Smart Beta Research Articles

Practical Applications of A New Metric for Smart Beta: Factor Exposure per Unit of Tracking Error

What really makes a good smart beta portfolio? How can asset owners and institutional investors differentiate among the multitude of smart beta methodologies that have emerged over the last decade? Jennifer Bender, Xiaole Sun and Taie Wang of State Street Global Advisors set out to answer these questions in their article A New Metric for Smart Beta: Factor Exposure per Unit of Tracking Error in The Journal of Index Investing. They introduce a new metric—risk per unit of tracking error—to assess what how well smart beta strategies are helping institutional investors to meet their risk–return goals and provide guidance on how indices can be built with this measure in mind.

Practical Applications of Smart Beta or Smart Alpha?

Smart beta offers passive implementation, low fees, transparency and potentially better risk-adjusted returns relative to both traditional capitalization-weighted benchmarks and actively managed funds. But are smart beta strategies a replacement for active managers? <b>Kenneth Winther of Tryg</b> in Denmark and hiscolleague <b>Søren Steenstrup</b> felt that the research on smart beta’s superior performances was focused on US retail investors, not institutional investors. In <b><i>Smart Beta or Smart Alpha?</i></b> they explain how they constructed actively managed institutional equity portfolios to capture the most widely used risk factors in smart beta strategies: value, size, low volatility and momentum. They found that other than the momentum portfolio, the actively managed portfolios generated superior absolute and risk-adjusted return, even after costs. They call this smart alpha.

Practical Applications of Alpha Signals, Smart Beta, and Factor Model Alignment

How can a portfolio manager properly identify the underlying drivers of return and incorporate this information into a reliable risk model? In the process of portfolio evaluation and rebalancing, there can be a misalignment between the manager’s understanding of what drives returns—whether it is alpha or smart beta factors—and the risk model. In Alpha Signals, Smart Beta, and Factor Model Alignment , Terry Marsh of the University of California, Berkeley and Quantal International and Paul Pfleiderer of Stanford University investigate smart beta and risk model forecasting. In this discussion with Marsh, he discusses the article and offers additional perspective on why he and his co-author decided to explore the drivers of return and the corresponding risk factors.

How Physics Solved Your Wealth Problem!

While Robert Solow suggested not to think of Economics as Science, Andrew Lo warned us about the dangers of using Physics to build economic systems. Physics has been a late entrant to the world of Finance. The subject has reached critical mass to answer some of the biggest challenges of Finance of the last 50 years. The Nobel Prize-winning Finance has failed to reconcile efficiency and inefficiency, forcing the global money to rely on the Efficient Market Hypothesis (EMH) as the last standing hypothesis, which can neither be rejected nor accepted. This has left trillions of dollars in Pension funds clueless regarding what to rely on. The consequence could be an unintended risk, which society is not prepared for.Though Smart Beta has brought in a rule-based thinking, it is primarily catering as an ‘Active’ solution to changing market cycles. Its foundation of financial theories remains structurally weak, the reason the industry does not have a coherent definition of the term Smart Beta. There is Factor proliferation, conflicting explanations of outperformance, lack of differentiation and a host of unanswered questions like why naive allocation is better than Factor selection? Is Multi-Factor better than Single Factor? Is everything that concentrates exposure of Market Capitalization (MCAP), smart? Whether timing helps or not? And larger questions like what drives the persistence of Premia? Will we ever take on Granger’s (1992) challenge to build a better alternative to EMH? The solution providers are simply riding on the growth of the segment relying on their institutional clients to comprehend the risk and invest.Finance needs Physics to bring the needed clarity, usher in a simplified Factor thinking, move away from causal thinking and look at markets in a very objective quantifiable way to answer the various questions and explain the need and persistence of Free Lunch (Premia). The author makes the case by restating market Alpha as a process driven by the ‘Reversion - Diversion’ Framework that is at the heart of the ‘Rich Get Richer’ phenomenon. The paper explains how a simple probabilistic classification can address the insufficiencies of a behavioral or market efficiency model, explain how Smart and Dumb Beta work together and how Physics and the science of complex networks can be the savior for Finance.

