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Thought leadership at Schroders:Harnessing dataReappraising commoditiesDiversifying with propertyControlling volatilityUnderstanding factors

Thought leadershipIssue 6, 2016

Contents

2Harnessing the data deluge

We are increasingly flooded with a mass of numbers, from Twitter trends to real-time electricity usage. Investors who want to stay ahead of the game need to harness the power of this deluge. However, just as important is to understand the limitations of data and the science used to manipulate it.

6Reappraising the case for commodities

Sentiment in commodities has rarely been worse. Despite that, they are one of the few asset classes that look genuinely cheap, provide a good hedge against inflation, and offer significant diversification benefits. Given relatively inefficient markets, active managers should be well positioned.

10The international route to a truly diversified property portfolio

Real estate is often seen as offering low correlation to a traditional equity and bond portfolio. It should therefore offer excellent diversification potential, yet investors in direct real estate often show a bias towards their home market. We suggest they would benefit by diversifying overseas.

16Managing volatility for better investment outcomes

Many investors want the returns that equities can give, but cannot live with the risks. We outline how volatility control techniques can reduce the risks while retaining many of the gains. The results will be different, we argue, but the risk-adjusted returns should be much improved.

22Primer: the factor drivers of investment returns

Factor investing is transforming portfolio construction, offering diversification, transparency and economy. The concepts underpin everything from smart beta to alternative risk premia. Factor investing is an important new investment tool, but understanding remains critical.

Bigger horizons, better results

It is often the big calls that matter most to investors − whether it be about which assets to buy or what investment approach to take.

Seldom have such issues been so important, as interest rates plumb new depths and political risk seems to mount daily. Against that background, this edition of Investment Horizons suggests investors look again at two asset classes that could help spread some of these risks, while highlighting a couple of investment approaches that we think should be better understood.

But we start off with a wide-ranging review of a big area that only seems to get bigger by the day. Data is omnipresent and investors need to know how to use it to their advantage or face being left behind. Our first article offers both a practical and philosophical roadmap through the data jungle.

Equally big is the question of inflation. Tentative signs are emerging that official attempts to rekindle inflation may at last be taking effect. If so, commodities may soon be back in fashion as an inflation hedge. We argue that their value goes far beyond this, bringing valuable diversification benefits to a traditional investment portfolio.

Diversification is also the theme of our piece on directly-held real estate. Investors in property seem to be particularly prone to the feeling that “east-west, home’s best”.

We argue this is short-sighted and that an international outlook can create a

more efficient portfolio, with greater potential for both return and risk reduction.

While diversification is vital, there are more systematic ways for investors to reduce risks. One where Schroders has done an enormous amount of work is in how to limit volatility in portfolios. We have distilled some of this research into our piece on controlling volatility, focusing on three practical ways such techniques can be used to improve outcomes.

Another route to better results is to look beyond asset classes to the fundamental drivers of investment returns. Factor-based investing has gained popularity on the back of the rise of smart beta. We argue in our last piece that, while factors can be powerful investment tools, they need to be carefully managed to be truly effective.

We hope you enjoy reading our thoughts, but whether or not you do, please get in touch. We always look forward to hearing from you.

Yours sincerely,

Gavin RalstonHead of Thought Leadership

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Harnessing the data deluge

Mark AinsworthHead of Data Insights, Investment

The process of collecting and analysing data has undergone a revolution. A whole mass of new numbers, from Twitter trends to real-time electricity use and demographic data, now floods in and can be manipulated in ways unheard of 30 years ago. If investors want to stay ahead of the game, they need to channel this deluge and harness its power to generate alpha in new ways. Just as important, however, is to understand the limitations of data and why data science can never replace the skills of a good portfolio manager.1

The information we are referring to here is large and “alternative” datasets that may be poorly configured for financial market analysis. Typically, alternative data is anything that is not already part of the core currency of investment research. Examples might include web traffic or geo-locational data (that is, information about where in the world things are happening). The dramatic increase

1 This article is extracted from a longer paper of the same name available at www.schroders.com

in computer processing power, storage capacity and information mean that the amount of such data that can be interpreted by an analyst or fund manager is growing at an exponential rate, and in a thoroughly unstructured fashion.

At the same time, a cadre of data science professionals is emerging to process the data. The tools available to these scientists are changing at a similar pace: traditional linear computational processes are rapidly being overshadowed by the newer techniques required to illuminate these much larger bodies of data in scale and at speed. Indeed, the label “Big Data” has emerged alongside the rise in open-source technologies for processing and analysing data in parallel across multiple computers. These new technologies work on standard desk-top computers, in contrast to the very specialist machines used by IBM, Oracle, Teradata and the like.

Figure 1: Both data and its analysis are rising fast Interest over time

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Taylor rule regression based on US Core Personal Consumption Expenditures (PCE) inflation less assumed core PCE inflation target of 2%, and US unemployment rate less assumed non-accelerating inflation rate of unemployment (NAIRU) of 6%. Okun factor of 2. Current implied policy rate = 2.4%. Regression coefficients are 1.53 and 0.47 respectively. Correlation is 0.84. Source: Bloomberg, as at 14 August 2014.

Ben WicksResearch Innovation

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Mark AinsworthHead of Data Insights, Investment

Inspiration. A collateral benefit of applying data science and alternative data to long-term investing is the scope to enhance collaboration. Large datasets tend to cut across multiple sectors. This means their exploitation can best be conducted centrally by a data science operation, working in collaboration with multiple end users. The combination can give large organisations a disproportionately large advantage. One area where this is possible is in using national customs data to gain a better measure of trade flows for use in a range of analysis, from individual companies to whole economies, by a range of analysts, be they retail, banking, consumer and economic, to name but a few.

Timing. Data scientists can ask helpful questions as well as helping to provide answers. Giving data scientists an explicit mandate to draw attention to patterns in alternative data should ensure that investors are grappling with issues that are relevant, even when they may not be the focus of general market attention. A case in point is where investors who want to keep a watch on the health of a particular brand use customer perception data to alert them to any change in sentiment.

Capacity. Perhaps the biggest impact of data science on the active manager is the freeing up of human intellectual capital that can be dedicated to further analysis and research. An investor who can rely on other specialists to help extend his or her vision in all the above ways will be that much freer to think and explore fresh sources of long-term alpha. The timing example above is one practical way data science can free up the manager’s time for more creative work.

Competence. Tracking an investor’s decision-making processes in a scientific manner can highlight weaknesses and improve ability. Good data science, combined with the lessons of behavioural finance, can shine a light on investor biases and optimise conviction. In doing so, it can help investors to know themselves better and improve their performance.

We can track the rise of both Big Data and data science, appropriately enough, with some Big Data: in this case the volumes of searches for these terms on Google (Figure 1).

One classic application of data science is “recommender engines”, such as those that suggest products to Amazon customers or films to users of Netflix. The algorithms behind these recommender engines are complex, and hard to execute in bulk. Recommender engines are practical expressions of scientific theories. As such, they can hugely enrich and improve the performance of any business that builds them into its operations.

These algorithms, along with the Big Data they feed on, can also hugely improve the quality of decisions made by fund managers. A good decision is a function of several factors. A fund manager with the right abilities, who has excellent information and able to form a coherent but differentiated view, drawing on a broad range of sources at the appropriate time, is likely to generate good performance. We argue that data science can enhance every one of these factors (and help with a few others):

Information. This is the lifeblood of both data science and investing. Information about such things as brand perception, customer affluence, location, even drive-times on roads, can provide useful perspectives on the prospects for a business. To take one instance, a comparison between the store locations of a retail chain and demographic and infrastructure data may play a key part of the share price valuation process.

Differentiation. Effective data science will unearth insight that is unlikely to be captured by others. The greater the quantity of data, the more permutations of analysis it becomes possible to conduct. By extension, the likelihood of others conducting the same analysis diminishes. A large investment organisation is likely to be able to deploy data science at scale to generate insight that cannot be precisely replicated. An example might be testing the success of a company’s overseas expansion policy by comparing its “share of conversation” on social media with that of local competitors.

Figure 2: Second guessing the competition authorities with Big Data

Source: Retail Locations and Schroders, as at June 2015.

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Here are some examples of unstructured data that investors are increasingly exploiting:

– Open-source data. This is freely available and often published by scientific bodies and governments. The US Food and Drug Administration’s Adverse Event Reporting System, a massive database used for recording safety concerns, is an example of open-source web-based data. Such data varies in availability, but there is a general trend across the globe to increase the amount of it that is publically available.

