VOX POP: Smarter than the average bull or bear?

Asset management experts discuss the chief characteristics of smart beta and tell us what we can next expect from the Sector.


In the current market cycle, which smart beta should investors be investing in?
In a time when there’s a return to volatility in equity markets, I’d look at dividends. Over the long run, dividends have been one of the biggest drivers of equity returns – and a stable dividend can imply resilient cashflows which can give some shelter in a volatile market. Dividends are particularly important in times of uncertain interest rate policy. Though interest rates have been rising, progression has been slow and income is still seen as valuable.

Dividend-focused smart beta funds have been around for over a decade, but I’d urge investors to be selective – some easily fall into the ‘yield trap’. A high yield can mean one of two things – perhaps the business might be a solid cash generator returning profits to investors. On the other hand, it could be a sign of trouble: a company paying out more than it can afford, loath to cut distributions and incur the market’s ire.

Remember, in 2008 yield forecasts rose, but many were never realised in the crash. Formerly secure businesses had to cut their distributions as the crisis worsened. It’s a double whammy – the investor loses the yield and share prices tumble in disappointment. And sadly, some dividend smart beta v1.0 indices really suffered here.

So to avoid the yield trap, I’d suggest investors look at quality income smart beta funds. There are a number of these which choose high-yielding shares whilst taking account of a company’s financial health. Controlling for leverage and profitability, looking for companies with a strong balance sheet should provide some security over the long run. If markets continue along this choppy path, I believe quality dividend should be a source of security in the long run.


What will be the main smart beta trends of 2018?
2018 is the year when investors in equity factor-based products become more ‘sophisticated’ by demanding: greater transparency on the index construction; enhanced details on the performance and risk attribution; and more flexibility in selecting factors and timing of their factor allocations.

The analytics being provided with factor-based equity products is more advanced, and investors are more analytical in the decision-making process of investment. Do the products in the market really have the exposures that the marketeers are promoting?

Investors will demand full transparency and understanding of the products they are investing in and will likely want to determine their own allocations rather than buying off-the-shelf products.

In addition to the increased specialisation of equity factor products, two new variants are coming to market: multi-asset class factor products and the overlay of ESG on the factor space.

However, both multi-asset class and ESG factor products suffer from the same problem: no agreed upon definition of the critical construction components.

The academic literature on risk premia in the multi-asset class realm is only now being turned into defined products but agreement on the definition of, say, quality in the fixed income world has proved elusive. Hopefully, a consensus will emerge as more products come to market.

The ESG market is even more fragmented. First, there isn’t agreement whether the decision to invest in ESG is alpha-driven or a desire to make an ‘impact’ on the corporate environment.

Second, there isn’t agreement on how to define each component of E, S and G or how to aggregate these disparate scores. Third, the data remains sparse.

That said, ESG is an important trend in the market. Market capitalisation weighted ESG products, thematic indices defined around ESG and factor-based ESG product are all going to be more prevalent in the market.


Where next for smart beta? What can we expect over the next three-to-five-year period?
Niels Bohr is credited with the tongue-in-cheek remark: “Predictions are hard, especially about the future.” As such, I shall perhaps stick to observations based on how I have seen clients reacting to the growing prevalence of smart beta strategies over recent years.

Firstly, I believe we will continue to see more granularity in the type of strategies offering clearer labelling than the catch-all and often abused ‘smart beta’. As such, I expect to see areas such as multi-factor, value, fundamentally weighted and low volatility create their own subgroups of managers and indices – hopefully making comparison and judgements easier across strategies and their relevance to an investor’s allocation decision.

Performance of many strategies will disappoint. This is active management, not alchemy, and many of the strategies rely on long-term cyclicality of factors to generate return, some I fear suffer from data-fitting and will not survive in the real-world environment. This quantitative Darwinism is good as, associated to this, I believe we will enter a period where the availability of live track records will reduce the reliance and number of backtests.

In 2018, a clear preference is developing to understand the level of risk being taken by many strategies and the degree to which unintended biases are creeping. I believe this is critical to the next stage of development, as marrying a strategy or index aims with the results it produces will create better scrutiny and challenges for firms in this space. If two strategies offer the same returns but one takes more risk, I’d anticipate investors preferring to spend their cost on the manager offering tight risk control.

One prediction perhaps – downward pressure on costs will continue, though we might see a revival of the performance fee.


Should smart beta indices be the correct option for measuring the performance of active asset managers?
Aside from exhibiting historically excess returns, another equally important reason for factor investing is that they account for a significant portion of active fund returns.

Industry studies, such as the ‘S&P Indices versus Active’ have consistently found that the average active manager generally does not outperform the cap-weighted benchmark net of fees, especially over a longer time horizon.

