ESG, superior return at lower risk, an oxymoron? Quest Equities

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2020

ESG, superior return

at lower risk,

an oxymoron?

Quest Equities

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ESG, superior return at

lower risk, an oxymoron?

The Pictet Quest equities franchise offers a range of defen-sive equity strategies that have built up a strong track re-cord over the past decade.

Managed according to a defensive approach, our flag-ship Quest Global strategy has delivered market-like per-formance with over 10% lower volatility than the global eq-uity markets since inception in 2012.

But Quest is not just about being defensive. We can de-viate significantly from the global equity indices, produc-ing portfolios with a high active share as we seek out the best investment opportunities. Quest also incorporates strict ESG criteria – either at best-in-class or full integra-tion levels depending on client requirements. This means we can help our clients achieve their investment aims with-out exposing them to sustainability risks.

In this paper we explain the history of the Quest ap-proach, how we manage portfolios today, and how it helps explain one of the best-known anomalies in the world of fi-nance: the low-volatility anomaly.

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1. The strategy’s history: rooted in responsibility 10

2. Capitalising on market inefficiencies 12

3. The 4Ps: a powerful investment engine 15

4. How the 4Ps help explain the greatest anomaly in finance 17

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10 e sg , s u pe r ior r e t u r n e t l o w e r r is k , a n o x y m or o n ?

ESG becoming the new normal

Climate change, workers’ rights, respecting minority shareholder rights. Issues like these have gained in promi-nence in recent years, compelling asset managers to think about how to take them into account in the way we manage our clients’ money.

Having previously been a concern mainly for sophisti-cated institutions, all kinds of investor are now demanding that their money be managed more sustainably. Therefore, integrating ESG criteria in investment approaches is rapid-ly becoming the new normal.

Regulators are also trying to address the issue, espe-cially in Europe which has unveiled a new “Taxonomy” reg-ulation which provides a framework to facilitate sustaina-ble investment. However, it’s still a work in progress for both the asset managers and investors who take ESG is-sues seriously.

ESG since the beginning

A good thing about Pictet Quest Equities is we have been running it according to a responsible approach for many years – long before ESG had become mainstream.

In fact, we launched it as a specialist ESG strategy, be-yond basic exclusion rules, in response to demand from some of our institutional clients for an approach that could meet their return objectives while investing in line with their values. Today, a proprietary ESG screen on all the stocks in our investment universe is an integral part of the strategy’s investment process and ensures that we only in-vest in companies with solid ESG practices.

Coupled with a strong performance engine

Back when we launched the strategy, ESG was still in its infancy. This meant the market wasn’t rewarding com-panies that took their responsibilities seriously or punish-ing those that didn’t in the same way that it appears to be doing today. In particular, our approach suffered during the Great Financial Crisis, as did many other ESG portfoli-os. It became clear that while ESG strategies reduce inves-tors’ exposure to long-term risks, they can go through rela-tively long periods in which they underperform the global markets.

1. The strategy’s history:

rooted in responsibility

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So while our approach was able to meet our clients’ needs in terms of sustainability, it was apparent that we needed to add a different dimension to the way we man-aged the strategy if we were to achieve the risk-adjusted returns that they were looking for.

As a team of investors with a learning mindset, this ini-tial performance setback became the foundation of an im-portant discovery – our proprietary 4P framework. where we focus on key determinants of long-term returns: profit-ability, prudence, protection and price.

We believe companies with stable profitability, healthy balance sheets and whose shares trade at attractive valua-tions – firms that we describe as “financially robust” – should fare better in periods of market turbulence, which means they can deliver superior returns over the long run. But there is more to a company’s “robustness” and resil-ience than financial factors.

For firms to be sound long-term investments, they also need to embrace ESG considerations in their decision-mak-ing. Our Quest team’s analysis shows that the most finan-cially robust companies according to our proprietary 4P

framework (of which more later) are already doing this – they tend to score better on ESG criteria. As FIG.1 shows, top quartile 4P stocks skew more heavily towards a higher

ESG rating (3 or 4 stars using our proprietary scoring sys-tem) than bottom quartile 4P stocks.