– Geospatial data. This combines mapping analysis with statistical and demographic data. It can be used to assess expansion plans, mergers and acquisitions, or competitive positioning at a very local level. For example, when the UK’s second- and third-largest betting shop chains (Ladbrokes and Gala Coral respectively) announced plans to merge, the deal was subject to a probe by the UK competition authorities. Speculation ranged widely about how many shops would need to be sold to pass the review. There was a clear investment advantage to gaining a more accurate estimate. We were able to map the two companies’ store locations, along with those of their competitors, and apply the conditions that the authorities might impose, as Figure 2 shows. On the left is an example of the sort of large-scale map we could analyse to initially identify problem areas, before zeroing-in on particular store overlaps, as shown in the map on the right. The result was intelligence that gave us a clear edge in assessing the deal.

– Brand sentiment. Data scientists can develop tools for analysing and tracking changes in a brand’s health, as well as how it is faring against competitors. Using techniques that analyse web-based activity trends, amongst other information, data scientists can formulate and maintain a real-time picture of a brand’s health.

– Web-based content. What is being written on the internet, whether in the news media or via social networks and forum discussions, can form a rich seam of data. Such data can be exploited to monitor awareness levels and attitudes towards a company, product or concept, and in particular to detect inflection points in perception that might otherwise go unnoticed until witnessed retrospectively in company results.

– Micro data: forecasting particular reporting lines. Tracking precise data on a particular product can yield powerful material for determining revenue forecasts. This reduces the margin of error in forecasting near-term results. But the detail available within such data also enables useful conclusions to be drawn for the longer-term, particularly when cross-referenced with other information about company strategy and local consumer behaviour.

– Macro data: understanding trade and individual events. Applying natural language processing techniques to large datasets of news and events can help in the development of regional indices of events, such as for bankruptcies, or to work out the correlation of different entities to event risk. Used in conjunction with large datasets of global trading activity, such information can

help us better understand the global economic picture and the relative exposure of different investments. Here, the data should allow the investor to be better positioned to understand and manage risk.

A properly focused small investment organisation can use the vast quantities of new data available highly effectively and in an agile manner. However, the data revolution in asset management also confers particular advantages on larger, well-structured, organisations, such as having:

More data to exploit. The scale and variety of the data available today require considerable engineering and data management resources if it is to be exploited optimally.

More local knowledge. Alternative datasets are typically extremely complex. The availability in a large organisation of detailed knowledge of multiple sectors and industries should help shape the work of data scientists more effectively.

More opportunities. Having a broad international footprint brings with it greater opportunities. Alternative data sets, such as direct surveying or mobile phone usage data which sidestep reliance on government channels, can create opportunities not available to rival investors.

More collaboration. The bigger the organisation, the greater the chance to innovate. Having more investors who hear about data science being used by peers should inspire them to consider more uses for the data themselves.

More data scouts. A good data science capability should turn every investor into a potential data scout. Investors then form a habit of flagging new data that they come across for possible wider exploitation by a data science unit, creating a virtuous circle.

It is important to note the key difference between long-term and short-term investment horizons when it comes to data science.

A short-term data science approach seeks to establish predictive models based on correlations between certain data and short-term share price performance. This is an effective but transient strategy: the more parties that establish the correlation, the quicker any inherent alpha will be competed away.

Long-term alpha, by contrast, results from deep insight about a company or industry’s fundamental prospects. In this case, any insight has been derived from a considered examination of a volume of sometimes obscure data. If alpha subsequently emerges, it is likely to be because excessive value or growth has been identified, potentially in a unique manner, which will in time be reflected in price, rather than because a group of other players has responded to the same perceived alpha signal from the same data.

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ConclusionData offers a huge opportunity for fund managers. The information revolution is creating hard-to-access “realms” of long-term alpha. The injection of new, and potentially unique, methods of data analysis into existing investment processes should enhance their ability to uncover this alpha. This should generate a competitive advantage for the well-equipped long-term investor. Organisations that successfully adapt to this data-heavy world will have a mindset of innovation and collaboration. They will also be large enough and have the technological prowess to compete. Those that do evolve, and remain agile enough to avoid the pitfalls while embracing change, will be in the best position to offer their clients sustainably differentiated returns.

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Reappraising the case for commodities

Duncan LamontHead of Research and Analytics

We think now is a good moment to consider a strategic allocation to commodities. Despite being one of the star performers of 2016, sentiment has rarely been worse. Previous poor performance means they remain one of the few asset classes that look genuinely cheap. They provide an infl ation hedge few other asset classes can match, while off ering signifi cant diversifi cation benefi ts. Indeed, we argue they can improve risk-adjusted returns even on a pedestrian outlook. Yet, given relatively inefficient markets, skilled managers should be able to extract high excess returns.11 Commodities have been on a rollercoaster ride. For a number of years since 2000, returns exceeded expectations as insatiable demand from emerging markets and China in particular spurred a “supercycle” in prices. (Figure 1).

But more recent performance has been disappointing. Even after this year’s upturn, the Bloomberg Commodity Index (BCOM) has fallen around 50% from its 2011 peak, while the S&P GSCI index is down around 60%2 . Investors are not surprisingly shunning commodities. Yet it is often

2 BCOM was established in 1991 and is more diversifi ed by commodity than the longer-standing S&P GSCI index (GSCI), which has an over 70% allocation to the energy sector, although the latter has a much longer track record, having been established in 1970.

Figure 1: The rise and fall of commodities

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Source: Datastream, Bloomberg, MSCI, data to 30 June 2016.

when sentiment is close to rock bottom that investors should reassess a potential investment. For commodities, there are at least three key reasons for a rethink.

Reason 1: Inflation hedgingCommodities have generally been positively correlated with inflation. Thus returns have tended to pick up when inflation has been rising and decline when inflation has been falling, making them potentially a better hedge than US Treasuries and equities (Figure 2, left-hand chart). Other asset classes typically considered inflation hedges include real estate and inflation-linked bonds, but they look very expensive compared to commodities just now.

1 This article is a summary of our longer paper, Reappraising the case for commodities, published in August 2016 and available at www.schroders.com.

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Figure 2: Commodities are positively correlated with inflation, unlike equities and bonds

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Given that commodities are literally the raw material for much economic growth, it is hardly surprising they perform well when high inflation results from overheating growth. However, they are also a key component of inflation indices, so it is axiomatic that commodity prices will be positively related. To delve a bit deeper, we segmented history into four different inflation environments and assessed the real performance of equities, commodities and US Treasury bonds in each. Our first division was between periods when inflation was high (defined as 3% or more) and low (defined as 1.5% or less). Then we further divided each period into times when inflation was rising and times when it was falling (Figure 2, right-hand chart).

This analysis showed that real returns from commodities tend to be positive when inflation is rising and negative when inflation is falling. Returns are particularly strong when inflation is both high and rising. In contrast, equities, bonds and cash have all historically generated real losses at such times. There is a cause and effect issue here as, more often than not, the cause of a spike in inflation is a spike in commodity prices. Thus, if an investor is worried about periods of high and rising inflation and a likely cause of such an outcome is rising commodity prices, then they should benefit from an allocation to commodities.

The correlation between commodities and inflation was persistently positive throughout the late 1980s, 1990s and 2000s, despite inflation being relatively restrained in these periods. However, it is fair to say that the other clear conclusion is that real commodity returns are negative when inflation is falling, with performance being particularly poor when inflation is both low and falling. This helps explain why commodities have been poor performers since 2008, a period when inflation has been low.

Despite the low level of inflation in recent years, there are signs that pressures are building. The 12-month change in the US core Consumer Price Index, which excludes commodity prices, has been steadily moving upwards (Figure 3, left-hand chart, overleaf). In addition, the US unemployment rate has fallen below the Congressional Budget Office’s estimate of the Non-Accelerating Inflation Rate of Unemployment (Figure 3, right-hand chart). This is an estimate of the ‘equilibrium’ rate below which inflation is expected to pick up. At the same time, consensus expectations for longer-term inflation remain above 2%.

So, while there continue to be deflationary pressures outside the US, it would be complacent to ignore the dangers of inflation. All the more so given the sums that have been pumped into the financial system and the policy bias towards generating inflation. After all, a bit of inflation would help governments reduce the real value of public debt burdens. Against this background, an allocation to commodities starts to make a lot more sense.

Dollar deliveranceHistorically, there has been a strong inverse relationship between commodity price indices and the dollar. Most commodity prices are denominated in dollars, so they become more expensive for non-dollar investors when the dollar strengthens, which has a negative impact on demand. Prices have therefore generally declined during periods of dollar strength. Similarly, demand from non-dollar investors picks up when the dollar is weaker, which puts upward pressure on prices. Unhedged non-US investors who are concerned about the impact that a weaker dollar could have on their other investments may find this characteristic attractive.

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Figure 3: US core inflation has been rising...

