The Tide Goes Out: Growth Trade Aftermath
The Tide Goes Out: asset allocation, equity mutual funds and hedge funds after the growth stock selloff
Russia. This interview in Italian daily Corriere della Serra is a disturbing look at the world according to Russia, as per Putin/Yeltsin advisor Sergey Karaganov. It’s entitled “We are at war with the West. The European security order is illegitimate”. In addition to Karaganov’s arguably distorted and at times absurd view of history, note his belief that Russia will eventually launch attacks on European countries supplying arms to Ukraine.
Market update. As I wrote in the March Eye on the Market, we expect the March 15 equity market lows to hold as long as there is no US recession. Some recession indicators are rising: first inverted 2-year to 30-year yield curve since 2007; a collapse in consumer sentiment to one of the lowest levels in 70 years; declining small business surveys; and ISM business survey orders falling below inventory levels for the first time since the expansion began. In addition, more signs of prolonged goods shortages and inflation: China’s supply chain delays and spikes in anchored containerships due to COVID, and additional sanctions on Russia in response to what has been described as executions, torture and other war crimes committed by Russian soldiers. Even so, I think a low growth period in 2022 in the US is more likely than a recession. Labor markets are very tight (there has never been a recession without a large spike in jobless claims), household and corporate balance sheets are in very good shape, and the release of the Strategic Petroleum Reserve lowers risk of recession in the near term (though it’s still a bullish sign for oil prices in the medium term). US recession risks look higher for 2023-2024.
The Tide Goes Out: Growth Trade Aftermath. As of February 28th, the median NASDAQ stock was down ~40% from its prior peak, a consequence of rising interest rates and the unsustainable increase in unprofitable companies. These declines are large but pale in comparison to the 2001-2002 selloff when the median NASDAQ stock was down ~75% from its peak, and when growth outperformance vs value was completely erased (this time growth has only given up a modest amount of its outperformance vs value). In this note, we look at asset allocation, equity mutual fund and hedge fund performance after the growth stock selloff.
Past performance is no guarantee of future results. It is not possible to invest directly in an index.
Analysis summary: a growth allocation generated substantial excess returns in pro-forma portfolios from 2017 to Feb 2022, even after the selloff. However, the four largest mega-stocks (Apple, Microsoft, Amazon and Google) accounted for almost half of these excess returns. Furthermore, only top quartile equity mutual fund and hedge fund managers delivered excess returns versus their growth benchmarks.
Asset Allocation: still a clear benefit from growth stock exposures in portfolios
To assess the benefit of an allocation to growth in equity portfolios, we compare an investment in the Russell 1000 Growth Index and the NASDAQ to other equity alternatives such as the S&P 500, Value stocks, Europe, US/Global Small Cap and Emerging Markets. The timeframe is of course a critical decision; looking back at our investment commentary over the last few years, I picked January 2017 as a starting point since that’s when we began to focus on the higher revenue growth and profit margins of tech and healthcare in a slower growth world. Different starting dates would of course yield different results.
Results. Through the end of February 2022, an allocation to growth generated higher returns than the other options shown with comparable levels of volatility. However, almost 50% of growth’s outperformance vs value was derived from exposure to the largest four stocks (AAPL, MSFT, AMZN, GOOG). For investors executing this view through passive index products, the performance discussion would stop here other than having to account for passive index fees which are generally comparable across the indexes shown1.
Past performance is no guarantee of future results. It is not possible to invest directly in an index.
Growth equity mutual funds: outperformance is scarce as diversification hurt returns
Assessing mutual fund performance is a straightforward exercise:
Use Morningstar to obtain a universe of funds in the Large Cap Growth category, excluding passive products
Narrow to those funds with performance from Jan 2017 to Feb 2022. This does create a survivorship bias issue since we ignore funds that used to be in this category but dropped out for whatever reason
Use the lowest fee share class for each fund as a proxy for its Institutional share class, and compute cumulative performance from 2017-2022
Compute excess returns for each fund relative to its stated benchmark. Most growth managers use the Russell 1000 Growth Index; other use the S&P 500 and a few use the Russell 3000 Growth Index
Results. Most funds with a R1000 Growth benchmark underperformed over this period. Many may have been reluctant to hold index-weight positions in the four largest stocks whose performance more than doubled the performance of growth stocks in recent years. Managers may also have been discouraged from doing so for regulatory reasons (see box). In March, the largest weights in the R1000 Growth index were AAPL 12.5%, MSFT 10.8%, AMZN 6.6%, GOOG 6.4%. As a result, just holding market weight positions would imply 36% in these four stocks, above the 25% diversification threshold that many mutual funds seek to comply with2, requiring them to be structurally underweight. As shown in the third chart, we estimate the cost of the diversification rule applied to the Russell 1000 Growth Index to be ~15% over the time horizon.
