AN ETF STREAM PUBLICATION // // Q2 … · 2019-06-25 · ETNs. We only looked at ETFs in the familiar sense. Here, it is interesting to note that the best performers in Q1 2019 were

FactorResearchHow to allocate smartly to smart beta

ParalaMethods for managing volatility in indices and ETPs

AxiomaEvaluating consistency across ESG vendor scores

Killik & CoWhat fund researchers look for in factor ETFs

AN ETF STREAM PUBLICATION // WWW.ETFSTREAM.COM // Q2 2019

BEY NDBETAINVESTIGATING THE SMART BETA, FACTOR & ESG INVESTMENT REVOLUTION

RIDING OUT THE STORMThe art of defensive investing with smart beta

10 14 24 26

Expertise | Technology | Data

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Global ETF & Index Managed Data Service PCF-Calculation

In this issueUPDATES

6 The best performing smart beta ETFs in Q1 2019 Returns from ESG based ETFs put under the microscope

8 New Q1 smart beta listings Our regular catch-up on the new smart beta ETFs listed from around the world over the first quarter

PERSPECTIVES

10 How to allocate smartly to smart beta Smart beta has come under pressure in recent years due to poor flows and popular factors such as value delivering negative returns. Nicolas Rabener, managing director at FactorResearch, examines the methods in which investors can allocate to smart beta without having their fingers burnt

14 Methods for managing volatility in indices and ETPs revealed The desire to control the investment journey by divsersifying away potential risks has always been a top priority for investors. Steven Goldin of Parala takes a deep dive into the ways investors can manage volatility through a number of factors and the pros and cons of popular methods

FACTORS IN FOCUS

17 Getting defensive with factors Last year marked the return to volatility. As central banks started withdrawing liquidity from the market, equities took a tumble. London-based global software provider Style Analytics studies how investors should play more volatile markets and what factors work best in certain conditions

21 Defensive strategies get the job done in recent turmoil Following the adverse market conditions in Q4 last year, Mark Barnes, managing director, head of US research at FTSE Russell, takes a look at which factors performed best during the more volatile period and whether it is worth seeking exposure in more defensive factors

CLOSING REMARKS

24 60 seconds from the buy-side: what fund researchers look for in factor ETFs ETF Stream editor in chief David Stevenson speaks to Kilik & Co’s head of fund research Mick Gilligan on the factors he uses to gain exposure, why academic evidence is crucial and the factors to take into account when selecting products.

26 Evaluating consistency across ESG vendor scores Anthony Renshaw, director of index solutions at Axioma Investment takes a deep dive into the inconsistencies in the way environmental, social and governance (ESG) issues are measured between different providers. While there have been numerous ESG methodology studies, the asset management industry is yet to find a one size fits all solution meaning metrics can vary from one provider to the other. Is there a solution to this issue?

About us

David StevensonDavid 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.

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 Q2 2019 BEYOND BETA 3

UPDATE CONTENTS

ETF 2025 is a one-day, invite only event, aimed at C-suite leaders within the passive funds space in Europe

JUNE 6th 2019

When: THURSDAY JUNE 6th 2019 Where: Dublin, The Westin Hotel, Bankers HallEMAIL: [email protected] VISIT: ETF2025.com

Hosted By SPONSORED By Supported by

ETF 2025 will provide a unique opportunity for Industry Leaders

to examine and exchange ideas on; Industry Growth,

Business Models and Transformation!

Tobias Sproehnle, CEO, Moorgate Benchmarks

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Isabell Moessler, ETF Business Development Manager, Euronext

Timo Pfeiff er, Head of Research, Solactive

Jamie Patturelli, Director - Exchange Traded Products, NYSE

Marco Boldini, Head of Financial Services Regulatory- Legal, PwC

Monica Gogna, Financial Services Partner, Dechert LLP

Philip Lovegrove, Partner - Asset Management Department, Matheson

Oliver Smith, Editor, AltFi

Terry McGivern, Fund Manager, AJ Bell

Nikolai Hack, UK Managing Director & COO, EXO Investing

Hany Rashwan, CEO, Amun AG

Iona Bain, Founder, Young Money Blog

Confirmed speakers include:


SMART BETA UPDATE EDITORIAL

COMMENT FROM THE EDITORS-IN-CHIEF

APRIL 2019

UPDATE

Hello and welcome to Beyond Beta – the one and only magazine dedicated to smart beta and quantitative investing. Smart beta is going from strength to strength. So much so that globally there are now more than 1,300 smart beta ETFs managing more than $700 billion in total assets. Not bad for an asset class that hardly existed 10 years ago.

While smart beta is booming, there has been little independent analysis of the nascent sector. Most of the literature examining smart beta has been product literature, which at times can be hard to distinguish from marketing material. With this in view, we created Beyond Beta to give an independent look at this important area.

In this edition we start by going through some of the major and more interesting developments in the first quarter of 2019. We survey every new listing and highlight some of the flashy products that caught our eye. We then go through the performers and highlight those that made their investors most proud. The magazine then breaks down into a series of articles which explore a range of popular topics, including defensive strategies in times of volatility; how index and ETF providers game ESG scores; how to choose which factors to buy – and more.

Beyond Beta is put together by ETF Stream, the London-based ETF trade publication.

Definitions: what is ‘smart beta’ and ‘thematic’ investing?Before we begin, a quick note on definitions. We define smart beta as non-market-tracking rules-based ETFs. For us, smart beta ETFs do not have to be index-tracking or passive. What matters is that they meaningfully deviate from the market weighted portfolio, while their portfolio managers make trades according to a set of rules. This means, for example, that an actively managed ETF with managers who make discretionary trades or ‘captain’s calls’ are not smart beta for us. While an ESG ETF that makes far reaching exclusions does qualify as smart beta.

We define thematic ETFs as non-GICS sector trackers. They seek exposure to a slice of economic activity – be it marijuana, millennial spending habits, self-driving cars, etc. – that falls outside the standard GICS breakdown. They are often market weighted. David Stevenson and David TuckwellEditors-in-Chief, Beyond Beta

Editorial Beyond Beta is published by

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requests. Please contact us on [email protected]

ETFSTREAM.COM6 BEYOND BETA Q2 2019

TOP ETF PERFORMERS DATA AND COMMENTARY

UPDATE

3-month performance (end 31 March 2019)

The US has an amazing smart beta ETF market that counts anything between 600-900 products, depending on how you cut the mustard. For us, we did not include some of the more exotic ETPs like like inverse and leveraged funds, nor did we include other strange structured products like ETNs. We only looked at ETFs in the familiar sense.

Here, it is interesting to note that the best performers in Q1 2019 were mostly tech and healthcare related. December 2018 saw a nasty correction in equity markets, with the S&P 500 declining as much as 20%. The sectors that seems to have come sprinting out of the woods were in healthcare and tech – the two most popular sectors the past decade. (Populations are ageing, creating a boon for healthcare; while once secure jobs are getting killed and replaced with software, which is good for tech.)

The top two performers in the US were both Invesco funds that use the famous Dorsey Wright stock picking strategy. The strategy, basically, is built around the idea that the rich will get richer; or successful companies will maintain their momentum and continue succeeding. Both PTW and DWAQ pick and weight companies based on how much their share prices have shot up in recent weeks. Looking at their performance, one begins to understand Professor Jeremy Seigel’s observations about equity markets today: “there are a lot of momentum traders in the market.” It seems to be working.

