Abstract: Recent periods of market turbulence and stress have created considerable interest in credible alternatives to traditional asset allocation methodologies. It would be preferred if portfolios can be decomposed into components that can be directly connected to independent risks and individually rewarded by the market for their level of risk. This can be achieved through factor-based investing, which relies on the observation that most return and risk characteristics for all asset classes can be well explained by particular building blocks, or factors.We describe main features of factors, factor investing and factor models, with emphasis placed on practical topics such as selection of significant factors associated to specific asset classes, differentiating between factors, anomalies or stylized facts, and preference for composite portfolios based on combining factors. We have also analyzed implementation details and the factor risk parity strategy.Then we consider improvements to factor-based investing through regime switching and sensitivity analysis. We present theoretical and practical frameworks for Markov switching models and for sensitivity analysis, and rely on representative examples to illustrate the benefits of efficiently incorporating regimes and sensitivity analysis into portfolio management.The final section describes features of good testing procedures for portfolio behavior and performance, in contrasts with possible testing pitfalls.

Abstract: Smart-beta products have captured the interest of investors. But where do they fit in their portfolios? The typical investor, who currently owns active and index products, should own active, index, and smart-beta products. This article introduces a framework that decomposes any strategy’s return over time into a broad capitalization-weighted index return, the return to static exposures to smart-beta factors, the return to timing smart-beta factors, and the return above and beyond smart beta. Smart-beta risk constitutes roughly one-third of the active risk of an average active equity manager, and roughly two-thirds of the active risk of an average fixed-income manager. Diversifying across active managers increases the fraction in smart beta. Most investors will want all of these return sources in their portfolio, and this framework facilitates optimizing the blend.

Abstract: Technological advances in telecommunications, securities exchanges, and algorithmic trading have facilitated a host of new investment products that resemble theme-based passive indexes but which depart from traditional market-cap-weighted portfolios. I propose broadening the definition of an index using a functional perspective — any portfolio strategy that satisfies three properties should be considered an index: (1) it is completely transparent; (2) it is investable; and (3) it is systematic, i.e., it is entirely rules-based and contains no judgment or unique investment skill. Portfolios satisfying these properties that are not market-cap-weighted are given a new name: “dynamic indexes.” This functional definition widens the universe of possibilities and, most importantly, decouples risk management from alpha generation. Passive strategies can and should be actively risk managed, and I provide a simple example of how this can be achieved. Dynamic indexes also create new challenges of which the most significant is backtest bias, and I conclude with a proposal for managing this risk.

Abstract: This paper takes another look at the recommendation of Blitz [2012] to allocate strategically to the value, momentum and low-volatility factor premiums in the equity market. Five years of fresh data shows that such a factor investing strategy continued to deliver out-of-sample. The potential added value of the two new factors in the Fama-French 5-factor model, operating profitability and investment, is investigated and found to depend critically on the performance metric that is considered most important. The paper also reviews the role of small-cap stocks, factor timing, long-only versus long-short portfolio construction, international evidence and factor investing beyond equities.

Abstract: In this article, we consider a new framework to understand risk-based portfolios (GMV, EW, ERC and MDP). This framework is similar to the constrained minimum variance model of Jurczenko et al. (2013), but with another definition of the diversification constraint. The corresponding optimization problem can then be solved using the CCD algorithm. This allows us to extend the results of Cazalet et al. (2014) and to better understand the trade-off relationships between volatility reduction, tracking error and risk diversification. In particular, we show that the smart beta portfolios differ because they implicitly target different levels of volatility reduction. We also develop new smart beta strategies by managing the level of volatility reduction and show that they present appealing properties compared with the traditional risk-based portfolios.

Abstract: There has been significant evidence that systematic equity investment strategies (so-called smart beta strategies) outperform the cap-weighted benchmarks in the long run. These strategies are usually marketed on the basis of outperformance. However, it is important to recognize that performance analysis is typically conducted on backtests that apply the smart beta methodology to historical stock returns. Concerning actual investment decisions, a relevant question is: How robust is the outperformance? The issue of robustness, as in extreme risk and performance attribution to well-defined risk factors, is not dealt with by index providers despite investors being wary of robustness of outperformance of various smart beta strategies. This article, with the use of single- and multi-factor indices, examines the causes of, and remedies for, lack of robustness and then provides a framework to evaluate the robustness of various smart beta strategies.

Abstract: In this article, we propose a framework for building advanced beta (factor) portfolios in a benchmark-centric context. The intuition behind benchmark-aware advanced beta portfolios is simple — overweight securities that rank high on the characteristic, or factor, the portfolio is meant to capture and underweight, or exclude, securities that rank low on that characteristic. We outline a general framework for building benchmark-aware advanced beta portfolios, what we refer to as portfolios “tilted” towards a certain factor. We discuss the various methodological extensions that can then be made within this framework, and relate current popular smart beta indices to this framework. The tilted framework has strong intuitive properties. It can be used to build “anti-tilted” portfolios, which overweight stocks that rank unfavorably along the characteristic. These anti-tilted portfolios underperform the benchmark, reflecting the coherency of this framework. We contend that as factor investing evolves, this benchmark-centric framework is a more consistent and coherent way to view advanced beta portfolio construction than benchmark-independent approaches.