Past performance is no guarantee of future results. It is not possible to invest directly in an index.
In contrast, most growth mutual funds using an S&P 500 benchmark outperformed. This may reflect the presence of core managers whose growth tilt was so high as to push them into Morningstar’s growth category, but the manager still benchmarks their performance to the S&P 500 Index. Whether investors give managers with a strong growth tilt credit for outperformance vs a core benchmark is up to them. Not sure I would.
Performance was mixed for the smaller number of mutual funds categorized by Morningstar as “growth” and who benchmark their performance to the Russell 3000 Growth Index. Given the underperformance of small cap shown on page 2 over this period, any manager with a structural underweight to small cap growth would have generated substantial excess returns over benchmark.
For legal and compliance reasons, I cannot cite JP Morgan Asset Management’s large cap growth performance; you will have to look that up on your own.
Hedge fund performance: plenty of assumptions and triangulation required
Measuring long only equity and fixed income mutual fund performance against stated benchmarks is a simple process. The prior section is one example of that.
As for private equity and venture capital, the development of the LP-sourced Burgiss performance database now allows for proper time-weighted performance measurement versus a variety of public equity market benchmarks without having to worry about survivorship bias or selective reporting. We discussed this in last year’s deep dive on private equity and venture capital. There is no easy answer to the question of what kind of illiquidity premium is “fair” to investors, but at least the magnitude of what investors earn in private equity and venture capital relative to public equity markets is much clearer.
In contrast, deciding whether a given hedge fund has performed well or not relative to its opportunity set is one of the more complicated questions in investment finance. The “LIBOR plus a spread” benchmark and the HFRI benchmarks that were popular in the 1990’s are used less often now, and the “stock/bond mix” benchmark approach is used less often as well. When investors have look-through access to a hedge fund’s exposures on a daily or weekly basis, they can construct a customized benchmark based on market factors to assess performance. But that is not a viable option when doing industry-level analysis with monthly data.
As a result, I’m going to use a simple approach to benchmark hedge fund performance. Many growth long-short hedge funds have “observed market betas” of ~0.45 relative to the Russell 1000 Growth index. In other words, over the long run their returns rise and fall at 45% of the rate of the Russell Index itself. So, we use 0.45 of the Russell 1000 Growth as a benchmark for growth long short hedge funds in this analysis. We sometimes look at a NASDAQ benchmark as well but since its performance is almost identical to the R1000 Growth Index, we only show the R1000 Growth benchmark in the charts and tables that follow. Note in the last chart how the beta-adjusted R1000 Growth benchmark is similar to a 50% S&P 500 / 50% Barclays Aggregate benchmark.
Then there’s the challenge of obtaining hedge fund performance data in the first place
Unlike the Burgiss database of LP-sourced private equity and venture capital flows, no such database exists for hedge funds. There are several aggregators that compile hedge fund performance but they all rely on hedge funds to consistently report their performance, and many of the largest hedge funds simply have no interest in doing that. Hedge fund managers may provide performance history to investors considering an allocation, but such data is often subject to non-disclosure agreements that prevent it from being used for research publications like this one.
Once we obtain hedge fund performance data, there are still issues that make returns harder to compare. Some managers have large private exposures as high as 50% of the fund’s NAV. When public equity markets decline, private exposures are often not repriced as quickly, making comparisons across funds harder. And of course, gross and net leverage differ across funds as well.
