an etf stream publication // // q1 … · 2020. 3. 12. · 19 analysing emerging market equity...

New ListingsNew smart beta ETFs listed around the world

Parala CapitalHow six EM equity factors behaved from 2007-2020

JP MorganSmart beta and fixed income in emerging markets

FactorResearchEmerging market debt: To hold or not to hold?

AN ETF STREAM PUBLICATION // WWW.ETFSTREAM.COM // Q1 2020

BEY NDBETAINVESTIGATING THE SMART BETA, FACTOR & ESG INVESTMENT REVOLUTION

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EMERGING MARKETSFRONT AND CENTREPlaying the assetclass with factors

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In this issueUPDATESPerformance and listings

6 The smartest beta We identify the top performing smart beta ETFs in the UK and the US

8 The newest beta We look at the new smart beta ETFs listed around the world and highlight ones to watch

PERSPECTIVES

10 ESG and emerging markets: Match made in heaven? Tom Eckett shows why incorporating ESG factors in emerging markets should be high on investors’ agendas when searching for a better risk-return profile

12 Why incorporating ESG in fixed income can deliver a superior risk-return profile Can an ESG screening have a bigger impact in fixed income than equities? ETF Stream’s editor-in-chief and Financial Times columnist David Stevenson examines why this may be the case despite the ESG community’s “confused” message

14 An analysis of the smart beta landscape in 2019 Kenneth Lamont, senior research analyst, passive strategies, at Morningstar, highlights the major moments from across the smart beta landscape last year, with the slowing number of launches a sign of a maturing market in Europe

19 Analysing emerging market equity factors over the business cycle Understanding how different factors perform over the business cycle is crucial especially in emerging markets. Steven Goldin, managing partner and co-CIO at Parala Capital, breaks down how six EM equity factors behaved between 2007 and 2020

FACTORS IN FOCUS

22 Why smart beta can produce fruitful results with emerging market debt Katherine Magee, investment specialist, Quantitative Beta Solutions, at JP Morgan Asset Management, explains how a smart beta approach can address the challenges of investing in traditional debt-weighted fixed income indices in emerging markets

26 ETF Panic Attack Vincent Deluard, global macro strategist at INTL FCStone, studies six million data points to answer the simple question of an important topic that receives relatively little attention, whether ETF flows impact short-term performance

32 Emerging market debt: To hold, or not to hold? Nicolas Rabener, managing director of FactorResearch, studies the potential benefits of an allocation to emerging market debt in terms of both a performance and diversification perspective

37 Is an X-Factor driving stock returns? Gavin Smith, managing director and portfolio manager on the Quantitative Equity Team at QMA, examines the possibility of an unknown fundamental x-factor driving recent stock market returns

CLOSING REMARKSWhat the buy-side says

40 Interview Tom Eckett speaks to AJ Bell’s head of passive portfolios Matt Brennan

42 Emerging markets flows George Geddes analyses the recent flows and trends from across the emerging markets space

About us

David StevensonDavid trained as an economist

before moving into financial journalism where he has

written about investing and finance for many years. David

is CEO and Editor in Chief of AltFiNews and is also a

columnist for the Financial Times (the Adventurous Investor),

Investment Week and Money Week. David is an experienced

media entrepreneur (he’s set up a number of online media

companies focused on online TV and viral videos) and investment

expert of retail repute.

David TuckwellDavid is an Australia-based

journalist who covers exchange traded funds and fintech. He formerly worked in the ETF

industry in London. In another life he was a top national Tetris player.

Tom EckettTom joined ETF Stream as a

senior writer in March 2019. He started his career at Investment Week in August 2016 as an asset

management correspondent covering ETFs. Outside the office,

he is a big boxing, football and cricket fan and can be found

most weekends at Victory Road supporting Leiston FC.

ETFSTREAM.COM Q4 2019 BEYOND BETA 3

UPDATE CONTENTS

Expertise | Technology | Data

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Global ETF & Index Managed Data Service PCF-Calculation


SMART BETA UPDATE EDITORIAL

COMMENTMARCH 2020

UPDATE

Hello and welcome to Beyond Beta – the one and only magazine dedicated to smart beta and quantitative ETFs. This issue will focus largely on emerging markets and the different ways factors can be implemented in the asset class.

How to play emerging markets is at the top of investors’ agendas amid the trade war between the US and China and the unknown market implications from the coronavirus. While these risks could impact prices in the short-term, the long-term outlook for emerging markets will certainly entice investors, especially when glancing at the low return environment across developed markets.

It is thought factors can deliver even greater returns in the less efficient emerging markets. The MSCI Emerging Markets Diversified Multiple-Factor index has delivered 5% annualised returns over the past decade versus 3.8% for the wider MSCI Emerging Market index. This outperformance is stronger than in developed markets over the same period. However, from a product construction perspective, issuers have been slower to introduce smart beta and environmental, social and governance (ESG) ETFs in this region and have been more focused on offering broad-based and single-country strategies to the market.

The issue begins with a market overview, looking at the best performing and newly listed smart beta ETFs from around the world. It then moves to a series of interviews and essays with top experts from across the quantitative investing landscape. Highlights include research from Steven Goldin, managing partner and co-CIO at Parala Capital, who examines how different factors performed over the business cycle in emerging markets, and FactorResearch’s Nicolas Rabener, who studies the potential benefits of an allocation to emerging market debt.

As always, a quick note from us on definitions. We define smart beta as non-market-weighted rules-based ETFs. For us, smart beta ETFs do not have to be index-tracking. What matters is that they meaningfully deviate from the market weighted portfolio, while trading according to a set of rules. (Where those rules, preferably, have some basis in peer-reviewed literature).

This means, for example, that actively managed ETFs with portfolio managers making ad hoc trades are not smart beta for us. While index tracking ESG ETFs that make consistent far-reaching exclusions can qualify as smart beta. Quantitatively, we would expect smart beta ETFs to have a correlation coefficient less than 0.95 with their broad market benchmarks. Smart beta ETFs that demonstrate a correlation higher than this, for us, count as “closet trackers”.Tom Eckett, deputy editor, ETF Stream

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ETFSTREAM.COM6 BEYOND BETA Q1 2020

TOP ETF PERFORMERS DATA AND COMMENTARY

UPDATE

3-month performance (end 31 December 2019)

The UK’s smart beta ETF market is relatively less interesting and less developed than the US. We tend to blame this on the fact that UK ETF issuers are making funds for other asset managers. These asset managers – such as discretionary fund managers and multi-asset managers – are conservative and do not like deviating from benchmark. The retail ETF market in the UK is far smaller than in the US, and smart beta ETFs are ultimately retail products.

The best performing UK smart beta ETF was the WisdomTree Cloud Computing UCITS ETF USD (KLWD). It uses a tiered weighting system to buy cloud computing companies. The purer a company’s exposure to the cloud computing, the more weight it gets in the index. The fund definitely meshes with the zeitgeist, as cloud computing companies – like Amazon – are trading on some of the richest multiples on the stock market.

Another noteworthy fund was the HANetf HAN-GINS Innovative Technologies UCITS ETF (ITEK). This fund buys companies based on how innovative and revolutionary their technology is judged to be. It is particularly interested in future cars – and has Tesla as its biggest holding at the time of writing. Like KLWD, the fund uses a tiered and equal weighted approach to weighting stocks.

Outside smart beta, there were some outstanding UK performers. We would like to take the minute to recognise the stellar returns delivered by rhodium and palladium trackers. Rhodium – an obscure metal that features in car catalysts – provided a massive 100% return in the past three months, meaning investors more than doubled their money. While the fund does not qualify as smart beta. But buying it three months ago would certainly have been smart.

One of our common refrains at Beyond Beta is that sector exposure matters more than factor exposure for short-term returns. Therefore, we almost always find that when examining quarterly performance, it is the thematic ETFs – which we classify as funds that weight their holdings based on exposure to themes – that tend to perform best (and worst). This edition is no different.

The best performing smart beta ETFs in Q4 2019

Ticker Fund Name – 3 Month Total Return % change

KLWD WisdomTree Cloud Computing UCITS ETF USD 10.83%

IUMD iShares Edge MSCI USA Momentum Factor UCITS ETF USD 6.24%

WTAI WisdomTree Artificial Intelligence UCITS ETF 5.67%

EUSV SPDR MSCI Europe Small Cap UCITS ETF 5.60%

BATT L&G Battery Value-Chain UCITS ETF 5.34%

XMVU Xtrackers MSCI USA Minimum Volatility UCITS ETF 1D 4.91%

LOWV SPDR S&P 500 Low Volatility UCITS ETF 4.90%

RUSG Lyxor Russell 1000 Growth UCITS ETF C-USD 4.78%

ITEK HANetf HAN-GINS Innovative Technologies UCITS ETF 4.60%

XRH0 Xtrackers Physical Rhodium ETC 109.50%

Cloud computing companies – like Amazon – are trading on some of the richest multiples on the stock market

Top performersUK

Top performers USA


SMART BETA UPDATE PERFORMANCE

3-month performance (end 31 December 2019)

The top US smart beta performers over the past three months are all thematic ETFs of various kinds. Their strong showing can mostly be explained by their exposure to just two stocks: Tesla, the electric car maker, and Enphase Energy, the Californian solar technology company. Enphase share price tripled in value from $20 last December to almost $60 in February, making it one of the best performing stocks in the world. Tesla, as is common knowledge, more than doubled its share price in this time after reporting a quarterly profit.

The best performing fund – Invesco’s TAN – held a 12% stake in Enphase. This made it the single ETF in the world with the biggest exposure to this stock. GRID – the ninth best performing ETF in our table – has the second largest holding of Enphase of any ETF. Other big winners, such as PBW, CNRG and PBD, owned smaller portions of both Tesla and Enphase.

Outside of these two stocks, there were four other ETFs to make the top 10. First, is the volatility loving Virtus LifeSci Biotech Clinical Trials ETF (BBC). This interesting fund buys biotech companies that have drugs that are still in the early phases of clinical trials. At this stage in the drug development cycle, a lot can go right and a lot can go wrong – making any investment extremely risky and rewarding. Interestingly, BBC has made it into our top 10 performers

Ticker Fund Name – 3 Month Total Return % change

TAN Invesco Solar ETF 49.62%

PBW Invesco WilderHill Clean Energy ETF 45.12%

CNRG SPDR S&P Kensho Clean Power ETF 43.05%

PBD Invesco Global Clean Energy ETF 30.39%

BBC Virtus LifeSci Biotech Clinical Trials ETF 29.09%

LOUP Innovator Loup Frontier Tech ETF 24.14%

KGRN KraneShares MSCI China Environment Index ETF 22.64%

KARS KraneShares Electric Vehicles and Future Mobility Index ETF 19.72%

GRID First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index ETF 19.05%

IBUY Amplify Online Retail ETF 18.92%

before – in Q1 of 2018. While this may make it sound like a good investment, the fund runs through -15% drawdowns on a frequent basis, meaning it has underperformed the S&P 500 since its inception in 2014.

Another two were from KraneShares, which seem to offer Chinese equivalents of Tesla and Enphase. The final fund to make the cut was IBUY, the online shopping ETF. IBUY has been a very strongly performing fund since it was listed in early-2016, thoroughly defeating the index. It weights companies based on how important online shopping is to their business. It then adds an equal weighting filter. Amazon is rarely one of its top 10 holdings.

Tesla more than doubled its share price after reporting a quarterly profit


NEW SMART BETA LISTINGS DATA AND COMMENTARY

UPDATE

New smart beta listingsThe big story for new listings so far this year has been the turn to active management within the ETF industry. The days where ETF providers uniformly denounced the greed of active money managers while singing the praises of index investing are well and truly over as this year’s listings so far

suggest. Active managers hoping to launch ETFs in the US were given a major boost late last year when the SEC gave a green light to non-transparent ETFs. This seems to be showing up already, as we have seen two major types of active ETFs list in January and February.

Sector rotators

The first is sector rotators. Sector rotators boldly claim to be able to routinely beat the S&P 500. To do this, they make judgements on which sectors in the US economy – be it healthcare, technology, real estate and so – are likely to outperform. They then buy market weighted ETFs that give exposure to these sectors. They also rule out sectors they judge likely to do badly, which in theory is relatively straight forward. (Our guess for 2020 would be energy). The two active ETFs employing this approach that have listed this year are:

• Anfield US Equity Sector Rotation ETF (AESR)

Day Hagan/Ned Davis Research Smart Sector ETF (SSUS)Both use proprietary research to predict which sectors will outperform and which will be duds. In the case of SSUS, Ned Davis – one of the world’s largest and best-known market

Domicile Ticker Issuer Fund Name Style Actively managed?

