A new paper^[1] from Hamish Seegopaul, Global Head of Index Product Innovation at STOXX, takes on the objective of deciphering those preferences, by analyzing ETF flows but also looking through to the underlying holdings. The exercise presents a view of investors’ granular preferences, ex-post, and can be a valuable source of information that comes closer to investors’ revealed preferences, as opposed to broad ETF categories, which may be closer to their stated preferences.

The study looks at US ETFs and employs a taxonomy of preferences across style, industry and regional factors. The author creates a ‘Flow Portfolio,’ which is comprised of securities that were theoretically bought or sold each year to facilitate ETF’s net flows. Key findings in the study of Flow Portfolios are:

• There has been a high degree of year-on-year variability in style, industry and regional exposures
• There is evidence of ongoing appetite for broad-based exposure
• Over longer time horizons, there is little clear preference for specific exposures
• No matter the time frame examined, investors favored ETFs with strong in-year performances

Performance rules all preferences

Some trends are persistent over time, Hamish writes. For example, across years the Flow Portfolios show a preference for ‘more’ – more holdings and more performance – compared to the entire ETF universe. The preference for higher returns is probably of little surprise, and the paper does not imply that investors were able to capture it.

That preference also coincides with a relatively constant preference for Momentum. The factor remains somewhat of an outlier, as all other styles show a degree of variability year after year (although that variability mostly disappears when measured over time). Sectors and regions exposures preferences, too, change over time but are fairly neutral over the long run.

“In the short run, preferences can be highly variable,” Hamish writes. “In the long run, there is one preference to rule them all – and that is performance.”

One other notable shift in aggregate exposure over time is the move away from Americas-based holdings and towards EMEA and Asia/Pacific ones, the report says, a sign of diversification preference.

“These findings support a rich product landscape, however, pose challenges for the industry,” the author adds. “Some challenges – such as the ‘returns gap’ – have been sticky. Others, such as understanding preferences prior to investment decisions, will lead to further innovation.”

We invite you to download the paper and explore its methodology and results.

^[1] STOXX, “Understanding Investor Preferences through Passive Investment Flows,” February 2024. 

But how can they screen out the best assets in terms of quality, financial clout and commercial activity? In other words, tokens that are akin to the blue chips of the equity world.

The STOXX^® Digital Asset Blue Chip Index was introduced last year and provides several tools for investors in the crypto world. Firstly, as a benchmark, it aims to act as a barometer of the underlying market’s behavior. Secondly, it helps navigate the space through a systematic and clear industry structuring as is the Bitcoin Suisse Global Crypto Taxonomy (GCT). The index also ensures asset price trustworthiness through a process that vets exchanges by volume and reliability. 

Finally, a comprehensive selection mechanism relies on crypto-native metrics to build a portfolio that is high-quality in the blue-chip sense, much like a similar equity portfolio will seek components with high revenues, established businesses, a large customer base and a solid balance sheet. 

Constructing a blue-chip crypto index

A new report^[2] delves into the STOXX Digital Asset Blue Chip Index’s asset selection process to unpick those metrics and understand the construction of a blue-chip benchmark in this segment. The asset characteristics considered in that process are:

Age: The age of a crypto asset helps gauge the commitment to the project and its adoption by the market.

Total Value Secured (TVS): The more value a protocol secures on its blockchain, the greater the trust, adoption and inherent applications the protocol has in securing transactions validity and immutability.

Active Addresses: The number of active addresses is used to measure adoption. This metric counts the number of unique sending blockchain addresses. 

Economic Activity: Strong fee revenue denotes usage and adoption, in addition to gauging the ongoing concern of the protocol and resilience in a competitive market landscape.

Developer Community: The developer community is a measure of innovative activity, growth and ossification at the same time. 

“Drawing direct analogies from the traditional equity markets while incorporating the intricacies of crypto is a challenge,” the report’s authors, led by Thomas Shuttlewood, Associate Principal for Product Research and Development at STOXX, write. “Blue-chip digital asset determination must consider the uniqueness of this distinct market, and must rely on criteria and fundamentals that are specific to the asset class.”