Architecture of Data

The rich do not always get richer. Explaining this probabilistically resolves a 100-year-old puzzle, opening up opportunities for smart beta portfolios, an architecture of complexity and eventually an architecture of data that could become Web 4.0.

Dimensions of Diversification

Within the investment industry, diversification now refers to not only the division of capital among a large number of securities but also the avoidance of risk concentration in any of a number of dimensions. Market-capitalization-weighted indexes often fail this requirement. The authors thus argue that although capitalization weighting makes a suitable benchmark, smart beta can provide a way to build indexes more suitable for investment. The authors present a methodology to measure and hence maximize diversification simultaneously across multiple dimensions. They show the practical value of this measure by using it to backtest equity portfolios. This provides an example of how the properties of assets, rather than historical returns, can be used to systematically construct well-diversified portfolios.

A New Metric for Smart Beta: Factor Exposure per Unit of Tracking Error

What does success look like for smart beta indexes? The authors argue that success is the ability of an index to provide a strong and consistent level of exposure to the targeted factor(s). Moreover, exposure should be achieved in a risk-aware way. Risk taken relative to the capitalization-weighted benchmark is active risk, and investors should be compensated with greater exposure to their targeted factor(s). The authors provide guidance on how indexes can be built with this measure in mind via screening and weighting decisions. Because screening and weighting decisions move tracking error and exposure in the same direction, most rules-based indexes naturally provide reasonably strong exposure per unit of tracking error.

Improving Smart Beta ETFs with Smart Beta. Revision 1.

In a previous working paper I analyzed the performance of pairs-trading from a universe of ETF stocks. This paper uses a similar approach but considers long-only strategies which belong to the general group of smart beta strategies: Alternative weighting schemes of the stocks and subset selection according to volatility, beta or momentum. One selects a smart sub-ETF. If the original ETF is already smart the subset is a sort of smart ETF of the second order. The working paper shows that the question of different weighting-schemes is over-weighted. It answers also the FAQ about the impact of different re-balance periods. The risk-adjusted performance can be improved by either low-volatility or small-beta subsets. Momentum and Mean-Reversal works for some but not all ETFs. Revision 1 adds the results for idiosyncratic volatility.

Improving Smart Beta ETFs with Smart Beta

In a previous working paper I analyzed the performance of pairs-trading from a universe of ETF stocks. This paper uses a similar approach but considers long-only strategies which belong to the general group of smart beta strategies: Alternative weighting schemes of the stocks and subset selection according to volatility, beta or momentum. One selects a smart sub-ETF. If the original ETF is already smart the subset is a sort of smart ETF of the second order. The working paper shows that the question of different weighting-schemes is over-weighted. It answers also the frequently asked question about the impact of different re-balance periods. The risk-adjusted performance can be improved by either low-volatility or small-beta subsets. Momentum and Mean-Reversal works for some but not all ETFs.

Rise of Factor Investing: Asset Prices, Informational Efficiency, and Security Design

We model financial innovations such as Exchange-Traded Funds, smart beta products, and many index-based vehicles as composite securities that facilitate trading common factors in assets' liquidation values. Through accessing a larger basket of assets in endogenously-chosen proportions, composite securities can benefit both informed and liquidity traders and attract all factor investors with optimal designs that feature selecting liquid and representative assets. Consistent with empirical findings, introducing composite securities leads to higher price variability and co-movements, larger trading costs and synchronicity, and lower asset-specific but higher factor information in prices, especially for illiquid assets. Trading transparency, distinction between bundles and derivatives, and endogenous information acquisition also significantly affect prices and security design.

