Factor investing in the wake of COVID-19
Man: Welcome to the Center for Investment Excellence, a production of JPMorgan Asset Management. The Center for Investment Excellence is an audio podcast that provides educational insights across asset classes and investment themes.
Lara Clarke: Welcome everyone. Thank you for taking time to join today. My name is Lara Clarke and I’m the Client Advisor and Client Segment Lead for Public Funds here at JPMorgan Asset Management.
It’s been a tumultuous 2020 across asset classes to say the least. We’ve also experienced significant move within asset classes i.e., at the factor level that have impacted clients in varying ways. Some clients have looked at factors to take advantage of opportunities, admit the fallout, others see a factor lend is a way of better understanding risks in their portfolio and so others are considering the roll that factors can play as they really reevaluate the broader structure of their portfolio and determine the ideal mix of active and passive strategy.
I’m excited to welcome our Quantitative Beta Solutions CIO, Yaz Romahi, Head of Fixed Income Research Niels Schuehle and Investment Specialist Garrett Norman to help address these topics across both equity and fixed income market.
We’ll spend about 30 minutes discussing questions I have prepared. The recent value drawdown and prospects for value continued to be a focal point of client conversation. Yaz, I know you’ve articulated your view on value in the recent factor views and white papers. So what has happened in 2020?
Yaz Romahi: Sure. So - hi everyone first of all and I hope you’re keeping safe in this environment. I think it is worthwhile stating that this is the worst drawdown to value that we’ve seen in 30 years, right. But it’s easy to get sucked into thinking in sector term where value has actually been in a decade long (unintelligible).
But no one talks about a factor drawdown. I think it’s important to kind of step back and think about it within sectors and it’s within a sector basis and so it doesn’t get muddied up with other risks. On this basis, value drawdown started in 2017. Now, that’s still pretty lengthy and given the size and the length of this drawdown, the question on everyone’s mind of course is value broken. In other words, is this drawdown structural?
So it’s important to (unintelligible) office and convince ourselves that this time is not different, right. And the first - and the most common argument (unintelligible) value in the recent drawdown has been that it doesn’t take into account intangible assets.
And on the face of it, that sounds like it might make sense. I mean the current economy is all about tangible assets and then one thinks about price to book for example, I mean book value is surely about tangibles rather than intangibles. But in fact, if you look at (PD) for example at price to cash flow, those measures of value taking stuff intangibles and tangibles and actually you find a drawdown almost identical. So even if one believes that (PD) may not be as relevant going forward because of the change in the economic make up, it certainly gets into an argument against the value more broadly.
Others point to for example, FANG stocks or tech or maybe the U.S. market or somehow that value has been a short disruption trade. Again, if you look across centers, if you look across regions, the value drawdown has actually been very, very broad based. And on the sort of construction argument, we looked at for example, neutralizing value for R&D and you see a very similar drawdown again. So again, it is much more of a broad based phenomenon.
And so the next question one might ask them is that, well, maybe simply it’s fundamentally justified. What’s interesting here is actually if you look at the growth dispersion between growth and value companies, that’s been narrowing. So in other words, people have been paying more and more for less and less growth in growth stocks. In other words, they’ve been overpaying for growth. So I think that’s the first thing that’s really interesting, because it highlights the fact that there is a bubble (forming).
The other point that’s important to remember is clearly I mean interest rates is something that a lot of people would raise as a potential catalyst and there are two aspects here. One is historically one doesn’t see a correlation that’s in the value factor (unintelligible). But in the current environment, we do believe the extended period of low rate has filled the bubble in red dots. And you actually see that, when you look at mid-cap companies in particular, you find that actually growth companies are carrying a lot more debt than value.
And so the gap is unprecedented, right. Growth funds are out of 40% premium to their average valuation whereas value companies are at a 14% discount. So that gap has to close.
Lara Clarke: Great. Thank you for your perspective. So coming back to your outlook, what would a catalyst for value look like and should investors be trying to time a rebound?