For those few managers who are able to outperform the market-cap benchmarks, academic studies have also found that tilts to well-known Fama-French factors, such as value and size, account for up to 50% of managers’ returns over market capitalisation-weighted benchmarks, using US institutional fund returns.

Undoubtedly, tilting towards these factors persistently should not be considered as part of manager skill. Manager skill should only include the excess return or the value-add that the fund manager is able to generate once all the systematic factors are taken into consideration.

Therefore, smart beta indices should be used to measure whether active managers have actually achieved any value-add or whether their ‘outperformance’ was merely the result of tilting towards certain well-documented factors, which can easily be achieved via smart beta ETFs.

At SPDR ETF, we have seen an increasing number of investors opting to replace their active managers with a smart beta ETF for factor exposures, such as value and low volatility. Apart from being low-cost options, smart beta ETFs are less likely to experience style drift, given their regular rebalancing frequency and the fact that investors are made aware of the methodology of the underlying indices in advance.


How are factors influencing portfolio construction?
The factor investing landscape has flourished in recent years, experiencing 11% organic asset growth over the past five years, making it one of the fastest-growing areas of the asset management industry – and it has revolutionised the way that investors allocate capital, diversify risk, and seek long-term performance.

Though the factor landscape in the past has been dominated by growth in the US, the appetite for factors in Europe is growing, with about $6 billion coming into smart beta ETF strategies last year alone, with total AuM around $46 billion.

Factors are likely to play an increasing role in portfolio construction over the next decade, especially through smart beta, an approach that is index-driven, transparent and low-cost – applying style insights to screen for securities with attractive characteristics (high quality, strong momentum, or attractive value, for example) and often implemented with smart beta ETFs. We expect the integration of smart beta into portfolio construction to be one of most significant drivers of growth – introducing managers to a whole new universe of clients. For example, many active managers have historically delivered returns that today can be obtained from factor portfolios (e.g. value) for much lower fees. This has resulted in increasing allocations to a range of smart beta strategies such as fundamental indexation, minimum volatility and others.

Investors have become more ‘factor-aware’ in recent years, with experienced investors tilting into more sophisticated versions of factor investing. We are seeing a greater use of long-short implementations – where premium returns from style can be targeted without taking on market risk, multi-factor smart beta (which blends several style insights in security selection) from single factors, and toward multi-asset rather than single asset class strategies.

We expect the industry to continue to grow at its current rate – reaching $3.4 trillion by 2022 (currently standing at $1.9 trillion in AuM), meaning equal potential for both alpha and index strategies.


Smart beta has become an industry buzzword, but what does it really mean and how should it be interpreted?
Is ‘smart beta’ an oxymoron? ‘Beta’ is passive exposure to a risk that is clearly defined. As such, this exposure can be replicated by anyone, by relatively straightforward means. ‘Smart beta’ typically refers to an optimised allocation to these risk exposures and a blend of such simple style exposures aggregates numerous risks.

These can offer some attractive diversification away from market cap-weighted indices for long-only implementations, or they can offer some lowly correlated returns in a long-short one. By diversifying the risk exposures using a smart beta blend, one does not fully neutralise the tail downside risk of each risk premium.

Nor does one typically increase the overall return of the portfolio relative to an average of the standalone risk premia, as allocation timing often doesn’t add significantly to long-term returns.

For example, investing into a size factor allocation would allow investors to capture a liquidity premium over the long term but they would be exposed to the tail liquidity risk of small caps – this risk would be impossible to diversify away.

Similarly, were one investing in a simple momentum factor, one would do well in highly dispersed, trending environments but would be exposed to large tail risk during reversals accompanying cyclical rotations. Once again, this tail risk is very difficult to diversify away.

In the end, the value-add of an overtly passive beta allocation methodology tends to be small in terms of the long-term returns generated for investors. Only a differentiated active strategy, exploiting market inefficiencies, has the potential to generate significantly higher returns than the market.

Furthermore, only a highly dynamic investment approach, driven by analysis and research, can make these high returns persistent over the long term and truly add value to investors.

As the sources of information multiply and the market inefficiencies evolve, being ‘smart’ – which I would interpret as ‘adding value’ – probably means being able to sufficiently integrate this new information and adapt one’s investment strategy in a way that maintains the alpha being delivered


Is smart beta not just another form of market timing, because it requires investors to leverage appropriate investment factors to reflect changes in market behaviour over time?
Factor timing is different from market timing in one important dimension: one forecasts the returns to multiple drivers of investment returns while the other forecasts the returns to just one asset class. This increases the potential value added from varying exposures based on the risk and return outlook for each factor.

The Fundamental Law of Active Management, factor timing’s theoretical underpinning, argues that the value added from an active strategy is a function of the quality of the forecasting model and the breadth of the strategy. The latter refers to both the number of forecasts made and the periodicity. Given a level of forecasting skill, the more frequent the decisions and the larger the number of assets, the greater will be the benefit.