Abstract: Using a comprehensive sample of 164 domestic equity Smart Beta (SB) ETFs during 2003-2014 period, I analyze whether these funds beat their benchmarks by tilting their portfolios to well-known factors such as size, value, momentum, quality, beta and volatility. I then test if Smart Beta funds harvest factor premiums more efficiently than their traditional cap-weighted benchmarks by periodic trading against price movements. While 60% of SB fund categories have beaten their raw passive benchmarks, I find no conclusive empirical evidence to support the hypothesis that SB ETFs outperform their risk-adjusted benchmarks over the studied period. Performance of SB funds is also insignificant when compared with the risk-adjusted blended benchmark that uses existing cap-weighted funds to provide low-cost passive exposure to market, size and value factors. SB ETFs exhibit potentially unintended factor tilts which may work to offset the return advantage from intended factor tilts. After decomposing total allocation component of SB funds into static and dynamic effects, I find that the benefit from dynamic factor allocation is neutral at best. This is consistent with the hypothesis that static factor exposure rather systematic rule-based rebalancing is the main driver of SB ETFs performance.

Abstract: Implementing a passive strategy with significant assets under management affects market values and thus gives rise to implicit costs in addition to the explicit costs, such as commissions and transaction-related fees. This article presents a conceptual framework and mathematical model for decomposing implicit trading costs and comparing them across active, passive, and smart beta strategies that do not involve frequent trading. The proposed framework, which improves on the weighted-average market capitalization/turnover approach, is intended to help providers and investors evaluate the investability or capacity of index-based strategies.

Abstract: In this article, we propose a framework for building advanced beta (factor) portfolios in a benchmark-centric context. The intuition behind benchmark-aware advanced beta portfolios is simple – overweight securities that rank high on the characteristic, or factor, the portfolio is meant to capture and underweight, or exclude, securities that rank low on that characteristic. We outline a general framework for building benchmark-aware advanced beta portfolios, what we refer to as portfolios “tilted” towards a certain factor. We discuss the various methodological extensions that can then be made within this framework, and relate current popular smart beta indices to this framework. The tilted framework has strong intuitive properties. It can be used to build “anti-tilted” portfolios, which overweight stocks that rank unfavorably along the characteristic. These anti-tilted portfolios underperform the benchmark, reflecting the coherency of this framework. We contend that as factor investing evolves, this benchmark-centric framework is a more consistent and coherent way to view advanced beta portfolio construction than benchmark-independent approaches.

Abstract: The past several years have witnessed the introduction of hundreds of so-called “smart beta” equity indices. These indices provide exposure to risk factors, such as value or low volatility, in order to seek excess return and/or risk reduction compared to cap-weighted indices. Although the set of risk factors that these indices target is relatively small, construction methodologies and historical performance have varied significantly, even among those indices seeking exposure to exactly the same factors. In this article, we introduce a simple metric we call the factor efficiency ratio that gauges the amount of active risk an index product derives from intentional, desired factor exposure versus active risk stemming from unintended or undesired bets. This ratio is a measure of how efficiently an index targets a group of intended factors and we demonstrate the strong relationship between efficiency and risk-adjusted returns. In doing so, we also highlight several potential problems with the design of existing smart beta indices.

Abstract: As a bull market in bonds that lasted for more than three decades comes to an end, investors increasingly question traditional means of accessing passive fixed income investments. Historically the main return driver of diversified fixed income portfolios has been treasury interest rate exposure, which has performed well in a long period of falling rates in developed markets. The prospect of rising rates in a low yield environment has sparked interest in new approaches to index-linked bond investing. Factor based investing provides a coherent framework for designing outcome-oriented fixed income indices. From that perspective we discuss shortcomings of existing fixed income indices, lay out the risk and return drivers inherent in the asset class, and propose alternative investment approached based on these insights. We conclude with examples of smart beta strategies that aim to provide enhanced diversification across credit and rates factors, an improved risk-return trade-off within a global treasury portfolio, and a value approach to investing in high yield corporate bonds.