As a result, we have to triangulate and use four separate self-reported universes of hedge funds, and their monthly returns from January 2017 to February 20223. Our Asset Management Hedge Fund Due Diligence team considers the first two more indicative of an institutional peer group, and shares the same reservations I have regarding the HFR dataset due to the lack of data from some of the largest well known funds.
Long-short hedge funds categorized by PivotalPath as “Tech-focused”
Long-short generalist growth hedge funds identified by JP Morgan Asset Management’s Hedge Fund Due Diligence team as “Tiger Cubs” in the PivotalPath database 4
Long-short hedge funds categorized by HFR as “Technology” or “Healthcare” (note: we do not use the HFR Equity Hedge Growth category since it includes a lot of hedge funds investing in Emerging Markets)
Long-short hedge funds in the eVestment database with observed market betas of at least 0.45 vs the S&P 500 Growth Index (eVestment does not have a Growth category, which is why we chose this method)
For each hedge fund universe, we identify the median, 75th percentile and 25th percentile manager5. For each of these managers, we show annualized returns, annualized volatility, return/risk, the fund’s current NAV vs peak levels (i.e., drawdown) and its correlation with the Russell 1000 Growth Index.
Are you excited yet? I am.
Hedge fund performance based on PivotalPath data
PivotalPath has data for 104 Technology long-short funds. However, many of these funds do not have consistent monthly data over our time horizon. We ended up with just 27 that we could analyze out of the original 104; some excluded funds began after January 2017, while others stopped reporting before February 2022. Data limitations are a frustrating and inescapable part of the hedge fund landscape.
The performance distribution looks “normal”: median Technology long-short hedge fund returns were close to our beta-adjusted growth benchmarks and also generated higher risk-adjusted returns. The 75th percentile manager’s outperformance was slightly larger than the 25th percentile manager’s underperformance.
We then analyzed the 29 funds in the PivotalPath dataset that our Hedge Fund Due Diligence team identified as generalist growth-oriented “Tiger Cub” descendants of the original Tiger fund. However, PivotalPath only has consistent monthly performance from Jan 2017 to Feb 2022 for 12 of them.
The 75th percentile Tiger Cub fund kept pace with our Russell benchmark, while the median manager experienced a correction in Q1 2022 that left the fund well below it. The 25th percentile Tiger Cub manager generated weak performance with high levels of volatility relative to returns. Some Tiger Cub funds exhibit very high volatility: one such fund generated very high returns (above the 75th percentile over the time horizon) but also generated very high volatility (16%) and experienced a sharp selloff whose drawdown reached 37% by February 2022 (i.e., current value / peak value of 63%). This fund has reportedly experienced further large drawdowns in March despite the recovery in the Russell 1000 Growth Index.
Past performance is no guarantee of future results. It is not possible to invest diectly in an index.
Hedge fund performance based on HFR and eVestment data
We were able to analyze 55 out of 96 long short hedge funds in the HFR dataset. As a reminder, we analyzed long-short hedge funds that were categorized by HFR as Technology or Healthcare. The results are similar to the PivotalPath Technology dataset: median manager close to beta-adjusted benchmark, with 75th and 25th percentile managers distributed on either side of them. Also similar: the 75th percentile manager outperformed by more than the 25th percentile manager underperformed. But to reiterate, we have concerns about the relevance of this dataset to institutional investors given which funds self-report.
The eVestment database has a lower rate of missing performance data than the other three datasets. We were able to analyze 231 out of 339 funds in their database. As a reminder, these funds were selected since they exhibited a beta to the S&P 500 Growth Index of at least 0.45 over the last two years. The results in most respects are almost identical to the HFR dataset results shown above.