My Factor Philippic

Arnott, Beck, Kalesnik, and West (2016) (ABKW) study smart beta or factor-based strategies and come to the following conclusions: (1) Aside from value, most popular factor strategies currently look expensive. (2) These expensive factor valuations portend lower future returns and a strong possibility of a future “factor crash” in which they go “horribly wrong.” And (3) many of these non-value factors were never real to start with because their historical performance was due to factor richening. That is, researchers mistook the one-time returns from factor richening for truly repeatable “structural alpha.” ABKW’s implied bottom line (their many protestations to only making modest recommendations aside): stick with value, dump the other factors. This essay elaborates on my response in Asness (2016). In summary: (1) I find non-value factor valuations moderately expensive, but not as expensive as ABKW. (2) I argue that ABKW exaggerate the power of factor timing by improperly using long-horizon regression techniques. More proper short-horizon regressions suggest some weak factor timing ability and given this predictability, I construct value-based tactical factor timing strategies to test them. Unfortunately, these strategies add little to portfolios that are already invested in the value factor. It turns out that this “newly” discovered timing tool is, yet again, mostly just a version of regular old value investing. And (3) I examine ABKW’s claim that factor richening drives much of non-value long-term factor performance and find that this very serious allegation about other researchers’ work is totally without merit. Overall, these results suggest that one should be wary of aggressive factor timing. Instead, investors are better off identifying factors they believe in, and staying diversified across them, unless we see far more extreme pricing than we do today.

The Last Smart Beta Paper You’ll Ever (Have to) Read: Nuggets of Hope beneath the Hype

The hype surrounding smart beta is drowning out valuable insights for improving investment outcomes. The core idea of smart beta—that factors independent of market direction are sources of sustainable and diversifying returns—is foremost among those insights. Unfortunately, the key selling points of most smart beta products—simplicity, transparency, and static implementation—actually make factor-based investing less effective. Smart beta also tends to be long only, equity focused, and single factor. The most promising forms of factor-based investing move beyond one or more of these constraints. In this article, the authors offer a “buyer’s guide” for linking factor-driven strategies to investors’ long-term objectives—one that may not mean “buying” any of the products on offer today. <b>TOPICS:</b>Analysis of individual factors/risk premia, portfolio construction

Alpha Signals, Smart Betas, and Factor Model Alignment

The authors consider the case for augmenting risk models to be used in portfolio construction to reflect information embedded in the portfolio manager’s alphas. They consider both smart beta models and cases in which alpha signals are partly factor driven but incorrectly perceived to be stock specific. In smart beta cases, the authors argue that mechanically augmenting the risk model can cause losses by distorting an otherwise-correct factor structure. The authors show that for cases in which asset-specific alpha signals might unexpectedly be related to hidden systematic factors, errors of omission—missing these hidden factors—generally result in larger expected losses in portfolio efficiency than do errors of commission—unintentionally including nonexistent “phantom” factors. When the alpha signals are very noisy, the practice of mechanically augmenting the risk model with a custom risk factor to offset that noise can improve portfolio efficiency. However, in those cases, the custom risk factor has nothing to do with underlying sources of true risk that all investors face, but instead serves as a penalty that in a back-door way tends to adjust for weak quality of the manager’s alphas.

A Taxonomy of Beta Based on Investment Outcomes

As the population of smart beta products has exploded, the quest to differentiate among them has focused on portfolio construction. The authors of this article aim to shift the conversation back to what actually matters to asset owners: investment outcomes. Their taxonomy of smart beta strategies, supported by a comprehensive analysis of simulated historical returns over three decades covering both developed and emerging markets, distinguishes between three stated portfolio construction objectives (enhanced diversification, factor investing, and fundamental weighting) and two investment outcomes (core exposure and style investing). The investment outcomes are characterized by performance patterns as well as such qualitative considerations as benchmark sensitivity, turnover levels, and underlying beliefs about market inefficiencies and the merits of broad diversification. Their classifications highlight how smart beta is more than just factor investing, with which it has become increasingly identified. Some strategies aimed at enhanced diversification are style neutral and thus suitable for core, while others, in fact, deliver backdoor style tilts. As in all things, with smart beta, caveat emptor.