Fee

Japan 2560 Mitsubishi MAXIS Carbon Efficient Japan Equity ETF Principles-based No 0.13%China 515700 Ping An Ping An CSI New Energy Automobile Industry ETF Thematic No N/AChina 159995 China AMC ChinaAMC CNI Semi-conductor Chip ETF Thematic Yes N/AUSA ARMR Exchange Traded Concepts Armor US Equity Index ETF Sector Rotation No 0.60%Hong Kong 2809 Mirae Asset Global X China Clean Energy ETF Thematic No 0.68%Indonesia XBES BNI Asset Management BNI-AM ETF MSCI ESG Leaders Indonesia Principles-based No N/AIndonesia XSRI Sinar Mas STAR ETF SRI-KEHAT Principles-based No N/AUSA MSVX Little Harbor LHA Market State Alpha Seeker ETF Asset Allocation Yes 1.16%USA SMDY Syntax Syntax Stratified MidCap ETF Size No 0.30%USA AWAY ETFMG ETFMG Travel Tech ETF Thematic No 0.75%USA STLG BlackRock iShares Factors US Growth Style ETF Growth No 0.25%USA STLV BlackRock iShares Factors US Value Style ETF Value No 0.25%USA AESR Regents Park Funds Anfield US Equity Sector Rotation ETF Sector Rotation Yes 1.42%USA SSUS Donald Hagan Day Hagan/Ned Davis Research Smart Sector ETF Sector Rotation Yes 0.78%USA LCR The Leuthold Group Leuthold Core ETF Asset Allocation Yes 1.45%USA TECB BlackRock iShares U.S. Tech Breakthrough Multisector ETF Thematic No 0.40%USA BUYZ Franklin Templeton Franklin Disruptive Commerce ETF Thematic No N/AUSA HELX Franklin Templeton Franklin Genomic Advancements ETF Thematic No N/AUSA IQM Franklin Templeton Franklin Intelligent Machines ETF Thematic No N/A

Sector rotator ETFs aim to make judgements on which sectors in the US economy – such as healthcare, – are likely to outperform


SMART BETA UPDATE NEW LISTINGS

research companies – will provide the intellectual property. AESR uses a forecasting model that Anfield have developed. In theory, the ETF that uses the best research should be the best performing. For this reason also, it is a brave venture for a research house like Ned Davis to sign up to (if the fund does very badly, it could impact their reputation for providing insightful market-leading research). However, as both funds are quite new, there is not much performance history to judge on at this stage.

There is a kind of commercial cleverness to listing sector rotating ETFs. All three (including Armor US Equity Index ETF, which is passive) have quite high fees. Yet as they only buy other ETFs, the cost of running the funds is relatively low. This helps ensure that profit margins are higher.

Asset allocation ETFs

The second active trend we have seen is asset allocation ETFs. These ETFs are kind of like sector rotators in that they buy other ETFs based on a proprietary reading of the market. However, they take it a step further – and dive in and out of different asset classes (cash, bonds, commodities, US stocks, etc) as well as riskier corners of the ETF market (leveraged ETPs, VIX funds, ETNs). Given the vast investment remit, these types of ETFs almost work like a portfolio unto themselves. The two we have seen listed:

• LHA Market State Alpha Seeker ETF (MSVX)• Leuthold Core ETF (LCR)

The two are very different. MSVX will buy pretty much everything that conservative investors try their hardest to avoid. It buys leveraged and inverse ETPs, it buys other actively managed funds, it buys strange ETNs and derivatives as well as VIX-related products. It does all this with the aim of generating alpha, however, the effort that it goes through is not for the feint hearted.

LCR for its part is a 70/30 portfolio made up mostly of other index funds. It is the kind of fund that conservative investors often like. The fund does not state what its benchmark is, which some investors might regard as a bit of a luxury (or cop out).

Both come from smaller lesser-known ETF providers – this is no coincidence. Both are riskier bets for as there is no guarantee at all that they will spark investor interest. There is very little history of asset allocation ETFs finding buyers. These days, it is the smaller ETF providers taking all the risk on building new products.

China new economy ETFs, helped by protectionism

Thematic ETFs have continued to grow in China, as Chinese retail investors often love to take a punt. In 2020, Chinese thematic ETFs have focused on semiconductors and electric

cars. These are two of the best performing and most promising pockets of China’s “new economy”. The new economy label is often given to China’s emergent healthcare and tech industries to distinguish them from older sectors like banking and energy, which are dominated by state-owned enterprises.

For us at ETF Stream, the ChinaAMC CNI Semi-conductor Chip ETF was especially interesting. Not only because Chinese semiconductor businesses have been some of best performing in the world the past 12 months. But also because of the sheer honesty of the factsheet that the fund was launched with. (It was in Mandarin, we used Google Translate). The factsheet says that the fund will likely benefit from Chinese protectionism (“import substitution”), which comes at the expense of US semiconductor companies. The fund also benefits from increasing competition between China and western powers for technological supremacy, this factsheet says, as increasing security competition will generate a steady stream of state subsidies.

Travel technology – great ticker

We have often observed that ETF tickers have been used to great effect as marketing devices. On this score we’d also take a moment to observe the new travel technology ETF from ETF Managers Group – the ETFMG Travel Tech ETF – and its ticker: AWAY. The fund buys companies that use the internet to deliver travel-related services. These include companies like Uber and Lyft, which provide car travel. But also online booking companies.

Thematic ETFs focussed on semiconductors and electric cars have continued to grow in China

Analysing emerging markets through an environmental, social and governance (ESG) lens is still in its early stages considering the poor data available

and the limited track records of ESG products. Despite this, a consensus is beginning to emerge that incorporating ESG factors into an emerging markets portfolio can produce even stronger results than in developed markets.

ESG investing has come a long way since the launch of the first ETF with an ESG tilt, the iShares MSCI USA ESG Select ETF, in 2002. Today, ESG ETFs represent $50bn of the $6trn ETF market, according to data from ETFGI, with 74% of investors planning to increase their allocations over the next year, according to a Brown Brothers Harriman survey.

Emerging market ESG ETFs only account for a small part of these assets, however, investors are starting to pour money into these products. For example, the Xtrackers ESG MSCI Emerging Markets UCITS ETF already has $297m assets under management (AUM) having just launched last October while Europe’s largest ETF of this kind, the iShares MSCI EM IMI ESG Screened UCITS ETF (SAEM) has $494m AUM.

Recent inflows will partly be due the strong performance emerging market ESG ETFs have exhibited over their parent counterparts. Over the past decade, the MSCI EM ESG Leaders index has delivered annualised returns of 7.5% versus 4.1% for the regular MSCI Emerging Markets index. If one compares this to developed markets, the MSCI


ESG and emerging markets: Match made in heaven?ETF Stream’s deputy editor Tom Eckett shows why incorporating ESG factors in emerging markets should be high on investors’ agendas when searching for a better risk-return profile

World ESG Leaders index has returned 10.4% over the past 10 years versus 10.5% for the MSCI World, as at 31 January.

While this is just a sample, it does highlight the greater impact incorporating ESG factors has in emerging markets compared to developed markets. The greater average annualised returns of 3.4% over a 10-year period when using an emerging market index with an ESG tilt can be explained by a number of factors.

As well as boosting performance, incorporating ESG factors can reduce the volatility of a portfolio, as Nicolas Rabener, managing director of FactorResearch, notes. “Intuitively applying ESG metrics should be rewarding in emerging markets as there is less regulation than in developed markets,” he says.

Supporting this, research from data provider Sustainalytics finds the FTSE Emerging index had an ESG risk score of 27.4 compared to 24 for the FTSE Developed index, suggesting emerging market investors are exposed to 14.2% more unmanaged ESG risk. “These differentials reflect the prevalence of weaker regulatory systems, poor corporate governance practices and lax disclosure requirements in emerging markets,” the report adds.

STATE-OWNED ENTERPRISESOne issue which is not prevalent in developed markets is the reality of state and family-owned businesses. Highlighting this, Goldman Sachs found state-owned enterprises (SOEs) represented 28% of the MSCI Emerging Market index in 2016.

The problem with SOEs, as a Cambridge Associates research piece notes, is “SOEs are influenced by interests beyond generating profits for shareholders, which can negatively impact operational aspects of the business”.

Evidence shows SOEs can have a significant impact over performance with those companies lagging private sector stocks by 40% over the five years to June 2016, according to Goldman Sachs.

Where ESG ratings have a positive effect is SOEs often score far lower than private firms due to governance issues. The Cambridge Associates research note adds: “Concerns over SOEs have seemingly been around for so long, one wonders how they could not already be amply discounted by markets, but the ESG ratings process has clearly been effective in identifying underperforming companies here.”

When studying the reasons behind the underperformance in the MSCI World ESG Leaders index, one factor is ESG stocks

underperforming in the US. Over the last decade, the MSCI USA has returned 14% per annum versus 13.4% for its ESG counterpart due to the index removing a number of the top performing US mega-caps including Amazon and Home Depot.

The Cambridge Associates report adds: “The data for developed markets have been more mixed, largely due to ESG ratings being a poor indicator of stock performance for US large-cap companies. Consideration of ESG quality can still add value in developed markets with the correct application, which may need to be more nuanced than using ratings in isolation.”

ESG DATATurning back to emerging markets, an area that remains an issue is the lack of ESG data available for providers and investors. Hortense Bioy, director of passive funds and sustainability research for Europe at Morningstar, says this requires more resources and more engagement with companies. “Poor data quality makes implementing ESG in emerging markets more challenging,” she continues. “However, considering ESG factors when investing in emerging markets makes a lot of sense because ESG standards in these markets are typically lower than in developed markets. Fewer emerging markets companies score well on ESG and focusing on these companies has proven to be a winning strategy.”

ESG data for emerging markets is becoming more detailed and comprehensive, according to Cambridge Associates: “ESG data for emerging markets has become more detailed and comprehensive in recent years.

“Investors…may have underestimated the value of now widely available information on the ESG strength of corporates in emerging markets.”


Intuitively applying ESG metrics should be rewarding in emerging markets as there is less regulation than in developed markets

PERSPECTIVESTOM ECKETT

CUMULATIVE INDEX PERFORMANCE

200

100

0

Sep07 Oct08 Oct09 Oct10 Nov11 Nov12 Nov13 Dec14 Dec15 Dec16 Jan18 Jan19 Jan20

-

n MSCI EM ESG Leaders n MSCI Emerging Markets

I’ve always believed the message coming out of the ESG community was slightly confused. If I had to reduce it to its bare basics it goes something like this.

Sustainable investing is one bit doing good, and one bit reducing downside risk, all without any obvious hit to performance. Obviously, the mix of these three factors depends on who you talk to, but doing good, reducing risk and not too much tracking error sounds like the holy trinity of investing. What’s not to like?

Back in the real world many of us have our suspicions. Let’s take the tracking error notion first. Many analysts such as Beyond Beta’s very own contributor Nicholas Rabener has crunched the numbers and remain unconvinced about the tracking point. Again, at the risk of over simplifying, one core observation is that the numerous ESG strategies have benefitted from a one-off boost because they avoided underperforming, value stocks such as energy businesses. Many ESG strategies by contrast have tended to favour either businesses with a tech bias or quality factor style skew. Both of these tilts might change rapidly in the future and ESG screens could serially underperform.

The doing good in a sustainable way can also sound confusing because it conflates very different debates about those businesses with an admirably long-term bias towards sales and earnings growth against those businesses that are very self-consciously trying to achieve an impact-based ethical outcome. Put simply one can believe that the likes of Diageo or Unilever are highly sustainable businesses (and quality stocks) without doubting for one moment that they are above all profit maximisation machines.

For me the least confusing notion in my holy trinity is that of risk reduction, especially in the world of climate change mitigation and impact. Here the argument for ESG screening seems powerful and difficult to argue with especially for fixed income investors. And that’s the crucial twist.

Most ESG strategies have been built on equity-based strategies and to date ESG fixed income investing is a pale shadow of its equity sibling. But it deserves to be much systemically important. Put simply at its core fixed income investing doesn’t care much about achieving some kind of alpha, rather the trick is making sure you get your principal original investment back in full plus those promised income streams. What should worry the fixed income investor is any scenario in which those, frequently long term, income streams might be impaired. And by any measure, climate change represents a real and tangible threat to some corporates future cash flows.