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...while a tight labour market also presages inflation

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Reason 2: Commodities diversify equity risk… but that doesn’t make them a tail risk hedge Despite their diversifying attributes, it would be unrealistic to expect commodities to offer protection against so-called tail risks, such as the financial crisis of 2008–09. In reality, the relationship between commodities and equities varies considerably. At times a negative correlation exists, but on average they show a low but positive correlation (Figure 4, top chart). The relationship weakens as the length of the holding period increases. For example, the correlation between equities and commodities on both a quarterly and an annual basis is around zero. This suggests that there may be significant diversification benefits from adding commodities to an equity-heavy portfolio. In contrast the relationship with Treasuries has been more persistently negative (Figure 4, bottom chart).

We would argue it is not necessary for commodities to be negatively correlated with equities for them to add value. Combining two asset classes with a correlation of less than one can lead to a reduction in the overall risk of an investment portfolio.

For example, assuming a 0.2 correlation between equities and commodities (a reasonable assumption based on experience), then adding a 10% commodity allocation to an equity portfolio could result in a 7.5% reduction in volatility from 17% to 15.7%. It is true that correlations can be highly unstable, but it is not necessary for them to be negative for commodities to reduce risk, even on reasonably conservative correlation assumptions.

Figure 4: Commodities offer significant diversification benefits

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Data 1970–2016. Source: S&P GSCI, Datastream.

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Moreover, returns do not have to be that good for commodities to merit a place in a portfolio. Our calculations suggest that, assuming a commodity-equity correlation of 0.2, commodities only have to generate returns of 2% a year or more to improve risk-adjusted returns compared with the equity-only portfolio. Even on the more conservative assumption that the correlation is 0.5, commodities only have to generate returns of around 4% to improve risk-adjusted returns.

Roll with the punches Commodity investments are typically made using futures contracts. To avoid being forced to take delivery when such a contract nears expiry, the contract must be ‘rolled’ – closed out and a longer-dated contract purchased. This results in a loss when the futures curve is upward sloping (in ‘contango’), as later-dated contracts are more expensive. While this is a drag on returns, it doesn’t necessarily mean losses. Indeed, on average, total returns have been positive in months when roll returns have been negative since the inception of the Bloomberg Commodity Index. Moreover, actively managed funds can limit or avoid negative roll returns by adopting certain curve and sector positioning strategies.

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Reason 3: Potential for attractive risk-adjusted returns Given what we have found so far, we would argue that there is a case for commodities’ inclusion in a portfolio even if returns are relatively muted. In fact, however, current conditions suggest that they could actually be much better than that.

While the past looks uninspiring, future returns should reflect the current low level of commodities prices. Certainly prices look cheap compared with their history, particularly against equities. Indeed, the prices of some commodities are also low relative to their production costs. Aluminium, nickel and copper prices, together representing over 80% of the industrial metals complex, currently trade below the marginal cost of producing them. Oil is changing hands for less than the industry’s average cost of production. Furthermore, return on capital among the major oil companies that together represent 30% of global production recently fell to an all-time low.

So, we would argue, there has rarely been a better time to buy commodities.

Moreover, commodities markets offer plenty of potential for active managers. For instance, they can profit from passive funds buying and selling in predetermined ways each month. Then the fact that different commodity sectors perform better or worse at different stages of the economic cycle allows managers to add value through sector selection. And, as noted above, it should be possible to add value by focusing on those markets which have downward sloping futures curves. With many commodities being poorly-researched, active managers should be able to add value.

ConclusionWe believe there is a particularly interesting opportunity in commodities. They look genuinely cheap, both with respect to their own history and their costs of production. For investors concerned about inflation, they provide protection that few other asset classes have been able to demonstrate. Moreover, they offer beneficial diversification to a portfolio. The result is that, even without making heroic assumptions about their prospects, commodities should be able to improve expected risk-adjusted returns in a multi-asset portfolio. And the inefficiencies of the asset class mean there should be a rich vein of opportunities for active managers to exploit.

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The international route to a truly diversified property portfolio

Real estate is often seen as a diversifier for a traditional equity and bond portfolio. Yet, while most investors are aware of the importance of portfolio diversification, many with property investments still exhibit varying degrees of ‘home bias’, a tendency to favour domestic markets over those in other countries. Investors in direct real estate (‘bricks and mortar’ as opposed to property securities tradeable in liquid markets) are, if anything, more susceptible to this home bias than others.

Clement YongStrategist, Research and Analytics

In this article, we analyse the prevalence of home bias in direct real estate and suggest why it may make sense to diversify into international property markets. We also show that the reasons to diversify may vary depending on the investor’s starting point in their home market and that there are specific factors to be taken into account when evaluating property investments.

The direct real estate market is large. Investment Property Databank (IPD), the data provider, puts the overall market at $6.2 trillion (as at 31 December 2015). Bricks and mortar represent approximately 10% of the total assets in global pension portfolios1 and also usually constitute the largest proportion of alternative assets in institutional investors’ portfolios. In fact, pension funds, insurance companies, sovereign wealth funds, wealth managers and banks allocate around 30-60% of their alternative assets to direct real estate. Endowments, foundations and fund of funds are the only institutional investors where direct real estate does not form the majority of their alternative asset allocation.

1 Willis Towers Watson’s Global Alternatives 2016 Survey and Willis Towers Watson’s Global Pension Assets Study 2016.

However, real estate investments tend to be concentrated in the investor’s domestic market. As Figure 1 (overleaf) shows, the domestic weight can be close to 100%. This contrasts sharply with institutional equity portfolios, which once exhibited a similar home bias but have diversified significantly over the past 20 years and now have only a 43% domestic allocation on average (Willis Towers Watson, MSCI, 2014).

Several reasons have been cited for this tendency to favour home markets:

Behavioural biases – Familiarity – investors are better informed about their

own market than foreign ones and hence feel more comfortable with making investment decisions there.

– Herding – investors are also likely to be more comfortable doing what others are doing. As most investors suffer from home bias, this ‘comfort in crowds’ is only likely to exacerbate the effect.

Figure 1: Too close to home?

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Returns look winningIt is not hard to see why investors have wanted to make an allocation to direct property. Several property markets have comfortably outperformed returns from developed market equities and bonds over a number of periods (Figure 2, overleaf)2. However, it is true that returns have tended to be lower in absolute terms over longer periods. This weakness can be largely explained by the property crash between 2007 and 2009 and its aftermath. Despite these poorer years, longer-term returns have still been respectable.

While the level of returns clearly matters, their composition is also important. One of the key attractions of real estate for many investors is its high and relatively stable yield. This is particularly important for investors in Germany and Switzerland who typically regard property as an alternative to corporate bonds and are less focused on total returns than, for example, UK and US investors.2 By way of comparison, the local currency returns for the MSCI World equities

index were 13.7%, 10.2%, 5.5% and 6.8%,and for the Citigroup World Government Bond Index 3.3%, 4.0%, 3.7% and 4.7% respectively over the four periods shown in Figure 2.

Hedging domestic risks – The liabilities many investors are trying to meet often

relate to domestic risks, such as inflation and interest rates. Domestic assets are the natural hedge for these liabilities, which is the main reason why fixed income portfolios tend to have a heavy home bias.

Transaction costs – Transaction costs in many markets may be higher due

to regulations, taxes, commission etc.

So why is home bias particularly prevalent when it comes to direct real estate? It is certainly true that this is an asset that is significantly less liquid than others and requires extensive due diligence and expertise to invest in successfully. Investors may therefore feel they understand property investments close to home better, and indeed that may make investing in property overseas a more time consuming and expensive exercise. It could be equally well argued, however, that these same characteristics should make investors more keen to diversify abroad in a search for better – or at least more consistent – returns, while reducing risk.

Figure 2: International property has offered healthy returns over most horizons...

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*Europe ex-UK offices only. All returns displayed are annualised and in local currency. Source: IPD, NCREIF, Schroders, as at 31 December 2015.

Figure 3: Seeking yields in a low rate environment

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Although yields have fallen, property still provides a superior income return to bonds in most markets. The size of this gap is likely to vary over the property cycle, depending on prospects for rental growth. When the economy is strong and rentals are expected to rise, investors may only require a small premium of 1-2% to compensate for the additional risks of depreciation and illiquidity. Conversely, when the economy is weak and rental income static, or expected to fall, investors may require a premium of 3-5% over bonds. As it happens, the current premium is around this level, which we believe is attractive given the prospects for steady economic and rental growth in most markets.

Looking in more detail, property yields not only vary across countries, but also across different types of property and according to the quality of the asset. The chart below illustrates the variation in yields across different types of property in France, Germany and the UK, using consistent data for prime properties (i.e. the best in class). The variation reflects two main influences. First, investors are broadly rational and in common with other assets, they will pay a lower yield for properties which are expected to see faster rental growth and income growth in the future. Therefore, shops in the heart of big cities where there are strict planning controls which protect historical buildings and limit new development (e.g. Mayfair, London, or Champs Elysées, Paris) command a lower yield than distribution warehouses, which are typically on the edge of cities where there are plenty of sites for new buildings.