Editor’s Letter

1. Brian R. Bruce 1. Editor-in-Chief Our Summer issue focuses on smart beta, or alternative index tracking, strategies. Smart beta strategies started with fundamental indexes and have grown exponentially to cover many strategies. Smart beta proponents argue they are an efficient means of

What Is in Your Smart Beta Portfolio?A Fundamental and Macroeconomic Analysis

With smart beta becoming increasingly popular, a swath of strategies have been designed to provide access to a wide array of return-enhancing risk in the marketplace. Many strategies claim to provide access to the same factors, and one might reasonably expect that they would be similar. Yet the ways they are constructed can vary widely. Seemingly small distinctions in index construction can lead to portfolios that have differential drivers of risk and return and unequal exposures to factor and sector biases. They can likewise have an effect on the macroeconomic environments in which the portfolios perform, which is particularly important in multifactor portfolios where a number of factors are blended. This article reviews some typical strategies that seek to track common factors (i.e., volatility, momentum, quality, growth, value, dividend yield, and size) in the U.S. market in order to better understand the characteristics of these strategies, from both a fundamental and a macroeconomic perspective. The same analysis is then extended to a multifactor portfolio. <b>TOPICS:</b>Analysis of individual factors/risk premia, mutual funds/passive investing/indexing, portfolio construction

Assessing the True Cost of Strategic-Beta ETFs

The report examines the differences in costs between strategic-beta (smart beta) exchange-traded funds (ETFs) and their more ordinary passive peers. Cost is an important consideration that isn’t always at the forefront of investors’ minds when selecting strategic-beta funds. The study assesses differences in fees, replication costs, and trading costs for a selection of European-domiciled, strategic-beta ETFs and market-cap-weighted ETFs linked to some of the most widely used broad equity benchmarks, including the S&P 500, MSCI World, and MSCI Emerging Markets indexes. The authors find a wide disparity in the fees charged by strategic-beta funds and that their total cost of ownership is, on average, significantly higher.

Influencing Retirement Saving: Smart Beta in Defined Contribution Default Options

The shift to defined contribution (DC) plans as a primary retirement vehicle is increasingly a global phenomenon. A trend that originated in the corporate and tax-exempt marketplaces, it is now carrying over to the public sector across the globe. In this article, the authors look at one of the most profound innovations in index investing, smart beta, and analyze exactly what features of this new family of strategies could make them a viable improvement for default DC options. They then introduce examples of strategies that aim to achieve predefined investment outcomes and explore ways to implement them in traditional multi-asset portfolios. They test this by using existing smart beta indexes in the context of a traditional lifestyle fund, where market-capitalization indexes are replaced with smart beta indexes on predefined allocations.

Incorporating Smart Beta into Portfolios: A Case Study with the Volatility Risk Premium

As the latest addition to the basic equity smart beta factors, the volatility risk premium (VRP) has become an attractive potential source of additional returns for investors. The VRP is generally defined as the difference between the implied volatility of options and the subsequently realized volatility. However, few VRP-based investment products are available that both deliver consistent returns and are low cost. The focus of this article is how investors can incorporate the VRP into typical portfolios, exemplified by a balanced 60/40 portfolio and an equal-weight, multi-asset diversified portfolio. The article explores two different methods, the dedicated VRP construct (long only) and the overlay VRP construct (long–short) and concludes that both methods can potentially enhance investors’ portfolio returns without significantly altering the portfolio’s risk profile because the VRP is an attractive and traditionally untapped source of returns that has exhibited low correlations with traditional risk premiums. The conclusions drawn in the article may be applicable to the task of incorporating other smart beta factors into portfolios.

Investors Care about the Purity of Factor Indexes: A Reply

In “Who Cares about Purity of Factor Indexes?” Amenc and Goltz (JII, Summer 2016) question the relevance of the factor efficiency ratio and the ultimate need for efficiency in factor index construction. This comment is intended to address their issues. So, “Who cares about purity of factor indexes?” Any investor that has to choose among the growing number of smart beta indexes should certainly care. <b>TOPICS:</b>Passive strategies, analysis of individual factors/risk premia