Since implementing this 4P framework in 2009 we have produced long-term equity outperformance of the broad global equity markets with lower volatility than markets – all the while protecting our clients from exposure to ESG

risks and helping them contribute to a better future for our planet. 35% 30% 25% 20% 15% 10% 5%

4P BOTTOM QUARTILE 4P TOP QUARTILE

ESG 4 STARS ESG 3 STARS

ESG 2 STARS ESG 1 STAR

FIG.1

A CLOSE REL ATIONSHIP: QUE ST 4P FR AME WORK AND E SG

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Let’s take a look at how we’ve done it.

Back to basics – evolution has made humans bad investors

First, let’s take a step back and consider why it’s im-portant to try to outperform the broad markets in the first place.

Passive approaches that are designed to closely track market-cap-weighted indices effectively outsource key in-vestment decisions to the index provider. We believe this potentially leads to a myriad of risks, not least with regards to ESG. For example, indexation reinforces inequality by overweighting past winners, which can lead to excessive concentration in certain key industries and sub-optimal

ESG outcomes. Possibly even more fundamentally, by defi-nition, a market-cap index is overweight momentum and underweight value: it naturally overweights those compa-nies that have performed best and have become expen-sive, while underallocating to stocks that are cheaper. We believe that this is a bad starting point for building a portfo-lio.

What’s more, there is lots of academic evidence to show that humans are prone to certain biases when they make decisions. These biases work well in most contexts, but when people apply them to complicated financial deci-sions they often fail miserably. Backing this up, research – conducted by Pictet and other companies – has conclu-sively shown that the stock markets are inefficient, at least in the short term1_{. Investors in the stock markets are}

hu-man, and humans make systematic mistakes. These be-havioural mistakes are the result of millions of years of evo-lution, in which the human brain learned to respond to very different kinds of problems to those we find in the financial markets today2_.

As a result, if the stock markets are inefficient, it must mean that it’s logical for sophisticated investors to try to outperform the market. The question is – what’s the best

1 https://www.nobelprize.org/uploads/2018/06/advanced-economicsciences2013.pdf 2 Humans’ behavioural biases have long been a persistent feature of investing. As the Nobel

Laureate Richard Thaler observes: “In an economy where rational and irrational traders interact, irrationality can have a substantial and long-lived impact on prices.”

An influential research paper by Norway’s central bank has found that there are a number of socioeconomic and psychological factors that underpin irrational preferences. These, it discovered, are just as likely to interfere in the decisions of professional investors as they are in those of non-professional ones.

One widespread misconception is that gaining greater rewards requires taking on more risk. This cognitive bias helps speculative stocks”.

Barberis, Nicholas and Thaler, Richard H., A Survey of Behavioral Finance (September 2002). http://dx.doi.org/10.2139/ssrn.327880

The Quality Factor, Discussion Note, Norges Bank Investment Management, 02.12.2015

2. Capitalising on market

inefficiencies

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way of doing so?

Fundamental or quantitative?

Many investors take a fundamental approach, analys-ing company characteristics in order to identify those best placed to outperform. Professional fundamental investors have been trained to recognise the mistakes that humans are prone to, but they are difficult to eradicate entirely.

This is why a quantitative approach designed to strip out human biases and take the other side of the trades gen-erated by investors’ systematic mistakes are seen by many as a good way of generating long-term outperformance. An additional important benefit of using models to systemati-cally screen the universe is that they can help uncover op-portunities that a small team of humans simply wouldn’t have time to find or analyse in depth.

However, computers have their limits too, and we be-lieve it’s unwise to rely solely on models in the way that many systematic managers do.

Combining the best of both worlds…

One well-known example of the limitations of comput-ers is how artificial intelligence often confuses pictures of muffins and chihuahuas – computers can identify that they look similar in some photographs, but only a human

can be sure which is which.

