If investors have the option to cheaply replicate their desired exposures to help solve their portfolio problems, then why shouldn’t they?
In our low expected return world, where leaking a percentage point of assets as fees every year to inconsistent active managers can’t be easily justified, this imperative now has added urgency.
Such are the driving forces behind an avalanche of savings moving out of active investment management and hedge funds towards passive approaches, a trend that has accelerated since the financial crisis challenged so many long-held assumptions.
Not all of these flows have gone to fully passive trackers. In fact, around $45 billion migrated into US smart beta and factor investing exchange-trade funds (ETFs) last year. Researchers have developed a proliferation of semi-passive or semi-active approaches to chose from. Depending on how you classify them, these approaches may be well on the way to fashioning a near-trillion dollar industry, according to Andrew Ang at BlackRock.
But what do we mean by smart beta and factor investing and how do they differ? Here is one good working definition for smart beta, courtesy of Ronald N. Kahn and Michael Lemmon in the CFA Institute Financial Analysts Journal®:
“They use simple, rules-based, transparent approaches to building portfolios that deliver fairly static exposures (relative to capitalization-weighted benchmarks) to characteristics historically associated with excess risk-adjusted returns.”
To understand the differences between smart beta and factor investing, consider a little background:
- The first iteration of semi-active approaches, often traced to Stephen A. Ross’s famous arbitrage pricing theory (APT) model, looked beyond the familiar cap-weighted market index (the market factor). Such researchers as Eugene Fama and Kenneth French attributed anomalous excess returns to value and small-cap factors.
- Thirty years later, a new generation of researchers, Robert Arnott among them, introduced fundamental index methodology, which championed the use of weighting methodologies based on accounting information like book value, cash flow, sales, and dividends. These were labelled alternative beta.
- More recent generations of quant researchers, using new econometric techniques and ever greater computer firepower, have analyzed and backtested over 300 factors extending into all asset classes. Researchers might refer to them as alpha rather than beta, but most agree they use smart approaches to isolate persistent excess returns.
Smart beta and factor investing are just fashionable marketing labels for a wide range of risk-based approaches that sit somewhere beyond active and passive investment management but possess attributes of both. In essence, smart beta and factor investing combine the disciplined rules-based approach of market-cap weighted passive funds with the discretionary selection of whichever chosen factors or index series those who use them hope to replicate.
Skeptics might see them as active management masquerading as passive, perhaps in order to make them more palatable to some investors. But even when the tier of the discretionary decision making has moved away from selecting competing securities to the level of selecting and combining factors, factor investing still seeks to solve familiar portfolio problems.
Using factors, one novel solution to this age-old portfolio formation problem was proposed in a recent article, “Fundamentals of Efficient Factor Investing,” in the Financial Analysts Journal, also summarized as part of our new In Practice series. Roger Clarke, Harindra de Silva, CFA, and Steven Thorley, CFA, apply to factor exposures the well-known mathematics used to create mean–variance portfolios and simulate optimal combinations of factor and security portfolios to capture improvements over the market portfolio’s Sharpe ratio. In other words, they use combinations of factors, taking a practical approach of using long-only assets, to form a perfectly optimized portfolio.
Scope and Challenges to Factor Investing
One of the recurring themes in Financial Market History is the slow and turbulent journeys of even the most powerful trends, such as the establishment of large institutional investors. Similarly, factor investing has not arrived at its current prominence without battling many challenges.
Writing in the Journal of Index Investing, Noël Amenc, Felix Goltz, and Ashish Lodh question why outperformance of smart beta ETFs is mainly due to the value and size factors. They took a closer look at why the portfolios outperform even if they are selected randomly as “monkey portfolios.” Other researchers also studied the lack of robustness, a charge often leveled at smart beta approaches, and proposed some remedies. They conclude with warnings about data mining and lack of transparency.
In another article from the Journal, “Will Your Factor Deliver? An Examination of Factor Robustness and Implementation Costs,” also summarized in the In Practice series, the authors find that many well researched factors lack robustness. Size and quality show weak robustness, whereas value, momentum, illiquidity, and low beta are more robust. The researchers determine that liquidity demanding factors may be better accessed by active management rather than indexation.