Second, the range in yields across different types of property reflects variations in liquidity and the ease with which assets can be sold without disturbing the price. Thus, investors will pay a lower yield for a new office in a major city than a comparable office in a regional city, because large cities typically have a very diverse set of domestic and international investors and there are always potential buyers in the market. By contrast, smaller cities are often dominated by domestic investors and liquidity may dry up in a downturn. Likewise, shopping centre yields are higher than shop yields, because fewer investors can afford to pay £500 million, or more for a prime shopping centre.

Figure 4: European prime real estate yields – 2016 Q3

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Office - regional cityShop- major city Office - major city Shopping Centre Distribution Warehouse

Source: CBRE, as at 30 September 2016.

In addition, yields on individual properties vary significantly according to the quality of the building (i.e. specification, sustainability, location) and the certainty of the rental income (i.e. financial strength of the tenant, unexpired lease term). Unsurprisingly, older properties in fringe locations let to start up firms on short leases sell at a discount when compared to new buildings in central business districts let to multi-nationals on long leases. The size of the discount will vary over the cycle, but will typically range from 1-4%. The discount can create interesting opportunities for investors who are quick to identify locations which are becoming more attractive, or buildings which are capable of being refurbished, or converted to another use (e.g. cinema to shop, office to apartment).

Risk can be spread more widely Of course, returns are only half the story. Indeed, one of the major advantages of real estate should be its potential for risk reduction. As we have said, investors often consider their home market to be less risky than unfamiliar foreign markets. This may be true when the investor lacks the necessary expertise, putting them at a disadvantage to local investors. However, this is not an argument against investing overseas, simply that appropriate expertise is required.

In truth there may be lower risks away from home. Take volatility, which is one way to define risk. Volatility varies quite widely from one region to another, when measured using ‘unsmoothed’ returns (Figure 5), calculated using a mathematical approach that attempts to recreate the volatility intrinsic to financial markets3. There should therefore be potential for investors to reduce risk by switching assets into foreign markets.

3 Amongst other things, this takes account of the inevitable lag and infrequency of real estate valuations, as well as valuers’ caution and their tendency to understate both peaks and troughs in market prices.


However, volatility is only one aspect and there are several other risks, many stemming from the simple fact that real estate is a physical asset, rather than a financial one:

Illiquidity. The extended due diligence required to buy bricks and mortar and relatively high transaction costs mean that investors need to take a long-term view and understand that they cannot sell quickly without compromising on price. Investor sentiment and liquidity also fluctuate significantly over the property market cycle.

Lease terms. Lease terms vary from one country to the next and indeed in the USA, from city to city. In some countries (e.g. Australia, UK) the tenant is usually responsible for all repairs and insurance, whereas in other countries the landlord typically carries some of these costs. Standard lease terms in France, Belgium, the Nordics and Japan are short, with either an expiry or break option every two to three years, whereas leases in Germany, the UK and US are typically between five and 10 years. However, there is a certain amount of inertia among commercial occupiers: tenants in France, for example, are more likely to renew their lease than in the UK. Furthermore, many continental European leases include an element of inflation indexation, whereas UK leases have no indexation, but include an upward-only rent review clause which prevents a cut in rent during the lease period.

Covenant strength. As with equities and corporate bonds, investors in real estate are exposed to the risk of default. However, one of property’s redeeming features is that, in the event of a tenant insolvency, the landlord should be able to re-let the vacant space, whereas a shareholder or bondholder in an insolvent company could lose everything.

Structural change and obsolescence. The long holding period means investors need to be aware of how structural shifts may change occupier demand, e.g. on-line retail, robotic process automation and driverless cars and trucks. They also need to anticipate how infrastructure and regeneration projects will change the attractiveness of locations. The other side of the coin is that real estate investors can add value in a number of ways, including changing and upgrading properties and extending leases.

Specific risk. The performance of individual properties is often quite idiosyncratic and in particular, lease events (e.g. new lettings, tenant failure) can have a major impact on returns. It is therefore important that investors take time to build a portfolio of properties in their target market which will give them a diversified exposure and that they do not spend all of their allocation on one trophy office building, or large shopping centre.

Implementation. Investors will need to decide on how to gain international real estate exposure and while it is not exactly a risk, it is an important consideration. One such route would be to invest in global cities. An attraction of large cities like London, New York, or Tokyo is that they tend to see faster economic growth and population growth than smaller cities in the same country. In part this is because they have scale – they have more people, more world-class universities, more good schools and hence more ideas – and in part because they have a wider range of amenities (e.g. museums, theatres). Big cities also have the advantage of relatively deep and liquid investment markets. However, on the downside, property yields in big cities are lower (see page 4) and they are also more likely to see speculative projects, whereby developers start schemes before a tenant has committed to the space. If a number of developers start building speculatively at the same time, then that can create a glut of space which undermines rents and big cities tend to have more pronounced rental cycles than smaller regional cities.

We would argue that investors should focus on a limited number of winning cities with certain key characteristics. These include a diverse economy, a skilled labour force, good infrastructure, proactive local government and good retail and leisure facilities. We particularly favour Amsterdam, Berlin, Boston, Brussels, Hamburg, London, Los Angeles, Munich, New York, Paris, Shenzhen, Stockholm, Sydney and Tokyo. We also favour certain smaller cities which share many of these characteristics including Bordeaux, Cambridge, Leipzig, Lyon, Malmö and Mannheim.

Figure 5: Using volatility as a measure of real estate risk

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Longer term volatility (p.a.)Volatility over last 10 years (p.a.)USUKJapanAustraliaPan Europe*

%

*Pan Europe data from 2006 only. Unsmoothing methodology devised by Schroders Research and Analytics team. Source: IPD and NCREIF, as at 31 March 2016.


Deceptive diversification. Investing in property inevitably involves picking cities and locations within those markets. Some countries are highly concentrated and dominated by a single city which serves as the economic, political and cultural capital (e.g. London, Paris, Stockholm, Tokyo). By contrast, other countries like Australia, Germany, the Netherlands and the USA are polycentric and the aforementioned functions are spread across a number of cities.

Investors also need to be careful they don’t squander the benefits of geographical diversification by picking cities with the same economic driver. For example, demand for offices in Frankfurt, Hong Kong, the City of London and New York is linked to financial services. Similarly, energy and commodities drive demand in Calgary, Houston, Oslo and Perth. As a result, a portfolio which includes cities whose economies are largely domestically orientated (e.g. Atlanta, Chicago, Cologne and Melbourne), or are seats of government (e.g. Brussels, Berlin and Washington), probably carries less risk than one dominated by cities tied to the global economy.

Figure 6: The best annual returns vary from year to year…

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Europe ex-UK 12.0% 12.9% 0.1% -4.7% 4.8% 6.0% 4.6% 5.2% 9.6% 13.4%

Australia 19.7% 18.4% -0.1% -2.3% 9.4% 10.3% 9.4% 9.3% 10.8% 14.0%

Japan 13.5% 11.3% -1.1% -6.3% 0.2% 2.9% 3.7% 6.2% 7.8% 8.9%

UK 18.1% -3.4% -22.1% 3.5% 15.1% 7.8% 3.4% 10.7% 17.8% 13.1%

US 16.6% 15.8% -6.5% -16.9% 13.1% 14.3% 10.5% 11.0% 11.8% 13.3%

All returns are in local currency. Source: IPD and NCREIF, as at 31 March 2016.

Figure 7: …while correlations between regions confirm the diversification potential

Pan Europe Australia Japan UK US

Pan Europe 1.00 0.87 0.83 0.72 0.90

Australia 0.87 1.00 0.92 0.54 0.87

Japan 0.83 0.92 1.00 0.42 0.81

UK 0.72 0.54 0.42 1.00 0.55

US 0.90 0.87 0.81 0.55 1.00

Unsmoothing methodology devised by Schroders Research and Analytics team. Source: IPD and NCREIF. Base date 31 December 2005, data as at 31 March 2016.

Discovering diversificationTaking these factors together, we can see from annual returns (Figure 6) that global real estate markets are seldom in synch. Thus, while the UK market was one of the first to be hit by the credit crunch in 2007, it was also the first to recover in 2009. The US was just behind and then experienced a much stronger recovery than virtually any other region. This lack of alignment between property cycles around the world suggests that an international portfolio should not only diversify returns, but also reduce risk.

We can further demonstrate direct real estate’s diversification potential with cross correlation figures for the five markets. The relatively low correlations displayed in the table (Figure 7) demonstrate the existence of past diversification opportunities. While there are limitations to using historical correlations to determine such opportunities, they are a good starting point. Take the US, for instance. It is relatively uncorrelated with the other markets, suggesting that its returns may be driven by distinctly different factors from other markets.