In the same vein, cutting-edge technology can play a huge role in maximising investment returns, but human oversight is necessary to protect from mistakes that

com-FIG.2

CHIHUAS VS MUFFINS

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puters might be making.

A great example of this idea in action can be found in the world of chess. The best chess computers have been able to beat the best human players for the past two dec-ades, but it’s been shown that a human-plus-computer combination – even if the human is only a moderate player – in fact plays more accurate games than computers alone. This could be because in some cases the human is able to point out the best potential moves and quickly rule out moves that are obviously unwise, thus optimising how the computer uses its time.

Along similar lines, satellite navigation systems in peo-ple’s cars always know what the best way should be to go, but only the human driver could know if there is going to be a huge pothole in the road that is best to avoid.

We believe the same principle applies to investing. While our Quest Equities approach is principally quan-titative in nature, it stands out from many other systematic strategies in that it benefits from human oversight of all the stocks it invests in. Once our model has produced a list of potential investments, our investment team performs a fundamental assessment of the stocks to ensure they are strong candidates. We see this as vital because no quant model is aware of all the latest developments at a company or the sector it’s in, and it helps us avoid unpleasant prob-lems down the line.

…without forgetting ESG

In parallel to this we run our ESG scoring process, which leverages on our 20 years of experience in sustainable in-vestment. Ranking stocks according to a number of differ-ent ESG metrics from several different data providers, we avoid investing in any stocks that could expose our clients to some nasty surprises down the line.

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Beware false signals

We’ve seen that a quantitative approach can help us systematically exploit inefficiencies in the market, but there’s a lot that can go wrong in designing a quant strate-gy. Computers poring over vast volumes of company char-acteristics and past performance data could easily identify just by chance what appear to be several causative rela-tionships, but if these false signals were to form the basis of an investment process they could result in extremely disappointing performance.

It’s therefore vital that quantitative investors under-stand the source of the inefficiencies that they’re trying to exploit, and that those inefficiencies have a rational expla-nation grounded in financial theory and human behaviour.

Four proven investment dimensions

In managing the strategy our model analyses each stock in our universe to determine their exposure to four investment factors that we call our “4Ps”: their profitabili-ty, price, prudence and protection.

All of these dimensions meet the criteria we set out above: they are rooted in financial theory and human expe-rience, and our research shows that all four are predictors of a stock’s long-term outperformance.

They are as follows.

— Profitability: highly profitable firms tend to produce

better-than-expected risk-adjusted results while loss-making or barely profitable companies are more likely to price in overly optimistic future growth scenar-ios

— Prudence: while robust, stable firms make less exciting

investment cases, companies embedding strong op-tionality (high financial gearing, high asset growth) tend to be priced as “lottery tickets”

— Price: all else being equal, a lower-priced company has

more attractive risk-adjusted return potential than a higher-priced firm and involves less downside risk

— Protection: the higher-risk equity segment is likely to

be subject to additional buying pressure and tends to deliver sub-par returns over a cycle, we prioritise the opposite.

3. The 4Ps: a powerful

investment engine

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It’s interesting to note that while each ‘P’ has strong performance potential in its own right, the intersection of the four is where the real “juice” is to be found in terms of risk-adjusted return potential. Similarly, stocks with low scores for each of the 4Ps perform particularly poorly com-pared to their higher scoring counterparts. We can see this in the following chart.

Indeed, the 4Ps form a powerful combination because all four factors are not highly correlated with each other and capitalise on different sources of market inefficiency. They provide a true 360-degree view of the companies we analyse and help us uncover hidden opportunities across the entire investment universe.

When combined with ESG research and fundamental oversight, our 4Ps have been shown to produce consistent outperformance of the broad market over the long term.