Sector leaders

For each metric, assets are assigned a score of 1 if they rank within the top 50% in their respective use-case sector: Cryptocurrencies, General Purpose Smart Contract Platforms, Decentralized Finance (DeFi), Utility and Culture. Those with a score of 4 or more are selected as constituents of the index.

Sector representation is an important consideration in benchmark creation and in representing the underlying economies and universe of the targeted asset class. To translate this notion into the digital asset space, one must rely on taxonomies that have been specifically created for this market, the authors explain. In likening crypto use cases to traditional industry sectors, one can select the leaders from each use-case sector into the index in much the same way as in the EURO STOXX 50^® design. 

Risk and returns

The authors then turn to analyzing the risk and return performance of the STOXX Digital Asset Blue Chip Index. The findings show that the index acts as a true reflection of the nature of the market, matching the average constituent return closer than any single component. In posting lower volatility than its individual components, the index’s standing as a blue-chip gauge is also enhanced. “A middling to strong returns for a relatively low level of volatility indicates that the index acted in a manner associated with a traditional blue-chip index in a defensive market regime,” the authors write.

Interest in the digital asset market is likely to continue gathering pace, so transparent and reliable data is vital. The space must be considered with the same respect as other asset classes, while still ensuring that its intricacies are included in the equation, the authors argue. An investment vehicle and market barometer in the shape of a blue-chip index allows this to happen. 

We invite you to download the report and explore its findings.

^[1] Source: CoinMarketCap.

^[2] “STOXX Digital Asset Blue Chip Index: A Benchmark for the Crypto World,” STOXX, March 2024.

Download

¹ The World Bank, 2021, Publication: The Economic Case for Nature: A Global Earth-Economy Model to Assess Development Policy Pathways, p47.

A new article published in The Journal of Beta Investment Strategies suggests that an optimized multi-factor strategy with low levels of active risk offers the potential to outperform the cap-weighted market. Drawing on data from the past 20 years, the authors — factor-investing experts at BlackRock, SimCorp and STOXX — show that multifactor strategies offer diversification at two levels: within factors and among the indi­vidual sub-components of each factor. Both help improve relative performance, backtest results indicate.

Evolution in factor investing

The study comes as investor demand for, and expectations from, factor strategies has evolved significantly in the past decade. As an example, the STOXX^® U.S. Equity Factor and STOXX^® International Equity Factor indices, launched in 2022, have tracking errors of around 1%, or roughly a fifth of that of single-factor indices introduced ten years earlier. 

Running the numbers on optimized factor portfolios

In “How Do Low Tracking Error, Multifactor ETFs Fit Into the Factor Investment Landscape?” BlackRock’s Andrew Ang, Bob Hum, Katharina Schwaiger and Lukas Smart; STOXX’s Anthony Renshaw, Hamish Seegopaul and Arun Singhal; and SimCorp’s Melissa Brown, analyze the performance of five single-factor portfolios and a multifactor one between March 2002 and June 2023.

The authors consider two parent universes: the STOXX^® USA 900 and STOXX^® Global 1800 ex USA indices. They select an alpha signal (a single factor, the multifactor signal^[1], or the individual components used to create the factors) and then construct a portfolio that maximizes its exposure to that alpha signal, subject to tracking error, turnover, active exposure, sector and country constraints. The single factors are Quality, Momentum, Value, Small Size and Low Risk. The optimization is performed using Axioma’s portfolio construction software.

The multifactor portfolio drawn from the STOXX USA 900 universe beat the Momentum, Value and Low Risk portfolios by around 1 percentage point per annum over the period (Table 1). Multifactor also significantly outperformed the Small Size factor portfolio, and came ahead of the Quality tilt only slightly. The multifactor portfolio’s Information Ratio (IR) was higher than all single-factor portfolios except for Quality. 

Table 1 – Performance Results 

_{Source: STOXX. Summary backtest active performance results for STOXX USA 900 index from March 2002 to June 2023.}

For the STOXX Global 1800 universe, multifactor also showed higher annualized returns over the period than all five single-factor portfolios considered, edging, for example, the Value and Low Risk signals by over 1 percentage point per year. The multifactor portfolio’s IR was higher than all single-factor portfolios.