Confirmation of these figures comes from the performance of real estate investment trusts, which are essentially market-traded collections of property. Long-term correlations for trusts operating in our five major markets are similar to those of the unsmoothed returns from direct real estate.


ConclusionWe hope we have shown that diversifying into foreign direct real estate markets can be beneficial, both from a return enhancement and a risk reduction perspective. How much to invest internationally and where will ultimately depend on the investor’s starting point. Some investors are principally looking for superior returns, while others are more concerned about achieving a certain level of income return. Either way, international real estate should offer more security for investors than concentrating assets in their own property market.


Managing volatility for performance and safety

The last decade has left investors painfully aware of the importance of volatility. Two major bear markets in 10 years have left many scarred by the experience. Clearly, an investment approach that captures the growth of equities together with a braking system that tries to prevent accidents would be ideal for navigating such difficult markets. We believe that investors can come close to realising this ideal by carefully measuring and managing the volatility in their portfolios.

John McLaughlinHead of Portfolio Solutions

Any mechanism that can smooth volatility in an equity portfolio is not only useful in its own right but, by improving the investor’s day-to-day journey, it may also help them to avoid major crashes. Volatility often accompanies market collapses. Figure 1 (light blue line) shows the volatility of the US Standard & Poor’s 500 Index between 1928 and 2012. It can readily be seen that many of the big corrections of this 84-year period, such as 1929, 1937, 1974, 1987, 2001 and 2008, have been associated with high volatility.

A simple portfolio braking system A basic mechanism for capping volatility is pretty straightforward to establish for a portfolio of risky assets such as equities. First decide on a maximum level of volatility, say 15% a year. Then monitor the portfolio volatility and whenever it exceeds this fixed 15% cap, sell enough risky assets to ensure that it falls back to 15%. Thus, for example, if portfolio volatility rises to 20%, bring it back down to 15% by selling a quarter of the stocks for cash.

Once the portfolio volatility moves back towards the 15% level, the cash can be gradually reinvested in risky assets. By following this simple, systematic rule, volatility should be effectively limited to a maximum of 15% a year (see Figure 1, dark blue line).

Over the very long term, the operation of such a volatility cap seems to be no material impediment to returns. A 15% per annum volatility cap applied continuously to the US stock- market over the last 90 years would have performed similarly to the uncapped equity market over the entire period. Moreover, it would have reduced losses in 12 of the 13 ‘crash years’ (calendar years where the market fell by more than 10%) that occurred during that time, and the benefit achieved would have been significant in seven of those cases (see Figure 2).

Figure 1: Volatility often presages problems

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Wall Street Crash

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1928 1934 1940 1946 1952 1958 1964 1970 1976 1982 1988 1994 2000 2006 2012

Source: Bloomberg and Schroders. As of December 2013. Indices used are the S&P 500 [Div Adjusted] (1928–1988), S&P 500 Total Return (1988–2012). Volatility on any day is measured as the annualised standard deviation of daily returns in the previous 30 days using closing prices and de-risking, if required, is assumed to occur at the close on the same day.


But rising markets are not always stable. Bear market rallies like 2009 (Figure 3c) can exhibit high volatility, leading to underperformance as the volatility cap forces de-risking despite the rising market – analogous to ‘riding the brake’ while keeping a foot on the car’s accelerator. Although this is not an ideal short-term outcome, it should not give rise to serious concern: over the whole two years from 2008 to 2009, the volatility-managed strategy outperformed the market, saving significantly more in the crash than it gave back on the rebound.

Furthermore, bear markets are not always volatile. On occasion a significant market loss can build up steadily over a lengthy period, as when the Internet bubble deflated between June 2000 and December 2002 (Figure 3d). In such a scenario the basic volatility cap remains inactive and does nothing to mitigate a substantial loss.

While history would suggest that this last scenario is a rare occurrence, the magnitude of the potential loss is still unacceptable to many investors who have limited risk tolerance. Could we adapt the basic volatility cap mechanism so that it will always limit losses to a specified maximum level, even when the loss develops gradually, as in the early noughties?

The variable volatility cap – setting a limit on lossesAs its name suggests, a variable volatility cap works by automatically reducing the level of the volatility cap as markets fall and portfolio losses develop. This makes the mechanism much more sensitive to volatility, and also increases the amount of de-risking applied, rather like a driver braking harder as she approaches a sharp bend in the road. When the market finally recovers and share prices begin to rise again, the level of the volatility cap will automatically rise with them, allowing the portfolio to participate in the market rally. Or, to extend our previous analogy, having negotiated the bend, the driver can now move her foot back to the accelerator.

Figure 4 compares the range of outcomes from a simple volatility cap strategy with that from a variable volatility cap strategy that aims to avoid losses of more than 15% over a rolling 12-month period. The starting point for both is a volatility cap of 15% per annum and in both the range of outcomes is narrower than the index. However, with the variable volatility cap, the negative outcomes have been effectively concentrated into a narrow range of zero to –15%, even though the average annual returns of the two strategies remain similar over the entire 84-year measurement period.

We can now revisit Figure 3, where the variable volatility cap is plotted as the dark blue line in each scenario. Charts 3a and 3d confirm the ability of the variable cap to achieve its –15% maximum loss target, even in a stable bear market scenario like 2000–2002. Stronger downside protection will always come at a higher cost, however, as the two middle charts attest. Even so, our analysis indicates that, over time, this method should be considerably more cost efficient than other downside risk management strategies, e.g. purchasing index put options.

Figure 2: Analysis of major corrections in the US equity market since 1928

Years with returns less than -10% Index return

15% volatility capped return Difference

1930 -27% -16% +11%

1931 -46% -20% +26%

1932 -13% -2% +11%

1937 -37% -22% +15%

1940 -13% -9% +4%

1941 -16% -15% +1%

1957 -12% -11% +1%

1966 -11% -12% -1%

1973 -15% -14% +1%

1974 -28% -21% +7%

2001 -12% -11% +1%

2002 -22% -16% +6%

2008 -37% -19% +18%

Source: Bloomberg and Schroders. As of 31 December 2013. Indices used are the S&P 500 [Div Adjusted] (1928–1988), S&P 500 Total Return (1988–2013).

As we noted earlier, short-term volatility can alert us quickly to the fact that a market correction is underway. The volatility cap responds automatically to this warning, de-risking the portfolio so that it is better positioned to withstand the blow. Then, after the market finally finds the bottom and volatility subsides, the portfolio can gradually re-risk and participate in the recovery.

Alas, no braking system is perfect Unfortunately, all risk management techniques have unwanted side-effects, and the basic volatility cap is no different. Consider the market scenarios illustrated in Figure 3. (Ignore the dark blue lines for now, we will return to those in the next section.)

Big market corrections such as the one we experienced in 2008 (Figure 3a) are typically violent. Our volatility cap was quickly activated and proved its worth by halving a potential loss of 40%. Furthermore, sustained bull markets are usually well behaved in volatility terms. Between 2003 and 2007 for example (Figure 3b), the 15% volatility cap was rarely triggered, so the capped portfolio remained almost fully invested throughout and hence delivered a very similar return to the market.1 1 You may be thinking as you read this paragraph: ‘If volatility is typically subdued

during rising markets, why don’t I boost my return by increasing market exposure when portfolio volatility is lower than 15%, as well as decreasing exposure when the volatility goes above 15%?’ In other words, 15% becomes a ‘volatility target’ rather than merely a ‘volatility cap’. There is a certain appeal in the symmetry of the volatility target mechanism, but we would urge caution for a couple of reasons. Firstly, it requires continuous adjustment of the hedging position to bring it back to the target level, which will gradually rack up substantial transaction costs. Secondly, it requires the portfolio to be leveraged when its volatility falls below the target. Although this should not give rise to serious concern (since you only gear up when risk levels are low), many investment guidelines prohibit any form of leverage. For those few who are not subject to this constraint, a ‘volatility collar’ may be worth considering. Here the volatility is not just capped at 15%, but also ‘floored’ at 9%, let’s say. When volatility exceeds 15% we de-risk back to that level; when it falls below the 9% floor we re-risk back up 9%; and when volatility lies between 9% and 15% no action is taken with the portfolio, thus saving on transaction costs. Historical analysis would suggest that a volatility collar may achieve better risk-adjusted returns over time than a simple volatility cap.


Figure 3: How well does the cap fit in different conditions?

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Source: Bloomberg and Schroders. As of 31 December 2013. S&P 500 Index. Above results are a back-test. Performance is net of transaction costs and gross of fees. Owing to the unpredictability of the behaviour of markets, there can be no guarantee that the volatility management strategy will meet its objectives.