IMPACT ON FUTURE CASH FLOWS Crucially it’s also increasingly easy to crunch the numbers that might give us some understanding of the impact on future cash flows. Take for instance the MSCI’s GMI database which now features due diligence data on over 90 governance factors across board, pay, ownership and control, and accounting activities. Inside that database is the MSCI Intangible Value Assessment (IVA) rating buckets for the Bloomberg Barclays Euro Credit Aggregate universe. Dig around inside this treasure trove of information and one can already see that higher-rated IVA issuers trade at tighter spreads than lower-rated issuers. In effect IVA ratings seem behaving like credit ratings. These data sets also easily allow bond investors to see credit risk deterioration across


David Stevenson trained as a economist before moving into financial journalism where he has written about investing and finance for many years. David is CEO and Editor in Chief of AltFiNews and is also a columnist for the Financial Times (the Adventurous Investor), Investment Week and Money Week. David is an experienced media entrepreneur (he’s set up a number of online media companies focused on online TV and viral videos) and investment expert of retail repute.

Why incorporating ESG in fixed income can deliver a superior risk-return profileCan an ESG screen have a bigger impact in fixed income than equities? ETF Stream’s editor-in-chief and Financial Times columnist David Stevenson examines why this may be the case despite the ESG community’s “confused” message

issuers – especially visible in sectors such as US unconventional oil and gas.

But lurking behind this increasing mountain of data we can also glimpse a more brutal, fundamental reality. Climate change presents real policy risks and opportunities which could impact those FI cashflows. Let me give one very real example.

In the summer of 2019, I attended the annual Amundi global investment forum which featured many speeches by the great and good, headlined by John Kerry, ex secretary of state in the Obama administration. One fascinating panel featured the governor of the French central bank who said that institutions such as his might not look too favourably in the future on climate change impaired collateral in the event of a crisis. So, if you are an investment bank with lots of energy sector loans impairing like crazy, you might start to think twice about offering those bonds up as collateral for any wholesale lending lines in the event of a future economic crisis. In the real world, that represents extra risk for the bank which must be reflected in the cost of debt, which must surely rise as the risk is articulated and measured.

PAST PERFORMANCE In my humble view, ESG screening makes sense for any fixed income investor who cares about future cash flows. But what of the past? Has an ESG based strategy delivered any meaningful performance numbers for fixed income investors? Going back to our alpha point in the holy trinity, would a bond investor be ahead of their peers if they’d have adopted ESG screens? The answer is yes according to a recent study looking at returns since 2014 by analysts at Amundi again.

They summarise their findings: “Since 2014, the integration of ESG has created alpha in euro-denominated fixed income portfolios. Indeed, for euro-denominated investment grade (“IG”) bonds portfolio, the annual excess credit return of long/short strategy between best-in-class bonds (20% best-rated according to ESG scores) and worst-in-class bonds (20% worst-rated according to ESG scores) bonds reaches 37 basis points.”

Curiously though this positive conclusion doesn’t hold true for American markets where the results are more “disappointing in absolute value, but the correlation between ESG and performance is positive”, according to the Amundi report.

“Indeed, ESG investing was a source of underperformance from 2010 to 2019 if we consider both long/short, best-in-class versus worst-in-class strategies and benchmark-controlled optimised portfolios. Nevertheless, we noticed that the large underperformance during the 2010-2013 period has

decreased significantly in the more recent period. The annual cost of ESG investing is 9 bps per year for benchmarked strategy since 2014 versus 24 bps from 2010 to 2013.”

Maybe the relatively restrained pace of policy initiatives, especially with the Trump administration, is imposing less of a policy drag on US investments.

THE BENEFITS OF ESG ANALYSIS These slightly disappointing stateside numbers shouldn’t blind investors though to an increasingly obvious reality – even huge US bond investing institutions are beginning to build ESG analysis into their credit risk analysis. In a 2018 paper, PIMCO’s Gavin Power points to what he calls straws in the wind such as their “early analysis of corruption allegations against South Africa’s president in 2015 [which] resulted in an active call to reduce exposure to the country”. According to Power, there is now a “growing body of evidence demonstrating, for example, that companies that effectively manage and integrate sustainability issues realise a range of competitive benefits – including resource and cost efficiencies, productivity gains, new revenue and product opportunities, and reputation benefits”.

So maybe that holy trinity of doing the right thing, avoiding risk and avoiding too much tracking error isn’t an impossible ask after all?


Companies that effectively manage and integrate sustainability issues realize a range of competitive benefits – including resource and cost efficiencies, productivity gains, new revenue and product opportunities, and reputation benefits

PERSPECTIVESDAVID STEVENSON

The European strategic beta (also known as smart beta) ETF market grew at a healthy clip in 2019 but the market is showing signs of reaching maturity.

Assets in these funds grew by more than a third in size to $71bn over the course of the year. A

combination of rising markets and consistently positive monthly net inflows has supported this impressive growth. Over the course of the year they collectively received $8.5bn in net new money with the share of the total European ETF market holding steady, finishing at 7.2%.

Dividend strategies received almost half of all net inflows in the year and retain the lion’s share of overall assets invested in strategic-beta ETFs. This reflects the demand for income producing funds among yield-hungry investors.

Risk-orientated and value strategies also benefited from meaningful inflows, with each raking in around $2bn in net new money over the course of the year. On the other hand, commodity strategies were the biggest losers, leaking almost $1.5bn in assets.

WINNERS AND LAGGARDSIt is no surprise that passive behemoth iShares retained the number-one spot in the European strategic-beta ETP space with a market share of 45% at the end of 2019. Its ‘Edge’-branded suite of ETFs that track factor indexes focused on core global, US, and European exposures has played a key role in cementing iShares’ standing in recent years. Their single factor minimum volatility and value funds were the main recipients of flows in 2019.

One of these funds, the iShares Edge MSCI World Minimum Volatility ETF, is the largest strategic beta fund in Europe with close to $4.4bn in assets at the close of 2019. It attempts to construct the least-volatile portfolio possible with stocks from the flagship MSCI World index under a set of constraints. These include limiting turnover, exposure to individual names, and sector tilts relative to the index, which improves diversification but also reduces style purity. While pure low-volatility portfolios hold only low-volatility stocks, this minimum volatility portfolio


An analysis of the smart beta landscape in 2019Kenneth Lamont, senior research analyst, passive strategies, at Morningstar, highlights the major moments from across the smart beta landscape last year, with the slowing number of launches a sign of a maturing market in Europe

EUROPEAN SMART BETA ETF NET FLOWS 2019 (USD BILLIONS)

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may include average- to high-volatility stocks because of the risk-diversification benefits they bring to the overall portfolio and the constraints built into the optimiser.

State Street Global Advisors’ SPDR ETF franchise remained in second place. However, this position belies a dependency on the fortunes of two products from the same stable: the SPDR S&P U.S. Dividend Aristocrats ETF and the SPDR S&P Euro Dividend Aristocrats ETF. These two funds harbour three quarters of SPDR’s assets in strategic-beta ETPs in Europe.

UBS, whose MSCI equity factor suite forms the backbone of its offering, is the third largest

provider in Europe. In particular, the UBS ETF MSCI USA Select Factor Mix ETF has proved to be the most popular multi-factor ETF in Europe, receiving close to $500m in net inflows in the year.

LAUNCHES TAIL-OFF AS MARKET REACHES SATURATION POINTFor all the growth in assets, just six new strategic-beta products hit the shelves in 2019, the lowest figure for over 15 years. The dwindling number of launches is the natural consequence of the strategic-beta market in Europe reaching maturity. With the market for more vanilla strategic beta all


PERSPECTIVESKENNETH LAMONT

but saturated, providers have moved to develop increasingly complex products in order to stand-out from the competition.

Most new entrants now employ multi-factor equity strategies, which wrap several factor exposures into one strategy.

These funds are routinely marketed as a way of improving the prospective risk-return profile of a market-cap index by diversifying across

factors, each of which can suffer long periods of underperformance.

The increased number variables in these products allows providers more scope to differentiate themselves and often charge a premium for their intellectual property. It is fair to expect that products of this type will continue to come to market. After all, multi-factor ETFs can be churned in many different combinations and cover a number of geographies.

The most high-profile multi-factor launch of 2019 came as part of Goldman Sachs Asset Management’s entrance into the European ETF market. The ActiveBeta US Large Cap Equity ETF shares its approach with a US-listed sister fund, which, listed in 2015 is currently largest multi-factor strategic beta ETF in the world. With an ongoing charge of just 0.14%, the European variant is the cheapest multi-factor ETF in Europe.



For all the growth in assets, just six new strategic-beta products hit the shelves in 2019, the lowest figure for over 15 years. The dwindling number of launches is the natural consequence of the strategic-beta market in Europe reaching maturity

Kenneth Lamont is a senior fund analyst for Morningstar. Within the Morningstar research team, Kenneth covers European passive funds. Before joining Morningstar in September 2013, Kenneth was a research analyst at Mergermarket, where he covered the infrastructure finance sector and previously an associate at Markit, where he held an operational role within the portfolio valuations team.

This launch was followed by a second fund; the Goldman Sachs ActiveBeta Emerging Market Equity ETF, which applies the same multi-factor approach to emerging market equities. This ActiveBeta strategy offers broad equity exposure, but tilts toward those with low valuations, strong momentum, high profitability, and low volatility.

The simple equal weighted factor approach employed is transparent, though a more integrated approach would likely lead to stronger style tilts and slightly higher returns.

As competition heated up, we saw JP Morgan also pursue a similar strategy. As part of their push into the European ETF market, they also borrowed two existing multi-factor equity strategies from the US. The JPM Global Equity Multi-Factor and the US Equity Multi-Factor ETFs select stocks based on momentum, quality and value factors while controlling for sector and regional biases.

The short live track records for many multi-factor strategies can make due diligence tricky. By ‘porting’ strategies which have already cut their

teeth stateside, both providers have given investors the luxury of a longer live track record to examine

The final multi-factor strategy ETF launched in 2019 was the UniCredit EURO iSTOXX ESG-X Multi Factor ETF. It selects eurozone stocks based on profitability, earnings yield, leverage, value and low volatility before weighting them using a multi-factor optimization process. It also applies exclusionary screens based on ESG criteria.

The only single factor launch of the year was the UniCredit EURO STOXX ESG-X Minimum Variance ETF. The fund attempts to differentiate itself from several cheaper rivals offering similar factor exposures by applying the same ESG screens as its stable mate.

Product development in the fixed-income space remains eerily quiet. The long-predicted flurry of innovation has failed to materialise, and asset levels remain subdued. One reason for this is that there is currently no consensus view on whether the factors recognized in equity investing can be applied to fixed income.



TOP EUROPEAN STRATEGIC-BETA PRODUCTS

Name Ticker Inception Date

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Fund Size – USD Billions (end 2019)

iShares Edge MSCI Wld Min Vol ETF $ Acc MVOL 30/11/2012 Global 0.30000 Risk-Oriented 4.25271

SPDR® S&P US Dividend Aristocrats ETFDis UDVD 14/10/2011 USA 0.35000 Dividend 3.62800

iShares Dev Mkts Prpty Yld ETF USD Dist IWDP 20/10/2006 Global 0.59000 Dividend 3.05299

iShares Edge S&P 500 Min Vol ETF USD Acc SPMV 30/11/2012 USA 0.20000 Risk-Oriented 2.28306

Ossiam Shiller BclyCp®US SectValTR 1C$ UCAP 22/06/2015 USA 0.65000 Multi-Factor 2.23342

iShares Edge MSCI Wld Val Fctr ETF $Acc IWVL 03/10/2014 Global 0.30000 Value 2.37520

SPDR® S&P Euro Dividend Aristocrats ETF SPYW 28/02/2012 Europe 0.30000 Dividend 2.12607

iShares European Prpty Yld ETF EUR Dist IPRP 04/11/2005 Europe 0.40000 Dividend 2.05811

iShares Edge MSCI Wld Qual Fctr ETF $Acc IWQU 03/10/2014 Global 0.30000 Quality 1.79637

Lyxor SG Global Qual Inc NTR ETF D EUR SGQI 25/09/2012 Global 0.45000 Dividend 1.75075

2019 EUROPEAN STRATEGIC-BETA PRODUCT LAUNCHES

Name Ticker Inception Date

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Ongoing Charge (%)

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GS ActiveBeta Em Mkt Eq ETF A USD Acc GSEM 04/11/2019 Equity EM 0.49000 Multi-Factor

GS ActiveBeta® US Lrg Cp Eq ETF USD Acc GSLC 23/09/2019 Equity USA 0.14000 Multi-Factor

UC EURO STOXX ESG-X Minimum Variance ECBV 15/07/2019 Equity Eurozone 0.40000 Risk-Orientated

UC EURO iSTOXX ESG-X Multi Factor ETF ECBF 15/07/2019 Equity Eurozone 0.40000 Multi-Factor

JPM US Equity Multi-Factor ETF USD Acc JPUS 09/07/2019 Equity USA 0.20000 Multi-Factor

JPM Global Equity Multi-Fac ETF USD Acc JPGL 09/07/2019 Equity Global 0.20000 Multi-Factor

EDHEC-RiskResearch Programmes

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220x280 ipe.indd 1 02/03/2020 09:16

Analysing emerging market equity factors over the business cycleUnderstanding how different factors perform over the business cycle is crucial especially in emerging markets. Here, Steven Goldin, managing partner and co-CIO at Parala Capital, breaks down how six EM equity factors behaved between 2007 and 2020

ETFSTREAM.COM

FACTORS IN FOCUSSTEVEN GOLDIN

Q1 2020 BEYOND BETA 19

THE BUSINESS CYCLE AND ITS INFLUENCE ON ASSET PRICESIt is common knowledge the business cycle moves through stages in which it expands, contracts and recovers and these stages have an influence on asset prices. During the early stage of a recovery, stocks and commodity prices tend to rise and during a contraction stock prices tend to weaken while bond prices strengthen.