“It is noteworthy that the multifactor case is at the top, or near the top, performance of all the portfolios constructed,” the authors write. “This is one of the advan­tages of multifactor indices, namely that they usually do well even when one or more of their underlying signals underperforms.”

As the authors note, the driving force behind this outperformance is diversification. Each of the underlying single-factor returns over the period shows low correlation with the others. 

“Through careful construction, multifactor indices can harness the diversification benefits amongst factors and signals,” the authors write, and “their associated premia can be efficiently harvested in a low tracking error format.”

Further, the authors explore how performance changed over the period considered by looking at the trailing 36-month, realized active returns of each factor strategy. Once again, the advantage of a multifactor approach becomes evident. Several of the single-factor portfolios — including Low Risk at different times, and Value and Small Size most recently — showed significant periods of underperformance. The multifactor portfolio, on the other hand, did not exhibit any sustained underwater periods.

Intra-factor diversification

A final consideration involves the construction of individual factors. Just as the combination of styles in a multi-factor portfolio can perform better than the average of their individual contributions, a composition of the individual parts of a particular factor should perform better than the average of the components. Here again, the study shows that returns for a given factor should be superior to a simple average of its constituting metrics.

The turnover for the factor strategies analyzed in the study is only slightly higher than the parent universe’s, the authors added. The tracking error is modest, so portfolio managers should be comfortable with the risk implications of these products, they said. 

Changing landscape

The construction of smart factor portfolios is of key importance in an increasingly competitive performance landscape. At the same time, the portfolios have become more attractive with decreasing management fees and transaction costs, while modern tools allow multifactor portfolio construction to target highly specific objectives. 

We invite you to download the article and explore the study’s findings.  

^[1] The multifactor signal was designed as 36% Quality, 27% Momentum, 27% Value, 5% Small Size, and 5% Low Risk.

A new whitepaper^[1] from ISS LiquidMetrix and STOXX seeks to answer this question by analyzing the trading costs involved in such a shift. The authors consider the cost of moving a standard holding of European equities as defined by the STOXX^® Europe 600 to two versions of the index that comply with the European Union Climate Benchmarks regulation: the STOXX^® Europe 600 Paris-Aligned Benchmark (“PAB”) and the STOXX^® Europe 600 Climate Transition Benchmark (“CTB”). 

CTBs and PABs follow EU provisions in terms of decarbonization trajectory, activity exclusions and sector exposure constraints. The two benchmark types differ in their level of restrictiveness and ambition. CTBs form a portfolio that is on a decarbonization trajectory, while PABs incorporate more stringent carbon limitations (“darker green”) that are in line with Paris Agreement global warming commitments.

In order to have a reference for a ‘typical’ (non-green) transition, the analysts also measure the cost of moving the benchmark to another standard index: the STOXX^® Developed Europe.

The authors compare the cost of transitioning a EUR 3.5-billion portfolio to the three alternative variants, with four trading strategies at different degrees of aggressiveness: trading the transition at 5%, 10%, and 20% market participation rates^[2], as well as using a full-day volume-weighted average price (VWAP) trading strategy. The ISS LiquidMetrix Pre-Trade Cost Estimate model^[3] is used to forecast the costs.

The cost of transitioning: impact and risk

The estimated trading cost is made up of two components: impact and risk cost. Impact is the cost associated with aggressive trading, crossing the market spread to secure the shares, and thereby raising the price. The more aggressive the trading (a higher participation rate) the greater the impact cost. The risk cost, meanwhile, is the cost from the price moving away. The longer it takes to complete the order (a more passive, smaller market participation rate), the higher the cost associated with risk.

Figure 1 from the whitepaper shows the analysis’ results. The composition of the total cost varies significantly depending on how aggressive the trading strategy is. Similarly, the weight of the components of implementation costs (impact + risk) vary.

While an ultra-passive 5% participation trading strategy provides lower impact costs, it carries a greater risk cost associated with overnight price movements. Conversely, a 20% participation strategy has a large impact cost but a lower associated risk cost as the time needed to complete the order is less.

Figure 1: Trading Cost Overview 

_{Source: ISS LiquidMetrix, STOXX.}

Overall, the 10% market participation strategy provides the lowest cost for implementing all three target portfolios, and the 5% participation the highest.