Suppose, for example, that you can forecast the outcome of a toss of a coin with a 55% chance of being correct. The greater the number of tosses, the greater the chance of being able to demonstrate the value of your ability. You can also increase the chance that you will have a good outcome by tossing more than one coin at the given time. For example, if one were to toss ten coins at a time over five time periods, it would be would be equivalent to tossing one coin over 50 time periods. Timing multiple factors is akin to tossing multiple coins – market timing is like tossing one coin. For a given level of skill in forecasting, the latter will take much longer to demonstrably add value.

The merits of any timing strategy will depend on the forecasting power of the underlying model. While there is some limited evidence around the forecastability of factor returns themselves, there is considerable agreement around the forecastability of factor volatility and the benefits thereof. Investors who do not incorporate this information into their allocation decisions are ignoring a potential contributor of value added to their investment portfolios.


Smart beta has been described as the middle ground between active and passive strategies. Is this an apt description?
In the early days of passive investing, market cap–weighted indices were used to replicate the returns of particular market segments. Since this approach did not involve security selection, these passive, index-based portfolios were regarded as ‘beta’ products. When regressing the return of a passive product against its reference index, the product should have a beta of one to that index, with little residual noise, or alpha.

As research emerged demonstrating that indices with alternative constructs – equal-weighted or weighted by volatility or fundamentals, for example – outperformed their cap-weighted counterparts, quantitative methods were developed to reconstruct these exposures in portfolios. As practitioners enhanced ‘regular’ beta to improve the risk-return trade-off, the resulting product was dubbed smart beta.

There is no single, industry-wide definition of smart beta. This is reflected in the different labels smart beta strategies are given in the asset management industry and the varying design and implementation of these strategies as managers began to differentiate themselves on construction competencies. However, we define smart beta as a rules-based approach to portfolio construction that weights stock positions differently than a cap-weighted index in order to generate outperformance.

In our view, smart beta investing is far from being a small step in the evolution of passive investing, despite that typically being an investor’s perception. The pursuit of generating consistent returns from investment anomalies entails significant research, market experience and risk management expertise.


In a time of increasing market volatility, which factors are going to bring the best returns in equities and fixed income?
Increased market volatility in traditional asset classes has been linked to concerns about equity market corrections and tightening monetary conditions. Facing this environment, investor interest has increased in long/short factors – systematic strategies aiming to deliver attractive risk-adjusted returns distinct from traditional assets. While factors may be less affected by the directionality of current market conditions, increased market volatility may affect them nevertheless, highlighting the importance of diversification within factor portfolios.

Factors can be classified into value, carry, trend and structural – where the latter navigates market anomalies related to structural market constraints. When implementing a specific factor category within various asset classes, correlations between implementations are low but the effect of volatility spikes is similar, making this discussion more about factor categories than asset classes.

Trend factors adapt swiftly to changes in market sentiment and may benefit from increased volatility, making them important building blocks for portfolios. More recently, however, trend struggled with sudden price reversals in whipsawing markets. Being more fundamentally driven and generally featuring lower turnover, value can provide an important complement. When assets experience negative trends pushing prices below fair value, a value signal balances overall positions with a positive view. Thus, combining value and trend makes the resultant portfolio responsive to price moves while grounded in fair value considerations, reducing adverse effects of price reversals. Carry and structural are factors providing further diversification. Thus, a thoughtful factor selection covering as many styles and asset classes as possible paired with robust portfolio construction featuring tight risk management becomes essential in successfully navigating market volatility.

Driven by market volatility and potentially resulting performance dispersion across factors, the siren call of factor timing can become alluring to some investors. Yet the diverse nature of themes driving volatility spikes indicates that factor timing remains challenging. Notwithstanding this backdrop, reduced exposures to some factors may be warranted by diminished return outlooks linked to structural market changes and factor crowding.


Can factor timing work and in what circumstances should it be considered? Is it fair to consider factor timing as a variant active strategy?
A common question when it comes to investing in styles is whether to tactically time or to simply maintain strategic allocations. We believe that while the idea of timing styles can seem very appealing, in practice, it can be very difficult (just as it is for the market).

Investors may try to time based on valuation metrics – increasing/decreasing the weight of a style when the spreads in valuation between the long and short sides are wide/tight; macroeconomic conditions – identifying the best market environments for each style; or momentum measures – increasing/decreasing the weight of a style when recent performance has been better/worse.

The evidence from several studies shows that timing is very difficult in practice and the benefit of timing strategies has been weak historically. For contrarian-based timing, this is further complicated by the fact that timing styles based on valuation is highly correlated with having direct value factor exposure in the portfolio – and therefore not additive to a portfolio that already has a strategic allocation to value. Timing based on macroeconomic conditions is doubly hard: you need to correctly predict the direction of macro indicators and how factors will behave in response to those changing economic conditions. While contrarian timing has proved disappointing, the use of pro-cyclical strategies – trend-following or momentum – does hold some promise with timing factors.

