizon. Which is usually what you look at as it relates to momentum.
In the case of quality, there's a variety of ways that you can think of capturing higher-quality versus lower-quality. What we're trying to capture here are companies that have good profit. That's probably the most well-known way to capture it, from the perspective of looking at gross profits over assets. That's effectively going to tell you a highly-profitable company.
There are other ways you could look for the underlying quality of a company. But there's a lot of evidence that higher-quality companies tend to outperform lower-quality or junkier companies, over time. That's effectively the premium that you're trying to capture here.
Similarly to value, these kinds of companies are boring or perhaps overlooked. That's some of the intuition in terms of why it should work, going forward.
As I mentioned, even though this is a systematic process that you can capture these ideas via a rules-based approach, they truly are based on fundamental ideas. Even if we go back to Warren Buffett and some of his philosophy in terms of how he thinks about picking stocks.
He had a quote that rings true, here. Especially as it relates to value, which is fair prices. And quality, which is quality.
Warren Buffett says, "Whether we're talking about stock or stocks, I like buying quality merchandise when it's marked down." That's not inconsistent with his philosophies. That's ultimately what we're trying to do -- to invest in fundamental ideas. The tendency for cheap, improving, high-quality companies to outperform expensive, deteriorating and junkier companies over the long-term.
This is the emphasis and the main idea behind a multifactor approach. It's taking advantage of these three investment themes in a way that takes advantage of some of the synergies that exist when you combine these ideas.
Certainly you could form a portfolio based on any one measure. We would support that investment idea from a single-factor or single-style perspective. When you combine these ideas, you get an even-better outcome from a perspective that these ideas all tend to pay off over the long-term.
There are characteristics in companies that you should get paid to invest in. These characteristics pay off at different times. That means when you put them together in a portfolio, it effectively means a more-consistent smoother return profile from the perspective of trying to generate returns above a benchmark by tilting toward companies that express these kinds of characteristics, and away from companies that don't have them.
That's ultimately what we're trying to do within the multifactor approach.
I will note that while we're talking about these labels, there are a lot of labels and a lot of styles that a lot of factor-based investors can agree up, there are so many different choices in terms of how you can actually implement these investment ideas.
Two value managers that are both systemic managers can look very, very different in terms of how their performance is and how their risk is. How they effectively behave over time.
A big part of that is due to some of the design choices that go into what signals you're looking at, how you're measuring these characteristics, and how you apply these different lenses.
What we're showing here is AQR-prefered approach to investing in these ideas. Which is characterized based on the fact that in each of the three categories of styles we tend to like there are so many different ways to capture these ideas.
In the case of value, again, cheap-versus-expensive. Trying to look at some fundamental measure, and relate that to price. Book-value, as mentioned, is the most well-known way to do that.
But there's other accounting information you can look at. You can look at cashflow statements. You can look at earnings. You can look at forecast earnings. The whole idea here is to get a better read on whether a particular company is actually cheap or expensive, and to do that through a lot of different perspectives.
As it relates to both momentum and quality, the exact same idea. When you're really trying to zero-in and focus on the investment ideas, there are a lot of different ways you can do it.
Price-based measures of momentum are probably the most well-known ways to do it, from the perspective of looking at changes in prices over the last 12 months. You can also look at what we're calling "fundamental" momentum, which are changes in fundamentals.
Asking the question, "Are particular companies' fundamental improving?" Are their margins growing? Are their earnings improving? Are analysts becoming more or less bullish or bearish on a particular company?"
These are all fundamental notions of improvement versus deterioration. It helps us isolate and get at the core components of trends in markets, and securities that perform well and continue to perform well.
Same idea for quality. We're looking at it through a variety of different lenses. These are the measures that we look at in determining whether a company meets these various lenses.
Then there's also the question of, "How do you apply those lenses?" That's effectively what we mean at the bottom of this page by, "Peer Groups."
For the most part, we think about capturing these ideas on a Levin Industry basis. That is comparing Ford-versus-GM rather than Ford-versus-Apple. That's one example.
The reason why we're doing it on a Levin Industry basis is a lot in large part because the most-comparable doesn't make a whole lot of sense to compare accounting measures for an IT company relative to a utilities company, as one example.
Specifically when it comes to accounting measures, in the case of value -- in the case of quality -- it's even more important. We're only going to make industry-peer comparisons.
How does Ford look in terms of its book-value relative to price versus GM? Those are the comparisons we make. That's one of the design choices that we make.