Other interesting front runners this past quarter were BBP and BTEC, both of which pick small cap US healthcare companies. BBP picks biotech companies that have attained FDA approval for their top drug. While BTEC buys into companies before their drugs even have any FDA approval. Such investments can be highly speculative and risky—but potentially very rewarding, as Q1 shows.

Unlike the Dorsey Wright funds, which are unapologetic momentum plays, it is difficult to put BTEC and BBP into traditional factor boxes. On the one hand they are a size factor play: all the companies they invest in are tiny. They’re also both high beta plays – with betas between 1.5 and 2.5. Yet they’re also both very much growth plays – with PE ratios firmly in the negative.

The best performing smart beta ETFs in Q1 2019

Top performers USADorsey Wright wins

Ticker Fund Name – 3 Month Total Return % change

PTF Invesco DWA Technology Momentum ETF 39.15%

DWAQ Invesco DWA NASDAQ Momentum ETF 37.07%

BTEC Principal Healthcare Innovators Index ETF 36.53%

BBP Virtus LifeSci Biotech Products ETF 35.82%

XES SPDR S&P Oil & Gas Equipment & Services ETF 35.63%

SBIO ALPS Medical Breakthroughs ETF 35.23%

OGIG O'Shares Global Internet Giants ETF 34.68%

XBI SPDR S&P Biotech ETF 34.50%

XITK SPDR FactSet Innovative Technology ETF 33.74%

PXMG Invesco Russell MidCap Pure Growth ETF 33.39%

The best performers in Q1 2019 were mostly tech and healthcare related


SMART BETA UPDATE PERFORMANCE

3-month performance (end 31 March 2019)

What do World War I and British smart beta ETFs in Q1 have in common? The Americans saved them.

Bad jokes aside, the UK market tends to have less extreme performers than the US – for the better and the worse. This tends to be because ETFs that trade in Europe are less experimental and arguably less risky. (As we all know: asset management in the UK is mostly institutional, which tends to cast a conservative glaze over product development.)

Not incidentally – the top performing ETF in Q1 was from an American provider, First Trust, which tracks the US market, no less. The First Trust US Equity Opportunities ETF (FPX) is an interesting case. It invests in companies six days after they have been IPO-ed or spun off. The fund chooses a six-day cut off to dodge the pump-and-dump that typically occurs in the first week of IPOs. New IPOs and spin offs are then left in the index for 1000 trading days at which point they’re sold (there are roughly 250 trading days in a year). The biggest company in the index is PayPal which was spun off from eBay in 2015. It is interesting to note that two more Invesco momentum products make the cut in

Ticker Fund Name – 3 Month Total Return % change

FPX:LN First Trust US IPO Index UCITS ETF 17.89%

UC99:LN UBS ETF (IE) Factor MSCI USA Quality UCITS ETF (USD) A-dis 14.15%

XRMU:LN Xtrackers Russell Midcap UCITS ETF 1C 13.91%

M9SV:LN Market Access Stoxx China A Minimum Variance UCITS ETF - EUR 13.72%

IPRV:LN iShares Listed Private Equity UCITS ETF 13.67%

IUSF:LN iShares Edge MSCI USA Size Factor UCITS ETF 13.53%

EQLT:LN UBS (LU) Factor MSCI EMU Quality UCITS ETF (hedged to GBP) A-dis

13.46%

PFTP:GR Invesco Tradable European Price Momentum UCITS ETF 13.45%

PFTE:GR Invesco Tradable European Earnings Momentum UCITS ETF 13.41%

RSGL:LN Lyxor Russell 1000 Growth UCITS ETF C-USD 13.37%

Top performers UKBring on the Americans

Europe, both of which use momentum to pick European large and mid-caps. (Although not Dorsey Wright in this case). One fund focuses on share price momentum – i.e. how quickly the share prices go up.

The other on earnings momentum – how quickly earnings have shot up. Both funds listed in November 2017, so are relative newcomers and potentially products to watch.


NEW SMART BETA LISTINGS DATA AND COMMENTARY

UPDATE

New Q1 smart beta listingsThe first quarter saw a freshet of smart beta listings, with North America – and surprisingly Canada – dominating product innovation. As may be expected, multi-factor funds were popular with ETF providers. This is likely because they fit well with the requirements of long only investors who do not like deviating too far from benchmark. And indeed, we have seen multi-factor ETFs listed almost everywhere and in almost every quarter the past several years.

Also among the top runners were a bevy of principles-based (ESG) funds, which have steadily grown in popularity since 2017. Some have speculated the timing of their popularity owes to the election of Donald Trump. (While Obama was president, these types of funds were few and far between). Below are some of our take-aways from Q1 listings.

Fun Fact: why do Korean ETFs look like casino machines?

There is a notion in Korea that Korean people are uniquely susceptible to the siren song of gambling. According to the Korea Centre on Gambling Problems, a government body put together in 2012, gambling addiction is two-to-three times worse in Korea than in other big economies. Therefore gambling, along with spread betting, remains illegal in most of Korea.

Fortunately, however, for Koreans with a gambling itch, ETFs are seen as a way of bypassing these laws.

This quarter we saw two listings that look to be in this spirit of things. First, for those wanting to play hot potato with potentially overvalued FANG stocks, the Samsung KODEX FANG PLUS ETF (H) arrived on exchange.

Second, for those wanting to play trend-following on the Kospi while undertaking a complex options trading

Ticker Fund Name % change

NFEDEF SJNewFunds Volatility Managed Defensive Equity ETF

Variance and Risk

NFEHGE SJNewfunds Volatility Managed High Growth Equity ETF

Variance and Risk

NFEMOD SJNewFunds Volatility Managed Moderate Equity ETF

Variance and Risk

ECAR LNiShares Electric Vehicles and Driving Technology UCITS ETF - USD (Acc)

Thematic

EMUD LNiShares MSCI EMU ESG Enhanced UCITS ETF - EUR (Dist)

Principles-based

EEUD LNiShares MSCI Europe ESG Enhanced UCITS ETF - EUR (Dist)

Principles-based

EEJD LNiShares MSCI Japan ESG Enhanced UCITS ETF - USD (Dist)

Principles-based

EEDS LNiShares MSCI USA ESG Enhanced UCITS ETF - USD (Dist)

Principles-based

EEWD LNiShares MSCI World ESG Enhanced UCITS ETF - USD (Dist)

Principles-based

WMVG GYiShares Edge MSCI World Minimum Volatility UCITS ETF GBP Hedged (Acc)

Variance and Risk

DYNF USBlackRock U.S. Equity Factor Rotation ETF

Multi-factor

ZWK CNBMO Covered Call US Banks ETF

Alternative

ZHU CNBMO Equal Weight US Health Care Index ETF

Size

ZZZD CNBMO Tactical Dividend ETF Fund

Fundamental

SRI FPBNP Paribas Easy - € Corporate Bond SRI Fossil Free - EUR UCITS ETF, Capitalisation

Principles-based

BNP Paribas Easy MSCI Europe SRI UCITS ETF, Capitalisation

Principles-based

BNP Paribas Easy MSCI Japan SRI UCITS ETF, Capitalisation

Principles-based

BNP Paribas Easy MSCI KLD 400 US SRI UCITS ETF EUR, Capitalisation

Principles-based

CMCE CNCIBC Multifactor Canadian Equity ETF

Multi-factor

CMUE CN CIBC Multifactor U.S. Equity ETF Multi-factor

CMUE.F CNCIBC Multifactor U.S. Equity ETF (CAD-Hedged)

Multi-factorFun Fact: Koreans use ETFs to solve gambling itch


SMART BETA UPDATE NEW LISTINGS

strategy there’s the KB Securities KB KOSPI200 Trend Following Option Mountain ETN 9 from the Kookmin Bank. As long as you don’t lose money this is all good fun!