Comparing costs

A key conclusion is that transitioning to a green index portfolio is generally more expensive — between 15% and 20% more — than moving to a similar reference portfolio (i.e., the Developed Europe index). However, there is little difference in transition costs between a “green” versus a “greener” portfolio — stimulating potential moves to the latter. The exception happens when implementing an ultra-aggressive 20% participation strategy, where there is only a slightly higher cost in moving to a PAB rather than a CTB portfolio. 

Interestingly, using a 5% market participation trading strategy, the costs of transitioning to the “green” or “greener” portfolio are less than the cost of the reference transition. While the impact of trading passively is similar for all three benchmark indices, there is more price volatility risk in the Developed Europe portfolio. 

Cost vs. exposure comparison

Further, the authors also look at the cost of transitioning vs. the level of “green” exposure obtained, another factor to be considered when determining the rationale of shifting portfolios. 

For example, transitioning from the STOXX Europe 600 benchmark to a “greener” PAB portfolio at a 10% market participation strategy, generates a marginal cost of 7 basis points, or 6%, relative to transitioning to the “green” CTB benchmark. This additional cost, however, is accompanied by a further 14% reduction in climate emissions intensity relative to the benchmark.

The importance of transaction costs

With sustainable investing moving mainstream in Europe and elsewhere, a lot has been written about the tradeoffs investors need to consider when shifting to a climate policy. Much of this literature, however, has been focused on investment risk and exposures. The latest whitepaper, on the other hand, turns the attention to transaction costs.

“Transaction cost analysis is a critical part of any transition planning,” the publication’s authors write. “There are unique insights, however, that can be drawn from incorporating a cost-aware view among traditional risk/exposure analyses. Our case study highlights the potential cost efficiency of darker green portfolios, which also varies depending on the trading method chosen.”

The combination of unique datasets, portfolio construction techniques and analytics is at the heart of many recent innovations in financial services, the authors add. With those tools at hand, investors can make better-informed decisions when shifting strategies.

The move to protect our habitats raises the regulatory liabilities for corporates and investors, already facing biodiversity-related physical, transition and systemic risks.^[1] Biodiversity plays a vital role in supporting life on earth, and enables economic and industrial development. To cite two examples, around 75% of global food crops rely on pollination, and one-quarter of all the drugs used in modern medicine come from nature.^[2]

The ISS STOXX^® Biodiversity indices were introduced in April this year, offering a comprehensive approach to integrate biodiversity challenges into investment portfolios. The indices address three different biodiversity goals and incorporate an additional climate objective (Figure 1). They exclude companies involved in activities that are controversial or cause harm to biodiversity, select securities with less negative impact on ecosystems and those contributing positively to relevant UN Sustainable Development Goals (SDGs). They also reduce the portfolio’s carbon emissions.

The indices are classified into two categories: “Biodiversity” and “Biodiversity Leaders.”^[3] The former tilt exposure to companies with high scores in seven biodiversity- and climate-related SDG objectives, while the latter include companies with high revenues derived from activities aligned with SDGs.^[4]

Figure 1: ISS STOXX Biodiversity indices framework

_{Source: STOXX.}

Looking into a biodiversity-focused portfolio construction

A new STOXX report^[5] explores the rationale behind integrating biodiversity considerations in an investment process and reviews what metrics are available to measure companies’ biodiversity impact. The authors then describe in detail the four steps in the ISS STOXX Biodiversity indices’ process, explaining how each step was put together and why certain measures were taken during index construction. Finally, they provide the results of implementing the strategy. 

The report may be of particular interest to investors considering applying a biodiversity focus or framework to portfolios. The main goal of the ISS STOXX Biodiversity suite is to enable the allocation of capital to companies with more sustainable practices and a lower biodiversity impact compared to peers. The framework can be used to customize and optimize existing indices, incorporating additional objectives and constraints. 

Measuring companies’ nature impact

A key component of the ISS STOXX Biodiversity framework is the Potentially Disappeared Fraction of Species (PDF)^[6], an output of ISS ESG’s Biodiversity Impact Assessment Tool. PDF seeks to measure how corporates affect our natural world by considering a set of environmental pressures on species and habitats across the entire value chain and different geographical locations.