Wrapping up: growth generated substantial asset allocation returns from 2017 to Feb 2022, but only top quartile equity and hedge fund managers delivered excess returns versus growth benchmarks
Asset allocation. An allocation to growth since 2017 generated benefits in portfolios despite the selloff that took place through February 2022
Equity mutual funds. Most growth mutual funds underperformed the R1000 Growth Index during this period. We believe that this reflects in part mutual fund manager reluctance/inability to hold market weight positions in the largest four stocks which outperformed the rest of the equity market by 300% from 2017 to February 2022, one of the largest such outperformance periods in history
Growth hedge fund performance
Median manager. Most median hedge fund managers tracked our beta-adjusted growth benchmarks even though they did not hold market-weight positions in the four largest stocks. The Tiger Cub manager was the exception, trailing the benchmark instead
Underperforming managers. The 25th percentile hedge fund managers all lagged our benchmark, and also generated from 1% to 5% higher volatility
Outperforming managers. The 75th percentile managers in three of the datasets generated large returns vs our benchmarks; the exception was the 75th percentile Tiger Cub fund which tracked the benchmark instead
Source: PivotalPath, HFR, eVestment, JPMAM. February 2022.
Volatility. Some hedge funds that experienced large drawdowns this year accumulated high prior returns, such that long term investors were still ahead of our benchmarks. This context is often missing from press articles6. However, volatility and risk may still be understated for funds with large private exposures
Benchmarks. Using a stock-bond mix or a beta-adjusted equity index is a simple approach that does not take into account the investment style of the manager. Hedge fund researchers often take performance measurement analysis to a deeper level to determine what a fund is doing with its capital, and measuring performance relative to a customized benchmark (see Appendix I). Such an approach is beyond the scope of our industry wide analysis given the limitation of monthly returns
Data issues. Selective reporting, survivorship bias and lack of comparability cloud the results. For the PivotalPath dataset, we were only able to analyze less than half the managers that existed during the time horizon due to missing data. Appendix II reviews the performance of partial managers which we excluded
Appendix I: Factor based hedge fund performance analysis
A large institutional investor can often obtain high frequency returns and leverage directly from a hedge fund. Hedge fund research teams can then regress these returns against market “factors” such as price-to-book, cash flow to enterprise value, price momentum, low volatility, etc. Each factor is constructed as a miniature long-short position; i.e. a price-to-book factor would show the daily returns on a portfolio that owned the “cheapest” stocks (lowest price to book) and was short the most expensive stocks (highest price to book).
If a hedge fund’s returns are highly correlated with one or more factors over time, that set of factors can be used as a benchmark with any residual performance differences measuring the manager’s excess return vs benchmark. The more customized a factor based benchmark is, the more the hedge fund is being measured against their assumed opportunity set. As a result, the benefit or penalty from investing in low volatility or low price to book stocks is assumed to be an asset allocation decision that the manager is not responsible for.
Other approaches require position-level transparency, which would allow for a hedge fund researcher to determine how much the fund made from market exposure, sector, country and style preferences, with any residual representing manager excess return.
Appendix II: Hedge fund survivorship bias and missing data
There’s not much we can do about missing data. But for hedge funds we excluded due to incomplete data, we can at least see if there is any performance skew for returns they did report during the 2017-2022 time horizon. As shown below, we unsurprisingly found a modest bias towards outperformance in the partial returns that these excluded managers did report. But the missing data remains a mystery, which is why we excluded these managers and their partial returns in the overall analysis.
Number of excluded funds out/underperforming R1000 Growth based on their partially reported returns
May contain references to dollar amounts which are not Australian dollars;
May contain financial information which is not prepared in accordance with Australian law or practices;
May not address risks associated with investment in foreign currency denominated investments; and
Does not address Australian tax issues.
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MR. MICHAEL CEMBALEST: Good afternoon everyone, welcome to the April 2022 Eye on the Market podcast. This month’s piece is called The Tide Goes Out, because we’re looking at the aftermath of the growth trade now that there’s been a selloff in growth stocks. At the end of February, the median NASDAQ stock was down around 40% from its prior peak. Those numbers are a little bit better now, so we do the analysis at the end of February, ‘cause we want to capture the most recent lows. Those median declines of 40% are large. Of course they pale in comparison to 2022 when at the end of the selloff, the median NASDAQ stock was down around 75% from its prior peak. And back then, all of the growth outperformance versus value was completely erased. This time around, growth has only given up around a quarter of a third of its outperformance versus value.
Anyway, I thought the end of February was a good time to look at the aftermath of the growth trade and specifically at asset allocation, equity mutual fund and hedge fund performance after this selloff took place. We start out with a discussion of the asset allocation benefits. Because if you think about it, when you make a growth allocation, the first thing you do is you make an allocation to growth. And then you can either implement that through ETFs and passive products, or you could engage an active manager instead.