Figure 4: The variable volatility cap vs the simple volatility cap

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But downside risk isspecifically targeted

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Annual Returns (%)S&P 500 Variable Volatility Capped S&P

Source: Bloomberg. Federal Reserve Bank of St. Louis and Schroders. As of 31 December 2013. For illustration only. The above results are based on a back test. The strategies trade once daily at the close of business. Indices used are the S&P 500 [Div Adjusted] (1928–1988), S&P 500 Total Return (1988–2012). Observations are for 12-month returns measured on a daily basis. These charts show the returns of the strategies applied to the S&P index. An observation of –10% indicates the return was between –10% and –19%.

Practical implementation Earlier, we implied that activating the volatility cap would require shares to be sold for cash. In practice, however, it is usually much more efficient to use equity index futures, which minimise transaction costs. Index futures are exchange-traded instruments and, provided their use is confined to the most liquid contracts (for example the S&P 500, EURO STOXX 50 or Topix indices), the transaction costs resulting from implementation of the volatility cap should be negligible over time.

So, for example, in the case of the US equity portfolio in Figures 1–3, instead of selling 5% of the equities for cash as a result of an increase in volatility above the cap, we can achieve the same economic effect by selling S&P 500 Index futures with a notional exposure equal to 5% of the value of the portfolio. Of course, it is a requirement of transacting futures on an exchange that the investor posts cash margin. Therefore it is advisable before embarking on a volatility control programme to first liquidate a small percentage of the equity portfolio for cash, so that additional margin can always be accessed readily when

volatility spikes suddenly (as it is wont to do). To ensure that the cash reserve doesn’t act as a drag on overall performance, it should be covered with a long equity future when the cap is de-activated.

It should be emphasised that the procedure described above – where index futures are used to adjust market exposures instead of selling and repurchasing physical stocks – is well understood and already widely applied by traditional equity portfolio managers. (The only difference in this case is that the size of each trade is determined by a systematic rule, rather than the decision of a fund manager.) It should never result in the portfolio becoming either leveraged or net short at an overall level. No over-the-counter derivatives are employed and so there is no counterparty default risk. We use only liquid, exchange-traded futures, and only for hedging purposes. Therefore volatility control satisfies the usual definition of ‘efficient portfolio management’ and should be permissible under even the most restrictive client investment guidelines.

So much for US equities – what about my portfolio?Thus far we have described volatility control only in the context of a US equity portfolio. What about other equity markets, or indeed other asset classes?

Each equity market has its own natural level of volatility: higher for an emerging market than for a developed market, for example. Provided that the cap level is set correctly in relation to the ‘natural’ level of volatility of the chosen market, our analysis suggests that volatility control should be effective across a wide range of equity markets. There may be an exception out there somewhere, but we haven’t found it yet.

For an international equity portfolio, we must create a composite of different equity index futures in order to hedge the market exposure. Statistical methods are used to minimise the basis risk – the possibility that the basket of futures fails to mimic perfectly the underlying portfolio and therefore fails to provide the expected hedging characteristics. Using such methods brings out yet another attractive feature of the volatility cap mechanism. Because it is designed to work at times of steeply falling prices – when correlations between different equity markets are often high – we find that we need look no further than the largest four or five equity indices to construct an effective hedge basket for the majority of portfolios. This should still be the case even for a concentrated stock portfolio, or one with a style or size bias.

In fact we would contend that the volatility cap can be usefully applied to any portfolio where the majority of the risk can be explained by equity markets. Consider, for example, a traditional 60:40 balanced portfolio of equities and bonds: although equities constitute only 60% of the capital value, they will be responsible for 80% or more of its total risk. Figure 5 shows how a variable volatility cap could effectively control downside risk in such a portfolio. Note in particular how the portfolio remains fully invested for long periods of time (the shaded area in the chart). This is the secret of the volatility cap’s success: like an experienced car driver, it steps on the brake only when necessary – and then decisively!


Figure 5: Performance of a balanced 60:40 portfolio, 2000–2012

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Dec 13Dec 12Dec 11Dec 10Dec 09Dec 08Dec 07Dec 06Dec 05Dec 04Dec 03Dec 02Dec 01Dec 00Dec 99

Multi-Asset Portfolio (LHS)-Asset (RHS)

Performance (5) Net Exposure (%)

Porfolio with Variable Volatility Capped (LHS) Target Protection (LHS) Allocation to Multi-Asset (RHS)

Source: Schroders. As of 31 December 2013. Model portfolio: 60% MSCI ACWI, 40% Bar Cap US Investment Grade Index, rebalanced monthly. Left axis measures portfolio NAV (rebased to 100% at inception) or target protection as % of portfolio NAV. Right axis measures net market exposure of portfolio as % of portfolio NAV, including volatility cap overlay.

In more diversified, multi-asset portfolios, the futures hedging basket should pick up not only the explicit equity exposures, but also the equity ‘beta’ hidden in certain other asset classes, such as higher yielding corporate bonds and certain commodities or alternative assets. Furthermore, for a portfolio that is actively rebalanced, it is straightforward to rebalance the hedging basket in tandem.

The overlay approach So how might an investor with cash to invest construct the optimal investment solution to deliver equity-like returns over the medium term, while limiting short-term losses? We would suggest the following:

1 First build a core portfolio of well-diversified global stocks to act as the engine of returns.

2 Supplement this core with a variety of other growth assets and employ an active asset allocator to improve the overall risk-adjusted return.

3 Finally, apply a systematic volatility cap for those times of market stress when active management alone cannot be relied upon to prevent significant loss.

In reality, of course, many investors are not starting with a blank canvas. Completely transforming an existing portfolio into the optimal solution described above cannot usually be justified, given the effort and cost involved.

Fortunately, it is possible to enjoy the benefits of volatility control without disrupting an existing portfolio. All this involves is the appointment of a ‘volatility overlay manager’ who will manage an overlay account comprising index futures and sufficient cash to provide margin. The overlay manager only needs to know the asset allocation of the physical portfolio, the market benchmark used for each of its component strategies and its daily total net asset value. In this way it is possible for an investor to retain any existing active managers, but also gain peace of mind knowing that their overlay manager will provide protection from what might be severe losses in the markets where they operate.

The overlay can be applied to the entire portfolio, or only to that part of it where the underlying market gives particular cause for concern. Either way, by separating the underlying management from the risk overlay, an investor is better able to understand from quarter to quarter how each is contributing to the performance of the overall portfolio.


ConclusionThe systematic volatility control techniques we have discussed have a number of important benefits for institutional investors, particularly those who are conscious that the current bull market for equities is now entering its sixth year: – They should provide comfort that the risk of large losses is being carefully controlled, whilst minimising the

drag on expected returns. – They should be much cheaper than alternative downside risk protection strategies, such as put options. – The tools used tend to be highly liquid and cheap to transact, which also means that the techniques are flexible. – They can be deployed in conjunction with passive strategies or to complement a more active approach. – They can be implemented as an overlay. – They are applicable to a wide variety of portfolio types. – They should satisfy even the most conservative of investment guidelines.

The next three years could be a bumpy ride, so prudent investors might be justified in concluding that now is an opportune moment to equip their portfolio vehicles with the sort of braking system that only volatility control techniques can provide.


Primer: the factor drivers of investment returns

Many of the concepts behind factor investing are nearly as old as investing itself. Much newer is the idea of bringing them together systematically. This is transforming the way investors think of portfolio construction, and the reasons are clear: factor investing offers diversification, transparency and economy. We review the underlying concepts and show how they underpin a number of apparently disparate developments from smart beta to alternative risk premia. Factor investing provides important new tools for the investor, but, like all tools, understanding remains critical to results.

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Ashley LesterHead of Research, Multi-Asset

Why factor investing?Traditionally, investors have thought of the world in terms of asset classes, such as equities and bonds. Factor investing looks beneath asset classes to allocate according to the factors that drive risk and return. Some of these so-called “risk premia” may correspond more or less directly to an asset class, such as the equity risk premium. Other asset classes represent a combination of factors. For instance, corporate bonds combine long-term interest rate risk (itself divisible into inflation and duration risk) and credit risk, and these two sources of risk are separable, at least in principle1.

The aim here is to achieve more stable diversification than asset class allocation alone. The risk premia underlying assets may have a more stable relationship than that between asset classes, since the mix of risks and expected returns in an asset class can vary. In October 2008, for instance, corporate bond risks and expected returns were dominated by credit risk, while by 2016 long-term interest 1 For instance, by hedging out the duration risk, or investing in credit default swaps.

For more on this subject, see “Putting a premium on risk”, Investment Horizons, issue 1, 2014.

rates (duration) were more important (Figure 1). These variations affect the risk, return and diversification of portfolios considerably. Factor investing seeks more direct exposure to the drivers of return, which have shown more stable relationships with each other and therefore more consistent diversification.