There are different design choices that other managers can make, but a lot of the choices we make are based on a lot of academic evidence. These investment ideas and how you capture them can be very different across managers. This is effectively a summary of how we think about capturing it.
Whether you have exposure to these investment ideas is a bigger question, and one that we would argue many investors should have exposure to these ideas. Then it's just a question of which manager gives you the best exposure to those investment ideas.
We'll pause here and turn it over to the sense that there are other questions.
Chad: Perfect! Thank you, Adrienne. Really appreciate you sharing those views. Especially from the AQR side of the house. I liked how you set it up talking about beta and alpha and how we come up with factors.
I really liked the intuitive part about imagining sitting across the table from a client and trying to explain the behavioral factors that allow these factors. Behavioral biases that allow these factors to continue throughout time, and why they add value.
That was pretty intuitive.
Of course, there are so many different ways to measure it, as you're showing here. I think it's a great, great setup.
I think we'll hold for questions now and we'll move on to our next panelist. Then at the end, we'll do a broad q-and-a for everybody involved.
I want to thank you, Adrienne.
We're going to move on now to Jeremy Schwartz.
Wisdom Tree is one of our newest members of the Research Alliance. We're really pleased to be working with them.
Just like AQR, WisdomTree just has a host of resources available to advisors. We're happy to have them on the platform.
WisdomTree launched its first ETFs in June of 2006. It's currently one of the largest ETP -- exchange-traded products -- sponsors in the United States.
They have offerings covering domestic, international and global equities, fixed-income, currencies, commodities and alternative strategies.
They pioneered the concept of fundamentally-weighted ETFs and active ETFs.
They're currently an industry leader in both categories, as measured by assets under management.
WisdomTree is the only publicly-traded asset-manager exclusively focused on the exchange-traded product industry.
Jeremy has served as executive vice-president and global head of research since November of 2018. He leads WisdomTree's investment strategy team in the construction of their equity indexes, quantitative active strategies and multi-asset model portfolios.
Jeremy joined WisdomTree in May 2005 as a senior analyst, adding to his responsibilities in February of 2007 as Deputy Director of Research, and thereafter, from October 2008 to October 2018 as Director of Research.
Prior to joining WisdomTree, he was head research assistant for Professor Jeremy Siegel, and helped with the research and writing of, "Stocks for the Long-Run," and "The Future for Investors."
Jeremy is also coauthor of the Financial Analyst Journal paper, "What Happened to the Original Stocks in the S&P 500?"
He received his BS in Economics from the Wharton School at the University of Pennsylvania, and hosts the Wharton Business Radio Program, "Behind the Markets," on Sirius XM 132. Jeremy is also a member of the CFA Society of Philadelphia.
Jeremy, thanks so much for joining us today. Please take it away. We're going to hand you the controls.
Jeremy: Chad, thank you. It's a pleasure to be here along with Adrienne on this topic. It is a great topic.
I think in terms of the partnership with Litman Gregory and the 360 Views, we're very happy to be a part of this program. Since we are a new member of the Research Alliance, just briefly on how WisdomTree got to the multifactor space.
When we started back in 2006 launching our first 20 ETFs, it was all built around this idea of alternatively-weighting indexes and trying to add value on top of just pure beta.
Our first 20 ETFs had a value and quality bias to them. They were bouncing back to relative value with the main idea of improving upon pure beta.
I loved how you described the original reason for this multifactor concept. It was combining the best of active and passive.
One of the things that we've been talking a lot about is, "What do you call these things?" Adrienne referred to this, too. Some of you may have seen our commercials with Professor Siegel, and we're doing a lot around the words, "Modern Alpha."
I know Cliff does a lot of publishing over at AQR. I know one of his pet peeves were the words, "Smart Beta." That phrase always bothered us, as well.
We had called a fundamentally weight, trying to improve upon cap-weighting with a rules-based rebalance. Protect from bubbles. Protect from things like the tech bubble in '99 and 2000, when Siegel got very dispassionate about cap-weighting.
That was the original dividend-earnings approach.
This "modern alpha," and what we mean by "modern alpha," is also combining the structure. The ETF structure, we do believe is a better structure. If you're going to have any sort of rebalancing, the benefit of ETFs -- to be able to do that very tax-efficiently, when that capital-gains season where everybody starts to pay attention to capital gains –
Any kind of rebalancing, we think the ETFs help you accomplish that efficiently. Modern Alpha is one of our funds, as we've described it.
Now when we got to this multifactor, today in the marketplace, we have three multifactor ETFs. We have a US multifactor, an international multifactor and an emerging markets multifactor.