The returns of equity sectors are also sensitive to the business cycle. Industrials and financials tend to strengthen during the early stage of a recovery while technology stocks begin to shine as the business cycle moves into an expansion.

Finally, the business cycle has demonstrably impacted risk premia for factors such as size, value, momentum and quality. For example, value stocks do particularly well in periods of recovery and less well during contractions. These relationships, of course, do not always hold and vary across markets.

In addition, there is the complexity of identifying the current state of the economy as the stages of the business cycle are better understood in hindsight. This article specifically focuses on a less explored segment of the market, looking at the variability of risk premia across the business cycle in emerging markets (EM). ANALYSIS DESCRIPTIONSix EM equity factors were analysed over the period 2007 to 2020 when they had full return histories including market, size, style, quality, momentum, dividend yield and low volatility. Macroeconomic variables were selected to represent the business cycle including default

spread, term spread, short rate and inflation. The sensitivities of each EM equity factor to these business cycle variables was estimated. We also looked at indices from different providers and found that differences in index methodologies can lead to differences in business cycle sensitivities even for the same risk factor. A LOOK ACROSS EM RISK FACTORSThe table below shows the latest business cycle sensitivities for twelve EM factors at 31 January 2020. The values can be thought of as the macroeconomic betas for the EM equity risk factors. The most meaningful comparison is for each business cycle variable column. MSCI indices were used for the above factor analysis as they had the most complete factor range with the longest performance histories

The results show that the majority of EM risk factors have different business cycle

BUSINESS CYCLE SENSITIVITIESDefault Spread

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Market -0.061 0.238 -3.802 -0.584 -0.684 -0.033Large Cap -0.136 0.179 -3.721 -0.524 -0.675 -0.033Small Cap 0.408 0.872 -5.400 -0.999 -0.798 -0.044Growth -0.756 0.103 -4.023 -0.228 -0.717 -0.035Value 0.656 0.372 -3.574 -0.958 -0.651 -0.031Quality -0.384 0.262 -3.830 -0.561 -0.665 -0.030Dividend Growth 1.094 0.482 -3.619 -1.482 -0.300 -0.024Dividend Yield 0.714 0.242 -3.634 -1.136 -0.489 -0.029Minimum Volatility 0.073 0.126 -2.795 -1.018 -0.379 -0.027Momentum -1.947 0.040 -3.279 0.324 -0.650 -0.033MSCI Indices were used for the above factor analysis as they had the most complete factor range with the longest performance histories

sensitivities with the exception of the market and large-cap risk factors which have low tracking error due to the market cap-weighting of both these indices.

The sensitivities are a useful guide for how securities are expected to perform in certain macroeconomic environments. As an example, if default spreads were to widen due to a credit crisis or general deterioration in economic conditions then having EM exposures with a positive sensitivity to the default spread such as the dividend and value factors would do better in relative terms than growth exposure, which is likely to underperform due to its negative sensitivity to this macroeconomic variable.

This is exactly what happened during 2007-2008. The US default spread (Baa-Aaa corporate yields) rose from 92 basis points in January 2007 to 3.37% by December 2008

and dividend and value factors outperformed growth (see graph above).

If an investor assumes that default spreads will continue their tightening trend of the past decade, then the case for holding growth and momentum factor exposures is strong. Rising inflation tends to have a negative impact on long duration and risk assets like equities. In a period of rising inflation, the EM equity risk factors with the lowest sensitivity to inflation are dividend growth and minimum volatility whereas small cap and growth would be expected to do better during periods of falling inflation due to their higher negative sensitivity.

This was the case in 2009 when inflation growth turned negative. Understanding EM equity risk factor sensitivities to the business cycle and a ‘house view’ of current business conditions or how they are likely to change, can be helpful in determining which EM risk factors are more or less attractive to hold.

SAME RISK FACTORS FROM DIFFERENT INDEX PROVIDERS SHOW DIFFERENCES IN BUSINESS CYCLE SENSITIVITIESMany index providers publish factor indices for the same category and ETF sponsors choose which reference index to use to provide investors with investable exposures. The table below left shows that even within the same category, the EM factor indices have different business cycle sensitivities. This is understandable given differences in methodologies but it does mean that one should expect different performance across the business cycle as macroeconomic conditions change.

For example, S&P EM Small Cap index has a lower (positive) sensitivity to the term spread than

ETFSTREAM.COM


20 BEYOND BETA Q1 2020

Understanding EM equity risk factor sensitivities to the business cycle and a ‘house view’ of current business conditions or how they are likely to change, can be helpful in determining which EM risk factors are more or less attractive to hold

CUMULATIVE PERFORMANCE 2007-2008 WHEN THE DEFAULT SPREAD WIDENED

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MSCI Emerging Markets -0.061 0.238 -3.802 -0.584 -0.684 -0.033FTSE Emerging Markets 0.145 0.135 -3.970 -0.846 -0.711 -033S&P Emerging Markets BMI 0.071 0.234 -4.002 -0.845 -0.704 -0.033

MSCI EM Small Cap 0.408 0.872 -5.400 -0.999 -0.798 -0.044S&P EM Small Cap 0.384 0.515 -5.638 -1.600 -0.809 -0.037

MSCI EM Value 0.656 0.372 -3.574 -0.958 -0.651 -0.031FTSE EM Value Factor 0.449 0.075 -4.014 -1.062 -0.687 -0.039

MSCI EM Quality -0.384 0.262 -3.830 -0.561 -0.665 -0.030FTSE EM Quality Factor 0.306 0.020 -3.775 -1.053 -0.651 -0.030

MSCI EM Min Volatility 0.073 0.126 -2.795 -1.018 -0.379 -0.027S&P BMI EM Low Volatility -0.024 0.106 -2.386 -0.899 -0.145 -0.029

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MSCI EM Small Cap index so if the term spread tightened (or went negative) as it did recently, you would expect the S&P EM Small Cap index to perform better and it did.

In 2019, the US Term Spread (10 year minus 3-month yield) tightened 73 basis points between January and August and a global measure of the term spread using 10 year minus 1-year government bond yields was negative throughout the year.

During 2019, the S&P EM Small Cap index outperformed the MSCI EM Small Cap index by 5.40%. It also has a lower (negative) sensitivity to VIX so if VIX were to spike dramatically, you would see less of an impact on the S&P EM Small Cap index. If these macroeconomic variables moved the other way then the MSCI EM Small Cap would benefit more.

Moving onto to the EM Quality factor, we can see that the FTSE EM Quality Factor index has a positive sensitivity to the default spread whereas the MSCI EM Quality index has a negative sensitivity.

If default spreads were to increase then the FTSE EM Quality Factor index would benefit whereas the MSCI EM Quality index would be negatively impacted. This happened in 2018 when a measure of the global default spread (intermediate corp Baa minus treasury yield) rose 76 basis points between January and December.

During this period, the FTSE EM Quality index outperformed the MSCI EM Quality index by 4.82%. If default spreads were to tighten, all else being equal, then MSCI EM Quality is better positioned. You can see evidence of this between

2009 to 2013 as credit conditions improved, the MSCI EM Quality index outperformed.

SENSITIVITIES CHANGE OVER TIMEIn order to consider how to best select and combine EM equity factor exposures, one must have a methodology (or system) which is able to evaluate the combinations of business cycle sensitivities simultaneously.

Additionally, one must take into account that the EM equity risk factors sensitivity to different macroeconomic conditions will change across the business cycle just as it is true that the EM equity factor indices have different sector exposures which may also change through time.

If we return to the comparison between the MSCI EM Quality and FTSE EM Quality Factor indices, we mentioned that the MSCI EM Quality index had a negative sensitivity and the FTSE EM Quality Factor index a positive sensitivity to the default spread as at January.

The chart below shows how the sensitivity of these two indices to the default spread has changed over time. We can see that understanding the sensitivity of EM equity risk factors to the business cycle can add substantial value when evaluating how these factors are likely to be impacted by changing macroeconomic conditions. It is also true that when considering how to combine different factor exposures, it is useful to have a methodology (and system) that is able to consider the impact of a range of macroeconomic conditions simultaneously while taking into account that these sensitivities will change over time.


Steven Goldin is the managing partner at Parala Capital, a London-based quantitative investment advisory firm with over $2 billion in assets under advisement. Earlier in his career, he worked at S&P Global, acting as their global head of strategy indices, and at Prudential, as the head of quantitative analytics.

CHANGING SENSITIVITY OF MSCI EM QUALITY AND FTSE EM QUALITY FACTOR INDICES TO THE DEFAULT SPREAD


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Investors are increasingly allocating to emerging market debt, attracted by the additional portfolio diversification that the asset class provides as well as higher yields on

offer compared to developed market bonds. In 2019, hard currency emerging market debt was one of the best performing fixed income asset classes, with a traditional benchmark, the JP Morgan EMBI Global Diversified index, returning 15% over the year.

Yet even in a bull market, emerging market debt investors face many of the same challenges and concerns that are present throughout the cycle – namely country-specific tail risk and changes in portfolio exposures that are out of their control. As an example, over the past year, countries such as Argentina, Venezuela, and Lebanon have experienced challenges, with debt

issued by these countries falling more than 50% over certain periods, even as the broader asset class has been positive.

Here we examine systematic ways to address these challenges. The research outlined underpins the development of a proprietary “smart beta” index – the JP Morgan Emerging Market Risk Aware index (the EMRA index) – which is tracked by JP Morgan Asset Management’s USD Emerging Markets Sovereign Bond UCITS ETF (JPMB). In developing this index, rather than simply weighting constituents by debt-outstanding, we instead considered the investor’s experience and sought to address many of the key considerations most relevant to them – country-specific risk, credit exposure, and liquidity – while providing a core exposure to the asset class.

ADDRESSING COUNTRY-SPECIFIC RISKWhen investing in emerging market debt, drawdown and tail risk can at times be substantial. These risks include isolated country defaults as well as more systemic crises, where some countries significantly underperform.

In Chart 1, we compare the historical spread-to-worst of the broad EMBI Global Diversified index with spreads for individual countries during times of stress. As shown, during episodes like the Argentinian default in 2001, the Ukrainian debt crisis in 2015, and the Venezuelan crises in 2017 and 2019, spreads on debt issued by individual countries can widen significantly and often abruptly, leading to a significant increase in volatility for investors. To address this challenge, we consider a quantitative


Why smart beta can produce fruitful results with emerging market debtInvesting in traditional debt-weighted fixed income indices comes with challenges, which can be particularly pronounced in emerging markets. Katherine Magee, investment specialist, Quantitative Beta Solutions, at JP Morgan Asset Management, explains how a smart beta approach can address these challenges

Countries such as Argentina have experienced challenges over the past year, with debt issued falling more than 50% over certain periods

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risk filter. We begin with the EMBI Global Diversified index, a traditional and widely tracked USD-denominated sovereign debt index. Emerging market countries within that index are then ranked according to their relative risk level and the riskiest 10% of the index by market cap is discarded.