What are the factors?The factors we have discussed so far are traditional elements of portfolios. But much of the recent excitement comes from less traditional factors, which are increasingly used as portfolio building blocks. The best known are equity strategies such as value, size, quality, low volatility and momentum. These sorts of “dynamic” factors, widely marketed as “smart beta”, require active management to maintain exposure, as a stock that was good value last month, for example, may be overpriced this month. These differ from traditional factors in another way: not everyone can hold them. If one investor holds a value strategy, all the other equity investors in the world must be collectively underweight value, relative to the market.

Figure 1: Dissecting corporate bonds into their risk premia

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Risk-free rate Credit risk premium Duration risk premium

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So here is our first note of caution: if factors are well known, either they may not continue to outperform, or there must be another reason why some choose not to use them.

At their most basic, factors are rule-based strategies that can generate outperformance over lengthy periods. A more helpful way to analyse them is to look at how the basic rule can be turned into different strategies of varying sophistication and risks (Figure 2).

Suppose we have a rule that ranks the desirability of stocks and then use it to build a portfolio: we have created a form of “smart beta”. The term derives from the idea that we have captured something systematic about stock returns over and above general market moves. “Alpha”, by contrast, is taken to represent above-market returns deriving purely from stock selection.

A key point about this portfolio is that the single largest driver of its returns is still the broad equity market as a whole. We hope that our rule tilts the risk and return of the portfolio in our favour, but if equity markets fall, for example, our portfolio will also tend to fall. If we are a bit more sophisticated, and use our factor not only to buy the desirable stocks, but to short the undesirable stocks, we have entered the world of long-short equity hedge funds or, more generically, of “alternative risk premia”. Of course, building a portfolio that includes shorts is operationally more difficult than building a long-only portfolio. And if the factor underperforms, the portfolio will not merely underperform the market, but actually lose money.

Hence our second cautionary note about factor investing: since all known factors experience periods of underperformance, seeking a diversified portfolio of factor exposures is a much better idea than focusing on just one or two.

Bringing these arguments together, we can say that factor investing aims to:

– enhance investment returns through historically demonstrated systematic strategies,

– improve diversification by breaking down risk into its underlying components, and

– reduce fees, since strategies can be implemented more efficiently.

Figure 2: The evolution of factor portfolios, from smart beta to alternative risk premia

Market

CheapExpensive Cheap

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Rank by factor

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Long-short “alternative risk premia”

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Source: Schroders, as at 29 July 2016.

While the concept and intentions are straightforward, the execution is not quite so simple.

Which factors?One of the canons of practical finance is that there are four or five fundamental factors, discovered in stock markets, that can be applied equally well in other markets, such as those for bonds and currencies. These factors are value, momentum, low volatility, and often size or quality. Conventional wisdom is, however, wrong in two important ways. First, there are many more than four or five plausible factors. Second, factor investing is just as relevant to other asset classes as to equities, but often involves quite different factors.

The notion that there are just four or five simply-described equity factors reflects the seminal findings of Eugene Fama and Kenneth French, then of the University of Chicago, starting in the early 1990s. In a series of research articles2, Fama and French showed that the returns from many different equity portfolios could be mostly explained using returns from the market, along with size and value. This was a major development in finance, as the significance of size and value shredded the previous academic view that market returns were the only systematically important and consistently rewarded risk in equity markets. (This was the basis of the famous capital asset pricing model developed in the early to mid 1960s.)3

Fama and French influenced a generation of students and practitioners, and their work laid the foundation for some of the most successful strategies of recent decades. But the students they influenced understandably were not content with accepting that two academics had discovered every conceivably important factor in equity markets. Their search for additional factors was spectacularly successful. Over 300 different equity “factors” have now been identified in the academic literature, with around one new factor having been discovered each month on average over the past 10 years.

2 E.g. “The Cross-Section of Expected Stock Returns”, Eugene F. Fama and Kenneth R. French, Journal of Finance, vol. XLVII, June 1992; “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics, vol. 33, February 1993; and “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. L, March 1995.

3 For a more recent discussion of this see “The Capital Asset Pricing Model: Theory and Evidence”, EF Fama and KR French, Journal of Economic Perspectives, vol. 18, summer 2004.


The academic quest now is to bring order to the “factor zoo” (a term coined by John Cochrane, also formerly of the University of Chicago4). In time, a new taxonomy may well emerge, but there is no particular reason to expect that it will resemble the remarkable simplicity of the model Fama and French propounded almost a quarter of a century ago.

It is no accident that equities have been a hotbed for new factors. Research is facilitated by freely available data, relatively straightforward return modelling and highly dispersed returns from many different stocks, allowing for the empirical examination of many different factor ideas. Achieving the same results for government bonds, commodities, currencies or, indeed, any other asset class is much harder. Few should be surprised, therefore, that nothing like 300 factors able to generate outperformance have been found anywhere beyond equities.

Nevertheless, systematic strategies have been established across all asset classes. One type of factor that is common across many is “carry” – roughly speaking, the amount an investor is paid to hold an asset, independently of price changes. This idea was originally applied to currency investing, where carry corresponds to a given currency’s short-term interest rate. In equities, dividend yield is often identified as “carry”. Similarly, momentum – the idea that recent winners go on winning – seems to be an empirical regularity across many asset classes and geographies. Other types of factors seem asset-class specific – for example, roll-down is the profit from holding a bond if an upward sloping yield curve remains fixed.

The existence of factors across many asset classes is important because it increases the possible opportunities for factor investing. And because of the low historical correlation between factors across asset classes, a multi-asset approach to factor investing should provide investors with greater opportunities for diversification benefits than focusing solely on equities.

Are factors generic?The concept of value investing dates back at least to Benjamin Graham and David Dodd’s classic work in 19345. Fama and French chose to formalise the idea by measuring value as the ratio of the book value of a company’s assets 4 “Presidential Address: Discount Rates”, John H Cochrane, President of American

Finance Association 2010, Journal of Finance, vol. LXVI, August 2011.5 Security Analysis, Benjamin Graham and David Dodd, Whittlesey House

(McGraw-Hill), 1934.

to its market value. There are, however, equally useful ways of measuring “value”, including, for example, earnings relative to a company’s market value. In one sense, the choice of measurement does not matter much – groups of stocks measured by different ideas of value tend to move up and down in similar ways; that is, portfolios formed from different types of value measurements tend to be quite highly correlated. In a much more important way, however, the choice of measurement is critical: cumulative returns of these portfolios can be very different.

Performance can differ even across portfolios of stocks selected from the same universe, by the same firm, using similar methodologies. Figure 3 shows cumulative returns from three different value indices created by MSCI, a leading index provider. The monthly returns from these indices are more than 90% correlated, and their “active” returns (those that differ from the capitalization-weighted benchmark) are around 60% correlated. But over 19 years, the Value Weighted index has added only around 10% to the performance of the benchmark, while the Enhanced Value index has added over 110%. So even small differences in index construction and portfolio management can lead to enormous differences in performance.

Investors may be wondering: if there can be such widely different returns produced by the best known factor from the same index provider, working on the same set of stocks, is factor investing truly systematic and reliable? The answer is that it can be, but it depends on the implementation: some factor implementations are more efficient than others. Because factor investing is quantitative, it is possible to study the different building blocks that go into factor construction and find out which approaches are more likely to produce acceptable returns in the long run.

An important example of this is how the stocks in a portfolio are weighted. If, for example, the selected stocks are capitalization-weighted, the resulting index is likely to have little effective exposure to the factor. This is largely because returns from many factors are more pronounced in smaller stocks, which are likely to be less well represented in the portfolio.

Again, we can use value as an example. Figure 4 (overleaf) shows the differences in returns from investing in different-sized stocks in four different value portfolios over the past 19 years. We divided each portfolio by size and compared the performance of the smallest 25% of stocks with the

Figure 3: Same factor, wildly different results

0

50

100

150

200 250

300

1997 2000 2003 2006 2009 2012 2015 ACWI Enhanced Value ACWI Prime Value ACWI Value Weighted

Three MSCI value indices compared to MSCI All Country World Index

Rebased to 100 from 1 January 1997. Total active returns compared to MSCI All Country World Index. Source: Bloomberg and Schroders, as of 31 December 2015.


largest 25%. In each case, buying “value” stocks and selling expensive stocks was handsomely rewarded among the smallest stocks, but generated almost no return at all among the largest stocks. This is evident across many factors, where returns are often more pronounced in smaller stocks than larger ones.

Clearly, just as with any other strategy, investors must pay close attention to implementation. We have found plenty of evidence of persistent systematic strategies, but that does not mean that any strategy labelled “systematic” is as good as any other. Fortunately for investors, careful analysis can illuminate differences among strategies in advance.

Who should hold factors?One other note of caution on factors relates to their dynamic nature. As suggested earlier, as a particular factor becomes widely used, either its previous outperformance is likely to tail off or there must be some other reason why non-users adopt an opposite strategy. Does this imply that factors become less attractive as their popularity increases? Here we can draw lessons from the past. One study considered 100 published equity factors and found longer-term evidence for about a quarter. So while the majority were not reliable, dozens of publicly-available equity factors were still found to produce significant positive returns, even after they became well known.