We started off purely as an index. But we're actually on our last two executions, because when we got to international and emerging markets, we did start running those actively, as described by my bio. Having the quant-active strategies.
We are increasingly going active.
The spirit of that was our original earnings indexes. Those were being run with a 2% tracking error and 1% value-added goal. We're rebalancing annually to that earnings concept, to lower P/E ratios. Increased quality of the portfolio.
Our latest multifactor is taking much more active risk.
We expect tracking-error of 4% to 5% compared to 2% from original earnings indexes. We're going to have more quarterly-type rebalancing. So a little bit higher turnover, going back to combining the benefits of the ETFs and doing it very tax-efficiently.
I would describe Wisdom Tree's original IP back 12 years ago as very value-and-quality based. Everything was sort of anti-momentum. We were selling the winners at every rebalance and buying the losers, in some ways.
One of the reasons we didn't want to rebalance more than annually was because you didn't want to keep increasing the negative momentum from that. This was one of our first steps of strategy that included momentum as one of the stock-question factors.
What's interesting –
When you see our multifactor process described here, it's interesting that I don't have to go back through all of the explanations that Adrienne did –
We do a lot of similar things on value-quality-momentum. And like she said, there are different ways of defining each. But you see some similar metrics in how we think about the value factor.
We similarly in our very original IP -- when you just looked at originally the earnings weightings or dividend weightings as a better example -- dividend weightings, you'll have 5 to 2 higher dividend sectors, away from the non-dividend payers -- which we like, over time. Small-cap growth has been a bad segment over time, so we don't mind it. But you will be persistently overweight things like utilities versus underweight the lower-yielding sectors.
We are getting more sophisticated in our execution of the factor-process, with our multifactor and the value-factor, looking within sectors in a similar concept. We have quality measures that look at both the levels of profitability as well as the trends. An earnings-momentum type quality gauge.
We didn't really think there was a lot of innovation and momentum, and there are a lot of different ways of calculating momentum. But in our spirit of trying to go after high alpha, where we're trying to target 4% to 5% value-added with 5% tracking, we were thinking about, "How do we manage the risk in a unique way?" So we used a risk-adjusted momentum as well as a low-correlation factor.
What's interesting even about having this panel of AQR and ourselves, I haven't seen a lot of publishing on low-correlation as a factor. We launched our multifactor index in the middle of last year. We've been doing the work on it throughout 2015 and 2016. Then I did see an AQR paper on low-correlation as a factor.
What's interesting is, I think we're one of the first -- I don't know how broadly they're using it in their factor strategies, but -- as part of our multifactor process, we have this combination of fundamental factors and technical factors, with risk-adjusted momentum and correlation being two of the technical stock-selection factors. We're combining them in a very equally-weighted fashion.
Every stock you can think about in the universe gets a value grade. Every stock getting a quality grade; a momentum grade; low-correlation. Then we're picking across. That's really true across the US and across international and emerging markets, approximately 200 stocks in every basket.
It is more concentrated, compared to our original 2,000 earnings-weighted stocks. You're much more concentrated with 200 stocks. Our weighting process is a combination of our multifactor score being our alpha signal, and a volatility score.
There's really an emphasis on managing volatility and managing the alpha-tilt. I think of this weighting process as like a Sharpe Ratio type of weighting concept. Where you're tilting toward your alpha signal as well as tilting to your volatility, all while managing your sector and countries relative to the market-cap universe. So you're not making any persistent bets toward a sector. In the US, at least, we're very sector-neutral.
In the international and emerging markets, we had some tilt versus the markets, but this was our most-active process.
We just launched the international and emerging markets version three months ago. They're live, up-and-running. I think it's going in its more-active spirit, which is the direction we've been going.
The research that we have -- just to give you a sense of how persistent these factors have been across the different universes –
The Fama-French high-minus-low across our four-factor definitions of value, quality, momentum and low-correlation –
We show you here across the US, the EAFE market and the MSCI emerging markets, how these good-versus-bad definitions work. When we describe good-versus-bad, this is really an interesting framework that you'll be able to see on the next slide, across a lot of the multifactor ETFs how they're exposed to these factors?
The way we're defining the source, we'll describe the good value stocks as being the top third of total market-cap on our value definition. Let's say there are 1,000 US stocks. You sort it 'til you get to the top one-third in total market-cap. That's your "good" basket. Then the middle one-third is your "okay," basket. Then the "bad" one-third is the poor value stocks.