Ultimately, the decision of whether to time styles (and by how much!) should depend on how skilled you are at timing, combined with what is already in your portfolio. Our advice is simple: if you want to commit the investing ‘sin’ of timing factors (or the market, for that matter), only do so on the margin – sin a little.


How long do you stick with a strategy that has not been performing for years?
It depends on the strategy and the initial investment rationale. A deep value strategy may only outperform in one out of five years, but in the years that it outperforms – the outperformance tends to dominate all else. For investors that are seeking consistent and high-magnitude excess returns, we advocate looking to thoughtfully designed multi-factor alternative index strategies or investing in time-tested active quantitative strategies with a demonstrated track record of consistent outperformance.

The risk that we believe is commonly overlooked is the inherent risk associated with traditional market capitalisation-weighted indexes. In other words, the most significant risk for investors is the absolute market risk, not the relative risk inherent in any given factor strategy. Passive investing weights companies according to market capitalisation (price times shares outstanding). As such, a capitalisation-weighted index by definition assigns increasing weights to companies that have appreciated in price and decreasing weights to companies that have depreciated in price.

The upshot is that capitalisation-weighted indexes expose clients to both the upside and the downside of periods of frothy valuations.

Recall that in the 1980s, Japan was nearly half of the global developed market index. Just a few years later – after a substantial market decline in Japan – the weight was about 10%. The impact on performance for a passive global equity investor was disastrous.

Since then, the late 1990s bubble and burst in technology, the financial services meltdown after the financial crisis, and other such episodes have imposed losses to passive investors. Factor investing and other such departures from capitalisation-weighted indexes are especially valuable to investors in such environments.


Should smart beta indices be the correct option for measuring performance of active asset managers?
There is a very strong argument that smart beta indices make a natural benchmark for assessing the performance of active asset managers. If you want to understand how effective a manager is, you need to compare performance versus a meaningful benchmark to work out how much value the manager is adding through stock selection rather than style bias.

For example, if you are investing in an active fund that has a value bias, looking at performance versus a broad market cap-weighted index will not tell you whether the fund is outperforming because the manager is picking the right stocks or simply because a value approach is outperforming.

Currently, most investors will solve this problem by comparing fund performance with that of other active value funds. Smart beta indices offer the potential for a more consistent benchmark.

Many smart beta indices are designed to capture exposure to systematic factors or stock characteristics that have been used by active managers for many years. More than half of the $750 billion in smart beta ETFs globally is invested in dividend, value or growth-based funds. All of these approaches have their roots in active management but have since been codified and tested by the academic community.

If we return to our previous active value fund example, a smart beta value index offers a more effective benchmark than either the broad market index or the peer-group comparison.

Smart beta ETFs also offer an effective comparator for actively managed funds either with or without a particular style bias. There are now smart beta funds with multi-year live performance history. This means that you can now compare the performance net of fees between a traditional active manager and a smart beta ETF such as the Invesco GS Equity Factor Index Ucits ETF.

Over the last three years, this ETF has outperformed more than 90% of European funds that are benchmarked to the MSCI Europe index.


Where next for smart beta? What can we expect over the next three-to-five-year period?
Following the growth in smart beta equity investing, we expect fixed income will be the growth opportunity in the future.

Smart beta strategies attempt to solve for flaws in traditional indexing and enhance our capture of the asset class. For example, in equities, we look to better capture the set of returns that drive markets (such as value, momentum, size, etc) and/or address concentration effects endemic in traditional indices.

The problem of inefficiency is even more acute in fixed income where indices are liability-weighted. In credit, for example, indices allocate most to the largest borrower. Hence, rethinking indexation in fixed income is even more important than it is in equities.

Yet despite this, we’ve always seen innovations in fixed income lag equities. The first equity indices, for example, were introduced in the 1950s, with fixed income indices launched in the 1970s.

Then the wave of traditional passive investing in equities in the 1970s was followed by fixed income in the 1980s. This is partly driven by the complexity of fixed income – where trading is not as rationalised or automated and the data itself is not as clean.

Yet while these hurdles have certainly delayed smart beta solutions in fixed income, they are not insurmountable and we believe we are reaching a tipping point.

Having conducted research ourselves, we see well-known factors like value, quality and momentum present themselves across global investment grade and high yield markets as effectively as they do in equities. As these metrics mirror those used in other asset classes, it also highlights the robustness of factor-based investing.

Given the drawbacks of traditional fixed income indices as well as the increased comfort of investors with factor-based investing, we fully expect this to be the biggest area of accelerated growth in the smart beta space in the near future.

©2018 funds europe



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