One to watch: NBI Canadian Family Business ETF

Leo Tolstoy’s happy families were all the same, while his miserable families were all different in their own way. One can say the same of ETFs of course: happy ETFs are all the same: solidly performing, flush with assets, and gaining admirers in the form of copycats. While the unhappy ones are all failures for different reasons. So the question for the family business ETF: which family will it belong to?

The economic basis for this ETF is fairly sound. Families are the most powerful economic unit in the world. Many of the world’s most famous companies are family-run: Wal-Mart, Volkswagen, Berkshire Hathaway, Samsung, Diageo – to name a few. In poorer countries almost every business is family-owned. While the image of big businesses being public corporations run by professional managers pervades popular consciousness, in reality 80% of world corporations are family-owned and run. For us, this ETF is one to watch.

Spotlight on: Direxion’s long short suite

Direxion’s suite of long/short ETFs allow investors to express views on key opposing macro trends. For example, do they believe US equity markets will keep ahead of the rest of the world? Or do they think large companies will do better than small ones?

For the macro trend investors are bullish on, the funds offer 150% long exposure. For the one they’re bearish on, the funds build in 50% short exposure. Thus the Direxion Russell 1000 Value over Growth ETF (RWVG) would go 150% long on value stocks while going 50% short on growth stocks.

For us, this suite is interesting because, in a way, it is how factor trackers were intended. That is: they go long on the factors they’re bullish on while also crucially having a short leg. Most – in fact, almost every – smart beta ETF is long only. That makes these funds an interesting play. (Please note: the daily resets on leveraged and inverse ETFs like this mean they will not look or perform like factor funds).

Ticker Fund Name % change

DANC CNDesjardins Alt Long/Short Equity Market Neutral ETF

Alternative

DRFE CNDesjardins RI Emerging Markets Multifactor - Low CO2 ETF

Multi-factor

DRFG CNDesjardins RI Global Multifactor - Fossil Fuel Reserves Free ETF

Multi-factor

RWIU USDirexion FTSE Russell International Over US ETF

Alternative

RWUI USDirexion FTSE Russell US Over International ETF

Alternative

RWCD USDirexion MSCI Cyclicals Over Defensives ETF

Alternative

RWDC USDirexion MSCI Defensives Over Cyclicals ETF

Alternative

RWDE USDirexion MSCI Developed Over Emerging Markets ETF

Alternative

RWED USDirexion MSCI Emerging Over Developed Markets ETF

Alternative

RWGV USDirexion Russell 1000 Growth Over Value ETF

Alternative

RWVG USDirexion Russell 1000 Value Over Growth ETF

Alternative

RWLS USDirexion Russell Large Over Small Cap ETF

Alternative

RWSL USDirexion Russell Small Over Large Cap ETF

Alternative

HEWB CNHorizons Equal Weight Canada Banks Index ETF

Size

HCRE CNHorizons Equal Weight Canada REIT Index ETF

Size

HUTL CNHarvest Equal Weight Global Utilities Income ETF

Size

ICFP IMInvesco MSCI Europe ESG Leaders Catholic Principles UCITS ETF Dist

Principles-based

580009 KSKB Securities KB KOSPI200 Trend Following Option Mountain ETN 9

Technicals

NFAM CN NBI Canadian Family Business ETF Thematic

NREA CN NBI Global Real Assets Income ETF Thematic

NALT CN NBI Liquid Alternatives ETF Variance and Risk

HOMZ US Hoya Capital Housing ETF Thematic

NETL USNETLease Corporate Real Estate ETF

Thematic

314250 KS Samsung KODEX FANG PLUS ETF (H) Thematic

SBEA CNSmartBe Global Value Momentum Trend Index ETF

Technicals

Many of the world’s most famous companies are family-run

How to allocate smartly to smart betaSmart beta has come under pressure in recent years due to popular factors such as value delivering negative returns. Nicolas Rabener, managing director at FactorResearch, examines the methods in which investors can allocate to smart beta without having their fingers burnt

TAKEAWAY VERSUS HOME-COOKED INVESTINGObesity rates in countries like the US or New Zealand are north of 30%, which increases health issues such as diabetes and effectively reduces life expectancy. The citizens of these countries should do everything within their powers to reduce their daily calorie intake. Unfortunately, new companies like Uber Eats are making it more convenient to order takeaway food, which tends to be less healthy, more expensive, and contain more calories than when cooking at home.

Factor investors face a similar choice between buying ready-made multi-factors products or creating these themselves. Multi-factor ETFs might be a convenient solution for harvesting returns from various factors, but these are more costly than single-factor ETFs and are often challenging to analyse.

Alternatively, investors can directly select single-factor products and create custom portfolios, either by allocating on a discretionary or systematic basis. The simplest form of the latter might a portfolio that allocates equally across smart beta strategies, but it is questionable if that is an optimal allocation model.

In this research note, we will apply four well-established asset allocation models to smart beta

strategies and evaluate if smart beta would have enabled investors to outperform the markets.

OUTSMARTING THE MARKET?The rise of factor investing is based on the broad effort to make investing more scientific, which has become easier given cheaper, better data and new technologies. Research by academics and finance professionals principally supports five factors namely Value, Size, Momentum, Low Volatility, and Quality. Dividend Yield can be considered a (poor) version of Value while Growth is a widely-followed investment style, but lacks backing from research.

These seven smart beta strategies have become available via smart beta ETFs at affordable prices and gathered approximately $1 trillion in assets. However, some strategies like Low Volatility were launched as ETFs only in recent years and have a limited price history. We therefore create smart beta indices going back to 1990 by selecting the 30% of stocks ranked most favorably by the factor definitions, which are in line with academic and industry standards. When we benchmark these theoretical smart beta strategies with the track records of live smart beta ETFs, they show only minor tracking errors.

Reviewing the performance of these seven strategies over the last three decades reveals that most generated alluring excess returns that explain the interest in smart beta. Based on these results, an investor might conclude that simply allocating equally to all the strategies might create an attractive portfolio for harvesting factor returns.

However, an astute observer might be perplexed as nearly all strategies outperformed the markets. In Europe and Japan any of these smart beta strategies would have been better than plain beta. Is it that simple to beat the market?


The rise of factor investing is based on the broad effort to make investing more scientific, which has become easier given cheaper, better data and new technologies. Research by academics and finance professionals principally supports five factors namely Value, Size, Momentum, Low Volatility, and Quality

(4.0%)

(3.0%)

(2.0%)

(1.0%)

0.0%

1.0%

2.0%

3.0%

4.0%

US Europe Japan

Smart Beta Excess Returns Per Annum: Last 10 Years

Value Size Momentum Low Volatility Quality Growth Dividend Yield

(1.0%)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

US Europe Japan

Smart Beta Excess Returns Per Annum: Last 30 Years

Value Size Momentum Low Volatility Quality Growth Dividend Yield

ETFSTREAM.COM

PERSPECTIVESNICOLAS RABENER

Q2 2019 BEYOND BETA 11

SMART BETA EXCESS RETURNS PER ANNUM: LAST 30 YEARS

SMART BETA EXCESS RETURNS PER ANNUM: LAST 10 YEARS

Source: FactorResearch


n Value n Size n Momentum n Low Volatility n Quality n Growth n Dividend Yield

n Value n Size n Momentum n Low Volatility n Quality n Growth n Dividend Yield

5.0%

4.0%

3.0%

2.0%

1.0%

0.0%

(1.0%)

4.0%

3.0%

2.0%

1.0%

0.0%

(1.0%)

(2.0%)

(3.0%)

(4.0%)

US Europe Japan

US Europe Japan

Research like the S&P’s SPIVA Scorecards highlights that more than 80% of the fund manager have failed to beat their benchmarks when measured over five years or longer. Perhaps these fund managers have not explicitly followed factor-based strategies, but the lack of performance needs to be reconciled with the high excess returns from the smart beta strategies.