Yaz Romahi: Yes. So I think that’s the million dollar question, right? I mean I brief - hinted at the leverage problem and how mid cap in particular, but growth companies in general do carry a lot more debt than (value) companies. So standing at the beginning of the year, I would have argued that a catalyst to evaluate that will be a market disruption. And fundamentally because we’re having market dislocation, the results in credit impairments and then that certainly would have a direct result in increasing financing costs for these leveraged growth companies, investors are focused in on the fact that - I mean that clearly hits the bottom line they’re then unable to grow themselves out of the step and you see a rebound in value versus growth.
Now, clearly the drawdown in March seem to have accelerated previous trends rather than actually result in alternative value. So the first question I ask myself is was our thesis wrong or is it something else at play. So the first issue of course is that at the beginning of the drawdown, everyone is focused on bankruptcy risk rather than financing risk. At the front end of the desk calendar, value companies actually carry more debt than growth companies even though at the two-year points and overall growth companies have a lot more debt, right. So because the focus was on bankruptcy risk, it was actually - the focus was on the front end which (hurt value customers).
So then one of the things that once being stabilized, when does it expect focus to shift the financing risk, but then what we had is central banks scrubbing the markets where the Fed even purchased high yield ETFs to the $200 billion to $300 billion encountering.
And so in effect, what they’ve done is they pushed off and they recommend, they allowed these companies to being able to continue financing their debts at low rate. We still believe the thesis could be the catalyst. But I think what it does highlight is, I mean we know that the gap is at unprecedented levels. It will inevitably turn. We believe it’s cyclical and not structural. And when it eventually turns, it could be aggressive. But I think what highlights is the factor timing is notoriously difficult. So while we know that, you know, the gap has to turn, the question is when it’s a lot more difficult.
Lara Clarke: Great. So in regards to diversification, what is your outlook for other factors? What about quality or what about min vol?
Yaz Romahi: So quality is actually a very important factor to consider in the current environment. I mean while it has actually done quite well and continues to do well, it essentially gives exposure to a more defensive core, right? And that has a value in the portfolio. I mean I think some people - I mean you mentioned min vol, some people think of min vol in the same light, but I just wanted to highlight, the min vol itself is actually (currently) expensive while a more broad quality factor is not.
But the other interesting thing about quality is actually the interesting section between value and quality and that’s quite important, because some growth companies may indeed grow themselves out of their debt. So having a quality lens, getting there is important to differentiate those potentially can grow themselves out of their desk versus those that are actually taking on potentially unsustainable levels of debt.
Lara Clarke: Great. Thank you, Yaz. I’m sure we’ll come back to some of these topics. But Garrett, let’s shift gears and how are clients thinking about allocating to factors now?
Garrett Norman: Sure. So I think we’re seeing engagement in three main areas and the first relating to the core exposures, the second to complementary roles that factors can play in a portfolio and the third is around targeted exposures or targeted outcomes. So with regard to first, the core role in a portfolio, I think you’re seeing factors increasingly play that role sitting in between active and passive. We’re all aware of the push towards lowering cost across investment plans and I think that’s somewhat inexorable in nature.
I think on the other hand there has been fear of how to generate returns that are going to meet plan needs or liability objectives which, you know, at certain points of this year might have been less onerous. But as we’re coming back to full evaluations in equity market, it’s wearing its hat again. So that’s that one area of looking through factors sitting at that middle ground where they can enhance return versus market cap, but they’re still able to provide exposure that’s lower cost in nature than active, that has a liquid profile to it and it’s very transparent in nature.
The second area that I mentioned was around completion opportunities. I think this one is particularly interesting as the technology around the factor investment ecosphere has evolved. So we and many others in the space have been running factor analysis for quite some time trying to help clients identify biases they might have in their portfolio, right. So given current trends in the market, managers might be drifting more towards quality or growth stocks.
And while this ability to analyze such a bias has existed now for a while, I think the way that we can all leverage technology and scalability in offering more of the scope factor, so we shouldn’t help lead these to more meaningful engagement in that case where there might be a growth bias, it’s easier today to manufacture and deliver a targeted value exposure than it had been years prior.