In determining relative risk, we use duration-times-spread (DTS) as a metric, which has a number of benefits, first:• It incorporates both the country’s spread as well as

its sensitivity to changes in spread.• It is a good forward-looking measure, forecasting

ex-ante spread volatility and identifying the highest risk countries based on both volatility and tail risk.

• Using DTS rather than a more momentum-based measure ensures that turnover is contained, thereby limiting transaction costs that would be incurred by the end investor and could be significant in emerging markets.

To illustrate the benefit of this risk filter in practice, Chart 2 shows the cumulative return of a portfolio invested in the 10% of market cap of the highest risk countries – those countries with the highest DTS. The portfolio is weighted by debt-outstanding and rebalanced semi-annually. We then compare this portfolio to the returns of the J.P. Morgan EMRA index. As shown, while the overall returns are similar, the volatility of the highest risk countries is nearly three times as high.

RECENT RESULTS: COUNTRIES IN CRISISIn 2019 and early 2020, even as the broad emerging market debt universe has rallied, this quantitative risk filter has added value. We examine two specific examples: Argentina and Lebanon.

Argentina was a relatively dominant story in emerging markets in 2019. As shown in Chart 3, from January through to July, the country’s debt returned 12.5%, roughly in line with the broader index. Yet in August, following the surprising scale of the defeat of incumbent president Mauricio Macri in the first round of Argentina’s presidential election, the country’s debt lost more than half its value in one month alone.

At the beginning of August, Argentina made up 2.3% of the traditional EMBI Diversified index, but thanks to the risk filter was not held by the EMRA index and was therefore also not held in the JPMB portfolio. Simply avoiding an allocation to Argentina led to a strong outperformance at the index level and a significant reduction in volatility.

Lebanon is another recent example of where the risk filter has been able to add value. The country


CHART 1: INDEX RISK VERSUS INDIVIDUAL COUNTRIES AT TIMES OF STRESS

CHART 2: IMPACT OF COUNTRY RISK FILTER: GROWTH OF 100

CHART 3: ARGENTINA SOVEREIGN DEBT: CUMULATIVE RETURN

Source: J.P. Morgan Asset Management, Bloomberg. JPMorgan EMBI Global Diversified Index as at 31 December 2019

Source: J.P. Morgan Asset Management, as at 31 December 2019. Past performance is not a reliable indicator of current and future results.

Source: J.P. Morgan Asset Management, as at 31 December 2019. “Countries Removed by Risk Filter” is comprised of countries removed via the risk screen in the EM Risk-Aware Index. They are cap weighted to illustrate returns and risk. Index inception date: 31 December 2009. Indices do not include fees or operating expenses. Past performance is not a reliable indicator of current and future results.

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FACTORS IN FOCUSJ.P.MORGAN ASSET MANAGEMENT

is undergoing its worst economic crisis in decades, has experienced significant anti-government protests, and is currently seeking assistance from the International Monetary Fund to restructure its debt. While Lebanon was held in the JPMB portfolio throughout 2018, the country was removed by the quantitative risk filter in March 2019. Since then, its debt has fallen more than 50%, as shown in Chart 4.

The exclusions of Argentina and Lebanon, as well as other crisis countries such as Venezuela, are strong illustrations of how the strategy is able to avoid some of the idiosyncratic issues associated with the highest risk countries, and thus reduce overall portfolio volatility for our clients.

ADDRESSING CREDIT RISKAnother challenge of investing in traditional, debt-weighted indices is that investors’ exposure is driven entirely by debt issuance patterns, rather than a desired investment outcome. This can lead to unstable credit ratings, unwanted interest rate sensitivity, or concentrations in areas of the market that are under-rewarded – simply because certain countries issue more or less debt.

Chart 4 illustrates this challenge in the hard currency emerging market debt market. In 2008, roughly 65% of the traditional J.P. Morgan EMBI Global Diversified index was rated high yield. Fast forward to today and about half the index is investment grade. This variation in credit rating has been entirely driven by debt issuance patterns and is out of the control of traditional passive investors.

To help to manage these fluctuations in credit exposure, we consider a credit stabilisation approach. After removing the highest risk countries, we then re-weight the index toward higher quality high yield issuers, seeking to maintain a consistent 75% risk contribution from high yield bonds and a 25% risk contribution from investment grade bonds.This approach leads to a number of benefits:• It provides investors with a more thoughtful and

consistent exposure to credit and duration• It aligns risk exposure to higher quality high

yield, an area of the market where investors have historically been more compensated

• It provides a yield that is similar to a traditional index. While removing the highest-risk countries improves an investor’s volatility, a standalone quality filter also reduces the strategy’s headline yield. This credit stabilisation step can help to enhance the yield profile.

The impact of this step is illustrated in Chart 6, which shows that the EMRA index is underweight


CHART 4: LEBANON SOVEREIGN DEBT: CUMULATIVE RETURN

CHART 5: HISTORICAL CREDIT RATING BREAKDOWN: J.P. MORGAN EMBI GLOBAL DIVERSIFIED INDEX

CHART 6: CREDIT QUALITY BREAKDOWN

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Lebanon Removed from JPMB

31 Mar 2019 to 20 Feb 2020:

-58.5%

Source: J.P. Morgan Asset Management, as at 20 February 2020. Past performance is not a reliable indicator of current and future results.

Source: J.P. Morgan as at 31 December 2019

Source: J.P. Morgan Asset Management as at 30 September 2019

120

110

100

90

80

70

60

50

40

30

100%

80%

60%

40%

20%

0%

06/18 08/18 10/18 12/18 02/19 04/19 06/19 08/19 10/19 12/19

AA A BBB BB B CCC and below

NR

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20190%

20%

40%

60%

80%

100%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

CCC or Below B BB BBB A AAn CCC or Below n B n B n BBB n A n AA

n JPM EM Risk Aware Bond Index n JPM EMBI Diversified Index

0%

5%

10%

15%

20%

25%

30%

35%

40%

AA A BBB BB B CCC andBelow

NR

JPM EM Risk Aware Bond Index JPM EMBI Diversified Index

40%

35%

30%

25%

20%

15%

10%

5%

0%


issuers rated CCC and below (the highest risk area of the market), which allows us to take more risk in higher quality high yield names rated BB or B.

RECENT IMPLICATIONS: GULF COUNTRIESOver the course of 2019, five new countries from the Gulf region – Saudi Arabia, Qatar, The United Arab Emirates, Bahrain, and Kuwait – were added to the EMBIG universes and made 13% of the standard J.P. Morgan EMBI Diversified index at the end of the year. Inclusion of these countries has tilted the EMBI Global Diversified index more towards investment grade, lowering its yield and giving investors more duration exposure.

There are many reasons for including these countries, whose share of debt has increased significantly over the last three years. That said, this change still has an important impact on investor outcome and the type of risk to which they are exposed. While investors have historically considered emerging market debt as a high yield asset class, the make-up of the market has changed over time, based purely on issuance patterns and methodology changes that are completely out of the hands of the investor. In JPMB, we can be more thoughtful.

BRINGING IT ALL TOGETHERAs outlined, gaining exposure to emerging market debt through a traditional passive index fund can be challenging. In designing the JPMB strategy, we focus on the investment outcome and use a unique process to address investors’ primary considerations: generating yield and return while avoiding country-specific tail events. The end result is a core exposure to the hard currency sovereign emerging market debt asset class (the proprietary JP Morgan EMRA index

has a historical tracking error to a traditional index in the range of 1%-1.2%), with a similar level of yield but the potential for better risk-adjusted returns. Chart 7 shows returns of the index tracked by JPMB which has successfully provided a core exposure to the asset class while generating an improved risk-adjusted return compared to a traditional index.

CONCLUSIONWhile there are benefits of traditional debt-weighted investing, a number of challenges remain. The JPM USD Emerging Markets Sovereign Bond UCITS ETF (JPMB) seeks to address some of these challenges – namely country-specific risk, credit exposure, and liquidity – in a systematic and rules-based way to provide a more thoughtful exposure to USD-denominated emerging market debt.

INVESTMENT OBJECTIVEThe Fund aims to provide an exposure to the performance of bonds issued by the governments or quasi-government entities of emerging markets countries globally which are denominated in US Dollars.RISK PROFILE• The value of your investment may fall as well as rise and you may get back less than you originally invested.• To the extent that the Sub-Fund uses financial derivative instruments, the risk profile and the volatility of the Sub-Fund

may increase. That notwithstanding, the risk profile of the Sub-Fund is not expected to significantly deviate from that of the Index as a result of its use of financial derivative instruments.

• The value of debt securities may change significantly depending on economic and interest rate conditions as well as the credit worthiness of the issuer. These risks are typically increased for below investment grade debt securities which may also be subject to higher volatility and lower liquidity than investment grade debt securities. The credit worthiness of unrated debt securities is not measured by reference to an independent credit rating agency.

• Emerging markets may be subject to increased political, regulatory and economic instability, less developed custody and settlement practices, poor transparency and greater financial risks. Emerging market currencies may be subject to volatile price movements. Emerging market and below investment grade debt securities may also be subject to higher volatility and lower liquidity than non-emerging market and investment grade debt securities respectively.

• The Sub-Fund is not expected to track the performance of the Index at all times with perfect accuracy. The Sub-Fund is, however, expected to provide investment results that, before expenses, generally correspond to the price and yield performance of the Index.


Katherine Magee is Investment Specialist, Beta StrategiesJ.P. Morgan Asset Management

CHART 7: YEAR BY YEAR PERFORMANCE OF SMART BETA INDEX

-10%

-5%

0%

5%

10%

15%

20%

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 YTD2020

JPM EM Risk Aware Bond Index JPM EMBI Diversified IndexSource: J.P. Morgan Asset Management, Bloomberg. As at 31 January 2020. Index inception date: 31 December 2009. Indices do not include fees or operating expenses. Past performance is not a reliable indicator of current and future results.

20%

15%

10%

5%

0%

-5%

-10%2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

YTDn JPM EM Risk Aware Bond Index n JPM EMBI Diversified Index


Over the past months, I completed a tedious study involving about six million data points to answer a simple question that many of our clients have asked. Do

ETF flows matter for short-term performance? ETFs are bought (and sold) because they provide exposure to broad market movements. But could the reverse be true? Do large ETF outflows create disruptions in the price of their underlying baskets? Does the tail sometimes wag the dog?

The first part will present the data and outline a first important finding: ETF flows are more than just random noise. ETF flows display serial correlation, i.e. large outflow days are likely to be followed by more selling, even though performance tends to rebound the day following large redemptions.

The second part will focus on the impact of large redemptions on the normal arbitrage relation between ETFs’ market price and the net asset value of their assets. Large outflows do not cause abnormal deviations from net asset value for the largest equity and bond funds, including HYG (iShares iBoxx High Yield Corporate Bond ETF).

However, large outflows can lead to significant discounts-to-NAV for smaller high-yield bond funds, leveraged loan ETFs, high-yield muni ETFs and emerging markets debt ETFs. Asia equity ETFs trade at very wide discounts to their NAVs on large outflow days, but the mispricing may partly be

explained by time zone differences and the use of stale data in NAV calculations.

The third part will show that large redemptions for smaller emerging markets funds, where dealers cannot properly hedge their exposure due to liquidity issues or trading constraints, lead to abnormally large losses and discounts to NAVs. These effects are typically reversed the following day, rewarding the brave investors who are willing to assume risk when the ETF crowd bails out.

OVERVIEW OF ETF PANIC ATTACKSI studied daily flows, assets, prices, and net asset value for the 1,000 largest US-listed ETFs since January 2014, or about six million data points. I defined a big outflow as a net redemption of more than 2% of outstanding assets, which corresponds to a sale of $6.2 billion for the largest ETF (SPY) and about $4.5 million for a relatively small ETF like EPHE (iShares MSCI Philippines ETF).