PDF is divided by each company’s Enterprise Value Including Cash (EVIC) to avoid size biases (Figure 2). The indices select the top 80% companies in each ICB Sector by PDF/EVIC.  

Figure 2: Average PDF vs. PDF/EVIC – An industry comparison.

_{Source: ISS ESG and STOXX as of June 2023. From “ISS STOXX® Biodiversity Indices: How to Incorporate Biodiversity Considerations in Index Construction.” PDF is defined over both area and time (unit: PDF.km2.year), and larger PDF values indicate a greater negative impact on biodiversity. EVIC is defined as the sum of the market capitalization of ordinary and preferred shares, and the book values of total debt and minorities interest at year-end. No deductions of cash or cash equivalents are made to avoid the possibility of negative enterprise values.}

Improvement in SDG exposures

The publication also presents an analysis of average SDG ratings per business sector. In the ISS STOXX Biodiversity indices (broad versions), 34 of 43 sectors have a negative average rating. However, 13 of those 34 improve to a positive rating when a top 80% SDG rating selection is applied.

On the other hand, a sector with a high SDG Solutions Score^[7] may not necessarily provide the most index constituents once we consider only companies that derive at least 20% of their revenues from products and services that make an overall positive net contribution to selected SDGs. Such a filter appears in the ISS STOXX Biodiversity Leaders indices and is intended to comply with the Article 9 classification of the Sustainable Finance Disclosure Regulation (SFDR). 

Performance comparisons

Figure 3 also comes from the report and illustrates the improvements achieved in the biodiversity-related objectives of the indices. The table shows that the indices significantly improve both the overall PDF and SDG scores, and also surpass the 30% carbon footprint reduction target. 

Figure 3: ISS STOXX World AC indices’ metrics vs. benchmark

Source: ISS ESG and STOXX as of June 2023. From “ISS STOXX® Biodiversity Indices: How to Incorporate Biodiversity Considerations in Index Construction.”^[8]

A more prominent feature of the investment process

As the authors write, capturing biodiversity risks and opportunities is becoming a more prominent feature of the investment process. With a transparent and systematic framework and better data available, investors can aim to effectively incorporate biodiversity considerations in portfolio construction.

We invite you to download and read the report here. 

^[1] Physical risks include the loss of raw materials and disruption of operating environments. Transition risks cover policy shifts, change in market preferences and voluntary commitments. Systemic risks mean things such as global pandemics. Regulatory or litigation risks include increasing legislation.

^[2] World economic Forum, “Nature Risk Rising: Why the crisis engulfing Nature Matters for business and the economy,” 2020.

^[3] The standard Biodiversity category includes indices that cover the World All Countries, Developed World, Europe 600, Developed Europe, US, Asia-Pacific and Emerging Markets regions. The Biodiversity Leaders category indices cover the World All Countries region.

^[4] ISS ESG’s SDG Impact Rating provides a holistic measurement of a company’s positive or negative impact on the 17 SDGs across more than 100 data factors. Companies are rated on a scale of -10 (significant negative impact) to +10 (significant positive impact).

^[5] STOXX, “ISS STOXX® Biodiversity Indices: How to Incorporate Biodiversity Considerations in Index Construction,” October 2023.

^[6] PDF quantifies the potential decline in species richness in an area over a defined period due to unfavorable conditions associated with environmental pressures. Species richness refers to the number of unique species in an area. Another metric that is available for inclusion in the framework is the Mean Species Abundance (MSA). MSA quantifies the mean abundance of original species relative to their abundance in undisturbed ecosystems. Species abundance refers to the total number of organisms in a given species.

^[7] In the ISS STOXX Biodiversity Leaders indices, companies are assessed in accordance with their revenues’ exposure to activities that are aligned with selected SDGs, and are assigned an SDG Solutions Score. The Score measures the positive and negative sustainable contributions of the companies’ products and services to the goals.

^[8] Biodiversity SDG Rating: Companies are assessed on the basis of their contribution to biodiversity- and climate-related UN SDGs. Revenue exposure to Biodiversity SDGs is based on the share of revenues a company derives from products and services identified as contributing to the achievement of a given objective, and ranges from 0% to 100%. SDG Solutions Score: see ^[7]. WACI stands for weighted-average carbon intensity.