A simple explanation might be cost. Mutual funds have reduced their fees below 1% in recent years, mainly due to the pressure from cheaply-priced ETFs. However, active fund managers like Fidelity have historically charged management fees above 2%, which consumed most excess returns and explains the failure to beat benchmarks. Naturally smart beta ETFs come to the rescue as they are


ETFSTREAM.COM

much more affordable than mutual funds. A second reason why the 30-year smart beta excess returns should be regarded with some skepticism is that the observation windows matter. If we rebased the same strategies in 2008 to highlight the excess returns for the last decade, then these look far less attractive.

The academic research highlights that only a few factors generate abnormal returns across time and even these are highly cyclical with long periods of underperformance. Investors should therefore have a long-term view when considering factor investing.

Given that smart beta strategies might be less attractive over short periods, it raises the question of how to optimally harvest these returns.

A HORSE RACE OF ASSET ALLOCATION MODELSThere are many asset allocation models that can be considered for creating a multi-factor portfolio out of smart beta strategies. We will evaluate the following four models, which are well-established frameworks:• Momentum (Total Return): The total return

12 BEYOND BETA Q2 2019

SMART BETA ALLOCATION MODELS IN THE US

SMART BETA ALLOCATION MODELS: RISK-RETURN RATIOS (2008-2018)

700

7,000

70,000

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

Smart Beta Allocation Models in the US

Momentum (Total Return) Momentum (Sharpe) Equal Weight

Risk Parity Market

70,000

7,000

700

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

n Momentum (Total Return)n Momentum (Sharpe)n Equal Weightn Risk Parityn Market

n Momentum (Total Return)n Momentum (Sharpe)n Equal Weightn Risk Parityn Market

1.00

0.61

0.42

0.630.68 0.66

0.59

0.70 0.69 0.70

0.57

1.05 1.06 1.06 1.06


US Europe Japan

ETFSTREAM.COM

PERSPECTIVESNICOLAS RABENER


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.

of each smart beta strategy is measured over the last 12 months and the best three out of seven strategies are selected, receiving equal allocations.

• Momentum (Sharpe): The Sharpe ratio of each smart beta strategy is measured over the last 12 months and the best three out of seven strategies are selected, receiving equal allocations.

• Equal Weight: Each of the seven strategies receives the same allocation.

• Risk Parity: The weight of each strategy is determined in inverse proportion to its volatility, which is measured over the last 12 months.

If we focus on the US, we observe that all multi-factor portfolios outperformed the market since 1991, which is expected given that most of the smart beta strategies generated excess returns on a stand-alone basis. The two Momentum-driven portfolios generated the highest returns while the Equal Weight and Risk Parity portfolios show a lower and almost identical performance.

The difference between the models was especially large around 2000, where the Momentum-driven portfolios allocated to Tech stocks via exposure to the Momentum, Growth, and Quality factors. Allocating to a multi-factor portfolio of smart beta strategies in Europe and Japan also resulted in returns above the market, regardless of which asset allocation model was utilized. However, as noted earlier, these strategies performed far less attractively in the last decade. We therefore compare the four asset allocation models from 2008 to 2018 and change the perspective to risk-adjusted returns.

The results continue to favor allocating to smart beta strategies as the risk-return ratios of the multi-factor portfolios were higher than those of the

market in two out of three regions. However, none of the asset allocation models can be considered superior, even the simple equal-weight model performed well.

Given the marginal differences between the allocation frameworks, investors should evaluate other characteristics such as the model complexity or transaction costs and tax implications as some like Momentum feature higher turnover.

FURTHER THOUGHTSSome investors have started to turn away from smart beta strategies as performance has been disappointing. The popular Value factor has generated negative returns since 2009 and last year even diversified multi-factor portfolios performed poorly. However, there are no good alternatives for investors that are mandated to beat their benchmarks. Investors should focus on harvesting factor returns as efficiently as possible, but with the perspective that factors are as cyclical as equity markets and require a long-term view.

If we focus on the US, we observe that all multi-factor portfolios outperformed the market since 1991, which is expected given that most of the smart beta strategies generated excess returns on a stand-alone basis. The two Momentum-driven portfolios generated the highest returns while the Equal Weight and Risk Parity portfolios show a lower and almost identical performance

Methods for managing volatility in indices and ETPs revealedThe desire to control the investment journey by divsersifying away potential risks has always been a top priority for investors. Steven Goldin of Parala takes a deep dive into the ways investors can manage volatility through a number of factors and the pros and cons of popular methods

CONTROLLING RISK HAS ALWAYS BEEN A DESIRED OBJECTIVEInvestors have always sought to manage the risks inherent in their portfolios whether through security selection, diversification, asset allocation or insurance. From the simple to the sophisticated, this area of interest has become a source of innovation for index providers and ETFs as they’ve moved beyond cap-weighted beta offerings.

The range of methods being used to control volatility is reasonably broad. In this article, we focus on some of the most popular ones, revealing how they work, the asset types upon which they are applied, the pros & cons and limitations as well as newer methods that are becoming prevalent in the world of indices and will likely soon be available via ETFs as investor interest grows.

Risk control methodologies to be covered can be seen in table 1 below.

VOLATILITY TARGETINGS&P Dow Jones Indices, MSCI, Stoxx and others offer volatility targeted versions of their flagship indices, typically with target volatility levels of 10%, 12% and 15%. They often use “Risk Control” in the name of the index and there are some ETFs

that track them like the Japan listed MAXIS TOPIX Risk Control 10% ETF offered by Mitsubishi Asset Management. Linking structured products to these indices is popular with banks active in that business. In fact, many of the methodologies were initially introduced by the banks themselves.

These indices are created by combining a risk asset, say a flagship index, and a riskless asset like Treasury bills. The weight of the risk asset is typically set to equal the target volatility divided by the realised volatility, usually calculated daily over a look back period between 20 to 60 days and often incorporating an exponentially weighted moving average.

For example, if the target volatility was 10% and the realised volatility was 20% than the weight in the risk asset would be 50% (10 divided by 20) and the remaining 50% would be allocated to the riskless asset so that the weights sum to 100%. The advantage of the approach is its relative simplicity, allowing an investor access to their desired index exposure at a volatility level they can tolerate. The disadvantage is that the method requires daily rebalancing to maintain the volatility target, making it expensive to implement for all but the most


Table 1. Risk control methodologies to be covered

Method Optimisation Based (Y/N) ApplicationVolatility targeting No Index levelVolatility filtering and weighting No Constituent levelBeta filtering No Constituent levelMinimum variance Yes Constituent or Multi-AssetRisk parity No Multi-AssetEqual weighted risk contributions Yes Multi-Asset

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PERSPECTIVESSTEVEN GOLDIN


liquid indices. Also, the actual realised volatility of the risk-controlled indices will vary over time due to the estimation error in using historical realised volatility as a proxy for expected volatility of the risk asset. See the chart above.