A major issue with ETF flows is that all outflows do not correspond to actual selling. For example, big US equity ETFs, such as SPY, IWM, QQQ, and DIA, are often used to temporarily park money when futures and options contracts are rolled over. Rebalancing can also lead to large technical creations or redemptions. Typically, these flows are reversed within a few days. Because these technical redemptions create a lot of white noise in the flow time series, I only considered outflows


ETF Panic AttackVincent Deluard, global macro strategist at INTL FCStone, studies six million data points to answer the simple question of an important topic that receives relatively little attention, whether ETF flows impact short-term performance

CHART 1: FREQUENCY OF LARGE OUTFLOWS FOR THE LARGEST U.S. EQUITY ETFS

350,000

300,000

250,000

200,000

150,000

100,000

50,000

0

80

70

60

50

40

30

20

10

0

n Total Assets (LS) n Count of Big Outlows since 2014 (RS)

44,082

88,912

308,832

204,022

52,221 49,253 54,829

132,932

50,454 46,843

140,990

67

43

32

8 7 6 3 2 00 0-

IWM QQQ SPYI VV IJH VUGV TV VOOI WF IJR VTI

Vincent Deluard is the global macro strategist for INTL FCStone, where he authors weekly commentary on global macro topics and advises pension funds on asset allocation. Prior to joining INTL FCStone, Vincent served as Europe strategist for Ned Davis Research where he created the firm’s Europe product.

which were not preceded or followed by a large creation in the same week.

Large redemptions are generally more frequent among the largest, most active ETFs although some very large ETFs never experience large redemptions. For example, the third largest ETF in the world, VTI (Vanguard Total Stock Market ETF) never experienced an outflow of more than 2% of its assets (see graph 1).

Studying the relation between flows and returns is a bit of chicken and egg conundrum. Investors might sell because of market losses, and their sales might cause the underlying basket to lose value. Indeed, average ETF performance has been negative on big outflow days for international and emerging markets ETFs. That effect is not observed among US equity ETFs, partly because of their greater liquidity and the prevalence of technical/arbitrage-driven trading (see graph 2).

Looking at flows the day following large redemptions may help disentangle the feedback loop between flows and returns. In general, outflows tend to carry over the following day. For example, the average Europe equity ETF has lost an additional 0.7% of its assets after days when it had posted redemptions of more than 2% of its assets. Outflows from US equity ETFs are much less persistent, which is consistent with the fact that flows are primarily driven by arbitrage and technical factors (see graph 3).

The persistence of outflow cannot be explained by serial correlation in funds’ returns because prices tend to bounce back after large outflow days. This effect is particularly strong among emerging markets funds, which usually have greater liquidity constraints. Intuitively, it is easy to see how a bad day for emerging markets leads to large ETF redemptions, which further accentuate losses, resulting in positive simultaneous correlation between flows and returns.

Selling may carry over to the next day after investment committee meetings are completed in the morning. However, prices tend to recover as the supply and demand imbalance created by abnormal selling flow gets digested by market participants, resulting in a negative next day correlation between flows and returns. Looking at the relation between ETF prices and their net asset values should help us better understand this relation, which we shall do now (see graph 4).

DO BIG ETF OUTFLOWS CREATE DISCOUNTS TO NAVS?As expected, the largest US equity ETFs barely diverge from their net asset values, even on big outflow days. Large outflows do tend to coincide with


CHART 2: AVERAGE EQUITY ETF PERFORMANCE ON BIG OUTFLOW DAYS

CHART 4: AVERAGE RETURN OF EQUITY ETFS THE DAY AFTER BIG OUTFLOWS

CHART 3: AVERAGE FLOW/ASSETS OF EQUITY ETFS THE DAY AFTER BIG OUTFLOWS

FACTORS IN FOCUSVINCENT DELUARD

n Since 2014 n Past year



-0.06%

-0.14%

-0.01%

-0.28%

0.02%

-0.19%

-0.09%

-0.32%-0.35%

-0.30%

-0.25%

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

-0.36%

-0.51%

-0.74%

-0.52%

-0.26%

-0.42%

-1.09%

-0.59%

-1.20%

-1.00%

-0.80%

-0.60%

-0.40%

-0.20%

0.00%U.S.

U.S.

U.S.

Developed International



European Region

European Region

European Region

Emerging Markets

Emerging Markets

Emerging Markets

0.07%0.05%

0.04%

0.19%

0.12%

-0.08%

-0.16%

0.09%

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

significant price decline for IWM, SPY, and IVV but well-oiled arbitrage mechanisms ensure that these funds always trade within a few basis points of their net asset values (see graph 5).

Fixed income ETFs are often seen as more vulnerable to liquidity events because of the fragmented structure of bond trading. However, most of the large bond funds stood the tests of large outflows. The largest bond ETFs have tended to trade at a small premium to their net asset values on large outflow days. This would be expected of funds which hold highly liquid Treasury bonds and notes, such as AGG or TLT, or high-grade corporate bonds such

LQD and VCSH. HYG (iShares iBoxx High Yield Corporate Bond ETF) has also been tested by large redemptions, losing more than 2% of its assets on 35 days in the past two years, but the fund kept trading in line with its net asset value (see graph 6).

However, not every high-yield ETF is immune to the effect of large outflows. For example, ANGL, which invests in “fallen angel” bonds, has traded at an average discount-to-NAV of 45 basis points on big outflow days. Smaller municipal bond funds, loan funds, and emerging markets bond ETFs also deviate significantly from their net asset values on heavy redemption days (see graph 7).



CHART 5: AVERAGE DISCOUNT AND PERFORMANCE ON BIG OUTFLOW DAYS FOR THE LARGEST U.S. EQUITY ETFS

CHART 6: AVERAGE DISCOUNT AND PERFORMANCE ON BIG OUTFLOW DAYS FOR THE LARGEST U.S. BOND ETFS

n Average Discount to NAV on Outflow Day n Average Return on Outflow Day


Source: Bloomberg

Source: Bloomberg

0.00%

-0.01% 0.00% -0.02%

0.01% 0.01% 0.00%0.07%

0.00% 0.01%

-0.69%

0.22%

-0.21%

-0.37%

0.21%

0.00%

-0.61%

-0.46%

-0.09%

-0.31%

-0.80%

-0.70%

-0.60%

-0.50%

-0.40%

-0.30%

-0.20%

-0.10%

0.00%

0.10%

0.20%

0.30%

IWMQ QQ SPY XLK VO VB IVVI JH VIGV UG

-0.02%

0.15%0.19%

0.05% 0.06% 0.02% 0.04%0.06%

0.01% 0.01% 0.02% 0.03%

-0.27%

-0.02%

-0.23%

-0.17%

0.03% 0.02%

-0.04%

0.20%

0.00%

0.29%

0.08%

-0.22%-0.30%

-0.20%

-0.10%

0.00%

0.10%

0.20%

0.30%

0.40%

AGG LQD VCI BND VCSH MBB BSV TIP SHV TLT IEF HYG

Massive redemptions are also associated with large discount-to-NAVs for most emerging markets equity funds. The effect is strongest for Asia-oriented funds. For example, EWY (iShares MSCI Korea) and EWT (iShares MSCI Taiwan) have experienced discounts-to-NAV of 2.0% and 1.3%, respectively, around large redemption days.

Part of this effect may be explained by time zone differences, rather than a deficiency of ETFs arbitrage mechanism. The Korean market is not open when the New York Stock Exchange closes, which means that the prices used to compute net asset value for locally traded securities are carried

over from the Korean close the prior day. Since large redemptions usually happen on down days, traders and market-makers of Asian ETFs adjust their quotes



CHART 7: AVERAGE PERFORMANCE AND DISCOUNT ON BIG OUTFLOW DAYS FOR THE LARGE BOND ETFS

CHART 8: AVERAGE PERFORMANCE AND DISCOUNT TO NAV ON BIG OUTFLOW DAYS OF LARGE EM ETFSSource: Bloomberg

Source: Bloomberg

-0.70%

-0.41% -0.40%

-0.70%-0.78%

-0.32% -0.31% -0.30% -0.30% -0.28%

-0.16%

-0.05% -0.08%

0.03%

-0.14%

0.11%

0.00%

-0.11%

0.23%

-0.06%

-1.00%

-0.80%

-0.60%

-0.40%

-0.20%

0.00%

0.20%

0.40%

ANG HYM BKL SHY MLN PGH EBN SRL CEM EML


n Average Discount to NAV n Average Return on Outflow Day

-0.93%

-0.38%

-1.99%

-0.71%

-0.38%

-1.21%

-0.59%

-1.27%

-0.37%

-0.71%

-0.99%

-0.21%

-1.20%

-0.59%-0.48%

-1.12%

-0.18%

-1.60%

-0.27%

-0.60%

-2.50%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

EEMM CH EWY FXI EWA AAX EWJE WT ASHE WH

Fixed income ETFs are often seen as more vulnerable to liquidity events because of the fragmented structure of bond trading. However, most of the large bond funds stood the tests of large outflows

based on prices observed during US market hours, leading to significant deviations between quoted prices and stale net asset values (see graph 8).

That being said, liquidity issues may be an aggravating factor. Latin American ETFs also

tend to trade at abnormally large discount to net asset value on big outflow days, even though most local bourses’ trading hours greatly overlap with those of the major US exchanges. For example, ECH (iShares MSCI Chile ETF) has traded at an average discount of 46 basis points to its net asset value on the 35 days when it experienced outflows of more than 2% of its assets. The fact the fund also experienced heavy losses on these days (78 basis point) and a mean-reversion bounce of 32 basis points the next day is consistent with the hypothesis that large ETF outflows cause temporary disruptions in the local market.

Of course, this does not mean that investors are able to pocket the entire bounce because market-

ETFSTREAM.COM


CHART 9: AVERAGE PERFORMANCE AND DISCOUNT TO NAV ON BIG OUTFLOW DAYS OF LARGE EM ETFS

CHART 10: RELATION BETWEEN FLOW AND RETURN FOR SPY

n Average Discount to NAV on Outflow Day n Average Return on Outflow Day n Average Return the Day after the Outflow Day

Source: Bloomberg

Source: Bloomberg

-0.17% -0.13%

-0.46%

-0.24%

-0.86%-0.78%

0.18%

0.05%

0.32%

-1.00%

-0.80%

-0.60%

-0.40%

-0.20%

0.00%

0.20%

0.40%

ILFE WW ECH

y = 0.0666x + 0.0003

-6%

-4%

-2%

0%

2%

4%

6%

-6% -4% -2% 0% 2% 4% 6% 8%

SPY

Retu

rn th

e N

ext D

ay

Flow as a % of Assets

Latin American ETFs also tend to trade at abnormally large discount to net asset value on big outflow days, even though most local bourses’ trading hours greatly overlap with those of the major US exchanges


makers may increase their spreads when they are overwhelmed with sell orders that they cannot easily hedge. For example, it is quite risky for market makers to take large sell-on-close orders in New York when the Santiago Stock Exchange is closed and the local market may not offer sufficient liquidity to execute the order the next day (see graph 9).

DO BIG ETF OUTFLOWS CREATE A BUYING OPPORTUNITY THE NEXT DAY?Investors should not expect to get rich simply by counting ETF shares. For most days and most funds, flows-based short-term trading strategies would have the same probability of success as a coin flip. As shown in the chart below, flows into SPY explain less than 1% of its performance the next day. Taking the other side of the ETF crowd offers no service for highly liquid funds with well-oiled arbitrage mechanisms (see graph 10).

This is not the case for smaller funds where the underlying basket cannot easily be traded due to time zone constraints, different settlement rules, political risk, and shallow liquidity. In a typical emerging market panic attack, bad news triggers waves of sell orders. Market makers and brokers may not be able to hedge their books, especially if the local market is closed or if circuit breakers constrain trading. Spreads usually widen to compensate for the

risk of holding unhedged shares overnight. In this case, contrarian investors who take the other side of the ETF trade do provide liquidity to the market. This service tends to be compensated: most small emerging markets ETFs tend to rebound by 30 to 70 basis points the day following abnormal outflows. Investors may not be able to capture the full rebound because wider-than-usual spreads will shrink their expected gains.

In addition, these gains are not without risk: investors are compensated for taking on risks that ETF sellers are no long willing to assume, i.e. buying Chilean stocks when riots erupt, buying Chinese stocks after threatening tweets from Trump, etc. But at least the strategy offers a positive trade-off between risk and reward, in a world where so many assets offer return-free risk and most safe assets trade at negative yields (see graph 10).