If you just look at the US numbers as an example, for the last 16 or 17 years, you've had premium for value over bad value that's been some 260 bps. The quality-premium was a little bit over 300 bps. Momentum –
There wasn't a big number, here. Now a lot of the academic researchers say the momentum was one of the best long-run factors. We use this risk-adjusted momentum.
I think one of the most interesting things is just how different the betas are.
You see the 150 bps for good-momentum versus bad-momentum, but you see a 66-beta versus a 140-beta for a good-versus-bad. So you can really get a sense of the risk-management that comes with using the risk-adjusted momentum.
Low-correlation -- there was also interestingly 260 bps on least-correlated stock. We've correlated it being the diversifier, zigging when the market's zagging.
You could also think of low-correlation in some ways as dynamic momentum. In a strong rising market, stocks that have low-correlation would be ones that are lagging. In a down market, what's going to have low-correlation are stocks that are outperforming.
There are dynamic elements of using low-correlation as a factor. Which is an interesting element of correlation, there.
You think about this 200-bps to 300-bps spread across the US, and in most of these goods-versus-bads, you do see a lower beta. If you look at the quality-beta, it's 86 versus 115. If you see low-correlation, it's 88 versus 109. The only one that had higher beta was really value. 110 versus 92.
What's interesting is how consistent that was across the EAFE markets and across the emerging markets. In the developed world, that middle section you see consistent, that 200-bps to 300-bps premium for good-versus-bad.
The quality was around 300; momentum as a little bit better in the developed world. Correlation was around that 300 bps.
Where you actually saw the biggest spread -- I clicked ahead -- you saw the biggest spreads in the emerging markets. That's where you saw some of the –
Where we think it's the idea that you had more inefficient markets in emerging markets, which is where the bigger opportunity sets for value are.
I use that framework for the third/third/third largely to help you also understand the tilt that you see across the multifactor strategies out there. There's a question of once you define your factor-tilts, how you construct these multifactor processes really do matter. On the bottom, if you just start with the bottom, I described how we define the "good, okay, bad," by third-market-caps.
So if you took the SPY and the S&P 500, you'll see approximately a third/third/third across all of these good-okay-bad. Across value/quality/momentum and correlation.
What's interesting is, if you go up -- let's just read the table from the bottom-up. If you take a look at the Goldman Sachs "Active Beta US Large-Cap," they describe it as "active" beta. But what's interesting is how very low-active tilts they are.
If you look at them, they're good value-tilts. 36 versus just being 33. It's a very small value-tilt.
Their biggest tilt is to quality. They go to 41% in good-quality. Momentum is roughly a third/third/third. A small tilt to low-correlation. But really, it's not that active. Interesting, the screening of active-beta.
As you go across the different providers of multifactor, you can see that JP Morgan Diversified Return US Equity -- JPUS –
Roughly, it's not that tilted, either. The one tilt they had was to low-correlation, which is a low-vol factor. One of the things, interestingly, when you read their description –
They had a composite-scoring methodology similar to how I described selecting, based on this multifactor score that we create. They only end up taking half to 75% of the universe, but then they weight the sectors based on volatility.
So they end up having a big tilt to sectors. Things like big tilts to utilities, which you see showing up in low-correlation. But it's really not that heavy of an active process beyond the low-correlation bet.
Then you go to the iShares multifactor. What's interesting is really how concentrated their factor-tilts are in one or two factors.
They have a strong value-expression. They don't have that strong of a quality expression nor momentum. Also, tilting to low-correlation.
You can really see that it becomes very concentrated in one or two of the specific "multifactors."
Across our US multifactors, part of this is definitional, because we are sorting it based on our definition of the factors. But you can see how we have very heavy 45% to 50% tilt to our good stocks; much less than if you were neutral on all of these factors, and have a third across all of them. You'd have less than 20% in the bad factors.
You really do see the idea behind how we're constructing these. This is really true across, if you looked in the US, across international and emerging. I don't have all those slides here, because it's a short time.
It gives you a sense that construction matters. How you create the multifactor combinations and how you select. How you weight has implications. We're trying to help you understand the factor tilts through different tools and analytics that we can help you with, understanding the various ETFs and funds.
One important factor across international that I think we're uniquely doing -- certainly in ETF form -- I haven't seen anybody else doing anything like this –
I know AQR talks a lot about currencies generally, but there's a lot of misperception on the role of currencies, generally, I'd say in the portfolio. There are lots of people being unhedged all the time, which I like to say is an uncompensated risk, to be saying, "I'm going to always be long the euro forever."