VOLATILITY FILTERING AND WEIGHTINGVolatility filtering and weighting is another method that is applied at the index constituent level. It typically involves two steps: (1) selecting the lowest volatility subset of constituents based on a ranking of their historical daily volatility (for example, selecting the 100 lowest volatility constituents) and then (2) weighting each constituent by the inverse of its volatility.

Weighting by the inverse of volatility gives a higher weight to the lowest volatility securities. This method was applied to many flagship indices by leading index providers and proved popular particularly in the US where, for example, ETFs managed by Invesco linked to low volatility versions of the S&P 500, MidCap 400 and SmallCap pulled in over $15 billion in assets through March 2019.

A potential drawback of this method is the selection of the lowest volatility subset leads to different sector concentrations than the parent index so you’d be mistaken to think you are

getting exposure to a low volatility version of your favourite headline index. Instead, you are getting exposure to the lowest volatility sectors of your favourite headline index. LOW BETA FILTERINGLow beta filtering is similar to volatility filtering, selecting a subset of the index constituents for example, the highest or lowest 30% based on their historically estimated betas rather than volatility. Typically, this method is applied at the sector or country level to ensure diversification and stocks are still weighted thereafter by their market capitalisation. This method has proven less popular for ETF issuance but there are a few such as the Invesco Russell 1000 Low Beta Equal Weight ETF and some high beta versions that are available to investors.

MINIMUM VARIANCEMinimum variance optimisation has its roots in the Nobel Prize winning work of Harry Markowitz from his 1952 paper entitled Portfolio Selection. The paper showed that covariance among a universe of securities rather than an individual security’s variance is the critical component to building an ‘optimal’ portfolio with the minimum variance.

Combining securities that have a low correlation with each other provides a

Combining securities that have a low correlation with each other provides a diversification benefit which reduces the total variance of a portfolio regardless of each security’s individual level of variance

Table 2. Volatility filtering and weighting

S&P 500 Index Largest Sector Weights S&P 500 Low Volatility Index Largest Sector WeightsInfo Tech 21.2% Utilities 24.8%Healthcare 14.6% Real-estate 20.4%Financials 12.7% Financials 18.0%

48.5% 63.2%

CHART 1. ROLLING 12-MONTH ANNUALISED VOLATILITY (DAILY VALUES)

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diversification benefit which reduces the total variance of a portfolio regardless of each security’s individual level of variance. The method requires the use of a portfolio optimisation tool to solve the ‘problem’ of which combination of securities and weights results in a portfolio with the minimum variance subject to a set of constraints (i.e. minimum or maximum constituent, sector or asset class weights) that might be required.

The method has proven very popular among investors, particularly in Europe with minimum variance ETFs offered by iShares, Lyxor, Ossiam and other leading ETF providers across developed and emerging market as well as regions and countries. There are two challenges to the method. First, a set of potentially subjective constraints must be applied in the optimisation process otherwise the resultant portfolio may have high concentrations in a small number of securities. Second, the method relies on the calculation of a variance-covariance matrix which is prone to higher and higher estimation error the more securities are included in the optimisation.

RISK PARITYRisk parity is a popular construction method among institutional investors for multi-asset portfolios and benchmarks have been created by S&P Dow Jones Indices among others. Large asset management firms like AQR are big proponents of the approach. Each asset (class) weight is set such that its variance contribution (weight x variance) is the same across all assets. An asset’s risk parity weight can be calculated as 1/volatility of the asset divided by the sum of 1/volatility for all the assets in the portfolio.

It does not require the variance-covariance matrix of all the assets or an optimizer tool to calculate the weights. One of the potential limitations of the approach is that the lowest

volatility asset (such as Treasury bills) will end up having a huge weight in the portfolio. Low volatility assets can be excluded or capped but one of the ways that this challenge is often addressed without excluding cash/equivalents is to specify a target volatility such as 5, 7, 10 or 12% for the portfolio.

Leverage is then applied to scale up the weights to achieve the target level of volatility. For practical reasons, futures are often used for the investable universe for this reason.

EQUAL RISK CONTRIBUTIONWhile risk parity and equal risk contribution share the same basic objective of equal risk weighting, risk parity weights assets inversely to their volatility whereas the equal risk contribution method takes the covariance between assets into account to reduce overall portfolio risk through diversification.

It requires a portfolio optimization tool to arrive at the solution. Several index providers have introduced index families around this concept including FTSE Russell, MSCI and Scientific Beta (Edhec) and ETF launches include the Global X Scientific Beta US ETF which has reached $100 million in assets.

The method is intuitive, but the calculation is complex and prone to estimation errors. First, solving for optimal weights based on equal risk contribution to portfolio variance which itself changes each time a weight is adjusted is circular and may lead to non-linear terms that need additional steps in the optimization process to solve. Second, the method relies on the variance-covariance matrix between securities where estimation error increases with the number of securities which requires sophisticated approaches to address.

CONCLUSIONSInvestors have always been keen to control, manage or diversify away risks in their portfolios. Over recent years, innovations in the world of indexation and ETFs have provided a broadening range of methods to do so and growing array of investable building blocks to construct portfolios. Investors should just keep in mind that outcomes from these methods to reduce and/or control risk have variable (and sometimes unexpected) outcomes. This is because the methods for calculating volatility are often backward looking and potential estimation error in calculating covariance increases with the number of securities.


Investors have always been keen to control, manage or diversify away risks in their portfolios. Over recent years, innovations in the world of indexation and ETFs have provided a broadening range of methods to do so and growing array of investable building blocks to construct portfolios

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.

Getting defensive with factors Last year marked the return to volatility. As central banks started withdrawing liquidity from the market, equities took a tumble. Bernie Nelson, President, North America at Style Analytics studies how investors should play more volatile markets and what factors work best in certain conditions

Since the beginning of the fourth quarter 2018, equity investors have had a roller coaster ride, with equity markets only recently recovering from a very weak

fourth quarter in 2018. This volatility, combined with the brief inverted yield curve in March in the US, has increased investor concern of a global economic slowdown or recession following an extremely long bull market. Many investors will be questioning whether to adopt a more defensive position in their equity funds.

Traditionally, equity investors take defensive positions by investing in non-cyclical sectors such as consumer staples, utilities or health care. Nowadays investors are being offered an array of funds promoting factor investing that proclaim “defensive” strategies such as Low Volatility or Quality. Low Volatility funds are often sold as single, factor-focused funds, whereas Quality tends to be positioned alongside Value or High Dividend, or integrated within a multifactor approach. There is now a dizzying array of funds for investors to consider, verify and compare. How can investors make sense of this?

LOW VOLATILITYDifferent measures can be used to estimate low volatility, including market-relative beta, or absolute volatility using daily or monthly stock returns typically over the past 1, 3, or 5 years. Often, a few measures will be combined to form factor portfolios, which is why it is important to understand the definitions used since they can lead to different stock choices. Much has been written on the low volatility anomaly across global equity markets, specifically the outperformance of low volatility stocks as compared to high volatility stocks on a risk-adjusted basis.

One hypothesis for this anomaly is an irrational investor bias towards high-risk, “lottery”-like

payoffs from highly volatile stocks. Other reasons put forward include the fact that fund managers judged against index benchmarks have an incentive to own higher-beta stocks, as well as investor restrictions on short selling or leverage potentially leading to higher demand for high-risk stocks and overpricing. Regardless of these reasons, we know that low volatility strategies have become popular since the global financial crisis. This may simply be a case of investors wanting to reduce portfolio risk and ensure downside protection by giving up some upside participation. The low volatility anomaly may encourage investors to maintain this strategy on a longer-term basis.