FIG.3

4P FR AME WORK : THE W HOLE IS GRE ATER THAN THE SUM OF THE PARTS

PRUDENCE Q1 11% 10% 9% 8% 7% 6% 5% 4%

8% 10% 12% 14% 16% 18% 20% 22% 24% 26% ANNUALIZED VOLATILITY TOP 4P PRICE Q PROFITABILITY Q1 PROTECTION Q1 MSCI WORLD PRUDENCE Q4 PRICE Q4 PROFITABILITY Q4 BOTTOM 4P PROTECTION Q4 ANNU ALIZED RETURN

Source: Pictet Asset Management, Datastream, Worldscope. Top rated are top quartile of universe, bottom rated are bottom quartile.

Simulated cap-weighted portfolios rebalanced on a quarterly basis. From 31.12.1992 to 31.12.2019 Total return before fees in USD

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CAPM in reverse

Since the advent of the capital asset pricing model (CAPM) in the 1960s, financial theory has been rooted in the notion that risk and return go hand-in-hand: the higher the return an investor wants to achieve, the more risk they need to take on.

This has been shown however, not to be the case in practice: empirical studies have demonstrated that over the long term, stocks exhibiting low volatility tend to out-perform higher-volatility stocks3_{. This holds true across}

different markets and different time periods. In fact, it has been suggested that the exceptional returns of the most fa-mous investor of all time, Warren Buffett, can in part be at-tributed to what’s been called the low-volatility anomaly.

The following chart shows this phenomenon: when the stocks in the global equity universe are split into four quar-tiles based on their historical volatility, we can see that those in the lowest-volatility quartile (Q1 in the chart) out-perform the broad market, while the highest-volatility stocks (Q4) significantly underperform. This is essentially

CAPM in reverse.

And it has puzzled practitioners and academics alike for many years.

3 Haugen & Hein first demonstrated the volatility anomaly in the 1970s in their renowned pa-per “On the Evidence Supporting the Existence of Risk Premiums in the Capital Market” (http://dx.doi.org/10.2139/ssrn.1783797). Since then, academic researchers have demonstrated that low-volatility anomaly is an ubiquitous phenomenon that works even in other financial markets (https://doi.org/10.3905/jfi.2014.23.4.051).

4. How the 4Ps help explain

the greatest anomaly in

finance

Source: Pictet Asset Management, MSCI World. Data from 31.12.1999 to 30.09.2020

3% 2% 1% 0 -1% -2% -3% -4%

4P BOTTOM QUARTILE PROTECTION 4P TOP QUARTILE PROTECTION

FIG.4

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CAPM through a 4P lens

To better understand this phenomenon, we look at the relationship between returns and risk for stocks with high

4P scores and for stocks with low 4P scores

In the chart below the first thing we can see is that stocks with poor 4P scores (the purple line) are more vola-tile (as represented by their beta on the x-axis) than those with high 4P scores (the orange line). We can also see that for stocks with poor 4P scores, the low-volatility anomaly persists: the stocks with the lowest volatility tend to out-perform. But for stocks with high 4P scores, we see the re-verse: the stocks with the highest volatility outperform, showing that for this group the CAPM theory holds true. We believe this is because our 4P framework is less prone to distortions induced by behavioural biases.

Why could this be the case? It seems that the riskiest stocks drag down the overall performance of the low-4P

group of stocks. It’s not difficult to imagine that companies with weak fundamentals (e.g. a very low-quality, border-line-distressed company) will have very volatile investment returns. However, for higher-quality stocks, classic finan-cial theory holds true because in general they are less sub-ject to behavioural distortions.

The good news is that we avoid stocks with poor 4P

scores: we only invest in good companies whose risk and return behaviour is predictable and consistent with both financial theory and our decades of experience. This has benefits for the way we construct our portfolio, as the opti-miser model we use implicitly expects a positive relation-ship between risk and return.

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 14% 12% 10% 8% 6% 4% 2% 1.4 A VERA GE ANNU ALIZED RETURNS BETA FIG.5

TOP 4P RECOVERS A RISK- RE TURN EFFICIENT PROFILE . RISK MISPRICING IS FURTHER AMPLIFIED IN BOT TOM 4P STOCKS

Source: Pictet Asset Management, MSCI World. Data from 31.12.1999 to 30.09.2020

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The track record of our Quest Equities approach over the past decade clearly shows the scope for an ESG-focused portfolio to provide strong returns when used in conjunc-tion with a robust return-generating engine complemented by human oversight. What’s more, our portfolio has pro-duced a stream of returns less volatile than that of the broad global equity markets, meaning it produces no un-welcome surprises for our clients.