That's a bet that's not paid off.
We tried to add a currency factor on top of our international and emerging markets. We'll more dynamically allocate to currencies. We have different factors in developed and in emerging markets in a way. The developed-market signal is a 3-factor value-momentum-carry. So how much you're paid to hedge as a carry factor is sort of a rewarded-risk-factor that we think in the long-run pays off.
Value has bands between how expensive and how cheap you have to be before you fully hedge or fully take off the hedge. Then momentum showing up across asset classes.
We believe this kind of currency factor can add another 100 bps to 150 bps over the long-run, between whenever you choose your benchmarks from unhedged, fully-hedged, half-hedged. Most true, I'd say, is the half-hedged benchmark. But it's designed to outperform over the long 10- to 30-year cycle over any of those benchmarks.
I think that's unique in the ETF world, to have any kind of currency-factor process. Particularly in emerging markets.
We were one of the leaders in currency-hedging for broad developed worlds with Europe and Japan. We hadn't ever done static emerging market hedging, because we thought it was expensive.
Our first time including currency as a factor for emerging markets was this new multifactor I just launched three months ago. It will only be hedged 20% of the time on average, based on our research.
Interestingly today, it's 75% to 80% hedged. So it's telling you all of what's going on in the emerging markets right now is a currency issue. Currencies have been in freefall. To have actually had some type of protection from that, we think is important.
Here, we're using mostly a momentum model. There are a few different triggers. Short-term, long-term, moving-average -- where all three have to be signaling. You go hedged or otherwise you're unhedged, which is why it's only ahead 20% of the time.
But we think that's unique and it turns static hedging emerging markets from a 4% cost over two decades, to actually adding some value while reducing risk. We do think it's a really unique element of this new emerging market multifactor ETF.
To summarize, I think within the ETF, I described a lot of what we're doing under that banner of "modern" alpha. I think the ETF structure helps you take a lot of this academic research that a lot of us are talking about, commercializing it into the best structure to manage taxes in the "best way," when you have rebalancing to do tax-efficiently through the ETF-creation/redemption process. That's one of the unique things.
Going back to Chad's very early point about doing it in a very low-cost fashion, from US to developed to emerging markets, the ETFs are 28, 38 and 48 bps. So, very, very cost-effective to get high active-share. 85% active-share type portfolios, with again unique different insights on international on the currency side.
We think it's a very efficient way to execute this factor approach.
With that, I think that sort of summarizes my section, Chad.
Chad: Perfect! Thank you, Jeremy.
I'm going to take back controls, here.
On the topic of that, our audience doesn't necessarily get to see this. But when we're using a webinar and passing controls back-and-forth, and you have an organizer like me that's running into the mouse and taking back controls, the audience doesn't really see that.
The presenters are unflustered, and were gracious in presenting. So I appreciate that. Thank you, both.
That was really I think well-juxtaposed. Adrienne really talked a lot about the top-down thinking. A lot of the behavioral factors and behavioral biases that promote these factors. The few factors that AQR is focusing on in their equity products.
Then Jeremy, you talked a lot more from a bottom-up point of view, about portfolio-construction and things like that. Adrienne, I just want to give you a chance, if you want to talk just briefly about AQR's process for their multifactor-construction.
Then I'm going to open it up to questions, as well. But if you want to kick this off there, that'd be great.
Adrienne: Sure. Yes. That'd be great. Thank you, Chad.
Yes. There are a few things I'd like to add in relation to some of the portfolio construction, as well as some of the factors that we look at.
I described how our three main styles relate to value, momentum and quality. Jeremy was talking about the low-correlation factor, which I think is another one of those terms or factors that have a lot of different terms underneath it.
It can be called, "Low-Volatility," "Minimum Variance," "Low-Beta."
Ultimately I would categorize it as a defensive-like posture from the perspective of taking into consideration the underlying risk of a particular company. That is also a factor that we believe in and believe in very strongly. We've done a lot of research to describe some of the behavioral-based reasons of why that factor works over the long-term.
I will say that it is a factor that we do trade in some of other multifactor strategies.
The difference in terms of how to think about implementing that is of course how to think about the overall beta of the portfolio.
Jeremy talked about describing a particular level of active risk. Active risk is also going to be determined by the overall level of beta. When we think about running our strategies, we're very explicit about running them at a beta of 1. Full market exposure. Not varying that market exposure.