And I think the last area comes back to the targeted exposures or targeted outcomes. We’ve seen I guess coming back to quality which Yaz mentioned is helping given more defensive core, we’ve seen quality serve as an interesting filter alongside yield in certain markets. So whether it’s a dividend strategy or looking in at liquid real asset or infrastructure stocks, having a factor lens or in this case quality has created an interesting return profile for certain clients.
I think another area where you might mix different objectives would be the intersection between ESG and factor mandates. There you could think of ESG as in some ways playing another role similar to quality as a factor and just incorporating slightly more targeted exposure or more targeted outcome.
Lara Clarke: Great. Thank you, Garrett. Niels, we just heard the case for equity factors. Can similar concepts be applied in the credit space? And if so, how do you think about defining credit factors?
Niels Schuehle: Sure. So the principles that we see for equity sectors, they carry over to the credit space. So what I mean by that and we looked at sectors in the credit space, something like a value sector, a quality sector, momentum sector, they can be implemented in the credit space.
However, the way you implement them is very different. And so let me give you a couple of examples. Think about value. The principle of value is you look at how the market value as a security relative to fundamental income. If you think about equities as Yaz described earlier, your market value will be usually the equity price.
However, the fundamental income is usually based on either the financial statement of balance sheet and we will think about the book value for equities. And also credit to fundamental and credit default risk. So it’s one measure of default. It could be default (probability), but past years have a measure. So for example, (unintelligible) default probability. So you see the principle of comparing a market value to a fundamental income is the same. However, the way they actually implemented is very, very different. So value (unintelligible) sectors.
Other sectors that we look at is quality, quality is a sector. So the quality is one of these sectors which actually does not take any information from the securities itself into account, it’s purely based on balance sheet of financial statement data as well as equity market information. So what it turns out that the characteristics of (unintelligible) some of that to activity, so firms or companies with strong fundamentals tend to outperform in the risk adjusted space. So this is quality as really a sector which works very well in the volatility I’ve mentioned. So strong fundamentals often is associated with low risk, so quality looks very well there.
Finally momentum is another sector we take a look at. And here, they’re also slightly different from equity. What we look at is we’re looking at the fund - the momentum is in the credit market, but we also look at the momentum in the equity market. So what we find and then we do our sectors, there is a lot of information in other markets. So keep in mind, bonds are written on an issue and the issue often has equity outstanding.
So if you look at both how the recent equity performance of that issuer was as well as how the performance in the credit market was and we combine these two. And in terms of that strong recent performance tends to continue to have strong performance in the future. So you see the factors, the principles and the factors carried through. However, the way we look at them, the way we define them is quite different.
So now, we’re not really reinventing the wheel here. So a lot of the variables that we look are very similar to what the fundamental credit analyst will look at. Once again, the credit is anchored in default risk. So a lot of fundamental credit or is the way what they look at is different measures of anchoring credit risks or default risks. So some of the variables that we use, something that could say a debt to cash flow ratio or debt to long term earnings ratio, we use them in our sectors as well.
The main difference between us and fundamental credit analyst is we have, A, very broad courage. We do not concentrate on the biggest companies. We really cover the whole market. Second of all, we utilize a lot of out of market information, in particular equity market information. We let that flow into our fundamental anchor here.
But the biggest difference between us and active manager or fundamental - as this looks (at), this is really implementation step. When we implement our sectors, we tend to neutralize with respect to other risk sectors. So for example, we tend to not take positions or views on sectors or duration. We tend to be neutral relative to a benchmark. Moreover, we tend to be very well diversified. We don’t tend to have concentrated positions. We have very tight issue of caps. Besides that, we in general do not work on single sector.
So if you look at these three single sectors, value, quality and momentum, each of these can have prolonged periods of underperformance. And these three factors really play different roles throughout the credit cycle. So what we tend to focus on is the multi-sector approach where we combine the single sectors into one multi-sector score and the multi-sector approach tends to result in a way more robust results in general and in particular through times. So their periods of underperformance tend to be a lot shorter once you go for multi-sector approach.