So the first question is how did the asset allocation decision do with respect to adding growth stock exposures in portfolios? And here, even after the selloff, the growth numbers look pretty good. We compare a growth allocation using either the Russell 1000 Growth or the NASDAQ compared to the S&P Regular Index, value stocks, Europe, US, and global small cap stocks, and emerging markets. And so if you look back three, four years, whatever kind of starting point you want to use, even with the selloff that took place over the last couple of quarters, growth stocks are still substantially ahead of those other asset allocation options. So from an asset allocation perspective, growth delivered.
Now it’s careful, it’s important to take a closer look at this, ‘cause when you look at the Russell 1000 Index as an example, growth outperformed. But a lot of that was due to the four largest stocks, Apple, Microsoft, Amazon, Google. As a matter of fact, almost half of the excess returns of growth over value from 2017 to the end of February of this year was simply due to the outperformance of those four largest stocks. And that’s going to become an important issue when we start to talk about the performance of equity mutual funds and hedge funds, because a lot of active managers simply did not decide to hold market weight exposures in those four stocks. So to summarize so far, the asset allocation decision to growth added value in portfolios even after the selloff.
So now let’s take a look at growth equity mutual funds. And the short answer here is that outperformance has been scarce since any attempt at diversification ended up hurting your returns. So we used Morningstar to get a universe of large cap growth mutual funds. We narrowed it down to funds that have performance over the timeframe we’re looking at. We used the lowest fee share class as a proxy for the institutional share class.
And then we start by looking at the growth managers that use the Russell 1000 Growth Index as their benchmark. And only the top quartile managers ended up outperforming. And I think the big issue here is that many managers may have been reluctant to hold index weight positions in those four large stocks. And I would go further than the word reluctant. A lot of them may have been discouraged from doing so for regulatory reasons. There is something called the diversification rule in the Investment Company Act. And in order to pass that diversification test, you really can’t own positions, any position over 5% has to sum to less than 25% of the fund. And at the end of March for example, the Russell 1000 Growth Index had 36% in Apple, Microsoft, Amazon, and Google. And so you would have failed that diversification test just by holding a market weight position in those stocks.
Now you don’t have to pass that diversification test, but there are a lot of client types, defined contribution plans is one example, that generally prefer and require funds that pass those diversification tests, which is why most equity mutual fund managers seek to pass that test. And we estimate that over the timeframe we’re looking at, there was about a 15% cumulative performance drag in any portfolio that simply adhered to that diversification test by not wanting to exceed 25% of an individual mutual fund in those four stocks. And that I think accounts for part, not all of, but a big part of the underperformance of equity mutual funds that are benchmarked against the Russell 1000 Growth Index.
There’s also a bunch of mutual funds that Morningstar categorizes as growth based on their actual positions but that use the S&P Core Index as their benchmark. The bulk of these funds outperformed. Now whether you as an investor should give managers with a really strong growth tilt credit for outperforming a core benchmark is up to you; I’m not sure that I would.
Now the question of hedge fund performance, there’s a lot of heavy lifting and assumptions required. Measuring the performance of the industry in terms of long-only equity and fixed income mutual funds is pretty easy. They all have stated benchmarks, and you can use Lipper and Morningstar to figure out how a bunch of managers did.
Private equity and venture capital, as we’ve written every two, three years in our deep dive on private equity and venture performance, performance measurements got a lot better because limited partners provide data to Burgess, which is a performance aggregator that allows for all sorts of time-weighted performance measurements versus a bunch of different public equity market benchmarks. And you don’t have to worry about survivorship bias or selective reporting, because it’s the LPs providing the information. And that kind of information was the basis of the piece that we wrote last year on private equity and venture.
Unfortunately, there is no such equivalent for the hedge fund industry. And even before you get into the question of performance, you have to decide on what benchmark you’d use in the first place. Suppose you had a hedge fund that returned 11%. The question over some period, the question of whether or not that was really good or really bad or just moderate is actually one of the more complicated questions that I’ve dealt with in my whole history in investments.
