The growth of factor investing is being driven not just by an extraordinary backlash against conventional asset classes and high-fee active management but also by a stream of innovative research.
Traditional portfolio diversification among and within different asset classes for many investors failed at its most critical test in 2008, although a little less badly for those with large cash allocations. Correlations across asset classes spiked and active managers everywhere floundered relative to passive funds tracking market-cap–weighted indices.
The first installment of the Shortcuts to Factor Investing series describes the intellectual history of factor investing, with its origins in the late Stephen Ross’s arbitrage pricing theory (APT). In this post, we take a deeper dive into equities and factor investing’s wider applications to other asset classes, including fixed income. The third installment will evaluate multifactor investing and benchmarking.
Let’s begin with a reminder of a working definition of a systematic factor, and why these factors are relevant to investment professionals, as outlined in a paper by Andrew Ang of BlackRock, summarized in CFA Digest. According to Ang, a factor must have the following characteristics:
- Be proven in the finance literature.
- Demonstrated notable excess premiums over benchmarks that are predicted to continue.
- Have a transparent return history that includes down periods.
- “Be implementable in liquid, traded instruments.”
Ang suggests that a large part (70%) of the recent active returns of Norges Investment Management, the world’s largest sovereign wealth fund, can be explained by systematic factors that could have been accessed less expensively through passive management, an effect which gets worse the larger the fund. The solution, at least for Ang, is a dynamic approach deploying leverage and across different asset classes to optimize the factor exposure.
So factor investing is not just about equities, and not just about well-known semi-passive and long-only approaches such as size, value, momentum, and low-volatility. As factor investing is applied to a wider range of asset classes, it suggests many potentially useful solutions to sophisticated investors and asset allocators. Equally critical, the wider application of factor investing demands novel thinking about combining factors with each other and with other asset classes as well as some new techniques for measuring performance in a rigorous, useful, and consistent way.
Equities and Beyond: The First Identified Factors
One reminder of factor investing’s roots in the equities sphere came recently in “A Five-Factor Asset Pricing Model,” summarized in the CFA Digest. As every MBA and finance student (should) know, the original one factor (the market) asset pricing model, the capital asset pricing model (CAPM), was extended by Eugene Fama and Kenneth French into a three-factor model with the addition of size and value factors, in what is the most widely cited paper in finance. The two authors have now extended this into a five-factor model to include two new factors: profitability and investment that may make the value factor redundant.
Value for many investors is a deeply held and sincere conviction based on the enduring principles of Benjamin Graham and David Dodd, but the meaning of the term and the definition of the value factor have recently come into question. The introduction of the first style indexes by Russell in 1987 was followed by a proliferation of formulaic accounting-based definitions. In one paper summarized in CFA Digest, Clifford Asness, Andrea Frazzini, Ronen Israel, and Tobias Moskowitz consider the poor performance of stand-alone formulaic value strategies, such as those for larger firms where research indicates the book-to-market (a measure of value) has no significant explanatory power on the cross-section of realized returns.
In a new study, “Facts About Formulaic Value Investing,” published in CFA Institute Financial Analysts Journal® and summarized in our new easy-to-read In Practice series, U-Wen Kok, CFA, Jason Ribando, CFA, and Richard Sloan distinguish the more formulaic type of value investing from other approaches. They suggest that these strategies should not be confused with value strategies that use a comprehensive approach in determining the intrinsic value of the underlying securities.
Another route to understanding the many linkages among factor investing approaches, including derivatives (leverage) and wider asset classes such as currencies, can be found in “Return of the Quants: Risk-Based Investing,” a summary of a speech Sébastien Page, CFA, head of asset allocation at T. Rowe Price, presented at the 69th CFA Institute Annual Conference. Especially in a low-yield, low-return environment, the authors propose a managed volatility strategy can adjust the asset mix over time to stabilize a portfolio’s volatility and reduce that portfolio’s exposure to loss in many forecast methodologies, asset classes, time periods, factors/risk premiums, and regions.
This approach can then be combined with a favored outperforming strategy to generate even better returns: Covered call writing, in which the investor sells a call option and simultaneously buys the underlying security, gives exposure to the volatility risk premium that is thought to take advantage of investors “demand for hedging” and to compensate for tail risks.
Returning to the (contentious) topic of definitions in factor investing, covered call writing forms part of a wave of approaches labeled “alternative beta.” This serves as a subset of the generally long-only “smart beta” amid the much broader universe of factor investing approaches. Alternative beta is distinguished from smart beta by use of short as well as long investing. Confused? A glossary of terms can be found below.
Fixed Income and Factor Investing
But what about opportunities for factor investing in asset classes outside of equities? One study by a group of researchers from AB Global, summarized in CFA Digest, finds that a limited set of factors — rate, growth, and volatility — explain the return on fixed-income portfolios.
Meanwhile, new research indicates that a fundamental indexing approach to global government bond markets outperforms a market-value-weighted index, according to a recent study by Lidia Bolla, CFA, published recently in the Financial Analysts Journal and summarized as an In Practice article. Results show significant exposures of fundamentally weighted indexes to six fixed-income factors: term and duration risk, default risk, convexity risk, liquidity risk, and carry trade risk that help explain the outperformance.
Factors can be identified not just in government bonds, but also in corporate fixed income. In a recent Journal article, also summarized in the In Practice series, two researchers from Robeco in the Netherlands, find that size, low-risk, value, and momentum factor portfolios generate economically meaningful and statistically significant alphas in the US corporate bond market.
Momentum and Low Volatility
We conclude this second part of our factor investing series with a special note on two individual factors. In fact, a trio of recent articles in the Journal explore momentum and low volatility, two of the original “smart beta” factors boasting long-run excess returns and low correlation, and the authors offer challenges to conventional wisdom on both. “Two Centuries of Price-Return Momentum” suggests that price momentum is dynamically exposed to market risk, conditional on the sign and duration of the trailing market state. “The Low-Volatility Anomaly: Market Evidence on Systematic Risk vs. Mispricing” finds that the relatively high returns of low-volatility portfolios cannot be viewed solely as compensation for systematic factor risk. “Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios” examines the performance of both the low-risk strategy and a beta-neutral low-risk strategy.
A full topical collection of recommended links for all the related material on factor investing covered in this series can be found below along with a Glossary.
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.
Topical Reading Collection
1. What Is 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, and 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. R. David McLean, CFA, and Jeffrey Pontiff 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.
The term “value investing” is increasingly being adopted by quantitative investment strategies that use ratios of common fundamental metrics (e.g., book value, earnings) to market price. A hallmark of such strategies is that they do not involve a comprehensive effort to determine the intrinsic value of the underlying securities. U-Wen Kok, CFA, Jason Ribando, CFA, and Richard Sloan argue that these strategies should not be confused with value strategies that use a comprehensive approach in determining the intrinsic value of the underlying securities.
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.
Asset classes can be broken down into factors that explain risk, return, and correlation characteristics better than traditional approaches, Eugene L. Podkaminer, CFA, explains.
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, Vasant Naik and Sébastien Page, CFA, observe.
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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