Lara Clarke: Thanks Niels. Factors and credit space are certainly very interesting. How come it’s taken longer for credit factors to be established than their equity counterparts?
Niels Schuehle: Well, there is really three main reasons for that. First and foremost is data availability, then second it’s relatively speaking a little more complex to implement sectors in credit than in equities and finally, the implementation itself, how do we actually build the portfolio is a bit more complex.
But let me go through them point-by-point. Data availability is a big issue in credit. So for equities, equity markets tend to be traded on an exchange, so it’s very easy to obtain the data. Credit markets tend to be traded or (unintelligible), so historically the data availability was (unintelligible) index provider, there was no one stop shop to really obtain all of the datas.
And this lets to the result that academics did not really have this much research historically. A lot of the sectors have been driven in equity markets by academic research historically. So this as of lacked for credit markets. So with the introduction of (TRACE) in the mid-2000, this has slightly changed and you see more and more academic style research supporting credit sectors and coming out in the recent couple of years.
Finally, even if you get the data, for credit, just getting a price is not good enough. You need to have complex pricing models to obtain analytics like think about (spec) duration, stress and so on and so on. You need the pricing model behind it to really get this analytics to do something meaningful just to data. Once again, in academics, this is a little harder to obtain and the entry hurdle is just higher.
Second point was to slightly more elaborate to credit sectors than equity sectors why is this? Well, if you think about equities, in general for each balance sheet, you have one primary equity traded against the balance sheet. That’s not the case for credit. So if you think about credit, there are numerous bonds written against the same balance sheet, in fact there are numerous entities potentially backed up by the same balance sheet. So if you think about a large company, a large company often has entities on that which each entity could issue debt. That debt can be then guaranteed by a credit (unintelligible). This entity mapping is very complex. You have to map each bond to the balance sheet that eventually backs it.
Finally, doing this right now is already hard enough. Doing this historically over the last 20 years to get backed us to get comfort in the credit sectors is very hard. So the entity mapping changes dynamically through time.
To give you an example, in 2008, JPMorgan took over their stocks (unintelligible) one date to the next date for equities. The equity (unintelligible) seize to exist. However, (unintelligible) had a lot corporate bonds outstanding. These corporate bonds on one day to the next day grew all of a sudden backed by the JPMorgan balance sheet.
So if you do your back test, you really have to get this entity mapping, get it correct through time with all the corporate actions historic. So the entry hurdle to just get started for credit sectors is a lot higher.
Finally, the implementation, even if you get your issuer scores and issuer sector, even then the implementation is a little harder. If you take a look at for example, JPMorgan in the U.S. Investment Grade Corporate Index, JPMorgan has many more bonds than 100 outstanding in the index. So which bonds should you buy for equities is quite easy, there is one equity outstanding.
For JPMorgan, you have to take into account the whole term structure, which duration portfolio do you want or where do you want to sit in the capital structure, whole senior or junior. So it’s a little more complex. Second to liquidity, credit markets tend to be a lot less liquid than equity markets. So you have to be able to especially for high yield markets and you have to be able to source equities. Besides sourcing securities to trading itself is more costly. So then you do your research, the implementation for the liquidity trading cost. It has to be part of your research process. So it’s just a little more complex.
So what we do in our credit sector approach, what we do is we really focus on issuer selection. So by default, we will neutralize other risk sectors such as duration. We will be neutral to the duration of the benchmark index. We will be neutral for example the sector of the benchmark index. So you see it’s similar to equities, it’s just a little more complex.
Lara Clarke: Great, thank you. What is the outcome of factor of investing in credit space or what should clients expect in terms of value add?
Niels Schuehle: So when we ran our back test, we really go back for two and a half, three credit cycles, so we go back into the late 90s (unintelligible). We see strong historical performance both across time. We use the same sector definitions across different market segments. So in investment grade and high yield, you have the same sector definition, you have the same sector definitions globally. So if you look at say the U.S. market versus the European market, we use the same sector definition. That gives us confidence that these sectors are not (susperious) or data mind, so historic performance is very strong.