QUALITYQuality is a more wide-ranging factor than low volatility and is based on company fundamentals

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FACTORS IN FOCUSSTYLE ANALYTICS


Style Analytics provides industry-recognised factor analytics, trusted by the world’s largest investment managers.

rather than historic returns. But what makes a company ‘quality’ to one investor can be quite different to another. Profitability is usually the cornerstone of most definitions, often measured by high return on equity or high profit margin. But some investors also look at indicators such as stability of revenues or earnings, or low financial leverage based on, for example, debt to equity.

Quality is considered a factor because higher-quality companies have tended to outperform lower-quality companies. One might think that these known attributes would already be reflected in share prices. Some explanations for the quality premium focus on behavioural investor biases, include excessive optimism for headline earnings regardless of their sustainability, or expectations

that high-quality and low-quality firms will mean-revert faster than they actually do.

COMPARING LOW VOLATILITY AND HIGH QUALITYWe can construct both Low Volatility and High Quality factor strategies based on cap-weighted, top-quartile screens using similar factor definitions as described earlier. Chart 1 shows the average active sector weights for each strategy over the past 10 years for US equities. While consumer staples and health care are overweight in both strategies, consumer staples is more dominant in Low Volatility. High Quality has been very overweight in Info Tech but Low Volatility has been very underweight, a difference of more

CHART 2. CUMULATIVE RETURN RELATIVE TO US EQUITY MARKET. 30 YEARS TO END MARCH 2019

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CHART 1. AVERAGE SECTOR WEIGHTS VS US EQUITY MARKET. 10 YEARS TO END MARCH 2019

n High-Quality n Low-Volatility

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than 20%. Utilities is overweight in Low Volatility but underweight in High Quality.

By continuing to compare and contrast these two strategies, we run the risk of merely explaining the difference in performance and characteristics between sectors. To avoid that distortion, and to focus more on genuine factor differences, we repeat the same portfolio construction but do so within each of the 11 GICS sectors to build sector-neutral or “sector-adjusted” (SA) factor portfolios. This allows us to focus on genuine factor effects rather than confusing sectors with factors.

Chart 2 shows longer-term cumulative performance of the sector-neutral High Quality and Low Volatility strategies over the past 30 years. High Quality has significantly outperformed Low Volatility, even though these two strategies have had a fairly high market-relative correlation of 0.7 over the past 30 years.

Chart 3 shows the average monthly relative performance of each strategy over the last 30 years in up and down market months. This shows that both strategies do better when the market is down than up. Low Volatility provides more downside protection although it suffers more in the up-market months that have been dominant in US equities over the past 30 years.

Examination of the range of factor tilts for each of these strategies versus the US market over the past 10 years to end March 2019 is revealing.

Using a holdings-based analysis of the Low Volatility strategy, Chart 4 confirms the constructed tilts away from volatility (red bars). The red markers show the tilts as at end March 2019. However, it is also interesting to see that Low Volatility has typically shown a positive quality orientation (purple bars) together with a bias to high yield (dark blue bars) that is often associated with low volatility. Low Volatility has almost always been expensive on the value measures (light blue bars) of book-to-price, cash flow yield, and sales-to-price, although less so on earnings yield and EBITDA-to-EV. Low Volatility is also currently showing strong momentum (black bars) based on the previous 12 months of stock returns.

Chart 5 shows that, as well as the constructed bias to high quality factors (purple bars), the High Quality strategy also confirms an orientation to lower volatility (red bars). High Quality has been typically more expensive than the market on similar value factors to Low Volatility but to a higher


FACTORS IN FOCUSSTYLE ANALYTICS

CHART 4. LOW-VOLATILITY (SA) – FACTOR TILT RANGES VS. US EQUITY MARKET. 10 YEARS TO END MARCH 2019

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Source: Style Analytics

CHART 3. AVERAGE MONTHLY RETURN RELATIVE TO US MARKET. 30 YEARS TO END MARCH 2019

Low-Volatility (SA) High-Quality (SA)Up months (235) -0.7% -0.3%Down months (125) 1.4% 0.9%



degree, consistent also with a stronger historic growth bias (left green bars). As at end March 2019 (red marker), High Quality is also biased to stronger upward estimate revisions (rightmost black bar) compared with the US market, reflecting analysts’ current optimism for quality companies.

FINDING FUNDS WITH DEFENSIVE FACTORSWe used Style Analytics Peer Insights to filter ETFs in the Morningstar US Large Cap Universe based on the same low volatility and quality definitions above applied to a holdings-based analysis of the entire universe. We screened down to around 20 funds for each style, and summarised the resulting fund names by word frequency in a word cloud. Charts 6 and 7 show the results for the Quality screen and the Low Volatility screen respectively.

It seems that the names of the more defensive US Large Cap ETFs in terms of quality and low volatility do generally corroborate how the funds

are actually oriented based on their underlying holdings, although there are some fund names that may not reveal their defensive nature so directly. The High Quality screen also selects funds labelled as Growth, and the Low Volatility screen tends to also select dividend funds.

Interestingly, there were only two funds that met both of these particular High Quality and Low Volatility screens as at end March 2019:- the SPDR MSCI USA StrategicFactors ETF (QUS) and the VictoryShares Dividend Accelerator ETF (VSDA).

Overall, despite some correlation between low volatility and quality, ETFs exposed to these defensive factors do have quite different holdings - it is not the case that similar funds are simply being disguised by different fund names. Investors who want to get defensive need to further validate and compare funds, and demand transparency to the underlying factor ingredients. This will help to ensure the best fund fit for their investment views.

CHART 6. US LARGE CAP ETF FUND NAMES – QUALITY SCREEN

CHART 7. US LARGE CAP ETF FUND NAMES – LOW-VOLATILITY SCREEN

CHART 5. HIGH-QUALITY (SA) – FACTOR TILT RANGES VS. US EQUITY MARKET. 10 YEARS TO END MARCH 2019

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Bernie Nelson is President of Style Analytics North America and is based in Boston, USA. Bernie speaks regularly at international investment conferences on factor investing and smart beta, environmental social and governance (ESG) investing, and style and risk analysis in asset management.

Defensive strategies get the job done in recent turmoil Following the adverse market conditions in Q4 last year, Mark Barnes, managing director, head of US research at FTSE Russell, takes a look at which factors performed best during this more volatile period and whether it is worth seeking exposure to more defensive approaches

The recent market turbulence has reawakened painful memories. It has also revived interest in defensive strategies that can provide long-term downside

protection when markets fall without sacrificing much upside participation when they rise.

But not all defensive strategies are created alike – they have distinct objectives that have produced very different return patterns over time. The December meltdown and subsequent snapback offer a live test case of this point.

In our recent research paper we compare three popular defensive approaches based on back-tested FTSE Russell index data since September 2003: Minimum Variance (Min Var), Low Volatility Factor (LVF), and Equal Risk Contribution (ERC) . They pursue very different goals: • Min Var seeks to minimize the portfolio volatility,

while maintaining sufficient diversification. • LVF explicitly targets consistent exposure to the

(low) Volatility factor. • ERC portfolios are built so that each

stock contributes equally to the overall portfolio volatility, with the aim of avoiding concentration risk.

To achieve these goals, each strategy employs its own implementation ground rules. As our research shows, even small differences in these methodologies can yield major differences in risk exposures and performance, especially over the short run. This was clearly the case in the most recent bout of market volatility.

As expected, all three defensive portfolios fell less than the benchmark (the FTSE Developed Index) in the Q 4 downturn and rose less in the January rebound, outperforming for the full four-month span. Excess returns were highest

for Min Var (at +2.2%), followed by LVF (+1.0%) and ERC (+0.85%).