To the extent that you have a process that tilts toward low risk or low statistical risk -- whether that's correlation or volatility or beta, you're effectively adjusting or changing the level of beta such that the portfolio will have a market exposure less than 1.
We have some investors that are interested in incorporating that particular factor within a traditional beat-the-benchmark long-only kind of process. But for us, when we think about offering these, and we offer these strategies in the three styles I described -- value/momentum/quality -- in a neutral kind of format, we separately offer a pure defensive-play strategy.
Those pieces effectively allow investors to determine the overall level of market beta that they're most comfortable with. This allows markets or investors to determine their overall view on equity markets.
We are of the mindset it's very difficult to try to time equity markets. What we're trying to do is add "modern" alpha above and beyond exposure to the cap-weighted benchmark. Where our exposure to the cap-weighted benchmark is going to be 1. We're not varying that.
We can incorporate low-risk tilts elsewhere by playing with different pieces of the overall portfolio. So I'd agree that's a compelling style. But it introduces a little bit more complexity as it relates to the overall market exposure that you're getting when you are tilting toward that particular factor in a long-only sort of context.
It's a design choice, going back to some of the decisions that each manager makes when they're forming their portfolios.
The last thing I'll note to correspond to how we think about constructing portfolios relates a little bit to some of the comments on currency hedging.
When we think about forming our factor portfolios, whether we're talking about US largecap or international or emerging, we have the same process. We're not trying to take country or currency bets or views from this process.
We are focusing on relative-value stock-selection, again going back to the GM-versus-Ford comparison.
What that means is that our overall exposure in terms of country and currency exposure is effectively going to match that of the cap-weighted benchmark.
Our perspective here is that there are separate decisions to be made. Whether that's what your overall level of market beta is or whether that's what level of currency risk you're comfortable with, if you think you can add value from currency risk.
We have processes that do that, as well. They rely on similar ideas from the perspective of capturing value in currencies. We can also do that. That's not the objective within the suite of multi-style mutual funds that we offer, and how we think about constructing them in a way that doesn't add any extra unintended exposure to countries or currencies beyond what you're already getting in the MSCI and emerging-markets benchmark.
Just a little bit of compare and contrast. Design choices. And some of the complexities that investors are faced with as it relates to choosing different managers and understanding some of the embedded risks. Particularly from a beta perspective. Particularly as Jeremy mentioned, from an active-risk perspective.
Then as well, in terms of the active-exposures and tilts that you're getting. Those are certainly very important considerations that I'd encourage all investors to think through, as they're evaluating various managers.
Chad: Yes, that's a great point, Adrienne. I appreciate you bringing that up.
These small nuances in process, construction or strategy -- design, as you're talking about -- can really have an impact on expectations for what the strategy is going to do.
I appreciate your raising that.
Along that same topic, before we move off this, the topic of the process for developing the strategy and designing the strategy. Determining what the underlying constituents of the portfolio are going to be.
We had a question come in that says, "What if the factors contradict each other?" For example, stocks with positive momentum score have a poor value score.
Jeremy, do you want to start us off from Wisdom Tree's end? What happens in your process and put in the other strategy that would account for that? Then we'll go back to Adrienne.
Jeremy: Yes. I might've gone quickly through my methodology slide, where we described the individual datapoints going through the factor-tilts. Every stock getting a value-score; every stock getting a quality-score; a momentum score and low-correlation.
This specific one is, every stock gets this composite multi-factor score. You can think about it having a 25% weighting across each score, and then you're selecting the top 200 with the best combined rank.
They're not good scores at the top boxes of all four of them. It's going to be the best overall combination of them. You might be able to score well on three of them but poorly on one of them. That's why when I showed the "Good/Bad/Okay," you were in that 50 to 45.
For our process, you're in the 50s a lot of the time in that "good" tilt. You were below 20% in the "bad," tilt. Because of this idea that you're taking the best composite characteristics versus saying, "I'm just going to buy only a value set of stocks," and combine them with only the best-quality stocks versus only the momentum.
It is getting a composite score.
That selection is another thing where you're differentiating across the different providers. Some of the providers will look at each factor separately and then create. They've sort of built a sleeve of the value stocks -- a sleeve of quality and then they combine them all together versus this kind of composite-scoring methodology, which is just not the way to do it.
Chad: Perfect. Makes sense. Thank you. Go ahead, Adrienne.
Adrienne: I wanted to just add that the structure that Jeremy describes is very much aligned with how we think about investing at AQR. This dynamic, which is effectively being very adaptably picked up on from the perspective that sometimes these particular measures can contradict each other. Cheap stocks tend to usually be beaten down. That means that they look pretty poor from a momentum perspective.