If you look at how these sectors worked, you in general either get the same return at significantly lower risk or you get slightly higher return and at the same time lower risk. In general, these sectors tend to outperform the strongest performance relative to market cap rated benchmarks during times of market distress. So as soon as the dispersion in the market picks up, these sectors tend to work a lot better and tend to outperform.
In terms of diversification relative to other credit managers, we find that sectors are diversifying to both pure passive managers as well as active managers. So when we looked into holdings of active managers, we found that they really do not have tilts towards sectors on average.
So adding a sector allocation is really diversifying to this. And if you think about what the (unintelligible) distinguishes us from active managers, we tend not to have directional bets. I already mentioned, we do not tend to have bets in terms of duration or sector bets. When we looked at active managers, they often have a (unintelligible) tilt towards credit. So (unintelligible) is in let’s say an investment (unintelligible) in the universe they tend to often talk to us higher risk names or potentially a higher yielding sector.
We found that some of the managers tend to go out of index to add alpha. So we tend to not do any of these, so we are purely issuer selection. So we tried to align all of the general risk dimensions to the benchmark and our hedge is really in the issuer selection. So it’s really diversifying to both active and passive.
If you think about this in a multi-asset portfolio context, if you think about what is the role of fixed income in a multi-asset portfolio. Well, if you look at the portfolio, equity on average drives most of the expected return, in particular the risk of a multi-asset portfolio. So the role that fixed income is playing in this portfolio is really diversification. What makes sectors really interesting, they tend to outperform in particular during periods of market distress.
And one of the important things is we do not take directional bets, in particular, we do not add credit data relative to a benchmark index on active manager, so no direction of that. So what happens here is essentially our correlation to equities is on average lower than a pure passive index or an active manager. So really the diversification benefit in the multi-asset portfolio is really an important aspect that sectors can add to multi-asset portfolio.
Lara Clarke: Great, thank you. How have things played out in 2020?
Niels Schuehle: So 2020 was interesting and we really - when we look at 2020, we really can split 2020 into three soft periods. We have the periods up to March 20, so around March 23 and we have the period between March 23 and like say mid-April and after. So what’s happened on March 23? So on March 23, there were actually two announcements by the Fed in 2020. We had the first announcement around March 23 when they announced that they will purchase investment grade corporate bonds. And made a second announcement about two weeks later when they announced that they will also purchase and help the market in the (unintelligible) part of the market.
So if you look at our performance, up to end of May, we’re head of the market cap weighted indices. We were significantly ahead of these indices in the first period up to March 23. So we had very strong performance and in particular in the month of March during the massive downturn we started to significantly outperform the market cap weighted indices, in particular in the drawdown both for high yield and investment grade.
And obviously the announcement from the Fed, the second phase started. So from late March to mid-April, the market started to rally. And this is a period where we relatively speaking underperformed the market cap weighted benchmark. And it’s really the market intervention by the Fed, the market started to trade away from the fundamentals.
Once this comes down and they will (price) into the markets in a third phase from mid-April to end of May we started to outperform again. So over the whole period (unintelligible) the Fed announcements to intervene in a market where we strictly underperformed.
So if you look at this high yield, our next drawdown was significantly better than the market cap weighted index for let’s say all (unintelligible) aggregate portfolio, the corporate piece in that up to mid - late April there were about 22 foreign angels year-to-date. Our sector approach in this particular example, we avoided all 22 foreign angels. So from the pure issuer selection, the sectors delivered exactly what they were assigned to deliver.
Lara Clarke: Great. Thanks Niels, I certainly appreciate hearing about fixed income factors and credit factors. Well, we hope you enjoyed today’s discussion and found it helpful. If you’ll be interested in reading more about the equity value factor, we’ll be releasing a white paper next week. White papers are also available on factors and the credit space.
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