Looking at participation ratios, Min Var was the most defensive of the three approaches (with participation ratios of 0.77 for both the Q 4 and January 2019), meaning that it captured 77% of the broader market’s selloff in Q 4 and 77% of the market’s rebound in January. By contrast, LVF was the least defensive (with respective ratios of 0.90 and 0.92), while ERC was in the middle, with respective ratios of 0.87 and 0.82.

Interestingly, Min Var’s similar participation ratios for both periods indicates that its outperformance came from the market not fully regaining lost ground. LVF, on the other hand,

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FACTORS IN FOCUSFTSE RUSSELL


EXHIBIT 1. TOTAL RETURNS (%)

Q4 2018 JAN 2019 TOTAL PERIOD

n Benchmark n Min Var n ERC n LVF

-16.0

9.06.9 7.3 8.2

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Source: FTSE Russell. Data from October 2018 through February 3, 2019. Data based on the FTSE Developed Index Universe. Past performance is no guarantee of future results.

FTSE Russell is a leading global provider of benchmarks, analytics, and data solutions with multi-asset capabilities

had a slightly positive participation difference (up minus down), meaning that it protected more on the downside than it gave up on the upside. Thus, if the market had completely recovered, LVF would have risen modestly, given these participation ratios. Meanwhile, ERC’s participation difference was negative.

Table 1 provides a more granular look at the sources of excess returns. Most of Min Var’s and ERC’s excess returns came from country and industry exposures, while very little came from factor exposure. By contrast, three-fourths of LVF’s excess return came from factor exposure.

CONSTRUCTION METHODOLOGYThis pattern makes sense given that both Min Var’s and ERC’s construction methodology revolve around portfolio risk, which acts to diversify country and industry exposures. For example, Min Var and ERC’s large exposures to Hong Kong

added to excess returns, as did their overweights to Utilities. LVF’s comparatively smaller weight in Utilities had a neutral impact on excess returns, while its negligible exposure to Hong Kong detracted from relative performance.

Factor exposures can also make a big difference in performance (Table 2). LVF benefited from its large (and specifically targeted) exposure to the (low) Volatility factor, which outperformed the benchmark over this period. While Min Var is usually overweight (low) Volatility, its exposure was neutral during this period, hindering performance. The same goes for ERC, which had negative exposure to (low) Volatility. Another important factor was Momentum, which lagged the benchmark during the period. Min Var’s and ERC’s negative exposures to Momentum contributed to their outperformance.

In our research paper, we delved into how these strategies behaved during previous major market shocks, specifically the Global Financial Crisis, the Lehman Collapse, the European Credit Crisis and the China Growth Scare of 2015. Although all approaches provided meaningful downside protection during these episodes, they did so in ways specific to their makeup.

VOLATILITY REDUCTIONSFor example, during the preliminary global financial crisis sell-offs from November 2007 to

Not all defensive strategies are created alike – they have distinct objectives that have produced very different return patterns over time. The December meltdown and subsequent snapback offer a live test case of this point


Table 1. Attribution Summary of Sources of Excess Returns (%) − Oct 2018 to Jan 2019

Country Industry Factors Residual Excess ReturnMin Var

Q4 2018 1.33 1.12 0.64 0.98 4.06Jan 2019 -0.77 -0.23 -0.57 -0.49 -2.07Total Period 0.56 0.89 0.06 0.48 1.99ERC

Q4 2018 1.71 0.48 -0.42 0.47 2.23Jan 2019 -1.42 -0.11 0.27 -0.30 -1.57Total Period 0.28 0.37 -0.16 0.17 0.67LVF

Q4 2018 -0.30 0.12 1.88 0.04 1.74Jan 2019 0.39 0.00 -1.13 -0.01 -0.76Total Period 0.08 0.12 0.75 0.03 0.99Source: FTSE Russell. Data from October 2018 through February 3, 2019. Data based on the FTSE Developed Index Universe. Past performance is no guarantee of future results.

Table 2. Select Factor Attribution Details − Oct 2018 to Jan 2019

Attributed Volatility

Effect

Average Active Volatility

Exposure

Attributed Momentum

Effect

AverageActive Momentum Exposure

Min Var -0.10 0.01 0.29 -0.06ERC -0.40 -0.15 0.40 -0.15LVF 0.71 0.31 0.00 0.03Source: FTSE Russell. Data from October 2018 through February 3, 2019. Data based on the FTSE Developed Index Universe. Past performance is no guarantee of future results.

March 2008, all three defensive portfolios provided relatively small reductions in volatility. Although they all benefited from industry diversification (for example, underweights to Technology and overweights to Utilities), Min Var and LVF held up better than ERC. This was mainly because of their positive exposure to the (low) Volatility factor, whereas ERC had negative exposure.

In the Lehman collapse from June 2008 to February 2009, our research shows that ERC was helped by its country diversification (for example, the overweight to Japan, which performed relatively well during that period). However, once again, ERC was underexposed to (low) Volatility, whereas Min Var and LVF were overweight. Moreover, exposures to (small) Size, which also performed well during part of this period, helped Min Var and ERC, but hurt LVF.

In the 2011 European Credit Crisis, we found that Min Var and LVF benefited considerably from their (low) Volatility exposure, but ERC had only a slightly positive active weight to the factor.

ERC was also hurt by its sizable overweight to Japan and underweight to the US. As with the previous episode, Min Var’s exposure to Size and Volatility provided considerable downside protection during that episode. During the China Growth Scare from August 2015 to January 2016, ERC once again benefited from its underexposure to (low) Volatility. Size, on the other hand, benefited Min Var and ERC but hurt LVF. Again, ERC held large active weights in Japan and the US, which detracted from performance.

CONCLUDING THOUGHTSThese analyses underscore the effectiveness of defensive strategies to perform as expected during times of heightened volatility. As important, however, it reinforces how their differing objectives result in different exposures and, thus, performance outcomes. It is important for investors to understand these differences when choosing the approach that can best addresses their investment needs.


Mark Barnes is Head of US Research for FTSE Russell Indexes. Dr. Barnes joined FTSE Russell in 2018 and is responsible for applied research and new index development for the United States.

FACTORS IN FOCUSFTSE RUSSELL

Evaluating Consistency Across ESG Vendor Scores

Anthony Renshaw, director of index solutions at Axioma Investment takes a deep dive into the inconsistencies in the way environmental, social and governance (ESG) issues are measured between different providers. While there have been numerous ESG methodology studies, the asset management industry is yet to find a one size fits all solution meaning metrics can vary from one provider to the other. Is there a solution to this issue?

SEVERAL RECENT PRESS ARTICLES HAVE HIGHLIGHTED inconsistencies in ESG scores across different vendors, which is troubling given ESG’s increasing use in investing. In our recently published paper, A Survey of ESG Vendor Data: Strategies for Managing Score Differences we looked at data from four ESG vendors and analyzed how pervasive ESG disparities are, where they arise, and what could be done to develop a more consistent ESG taxonomy. In this article, we highlight some of those findings from the research. In a nutshell, we find inconsistencies of varying degrees across individual E, S, G components.

Breaking down the E, S, Gs: Industry Average E (Environmental) ScoresTable 1 below lists the scores available to us from each of four ESG vendors. Vendor A gives its E score in USD (e.g., millions of

dollars of greenhouse gases, carbon emissions, etc.); vendor B reports its E score as a percent of company revenue (e.g., dollars of environmental impact divided by company revenue); vendors C and D give scores between 0 and 100 for E, S, G, and ESG combined.