Spotting invalid approaches that can be the result of data mining is a technical as well as practical challenge for many researchers and end investors. In practice, researchers commonly adjust results by a “haircut” to minimize data-mining risks. In another article in the Journal of Portfolio Management, a new approach is proposed to calculate a haircut to the Sharpe ratios to account for data mining and multiple testing.
Small cap, value, momentum, low beta, and other factors have been commercialized into financial products, but in another large study summarized in CFA Digest, researchers ask, “Does Academic Research Destroy Stock Return Predictability?” This is an important risk, even if the authors’ findings are far from definitive. The authors evaluate whether premiums remain persistent or are arbitraged away after the publication dates of research and suggest grounds for optimism about persistence when considering major anomalies.
Finally, there are timing risks from attempting to time entry and exit from factors at the most opportune moments and portfolio risks when trying to combine them. Crowding and capacity issues, the subject of regular warnings from industry leaders, are additional risks in the field of quantitative investing. This was dramatically demonstrated by the quant meltdown of August 2007.
The next articles in this series will survey some asset class variants before focusing on multifactor investing and benchmarking.
A full topical collection of recommended links for all the related material on factor investing covered in this series along with a glossary can be found below.
Factor Investing: Applies to a wide range of risk-based approaches that sit between active and passive investment management but possess attributes of both. Uses both long and short techniques.
Smart Beta: A marketing label describing simple, rules-based, and transparent approaches to building portfolios that deliver fairly static exposures (relative to capitalization-weighted benchmarks) to characteristics historically associated with excess risk-adjusted returns. Often long only.
Alternative Beta: A subset of “smart beta,” alternative beta is distinguished from smart beta by its use of short as well as long investing.
Fundamental Indexation: Another subset of “smart beta” with a focus on using accounting, economic, and weighting data to develop new indices.
1. What Is Factor Investing? Smart Beta vs. Factor Investing
“I have got bad news as a starter,” Antti Ilmanen told the audience at the 2016 CFA Institute European Investment Conference. “It is not only a low interest rate world, it is also a low expected return world on any long-only investment.” Ilmanen, a principal and researcher at hedge fund AQR said low expected returns are going to anchor bad news for all of us for the rest of our working lifetimes. And maybe beyond.
Smart beta products are a disruptive financial innovation with the potential to significantly affect the business of traditional active management. Ronald N. Kahn and Michael Lemmon provide an important component of active management via simple, transparent, rules-based portfolios delivered at lower fees.
The quant manager has the same set of tools that any active manager has: Quants simply apply them using the ever-increasing power of computers, Gina Marie N. Moore, CFA observes. These tools allow the manager to pursue reward and deal with risk, costs, fees, and buying themselves the time necessary to distinguish investment skill from luck.
Combining long-only-constrained factor subportfolios is generally not a mean–variance-efficient way to capture expected factor returns, Roger Clarke, Harindra de Silva, CFA, and Steven Thorley, CFA, observe. For example, a combination of four fully invested factor subportfolios — low beta, small size, value, and momentum — captures less than half (e.g., 40%) of the potential improvement over the market portfolio’s Sharpe ratio. In contrast, a long-only portfolio of individual securities, using the same risk model and return forecasts, captures most (e.g., 80%) of the potential improvement.
2. Industry Scope and Challenges to Factor Investing
Since the beginning of the 20th century, institutional investors have gained prominence in UK and US financial markets not only because of changes in economic access, but also because of changes in the way governments protect investors, according to Janette Rutterford and Leslie Hannah.
Financial research has uncovered many new factors (e.g., small cap, value, momentum, low beta) that explain stock returns; in fact, many of these factors have already been commercialized into financial products. The authors examine whether these historical insights and return patterns remain after the academic research discovering them is published.
Because of the potential for data mining and multiple testing, it is common practice to haircut reported Sharpe ratios by 50% when evaluating backtests of trading strategies. Campbell R. Harvey and Yan Liu propose an approach that calculates a haircut to the Sharpe ratios to account for data mining and multiple testing.