Some of the providers that are forming these stand-alone portfolios and then combining them after the fact so that it's building a separate value portfolio from a separate momentum portfolio and then combining it, are going to miss out on this composite-score ranking.
It ends up being a lot more inefficient from a trading perspective. On top of that, we've done a huge amount of research on this exact topic at AQR and written a lot of papers on it.
What we effectively refer to as an "integrated" approach, which is what Jeremy is describing as a composite-score approach -- we're taking into consideration how companies look on all of these dimensions before we're investing.
The companies that we tend to like the most are going to have the best blended-composite ranking. That's another way of saying the kinds of companies we like the most are not necessarily going to be the very best in any one of those categories. They're not necessarily going to be the cheapest or the best momentum. But they're going to look pretty good across all of those dimensions.
We like companies that in-aggregate are cheap but are also showing signs of improvement and are also high quality. Those characteristics, when you look at them all that the same time, really help isolate and identify the very best companies to invest in.
Even if you think about it in the case of value, for instance, we don't want to buy companies that are cheap for a reason. We like companies that are cheap but are also turning around in terms of their momentum. And that are also high-quality. That helps clean out and protect against some of the dangers of value-investing.
That's really a key component of this multifactor approach and this composite or integrate approach relative to -- call it -- an a la carte or mix approach, where you're investing to each of the different sleeves or each of the different managers on a stand-alone basis.
Chad: Okay. Perfect . Understood. Thank you for sharing that from AQR's viewpoint, Adrienne.
I want to try to combine a couple of questions here that I think are similar. Also on the topic of portfolio construction, but away from the individual portfolio-strategies that AQR and Wisdom Tree are running as asset managers. More from the audience as wealth-managers' perspective.
The questions are around constructing a globally-diversified multi-asset-class portfolio. So from one viewpoint, some people are asking --
A lot of the focus here today was on equity strategies on the factor side. What if I want to construct a globally-diversified multi-asset-class portfolio of multifactor strategies that include fixed-income and alternatives? Is there a portability of these factors amongst different asset-classes and geographies and capital-structures?
The second part of this question is if someone doesn't want to construct a portfolio of just all factor-based strategies, what if they have a current client portfolio that uses some active and maybe a little market-cap-weighted? Is there literature or resources available to help them combine these multifactor strategies with their existing strategies?
I know that's a lot. But I wanted to try to combine the idea of integrating these solutions into a portfolio for the audience. Jeremy, if you want to go ahead with that.
Jeremy: Yes. Let me give you something and you can build up on it, Adrienne.
One of the things -- there are two ways I'll answer that. We can obviously go in many different directions.
One of the reasons why we designed our multifactor family to this higher activeness, to the idea of the 5% tracking-error and 80% active-share -- higher value-added --
Was the view that people have a lot of beta ETFs. There are a lot of beta-ETFs that they're not going to just want to get rid of. Depending on your conviction to process, being able to --
The smaller your tilt that I've described -- Wisdom Tree 1.0 back in 2006 -- our earnings weighting. If you took those 2,000 profitable companies, it looked like the market was tilted toward value and quality.
This newer expression is very different than the market. It's actually closer to equal-weighting. It's higher activeness. That blends I think better with peoples' existing beta positions. That's just a design question on how we think we can complement your existing portfolios with the multifactor.
We do also believe that these approaches work. The factors work with fixed-income. We launched a factor strategy with fixed-income a few years ago. Whether it's yield-enhancing bonds or applying fundamental credit strategies, we have a suite of ETFs there.
But one area I feel there could be actually be more combinations of Wisdom Tree and AQR -- we did a fund not so long ago called the Wisdom Tree 90/60 US Balanced Fund. I actually linked to --
Well actually Cliff linked to our blog I'm talking about. He had done research on a levered 60/40 back in 1996, where he'd done research on the leveraged 60/40. Our ETF is 90/60 NTSX, which is basically core beta equities with bond futures to maximize your core exposure.
You could complement diversifying alternatives around it, and in some ways I think that's a good core. You complement with other alternatives around that. I think that is an interesting thing for people to think about -- applying this multi-asset stuff with a useful low-cor leveraged 60/40 idea.
I would sort of add to that. Our philosophy of course is that we think these ideas have a place in an overall portfolio. Whether that's a stand-alone equity-only portfolio or whether that's a multi-asset strategy.