Some of these factors are likely to be of little use for most investors. Others, however, may continue to provide attractive returns. Many of the latter have probably been known about, albeit with different labels, for a long time.

Figure 4: Size can make a weighty difference

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Book to price

Differences in returns from four long-short value portfolios divided by stock size (%)

Dividend yield Earnings yield Operating cash flow

Smallest 25% Largest 25%

Investment universe is divided into quartiles by size, then each size bucket is further divided into quartiles for a given value factor. Value portfolios created by ranking investment universe from highest to lowest based on, respectively, book value to price, dividend yield, earnings yield and operating cash flow. Each column represents the difference in performance between the 25% cheapest and the 25% most expensive stocks in the smallest and largest size buckets for each value factor. Data are for July 1997 to June 2015. Source: Schroders.

After all, investors were seeking “cheap” stocks long before Fama and French appeared in the early 1990s. So even if investment flows into “value” factors are new, flows to managers seeking to exploit value as an idea certainly are not. This is another reason to think that such well-known factors will probably not be competed away.

If a well-known factor can persist, why wouldn’t everyone invest in it? In truth, investors have different goals and needs. Consider those saving for retirement. Some are at the start of their working lives and will not need retirement savings for years; others are close to retirement, with little opportunity to make up for investment losses. Suppose a factor performs well in most markets, but does badly when prices plunge. This risk may be acceptable for young savers, who have a long investment horizon, but older investors should avoid it, given the possible impact of losses. This points younger investors towards factors like size and value, and older ones towards low volatility and quality.

Similarly, most contributors to defined contribution pension schemes probably want relatively straightforward investments, implemented cheaply, with easily-understood risks. For these investors, long-only factor investing is a sensible way to try to provide extra return. At the other end of the spectrum, sophisticated institutions or family offices may be willing to take on more opaque risks, particularly if they help to diversify their existing portfolio more effectively. For those investors, taking on intensified factor exposure using alternative risk premia may make more sense.

ConclusionFactor investing is a powerful tool for managing investments. By breaking down assets into risk premia, it can provide greater transparency of portfolio construction and greater control over the drivers of risk and return. Dynamic risk premia, such as value or momentum, can diversify the sources of return in a portfolio beyond traditional assets. But successful implementation of factor investing requires care and skill. Creating a portfolio of dynamic risk premia means sorting those with lasting value from those that are ephemeral or illusory, concentrating factor exposure on securities where it is rewarded, broadening exposure across asset classes, and staying on the leading edge of financial research. Meeting these challenges should enable investors of all types – from the least engaged to the most sophisticated – to gain access to new sources of return in the form that best fits their investment needs.


Further reading: Investment Horizons

Issue 4, 2015

Retirement planning: an income strategy for old ageThere is no one way to make increasingly inadequate pension savings cover ever-lengthening lives. However, we argue that any solution needs to combine investment income with longevity insurance.

Exploiting economic conditions to pursue growth with less riskA good way of reducing risk is to position a portfolio to best address the prevailing economic environment. But investors first need to identify the environment and then be able to adapt to it.

Interest rates: are investors in for a nasty shock? The consensus view is that interest rates will rise more slowly and peak at more lowly levels than before. Nonetheless, the underlying drivers of inflation remain, even if they are currently quiescent.

Putting a price on climate changeOur economics team has used the increasing body of academic research to try to quantify the financial effects of climate change, identify the main losers and highlight any winners, if there are any.

Is the spectre of illiquidity again stalking global bond markets?There has been much debate about the state of liquidity in global bond markets since the crash of 2008–09. While some markets seem unaffected, we highlight others where alarm bells are starting to sound.

Issue 5, 2016

Does “Big Food” face a showdown over sugar?The rise in sugar consumption and its link with ill health could create serious problems for the food and beverage sector. There are close parallels with the issues faced by the tobacco industry, and that could spell bad news for investors.

The case for small caps in a world of disruption and deflationThe underperformance of small caps has prompted suggestions that the traditional arguments in their favour no longer hold. But in the current economic circumstances, we argue that small caps can bring unique characteristics to a wider investment portfolio.

Consistent returns are the key to better pensions Financing retirement is one of the biggest problems faced by individuals. We’ve looked at more than 300 years of UK investment history and concluded that the best results come from minimising investment surprises, rather than maximising contribution levels.

Is inflation overstated?Official measures of inflation are crucial to the smooth functioning of markets, but there is strong evidence to suggest that official benchmarks have consistently overstated the rise in the cost of living. This is likely to have serious ramifications for investors.

Primer: taking correlations out of the black boxWe look at the shortcomings of correlation and suggest that overcoming them requires us to make some educated forecasts about what the future will be like and then to adjust our expectations accordingly.


Issue 2, 2014

Harvesting bond returns as rates rise We argue that it is still possible to generate bond returns by adopting an unconstrained, global strategy, able to allocate risk wherever it is rewarded, without being unduly tied by benchmarks.

Is volatility risk? Low volatility can be the calm before the storm, while raised volatility can signal danger or opportunity. The key to determining which is to a large extent dependent on investors’ time horizons.

Should investors be increasing equity allocations to emerging markets? We think it’s time to start taking a second look at emerging markets. The strategic case remains firm, while the tactical arguments for investing are increasingly compelling.

Tapping into behavioural biases can create repeatable returns Investors often behave irrationally, taking shortcuts when making decisions that throw up opportunities for others. We identify some of the strategies that can be used to exploit these opportunities.

The hidden risks of going passive Index-based investors may not always realise the risks they face. They may also be missing out on outperformance from better portfolio construction and certain types of active management.

Pointers towards a better pensions landscape We outline a number of principles we think the defined contribution pensions industry should follow if it is to meet the needs of the increasing number of retired people who must provide for themselves.

Issue 3, 2015

Building and measuring outcome-oriented investment strategies Tools for measuring the performance of portfolios are often ill-suited to the investment problems they are aiming to solve. We suggest a more realistic alternative to capital-weighted benchmarks.

Why focusing on risk can result in better wealth preservation Is it possible to strike a balance between generating sufficient growth and protecting invested capital? We have developed a portfolio solution that aims to achieve both goals.

Listed real estate: an unexpected buttress against rising rates? Many wonder whether property will struggle with a possible rise in interest rates on the horizon. We argue that listed property can not only provide growth for a portfolio, but also diversification.

Can investors do well while also doing good? Many investors are reluctant to embrace “sustainable” investing, fearful of the effect on performance. We highlight recent research that suggests such worries may be misplaced.

Have cars reached the end of the road in the developed world?We believe developed country car markets are in structural decline. Future growth will only come from the emerging world. To thrive in this environment, car companies need strong brands or competitive costs, or both.


Further reading: Investment Horizons

Issue 1, 2014

Managing volatility for performance and safety Many investors are looking for a way of reducing and, if possible, avoiding major losses. Measuring and managing volatility can offer an effective solution.

Putting a premium on risk Simply owning different assets doesn’t amount to a diversified portfolio. A risk premia approach is a much better way to ensure that risk is being properly rewarded.

How chasing storms can generate uncorrelated returns Insurance linked securities are becoming increasingly mainstream. Offering good returns and true diversification, we think they deserve a place in many more portfolios.

A framework for action in fixed income Bond investors still need the certainty and income that fixed income has provided, but can’t see where returns are going. One answer is to loosen or break the link to benchmarks.

Is there a smarter alternative to smart beta? Smart beta seems to offer active-like performance at a passive-like cost, but it’s no panacea. We outline a better way to achieve the same ends.

Engaging for alpha: why more involved fund managers can create better returns for all The long-term effects on companies of decisions made by equity and bond holders are increasingly under scrutiny. We think fund managers can do better.


Important information: The views and opinions contained herein are those of the authors, and may not necessarily represent views expressed or reflected in other Schroders communications, strategies or funds. This document is intended to be for information purposes only and it is not intended as promotional material in any respect. The material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. The material is not intended to provide, and should not be relied on for, accounting, legal or tax advice, or investment recommendations. Information herein is believed to be reliable but Schroders does not warrant its completeness or accuracy. No responsibility can be accepted for errors of fact or

opinion. Reliance should not be placed on the views and information in the document when taking individual investment and/or strategic decisions. For your security, communications may be taped or monitored. Third party data is owned or licensed by the data provider and may not be reproduced or extracted and used for any other purpose without the data provider’s consent. Third party data is provided without any warranties of any kind. The data provider and issuer of the document shall have no liability in connection with the third party data. Issued by Schroder Investment Management Limited, 31 Gresham Street, London EC2V 7QA, which is authorised and regulated by the Financial Conduct Authority. SCH51425.

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