Exchange-traded funds (ETFs) have been growing in popularity with recent developments in factor-tilted strategies. Some investors have observed that these portfolios derive most of their outperformance from exposure to only two factors — value and small size — and the portfolios outperform even if randomly put together or turned upside down (monkey portfolios), according to Noël Amenc, Felix Goltz, and Ashish Lodh.
Investors are wary of the robustness of the outperformance of smart beta strategies. Noël Amenc, Felix Goltz, Sivagaminathan Sivasubramanian, and Ashish Lodh address this concern by providing measures of relative and absolute robustness. They examine the causes of a lack of robustness and propose remedies for these problems. Their conclusions focus on the dangers of data mining and a lack of transparency.
The multifactor investing framework has become very popular in the indexing community. Both academic and practitioner researchers have documented hundreds of equity factors. But which of these factors are likely to profit investors once implemented? Noah Beck, Jason Hsu, Vitali Kalesnik, and Helge Kostka find that many of the documented factors lack robustness.
3. Equities Factor Investing
Eugene Fama and Kenneth French introduce a five-factor asset pricing model that outperforms the well-known Fama–French three-factor asset pricing model in explaining stock returns. Surprisingly, when the two additional factors of profitability and investment are added to the original three-factor model, the value factor becomes superfluous. Although the five-factor model is not without its challenges, it is useful in describing the cross-sectional variance of the factors’ expected return.
By adding profitability and investment factors to their earlier three-factor model, Eugene Fama and Kenneth French explain the market β, net share issues, and volatility anomalies. The accruals and momentum anomalies cannot be explained by the five-factor model.
Managed volatility and covered call writing are two of the few systematic investment strategies that have been shown to perform well across a variety of empirical studies and in practice. So far, they have been studied mostly as separate strategies. It turns out that when combined, these two strategies create a powerful toolset for portfolio enhancements, according to Anna Dreyer, CFA, Robert L. Harlow, CFA, Stefan Hubrich, CFA, and Sébastien Page, CFA.
The idea that seemingly cheap securities, according to measures of fundamental and intrinsic value, outperform seemingly expensive securities has been scrutinized by academics for more than 30 years, yet the value strategy is still widely misunderstood. Recent research that updated the extensively cited Fama–French three-factor model introduced two new factors that claim to make the value factor redundant. Clifford Asness, Andrea Frazzini, Ronen Israel, and Tobias Moskowitz identify a number of facts and fictions about value investing that need clarification.
In factor investing, assets are viewed as bundles of underlying risk factors, according to Andrew Ang. Investors should hold factors whose losses they can endure more easily than the typical investor can. Ideally, the benchmark for factor investing is dynamically based on investor-specific circumstances rather than on market capitalization.
4. Bond Factor Investing
A risk factor–based approach can be used for managing fixed-income portfolios. Ramu Thiagarajan, Douglas J. Peebles, Sonam Leki Dorji, Jiho Han, and Chris Wilson show that a limited set of factors — rate, growth, and volatility — explain the return on fixed-income portfolios. Investors can use this approach in managing and analyzing their portfolios and in incorporating their macro views into their asset allocation decisions.
To investigate the fundamental indexing methodology, Lidia Bolla, CFA, applies it to global government bond markets and examines its exposure to several newly introduced risk factors. She finds that the fundamental indexing approach outperforms a market-value-weighted index. However, her results show statistically significant and economically relevant exposures of fundamentally weighted indexes to the risk factors term and duration risk, default risk, convexity risk, liquidity risk, and carry trade risk. The increased risk exposure explains the outperformance of the fundamental indexing methodology in government bond markets.
Fixed-income attribution explains the sources of a manager’s active return, Deborah Kidd, CFA, observes. A complex process, attribution can be challenging to implement and often plagued by large, unexplained residual returns. Understanding the assumptions underlying a manager’s attribution model and their relation to the investment process, along with a qualitative assessment, can help determine how well the attribution reflects the manager’s decision-making skills and provide a clearer picture of performance.