Our 4P framework has been a vital addition to our origi-nal ESG approach, harnessing all the important cognitive biases that plague many investors’ decisions. And yet we are fully aware that the quest for strong investment returns is an ongoing process and that we must never rest on our laurels. With this in mind, we continuously research the best way of implementing our approach and constantly re-view new sources of data and use new techniques.

With over 20 years of experience and a deep talented team, we are convinced that the power of a quantitative process managed and supervised by experienced manag-ers offmanag-ers investors the best chance of reaching their in-vestment goals in the a more predictable manner.

In short, no, “ESG, superior returns and lower risks” needs not be an oxymoron. This is good news for investors.

5. Conclusion

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Disclaimer

This material is for distribution to professional investors only. However it is not intended for distribution to any person or entity who is a citizen or resident of any locality, state, country or other jurisdiction where such distribution, publication, or use would be contrary to law or regulation.

Information used in the preparation of this document is based upon sources believed to be reliable, but no representation or warranty is given as to the accuracy or completeness of those sources. Any opinion, estimate or forecast may be changed at any time without prior warning. Investors should read the prospectus or offering memorandum before investing in any Pictet managed funds. Tax treatment depends on the individual circumstances of each investor and may be subject to change in the future. Past perform ance is not a guide to future performance. The value of investments and the income from them can fall as well as rise and is not guaranteed. You may not get back the amount originally invested.

This document has been issued in Switzerland by Pictet Asset Management SA and in the rest of the world by Pictet Asset Management Limited, which is authorised and regulated by the Financial Conduct Authority, and may not be reproduced or distributed, either in part or in full, without their prior author isation.

For investors, the Pictet and Pictet Total Return umbrellas are domiciled in Luxembourg and are recognised collective investment schemes under section 26 4 of the Financial Services and Markets Act 20 0 0. Swiss Pictet funds are only registered for distribution in Switzerland under the Swiss Fund Act, they are categorised in the United Kingdom as unregulated collective investment schemes. The Pictet Group manages hedge funds, funds of hedge funds and funds of private equity funds which are not registered for public distribution within the European Union and are categorised in the United Kingdom as unregulated collect ive investment schemes. For Australian investors, Pictet Asset Management Limited (ARBN 121 228 957) is exempt from the requirement to hold an Australian financial services licence, under the Corporations Act 20 01. For US investors, Shares sold in the United States or to US Persons will only be sold in private placements to accredited investors pursuant to exemptions from SEC registration under the Section 4(2) and Regulation D private placement exemptions under the 193 3 Act and qualified clients as defined under the 19 4 0 Act. The Shares of the Pictet funds have not been registered under the 1933 Act and may not, except in transactions which do not violate United States securities laws, be directly or indirectly offered or sold in the United States or to any US Person. The Management Fund Companies of the Pictet Group will not be registered under the 19 4 0 Act. Past performance is not indicative of future results, which may vary. Projected future performance is not indi cative of actual returns and there is a risk of substantial loss. Hypo thetical performance results have many inherent limitations, some of which, but not all, are described herein. No representation is being made that any account will or is likely to

achieve profits or losses similar to those shown herein. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. The hypothetical performance results contained herein represent the application of the quantitative models as currently in effect on the date first written above, and there can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. There are numerous other factors related to the markets which cannot be fully accounted for in the preparation of hypothetical performance results, all of which can adversely affect actual performance results. Hypothetical performance results are presented for illustrative purposes only. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. There is no guarantee, express or implied, that long term return and/or volatility targets will be achieved. Realised returns and/or volatility may come in higher or lower than expected. A full list of the assumptions made can be provided on request. Issued in November 2020 ©

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