CHART 11: AVERAGE PERFORMANCE THE DAY FOLLOWING BIG OUTFLOW DAYS OF EM ETFSSource: Bloomberg

n Average performance the day after large outflow (LS) n Fund Assets (RS)

1.14%

0.74%0.67%

0.60%

0.53% 0.51%

0.43% 0.40%0.44%

0.38%0.34% 0.32% 0.31% 0.32% 0.31%

2.21.2

4.6 3.7

28.9

10.4

0.6 0.5 0.4 0.2 0.8 0.5 0.3 0.5 0.5 0

5

10

15

20

25

30

35

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

EPP9 cases

RSX35 cases

MCHI15 cases

SPEM16 cases

EEM26 cases

EWZ20 cases

KBA30 cases

EIDO81 cases

EDIV13 cases

ECON17 cases

INDY15 cases

EWX24 cases

SMIN19 cases

ECH68 cases

EWM44 cases

Investors should not expect to get rich simply by counting ETF shares. For most days and most funds, flows-based short-term trading strategies would have the same probability of success as a coin flip

Emerging market debt: To hold, or not to hold?Nicolas Rabener, managing director of FactorResearch, studies the potential benefits of an allocation to emerging market debt in terms of both a performance and diversification perspective

Forecasting the short-term outlook for the S&P 500 is like predicting the weather in Scotland. The likelihood of getting it right is not particularly

high and anything can be expected. However, evaluating the long-term prospects of a stock market seems to be a function of its valuation. It is not a perfect relationship, but the higher the multiple on average, the lower the subsequent return over a 10-year time horizon.

In fixed income, forecasting returns is somewhat easier as the current yield is a significant indicator of the long-term total return. Stated differently, what you see is what you get. Unfortunately for pension funds, insurance companies, and savers in general, bond yields are currently negative or extremely low in most developed markets.

Investors seeking yield, diversification, or both, could turn towards emerging market (EM) bonds. The consensus is these offer higher expected returns given more risk than bonds

of developed markets, although capital market assumptions vary significantly across financial institutions. JP Morgan expects long-term returns of 5.3% for EM debt, compared with only 1.6% by GMO.

Unfortunately investing in emerging market debt is complicated as it is a complex asset class. Bonds are issued by countries and companies, either in local or hard currency, which is typically in the US dollar. The universe of available instruments includes mutual funds, ETFs, and single bonds.

Adventurous investors have provided financing for Mozambique’s fleet of tuna-fishing boats in 2016 and granted Argentina with capital for a century in 2017, shortly after the country emerged from a debt restructuring.

In this short research note, we will contrast emerging market government versus corporate debt and investigate if an allocation would have been accretive for a traditional US equity-bond portfolio in recent years.


GRAPH 1. EMERGING MARKET DEBT PERFORMANCE

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

2500

2000

1500

1000

500

0

n Government Debt (USD-Denominated) n Government Debt (Local Currency-Denominated) n Corporate Debt (USD-Denominated)

Nicolas Rabener is the managing director of FactorResearch, which provides quantitative solutions for factor investing. Previously he founded Jackdaw Capital, an award-winning quantitative investment manager focused on equity market neutral strategies. Rabener holds a Master of Finance from HHL Leipzig Graduate School of Management, is a CAIA charter holder and enjoys endurance sports.

Source: FactorResearch

EMERGING MARKET DEBT PERFORMANCEWe focus on emerging market bond ETFs issued in the US, which mostly track benchmarks such as the JP Morgan Emerging Markets Bond index. The ETFs have approximately $30bn assets under management (AUM) and charge on average 40 basis points of management fees per annum.

We create three equal-weighted indices that represent the three types of EM debt. Only EM government debt denominated in USD has a sufficiently long track record that covers a complete market cycle. We observe that these bonds had a 29% drawdown during the Global Financial Crisis from 2008 to 2009, which raises the question of the attractiveness of EM debt from a diversification perspective.

Contrasting government versus corporate debt shows that these generated similar performance from 2012 to 2020, if denominated in USD. Local currency-denominated government

debt performed significantly worse from 2013 onward, which can be explained by the strong performance of the USD against almost all other currencies (See graph 1 opposite).

EMERGING MARKET DEBT CHARACTERISTICSMost investors will be familiar with many of the constituents of the S&P 500. Stocks like Microsoft, General Electric, or Goldman Sachs are household names. However, there is likely less familiarity with countries such as Bahrain or Sri Lanka and companies like OCP or Banco de Bogota.

ETFSTREAM.COM

FACTORS IN FOCUSNICOLAS RABENER


Investing in emerging market debt is complicated as it is a complex asset class... The universe of available instruments includes mutual funds, ETFs, and single bonds

ETFSTREAM.COM

The easiest approach for investors to manage country or company risk is by diversifying. Aggregating the exposure to the top 10 countries reveals that USD-denominated EM government debt is significantly more diversified across countries than the local currency-denominated government or corporate debt, which likely contributed to the lower volatility in the period from 2012 to 2020 (See graph 2).

Despite USD-denominated EM government debt being less exposed to a few countries and exhibiting lower volatility, these bonds are not trading at lower yields that would indicate higher credit quality. The average credit rating for all three types of EM debt is BB, according to Morningstar.

EM corporate debt is currently offering the lowest yields, which indicates that investors have more faith in corporate than sovereign credit. However, it is worth noting that the EM corporate debt ETFs include some issuers from developed markets like Hong Kong, Singapore, and Taiwan, which represent less credit risk. Companies like the Singaporean telecom provider Singtel or property firm Global Logistic Properties operate in emerging markets but are not typically classified as emerging market companies (See graph 3).

EMERGING MARKET DEBT FOR DIVERSIFICATION?The analysis highlights that investors would have been better off buying USD-denominated EM debt in recent years, regardless if from governments or corporates. However, this perspective is backward-looking, and the USD might underperform going forward, which would make local currency-denominated bonds more attractive for US investors. Furthermore, yields and credit quality are comparable across the types of EM debt, which further adds to the complexity of evaluating EM bonds as an investment opportunity.

From a diversification perspective, investors should prefer strategies that feature low correlations to traditional assets. We calculate the correlations to the S&P 500, US investment grade bonds, and US high yield bonds in the period from 2012 to 2020. EM corporate debt was most attractive for diversification given the lowest correlations while EM government debt was highly correlated with US high yield bonds, which suggests investors view these as similar instruments (See graph 4).

Finally, we simulate how accretive a 10% allocation to EM debt would have been for a traditional US equity-bond portfolio from 2012



EM corporate debt is currently offering the lowest yields, which indicates that investors have more faith in corporate than sovereign credit. However, it is worth noting that the EM corporate debt ETFs include some issuers from developed markets like Hong Kong, Singapore, and Taiwan, which represent less credit risk

GRAPH 2. EMERGING MARKET DEBT: EXPOSURE TO TOP 10 COUNTRIES & ANNUALISED VOLATILITY

Government Debt (USD-Denominated)

Government Debt (Local Currency-Denominated)

Corporate Debt (USD-Denominated)

n Exposure to Top 10 Countries n Annualised Volatility


42.7%

69.7%

8.0%

55.2%

6.6%5.2%

GRAPH 3. EMERGING MARKET DEBT ETFS: YIELDS

Government Debt (USD-Denominated)

Government Debt (Local Currency-Denominated)

Corporate Debt (USD-Denominated)

n SEC Yield n Yield-to-Maturity


4.3% 4.4% 4.56%

3.6%4.04%

4.82%

to 2020. We observe the return decreased in all cases, which is simply explained by less exposure to the S&P 500, which outperformed almost all asset classes in recent years. However, the portfolio volatility decreased further, which results in slightly higher risk-adjusted returns when including EM debt.

We also provided a scenario with a 10% allocation to US high yield bonds given the high correlations to EM debt, which also would have increased risk-adjusted returns, but not reduced the maximum drawdown as significantly as EM debt. US high yield debt is more correlated to US equities and bonds than EM debt and therefore provided fewer diversification benefits (See graph 5).

The analysis can be challenged given that we mainly focused on the period from 2012 to 2020, which effectively represents a bull market in equities as well as bonds and therefore only a part of a complete market cycle. However, there is a longer data history available for USD-denominated EM government debt, which includes the global financial crisis from 2008 to 2009.

Evaluating the benefit of a 10% allocation to this type of EM debt highlights an increase in risk-return ratios from 0.90 to 0.96 for a traditional US equity-bond portfolio and a reduction of the maximum drawdown from 28.7% to 27.1%.

FURTHER THOUGHTSGiven high levels of public, corporate, and household debt, as well as poor demographics in most developed countries, inflation and economic growth is likely to remain low for the foreseeable future. It is challenging to see interest

rates and bond yields in developed markets rising significantly in such an environment, which should continue to keep the interest in high-yielding EM debt elevated.

An allocation to EM debt has been accretive for US investors and is likely superior to US high yield bonds given lower correlations to traditional assets. However, there are strategies like managed futures or liquid alternative products that offer higher diversification benefits.

It is also worth noting that many emerging markets feature the same structural issues as developed markets. For example, China, which features prominently in the EM debt ETFs, has one of the highest debt-to-GDP ratios globally as of today, but is expected to lose 400 million people until 2100. Paying interest and repaying debt will become challenging over time.

ETFSTREAM.COM



GRAPH 4. EMERGING MARKET DEBT: CORRELATIONS TO US EQUITIES & BONDS (2012-2020)

S&P 500 US Investment Grade Bonds US High Yield Bonds



n Government Debt (USD-Denominated) n Government Debt (Local Currency-Denominated) n Corporate Debt (USD-Denominated)

n CAGR n Annualised Volatility n Max Drawdown

0.38

9.0% 8.7% 8.3% 8.7% 8.7%6.4% 6.1% 6.3% 6.0% 6.3%

(10.6%) (9.8%) (9.7%)(9.7%) (10.2%)

0.45

0.12

0.32

0.13 0.17

0.56 0.53

0.20

GRAPH 5. ADDING 10% EMERGING MARKET DEBT TO A US TRADITIONAL PORTFOLIO (2012-2020)

Traditional Portfolio(S&P 500 & US IG Debt

(55%/45%))

Traditional & EM Gov Debt (USD-

Demoninated)

Traditional & EM Gov Debt (Local

Currency-Demoninated)

Traditional & EM Corp Debt (USD-

Demoninated)

Traditional & US High Yield Bonds

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Is an X-Factor driving stock returns?Gavin Smith, managing director and portfolio manager on the Quantitative Equity Team at QMA, examines the possibility of an unknown fundamental x-factor driving recent stock market returns


Over the last 18 months, we have seen value strategies struggle. Prices have continued to pull further and further away from fundamentals. This has been

particularly evident among speculative growth stocks. Prices for such stocks are now at extreme levels, as investors have become overly optimistic about their future growth potential.

A counterpoint is that speculative growth stocks are not actually expensive – they are priced correctly. In this argument, an unknown x-factor is responsible for driving the returns of speculative growth stocks.

Effectively, all fundamental valuation methodologies are flawed if they are not properly accounting for this x-factor (whatever it may be).

Perhaps this argument can be supported through the lens of what some call “weightless” firms, which are shifting away from physical capital investments to a capital-light model centred on investments in intangible assets, such as research and development, data, software, internet protocol, technology platforms and brand. Such intangibles are less likely to be fully reflected on the balance sheet. They are

FACTORS IN FOCUSGAVIN SMITHH

also somewhat less observable and harder to measure, yet critical to the success of a firm. Are such intangible assets examples of the unknown x-factor driving returns?

TESTING FOR THE X-FACTORIf intangible assets are, in fact, the x-factor responsible for pricing differences, we should be able to build valuation measures to capture them. One method is to integrate this presumed x-factor into valuations. Some academic studies have taken this approach. But we come at the problem from a different direction.

Since we are not sure what the x-factor really is, we would have to build all possible x-factors into our valuation model. Instead, we choose to evaluate the earnings growth an x-factor would be expected to deliver. Then we determine whether the results appear sensible. If earnings are expected to grow at unreasonable levels, it would be hard to justify the existence of an x-factor (or at least that the x-factor is motivated by fundamentals).

We utilise a discounted cash flow framework in our analysis. This assumes that the stock price accurately reflects its intrinsic value – basically, that the stock is priced right. We then specify earnings that grow at a constant rate (10 years, in our case) followed by a terminal value, all of which are discounted back to today’s values, using conservative firm specific discount rates. In this framework, we are solving for the market implied growth rate. We can then use the implied growth rate to examine the median growth rate for stocks we consider cheap and expensive (see graph 1).

RESULTSOur analysis shows that the median growth rate for expensive stocks is 25%. This means that median stocks would need to grow their earnings at 25% for ten years, in order to justify their current valuations. For instance, a company would be expected to grow $1 to $9.31 in 10 years’ time.

Frame this in terms of how much the median growth rate for expensive stocks exceeds GDP growth – and consider that the median firms are expected to deliver this rate of growth for 10 years. From an industry perspective: how could so many firms possibly grow at such extraordinary rates? Would not they compete against each other for market share? In contrast, the approximate implied growth rate for cheap stocks is a more reasonable 4%.