The ideas that are expressed here -- how you think about sizing these exposures in your overall portfolio --
We're not necessarily arguing to replace all of your market exposure with long-short market-neutral style exposure across asset classes. We have strategies that do that.
Those, we think, have a truly diversified alternative sources of return that help diversify our overall strategies.
If you think about a 60/40 strategy as the big one from an asset-allocation perspective -- then as you think about allocating 10, 20 or 30% of that on maybe a pro rata basis to alternative strategies -- that's a very good place to be.
You can take advantage of these ideas across asset classes. Across a variety of different contexts. We might argue that going purely long/short is the best expression of these ideas.
Certainly you can take advantage of it in a traditional equity portfolio. Or even a traditional fixed-income portfolio, as deviation relative to market indices. But the purest expression is purely long-short, and to take advantage of that within the overall portfolio context allows general diversification from the perspective that --
Anything market-neutral helps. Having exposure to these investment styles is maybe even better. They're really nice complements in the overall portfolio. We can have that perspective in terms of asset-allocation and how to allocate from a building-block perspective or even just within an equity sleeve of the portfolio. How to think about getting exposure to these ideas.
We've certainly seen that over time. Investors have adapted these ideas and become more comfortable with factors and the role that they have within an overall portfolio. To the extent that they add something different than what you're already doing in your portfolio, if you don't have value exposure, that will be valuable. I guess pun intended.
Really, it's about what's in your portfolio and what's different than what's in your portfolio, and how you can add that to your overall thought process.
Chad: Perfect. Yes, I appreciate that perspective. More complementary in that point of view.
Well, we're up against the hour. There's one more question and there are a bunch of people that have hung in with us. I want to be respectful of your time.
If we can address maybe one more question quickly and then we can wrap it up.
It's kind of a broad question from the audience. How does an asset-manager like Wisdom Tree or AQR go about identifying factors? To easily explain to a client, what's the broad reason for including some but excluding others? Is it the strength or relative strength?
I saw AQR really focused on value/quality/momentum. Wisdom Tree had value/quality/momentum but also low-correlation. Maybe just a couple of minutes on each from Adrienne and Jeremy. Then we can wrap up.
Adrienne: Sure. I can go ahead with this one.
I think that the main thing you need to look for when you're investing in these investment ideas is that you can evaluate the effectiveness or how much return you can generate from investing in these concepts, in as many different places as possible.
You can apply the ideas across regions and market-caps -- across time periods, and to some extent across asset classes. That sort of very lengthy and extensive amount of research and evidence on the efficacy of these ideas is one of the most important things to look at.
The second thing is just whether it's economic intuition of why it works and why it should continue to work, going forward.
I think we can all agree that your value/momentum/quality may be defensive to the extent of low-risk. We generally believe in that idea, as well. These are all investments ideas that pass those tests. They work in so many different contexts. We can explain why they work.
Some of the other investment ideas that maybe some of the other providers may look for that neither of us on this call have addressed relate to something like the size premium. For us, our perspective is that size is pretty isolated to individual stocks. It's harder to test that idea in fixed-income or commodities.
Things like that help us have confidence in these ideas, and think about which ones are most important for our process, and how we want to think about allocating to the relative importance of these investment concepts.
Jeremy: Yes. Fortunately, neither of us are really disagreeing with what the other is saying. You're not going to hear a lot of debate. I think it does come down to how you're achieving the exposure and if it's value-added to your process.
I think when we started, when Wisdom Tree created it 12 years ago, it was all about solving for deficiencies in cap-weighting, which we thought got exposed to all of the major bubbles across time. I had no measures to manage valuation risk. That was what got us started in going to rebalance back to dividends and earnings, which had this value-quality bias to them.
As we started to think about how we evolve our process and get more sophisticated in the execution of these factors, it was just a matter of looking at the academic research similarly, and thinking about what we thought had the most robustness and would last.
It's not just that it looks good in a back-test. We think there's a good reason why it's going to continue to work or add value to your process.
It's a very similar approach.
Chad: Perfect. Makes sense. Okay. With that, I'd really like to thank our audience for joining us today. And really give a special thanks to our Research Alliance panelists -- Jeremy and Adrienne -- for being with us and sharing a wealth of experience and their perspectives.
If you'd like to stay current on Litman Gregory's views and strategies -- of course as well as those of our Research Alliance members, please visit our website --
www.AdvisorIntelligence.com. Or you can contact us, of course, by phone or e-mail.
Thanks very much for joining us, everyone. Have a great day.
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