In the 1990s, people used LIBOR plus a spread as a benchmark for hedge funds. Then people compared them to other hedge fund managers in the HFRI Index. People don’t use that as much now because of reporting issues and what managers choose to provide performance to HFR. Sometimes we’ll use a benchmark that’s 60% equity, 40% fixed income under the notion that well, if you’re investing in hedge funds, you’re probably taking money out of both equities and fixed income in amounts that reflect the volatility of the hedge funds.
But when we’re doing the kind of analysis we do this time, we’re going to use an even simpler approach, which is over the long run, many long short hedge funds have a market beta of .45 versus the equity markets. In other words, their returns rise and fall at around 45% of the rate of the market itself.
So in this analysis, we just took the Russell 1000 Growth Index, and we multiplied it by .45, whether it goes up or down, and we’re using that as a benchmark for long short hedge funds in the analysis. And we’ve got some data here showing that that’s generally, a range of 40 to 50% is generally where those market betas have been over the last few years.
So getting back to this question of where do we even get hedge fund performance, we end up having to triangulate and use a bunch of different self-reported universes of hedge fund monthly returns. Again, these databases are only as good as the managers that elect to provide information and the managers that do so consistently. Because if you’re missing a bunch of months, it doesn’t do you any good, the whole thing, you can’t use any return information for that manager. We use a database called PivotalPath. We take another look at HFR data. There’s another database we used called eVestment. And so we a lot of triangulation in this piece to see how hedge fund performance turned out. We also take a close look at the Tiger Cubs because the amount of assets they manage as generalist hedge funds.
And of course, lots of issues come up. Different managers have different levels of growth and net leverage. Some managers may have private exposures that are as high as 50% of the funds in AV [phonetic]. So that when public equity markets decline, some of those managers’ returns are not reflecting the likely eventual repricing of a lot of those private positions. And so sometimes you have to wait a really long time before those kind of managers are publishing the kind of returns that really reflective of the risks in the portfolios.
Anyway, there’s a bunch of charts and tables here that go through the gory details of how we triangulated hedge fund performance. The bottom line is that only the top quartile managers outperformed our benchmark, which was 45% of the Russell 1000 Growth Index. And whether we looked at technology or generalist hedge funds, healthcare, or other categories, the results were pretty consistent. The top quartile managers did pretty well. The median managers tracked the benchmark, and the 25th percentile hedge fund manager trailed the benchmark. The exception in many cases again were the Tiger Cub funds where even the best, even the top quartile Tiger Cub funds simply tracked the market, and the median Tiger Cub fund tend to trail the market.
Anyway, you can take a look at all of the information in this month’s piece. And I thought it was an interesting time to look at this. I don’t know that the growth selloff is completely finished yet. I don’t think that we’re going to have a recession in the US this year. There are certainly plenty of indicators that suggest there’s going to be a recession, the inverted yield curve, a collapse in consumer sentiment to one of the lowest levels in seven years, in inversion in ISM business survey orders falling below inventory levels, et cetera, et cetera.
But the labor markets are very tight, and there’s never been a recession without a pretty big spike in jobless claims. And household and corporate balance sheets are in very, very good shape. So I just don’t think that a recession is in the cards in the US for this year. Looking out to the end of 2023/early 2024 to me is when the recession risks look a bit higher.
In any case, take a look at the piece. For investors that have been participating in the growth trade, I think this is a helpful piece to try to think about where value was added and where it wasn’t by separating the asset allocation and the manager decisions in the process. Thank you very much for listening. The next Eye on the Market in all likelihood in May will be our 13th annual energy paper. So I look forward to talking to you all at that time. Have a great day, bye.
FEMALE VOICE: Michael Cembalest’s Eye on the Market offers a unique perspective on the economy, current events, markets, and investment portfolios and is a production of J.P. Morgan Asset and Wealth Management. Michael Cembalest is the Chairman of Market and Investment Strategy for J.P. Morgan Asset Management and is one of our most renowned and provocative speakers. For more information, please subscribe to the Eye on the Market by contacting your J.P. Morgan representative. If you’d like to hear more, please explore episodes on iTunes or on our website.
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