Table 1. A comparison of the different ESG scores available for this study

Vendor Score(s) UnitsA E USDB E % of RevenueC E, S, G, ESG 0-100D E, S, G, ESG 0-100

One method to assess the consistency of the E scores is by computing the rank correlations of industry averages across different equity universes. Table 2 shows the rank correlations


of the average GICS industry E scores across the four vendors between the different pairs of E scores for several equity universe as of the end of August 2018. Also included in the table is the industry-industry rank correlation for the Axioma Book-to-Price and Earnings Yield factors, as given by the Axioma Global Equity Fundamental Factor Risk Model (AXWW4-MH). The comparison of these two value descriptors provides a benchmark for the kind of rank correlation (68%) to expect from a well-known, traditional pair of similar (but not identical) factors. The table also shows the average rank correlation across each of the five regions.

All of the rank correlations in Table 2 are positive, most strongly positive, indicating that they are all capturing the same sense of E. A couple of the rank correlations are larger than the Axioma value pair rank correlation, and even the lowest regional average rank correlation (36% between B and D) is reasonably high and positive. The correlations in the US universe are particularly high (minimum of 73%) across all pairs (except the Axioma value pair). E scores therefore show a high level of consistency across vendors.

S (Social) ScoresNext, we examine the consistency of S (social) scores between vendors C and D (vendors A and B only provide E scores). Ideally, we would report results with more than two data vendors but as we

cannot, we should avoid over-generalizing the following findings. Table 3 shows the rank correlation of the GICS industry average S scores for each equity universe. All the correlations are about 70%, with the exception of the US All Cap universe, which only has a 32% rank correlation. There appears to be reasonable agreement between these two S scores, particularly outside of the US.

So, from the point of view of rank correlation, the S scores are fairly consistent.

G (Governance) ScoresFinally, we examine the consistency of G (governance) scores between vendors C and D. Table 4 shows the rank correlation of the GICS industry average G scores for each equity universe. The G score correlations are notably lower than those for E and S (with the exception of the Asia-Pacific universe).

The fall-off in correlation (and therefore consistency) is perhaps expected. The E and S scores are linked, at least in part, to what each industry does. Governance, on the other hand, is an equal opportunity factor: any company in any industry can be well managed. This lack of industry influence appears to lead to less consistency in the G scores for these two vendors.

Conclusion As more vendors enter this space and more work is done to standardize the ESG definitions, we think that in the near-term, granular scores (E, S, G, and sub-components of these) should be considered over composite ESG scores. Portfolio managers could integrate the ESG characteristics most important to them to ensure they are correctly reflected in their portfolios. For the limited data set available to us, we found that the E score has the highest consistency, followed by S scores. G scores appear to require care due to their relatively poor consistency.

ETFSTREAM.COM

CLOSING REMARKSESG VENDOR SCORES

As more work is done to standardize the ESG definitions, we think that in the near-term, granular scores (E, S, G, and sub-components of these) should be considered over composite ESG scores


Table 2. Rank correlations of industry averages between different pairs of E scores for different equity universes.

First Vendor Second Vendor Average Across Regions Global Core US All Cap Devel. Europe

All Cap

Devel. Asia Pacific ex Japan

All CapEmerging Core

A B 84% 89% 85% 83% 74% 87%A C 74% 69% 84% 72% 82% 66%C D 73% 80% 86% 43% 83% 74%Axioma a Book-to-Price Axioma Earnings Yield 68% 62% 46% 71% 79% 80%B C 65% 66% 73% 63% 63% 61%A D 43% 48% 84% 29% 13% 40%B D 36% 44% 83% 6% 8% 37%

Table 3. Rank correlations of industry averages between the S scores of vendors C and D for each equity universe


All Cap



C D 64% 70% 32% 72% 73% 71%

Table 4. Rank correlations of industry averages between the G scores of vendors C and D for each equity universe


All Cap



C D 23% 4% 6% 29% 55% 21%

60 seconds from the buy-side: what fund researchers look for in factor ETFsETF Stream editor in chief David Stevenson speaks to Kilik & Co’s head of fund research Mick Gilligan on the factors he uses to gain exposure

David Stevenson: Do you use smart beta or factor-based ETFs within your client’s accounts, and if so, why?Mick Gilligan: Yes. There are a number of well-established factors that have persisted over long periods of time, which appear to deliver superior returns to that of their broad market index. These factors have credible explanations as to why they might persist – they reflect systematic sources of risk (i.e. sources of risk that cannot be diversified away and provide investors with a risk premium) and they are a reflection of investor biases that tend to persist over time.

How do you view smart beta/factor-based ETFs? As part of the passive toolkit or as a substitute for actively managed funds? Or something inbetween?In our Multi-Manager portfolios we largely view them as a low cost source of factor return. We take the view that the well-established investment factors should be represented in portfolios and then look for the lowest cost way of getting this exposure. ETFs are often a useful means of doing so.

Which parts of the smart beta/factor-based spectrum (including thematic ETFs) interest you most? If you had to identify one factor that is really compelling what would it be? Which kind of smart beta/Factor LEAST interest you?There are a huge number of factors that investment professionals, academics and financial engineers have come up with. However, there are only three in particular that we feel have stood the test of time and appear to have persisted. These are size (the smaller company effect), value and quality. We take a portfolio approach and do not seek to ‘bet the ranch’ on any one factor. Many studies have looked at the issue of factor timing but few, if any, approaches have proved useful return forecasters. Beyond that we invest in socially responsible investment (SRI) ETFs as they allow investors to have greater influence on the world and are a low cost way of making more responsible investments. It is early days for ESG but the evidence regarding investment performance is very encouraging. When you focus on a particular ETF product to include on your lists what features do you investigate i.e. fund structure, index composition? For the index we look at the logic of the structure and the underlying constituents. We consider rebalancing

frequency, withholding taxes for a UK investor, liquidity constraints. For the ETF, we consider the listing exchange, whether it is UCITS, whether it has reporting status, fees, turnover level of the strategy, stock lending approach, etc. Are you concerned by the recurring accusations of hacking and data mining levelled at all factors and smart beta strategies? Are the identified premiums really that robust?Yes, this is one of the primary reasons that we stick to the aforementioned factors. A lot of ‘new’ factors look very good in back tests but lose efficacy once they get launched within a product. Alongside smart beta and factor based investing, we have also seen the rise of thematic based investing using ETFs (robotics, ageing society for instance) – does this interest you?Yes, we like the option to invest in themes that look like they are likely to have a strong tailwind for many years to come. Robotics and Cybersecurity are two such themes we access via ETFS. How do you engage with clients about smart beta – is there any interest and if there is interest do clients raise any concerns?We produce a quarterly Factor Investing Bulletin. This is helping to raise interest from low levels.

Are there any specific areas where you would like to see new products emerge? For instance, does the idea of factor-

based fixed income ETFs interest you? Anything that can help to bring costs down is worth

considering. In the fixed income space, we like funds that allow you to keep the portfolio within predetermined

maturity ranges i.e. iShares Sterling 0-5 Maturity.

By 2025 do you think you’ll be making extensive use of smart beta

products and factor ETFs? What proportion of portfolios do you think they’ll comprise?This very much depends on product issue. I do not foresee our portfolios embracing too many factors unless there is strong academic

evidence to support the case.


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AN ETF STREAM PUBLICATION // // Q2 … · 2019-06-25 · ETNs. We only looked at ETFs in the familiar sense. Here, it is interesting to note that the best performers in Q1 2019 were