We believe that such a high implied growth rate for expensive stocks suggests that markets are


GRAPH 1: MARKET IMPLIED GROWTH RATES AS OF 9/30/2019

GRAPH 2: DISTRIBUTION OF MARKET IMPLIED GROWTH RATES AS OF 9/30/2019

GRAPH 3: MARKET IMPLIED GROWTH RATE SPREADS – (EXPENSIVE – CHEAP)

30%

20%

10%

0%Cheap Expensive

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

2011 2012 2013 2014 2015 2016 2017 2018 2019

Implied Growth Distribution

Implied Growth Rate

Num

ber o

f Sto

cks

Impl

ied

Gro

wth

Rat

e D

iffer

ence

FACTORS IN FOCUSGAVIN SMITH

Sources: QMA, FactSet, Compustat, FTSE Russell



100

80

60

40

20

0

25%

20%

15%

10%

5%

0%

n Cheap

n Expensive

reflecting overly optimistic expectations rather than some fundamental x-factor (see graph 2).

HISTORICAL PERSPECTIVETo help put the current implied growth rates into perspective, we examine the growth rates through time. We find that the current difference in implied growth rates between expensive and cheap stocks is at the highest level since the Global Financial Crisis (GFC). The difference in implied

growth rates is beyond 20%. That is extraordinary. This difference is most extreme at a time when concerns about global economic growth is at its highest (compared to recent years). Clearly, something is amiss (see graph 3).

ACTUAL VS IMPLIED GROWTH RATESMaybe the implied growth rates are not as unusual as they appear. We look at realized growth rates to see if companies have actually delivered this level of growth in the past. Over the last five years, expensive firms have delivered earnings growth of 12%. This is half of what is currently implied by their prices. Cheap stocks, by contrast, have delivered earnings growth of 5%, modestly above their implied rates (see graph 4). Interestingly, when we look at the distribution of growth rates, expensive stocks in the 75th percentile have delivered growth in the ballpark of 30%. So some firms do deliver growth close to what is currently implied. Remember though, that this is only at extreme levels, rather than the norm.

CONCLUSIONOur analysis shows that implied growth rates are at extreme levels, which median firms have not delivered throughout time. It is unlikely that prices and valuations are being driven by some fundamental x-factor.

We believe, rather, that prices are being driven largely by sentiment. Investor exuberance is driving price movements. Such a dislocation between prices and fundamentals can increase return potential for value investors. It can also set non-price conscious investors up for dramatic losses in the event of a sentiment crash.


Gavin Smith, PhD is a managing director and portfolio manager at QMA working within the Quantitative Equity team. In this capacity, he serves as a subject matter expert, and performs research and analysis for Quantitative Equity portfolios. Prior to joining QMA, Gavin led the North American Quantitative Research team at Macquarie Capital, where he was named a Rising Star for quantitative research in the Institutional Investor All-American Research Survey.

GRAPH 4: ACTUAL ANNUALIZED 5 YEAR NET INCOME GROWTH RATE AS OF 9/30/2019

40%

30%

20%

10%

0%

-10%

-20%

-30%

25th Percentile Median 75th Percentile

FACTORS IN FOCUSGAVIN SMITHH


n Cheap

n Expensive

60 seconds with the buy-side: What fund researchers look for in factor ETFsETF Stream’s deputy editor Tom Eckett speaks to AJ Bell’s head of passive portfolios Matt Brennan on disliking backtests, avoiding multi-factor products and the reasons why the smart beta story has much further to run

Tom Eckett: Do you use smart beta or factor products within your clients’ portfoliosMatt Brennan: Although it is a fairly clichéd answer, we do not characterise investments as ‘passive’ and ‘smart beta’. Instead we prefer to just consider rules-based investments. To that note we are agnostic if the strategy is straight up market cap weighted or takes a smarter approach to index constitution. However, one important factor we do look at when using more complex strategies is cost. Do the extra costs generate better risk-adjusted returns?

Areas where we consistently use smart beta are where the portfolio objective is a bit more complex than maximising total returns. For example, when the portfolio has an income objective we are more likely to use smart beta strategies. This is because we want to make sure that the income is not just higher than average but sustainable in the long term. Other areas include thematic investments and when we are trying to achieve alternative like exposure. How much of your portfolios does smart beta typically make up?Our ‘standard’ growth portfolios are still largely dominated by simple strategies. We do however hold a small portion of smart beta strategies to enact our property view. However, over time we expect to see the number of smart beta strategies we use to increase, but it is really just a case of waiting for track records to build (we hate back-testing!)

and for costs to come down. For our income portfolios the majority of the equity component is made up from more complex index strategies. How do you view smart beta/factor-based ETFs?Smart beta is such a broad term, and probably encompasses both elements. For example a fixed income ETF that adds in liquidity screens is certainly closer to ‘pure passive’, whereas a robotics ETF that has a rules based strategy blended with a discretionary element, such as a panel of experts, is much closer to the active side of the spectrum. It is therefore very important to get under the hood of the investment. Which parts of the smart beta/factor-based spectrum interest you most at the moment?The biggest challenge for a multi-asset fund manager using passive investments only is to achieve alternative like exposure in the portfolio to improve the risk-return characteristics.

In the US, we have seen several strategies looking to replicate hedge fund returns, and in Europe we have seen products using managed futures, going long/short or trying to replicate physical property or infrastructure investments. We feel these are still at a nascent stage but is something we are watching closely. We also feel it is one of the last barriers that stop people switching from an active strategy to passive, so we are advocates of further development in this space.

On the other side of the coin, we really do not like momentum-based strategies. It feels like it is something that works, until it does not – they usually involve high turnover, which is against our philosophy of what a ‘passive’ investment style should look like. When you focus on a particular smart beta product to invest in what factors do you take into account?Cost is a big factor – what is the strategy giving me access to and does this justify the premium I am paying compared to a simpler strategy – could I replicate the factor using a combination of other ETFs. It is important to us that the strategy is not a ‘black box’. We want to make sure we can understand how the factor is systematically harvested, and more importantly the factor makes sense (e.g. we would expect less liquid stocks to outperform, however why should momentum work – just because a


CLOSING REMARKSA FUND RESEARCHER’S VIEW

Areas where we consistently use smart beta are where the portfolio objective is a bit more complex than maximising total returns. For example, when the portfolio has an income objective we are more likely to use smart beta strategies. This is because we want to make sure that the income is not just higher than average but sustainable in the long term

stock has performed well recently, there is no justification for further outperformance). Alongside smart beta, we have also seen the rise of thematic based investing using ETFs. Does this interest you? In our higher risk portfolios we have a small allocation to an automation and robotics ETF. We find the use of these products interesting on two levels.• On a standalone basis it allows to access strategies that will

give us access to long term growth trends.• On a portfolio basis they tend to improve the overall risk-

return profile.

However, a word of caution, something like an E-vehicle ETF often has high exposure to highly cyclical companies, such as traditional car manufacturers pivoting towards electrical vehicles. It is therefore important to ensure the thematic ETF actually gives you exposure to the theme! Are you concerned by the recurring accusations of hacking and data mining levelled at all factors and smart beta strategies?Data mining is one of our biggest concern, and therefore we always ask ourselves before investing if the factor actually makes sense. We usually avoid any strategy that is ‘multi-factor’.

We really see factors as a tool to build more diversified portfolios, we are sceptical about outperformance. For example, having an allocation to both quality companies and value companies, and thinking of them separately allows us to deliver performance depending on which strategy is in style, lowering volatility. It may not however lead to outperformance. In sample back testing, it is a statistical travesty! How do you engage with clients about smart beta?We recently dropped the ‘passive’ name from our fund range to allow us to focus more on ‘rules-based strategies’,

encompassing smart beta. We avoid using this term, and instead focus on education, explaining exactly why we are using the more complex strategies, and what extra value they bring. Clients are only concerned if the cost of investment significantly increases, hence our focus on product cost! Are there any specific areas where you would like to see new products emerge?Income investing is still at an early stage. We really like products that add a quality tilt to the income generation, however, the number of investment options remain few and far between. Costs still need to come down, and more education is required, however given low bond yields, we feel this is a growth area. Does multi-factor investing interest you? No – our motto is to build simple, transparent low-cost

solutions. It does not tick any of these boxes.

By 2025 do you think you will be making extensive use of smart

beta products and factor ETFs?The best analogy I have here (and

it still a poor one!) is Google. In the 90s, it was a very crude

search engine, returning results for the exact phrase you typed. Over the next 25 years it has transformed to a much more complex beast but is still ultimately rules driven. It can suggest results, recognise errors and identify patterns. ‘Passive investing’ is the Google of the late 90s

– we have seen strong growth, but the rule sets are still pretty

crude.Over time strategies will develop that

are little bit more nuanced and learn from previous mistakes, however, they will still be quantitative strategies.

This is where smart beta will sit. Ultimately, most of our portfolios

will have some element of a smarter rule set.

CLOSING REMARKSA FUND RESEARCHER’S VIEW


China, which holds a dominant market cap weighting within most emerging market ETFs, has been tackling several political issues in recent months and things are not getting any better following the recent outbreak of the coronavirus.

The country was impacted heavily by the trade war with the US and consequentially saw ETFs with this exposure slide in performance. The dispute erupted in mid-2018 and did not see a phase one deal being implemented until mid-January 2020, pausing further tariff hikes.

Shortly after this agreement, news started to emerge of the seriousness of the coronavirus outbreak which spread throughout Asia within weeks and resulted in many towns going into lock down. As a result, flows into emerging market ETFs domiciled in Europe dropped from €2.7bn in January to €364m in February up until 24th, according to data from Bloomberg.

Following negative flows in Q3 2019, global emerging market equity ETPs managed to pull in $24bn in net flows the following quarter, according to BlackRock. These inflows were dominated by broad emerging market equity ETPs with $13.1bn and South Korea equity ETPs with $6.1bn. Emerging market equities was the sixth most popular exposure for the quarter behind US large cap, global developed market equities and investment grade bonds.

The region’s equity inflows for the quarter meant its total net flows for 2019 was $32bn, three-quarters of which came in Q4.

For fixed income, emerging market ETPs saw modest inflows of $4.4bn in Q4, bolstering the year’s net flows to $19.9bn. This was

ahead of gold which had a popular year during numerous volatile periods and saw inflows of $18.8bn.

In 2019, the iShares MSCI EM UCITS ETF (IEEM) saw its net asset value (NAV) climb 11.8%. Since then, IEEM’s NAV fell 6% in January but has recovered slightly in February. Similarly, the Vanguard FTSE Emerging Markets UCITS ETF (VFEM) climbed 14% in 2019 and fell 5.9% in January.

China accounts for 35.4% of IEEM’s geography exposure as well as 37.3% for VFEM. Looking at China uniquely, the HSBC MSCI China UCITS ETF (HMCD) climbed 22% in 2019 before falling 6.6% in January alone.

As fixed income ETF adoption grows, so does investors’ allocation to emerging markets debt. According to JP Morgan Asset Management, investors are attracted by the diversification, higher yields and improving credit quality and fiscal strength of emerging market sovereign issuers. However, emerging market investors still face the same challenges as developed market investors such as idiosyncratic country risk and unstable credit risk exposure.

At the end of 2019, most investors were bullish towards emerging markets’ development in 2020. A potential downfall of the US dollar meant the growing likelihood of emerging market currency appreciation and is positive for financial conditions, according to Schroders. Therefore, the balance of risks looked favourable for equities with EM exposure, especially as the trade war with the US was the biggest concern heading into 2020 and quickly calmed in the early days of January.

CLOSING REMARKSESG FLOWS


Emerging markets ETF flows remain positive despite China’s woesETF Stream’s senior writer George Geddes analyses the recent flows and trends from across the emerging markets space

GLOBAL ETP Q4 FLOWS ($BN)

US Large Cap

Global DM Equity

FI Multi-sector

Broad US Equity

Investment Grade

EM Equity

EAFE Equity

US High Div Equity

Broad Europe Equity

US Treasury

28.5

19.7

17.0

15.6

13.4

13.1

10.5

9.2

8.9

8.8 Source: BlackRock

DEFINING AND UNDERSTANDING THE SMART BETA, FACTOR INVESTING AND

ESG INVESTMENT REVOLUTION

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an etf stream publication // // q1 … · 2020. 3. 12. · 19 analysing emerging market equity...

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