5. Other Asset Classes
Patrick Houweling and Jeroen van Zundert, CFA, offer empirical evidence that size, low-risk, value, and momentum factor portfolios generate economically meaningful and statistically significant alphas in the corporate bond market. Because the correlations between the single-factor portfolios are low, a combined multifactor portfolio benefits from diversification among the factors: It has a lower tracking error and a higher information ratio than the individual factors.
The number of alternatively weighted equity indices, also called “alternative beta indices,” has risen dramatically since their introduction into the index scene in the mid-2000s. Deborah Kidd, CFA, provides an overview of popular alternatively weighted index schemes and gives investors a framework with which to understand and gauge the suitability of an alternative index for their desired risk exposures.
Having created a monthly dataset of US security prices between 1801 and 1926, Christopher C. Geczy and Mikhail Samonov conduct out-of-sample tests of price-return momentum strategies that have been implemented in the post-1925 datasets. The additional time-series data strengthen the evidence that price momentum is dynamically exposed to market risk, conditional on the sign and duration of the trailing market state.
Xi Li, Rodney N. Sullivan, CFA, and Luis Garcia-Feijóo, CFA, CIPM, explore whether the well-publicized anomalous returns associated with low-volatility stocks can be attributed to market mispricing or to compensation for higher systematic factor risk. The results of their study, covering a 46-year period, indicate that the relatively high returns of low-volatility portfolios cannot be viewed solely as compensation for systematic factor risk.
Research showing that the lowest-risk stocks tend to outperform the highest-risk stocks over time has led to rapid growth in so-called low-risk equity investing in recent years. Luis Garcia-Feijóo, CFA, CIPM, Lawrence Kochard, CFA, Rodney N. Sullivan, CFA, and Peng Wang, CFA, examine the performance of both the low-risk strategy previously considered in the literature and a beta-neutral low-risk strategy that is more relevant in practice.
6. Multifactor Approaches
After completing this chapter of Quantitative Investment Analysis, Third Edition, readers will be able to describe arbitrage pricing theory (APT), including its underlying assumptions and its relation to multifactor models; define arbitrage opportunity and determine whether an arbitrage opportunity exists; calculate the expected return on an asset given an asset’s factor sensitivities and the factor risk premiums; and more.
This monograph, by Vasant Naik, Mukundan Devarajan, Andrew Nowobilski, Sébastien Page, CFA, and Niels Pedersen, draws heavily on the vast body of knowledge that has been built by financial economists over the last 50 years. Its goal is to show how to solve real‐life portfolio allocation problems. The authors have found that using a broad range of models works best and prefer simple over complex models.
Diversification in portfolios is desirable, but the models we use to achieve this objective may be misleading because they are divorced from macroeconomics. Individual and institutional investors should pay close attention to improvements to the traditional approach to asset allocation, including consideration of forward-looking macro views. This presentation, by Vasant Naik and Sébastien Page, CFA, offers a tutorial on linking macro views to optimal risk factor–based asset allocation, as well as a discussion on the stock-bond correlation.
Despite the shortcomings of traditional asset allocation policies, most investment portfolios are still constructed based on direct asset class exposure. In addition, it may not be feasible for investors to implement policy-level decisions using a factor-based allocation framework. Daniel Ung, CFA, and Xiaowei Kang, CFA, discuss three approaches to risk factor–based portfolio construction and offer their reflections on the practical aspects of implementation.
7. Performance Measurement of Factor Investing
The recent advances in computational and financial technology and resultant financial innovation have created the possibility of a new perspective on indexes, indexation, and the distinction between active and passive investing, writes Andrew Lo.
Many sources of alpha have become easy to identify and widely replicated over time, Deborah Kidd, CFA, notes. Such systematic return drivers, or factors, occupy the space between traditional beta and alpha. They represent investment strategies that require skill beyond passive investing but not the complexity necessary for alpha generation
In attempting to profit from the anomaly that the observed returns for high-beta stocks inadequately compensate for their higher exposure to market risk, practitioners have increasingly “bet against beta” — selling short high-beta assets and buying low-beta assets. Scott Cederburg, CFA, and Michael S. O’Doherty challenge the existence of any such anomaly.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
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