Abstract: In this chapter, we consider a new framework for understanding risk-based portfolios (global minimum variance (GMV), equally weighted (EW), equal risk contribution (ERC) and most diversified portfolio (MDP)). This framework is similar to the constrained minimum variance model of Jurczenko et al., but with another definition of the diversification constraint. The corresponding optimization problem can then be solved using the cyclical coordinate descent (CCD) algorithm. This allows us to extend the results of Cazalet et al. and to better understand the trade-off relationships between volatility reduction, tracking error and risk diversification. In particular, we show that the smart beta portfolios differ because they implicitly target different levels of volatility reduction. We also develop new smart beta strategies by managing the level of volatility reduction and show that they present appealing properties compared to the traditional risk-based portfolios.

Abstract: Smart beta strategies promise to deliver market-beating returns with simplicity and low cost, but the reality is more complicated. Contrary to popular perception, smart beta strategies are neither passive nor well diversified. Nor can they be expected to perform consistently in all market environments. Perhaps most importantly, because of their focus on only a limited number of factors, smart beta strategies fail to exploit numerous potential profit opportunities.

Abstract: Traditionally, portfolio managers have been discouraged from timing the market, for example, equity managers have been forced to adhere strictly to a benchmark with static or relatively stable components, such as the SP that is, to increase exposure when one anticipates that the market will rise, and to decrease it when one anticipates that the market will fall.

Abstract: In contrast to factor-based smart beta, diversification-based smart beta assumes that, seemingly naively, all investments are the same in some dimension. The four possible dimensions—portfolio weight, expected return, risk-adjusted return, and risk contribution—lead respectively to four naive beta portfolios: equally weighted, minimum-variance, maximum-diversification, and risk parity portfolios. In this article, the authors show how these four portfolios outperform the capitalization-weighted S&P 500 Index due to their sector differences and, especially, the index’s aggressive shifts into specific sectors. Among the three naive beta portfolios that use risk inputs as part of their portfolio construction, both minimum-variance and maximum-diversification portfolios tend to be highly concentrated in certain sectors. The authors develop an analytic framework based on a partitioned correlation matrix, modeling sectors as two groups (mostly defensive versus cyclical). The results shed light on material differences between the risk parity and optimized portfolios in their level of diversification. Empirical examples of sector portfolios within the universe of the S&P 500 Index show the triumph of naive beta over the index, which suffers from strong sector biases and ill-timed allocation shifts.

Abstract: As index investing continues to grow, we are witnessing an expansion of what the beta continuum encompasses. During the 2000 dot-com bubble and the 2007–2008 financial crisis, market-capitalization weighted strategies revealed some of their limitations. As a result, increasing attention is being devoted to index solutions that go beyond the traditional space—often referred to as “smart beta.” In this article, we focus our attention on a sample of beta strategies within the equity space. By analyzing each beta’s benefits and limitations, we highlight their ultimate nature as outcome-orientated solutions. We believe the results of our analysis encourage investors to think about beta as a range of complementary tools that should be used to actively rotate portfolios from one indexed strategy to another, based on investors’ goals and on prevailing market conditions.

Abstract: In remarks given at Wharton’s Jacobs Levy Equity Management Center for Quantitative Financial Research Spring Forum, Bruce Jacobs observes the parallels between smart beta and a popular, but ill-fated, investment strategy from the 1980s, portfolio insurance. He also notes that actual (rather than back-tested) smart beta portfolios have to date shown little evidence of significant risk-adjusted outperformance, and asserts that factors are best exploited in a dynamic, multifactor portfolio that employs numerous, proprietary factors simultaneously.

Abstract: In contrast to factor-based “smart beta”, diversification-based “smart beta” assumes that, seemingly “naively”, all investments are just average or the same in some dimension. The four possible dimensions: portfolio weight, expected return, risk-adjusted return, and risk contribution, lead to four “naive beta” portfolios respectively: equal-weight, minimum variance, maximum diversification, and risk parity portfolios. In this paper, we show how these four portfolios outperform the capitalization-weighted S&P 500 index due to their sector differences and especially, the index’s aggressive shifts into specific sectors. Among the three “naive beta” portfolios that use risk inputs as part of their portfolio construction, both minimum variance and maximum diversification portfolios tend to be highly concentrated in certain sectors. We develop an analytic framework based on a partitioned correlation matrix, modeling sectors as two groups (mostly defensive versus cyclical). The results shed light on material differences between the risk parity and optimized portfolios in their level of diversification across the sector and stock dimensions. Empirical examples of sector and stock portfolios within the universe of the S&P 500 index show the triumph of “naive beta” over the index, which suffers from strong sector biases and ill-timed allocation shifts.

Abstract: As index investing continues to grow, we are witnessing an expansion of what the beta continuum encompasses. Since the financial crisis, during which market-capitalization strategies have shown their limits, increasing attention is being devoted to index solutions that go beyond the traditional space—often referred to as smart beta. Since the publication of the first seminal papers in the early 1990s, equity investors have been aware of factors-based approaches reflecting systematic exposures to themes such as valuation, momentum, quality, or size. On the other hand, the progress on the fixed income side has been much slower and not until recently have investors started to investigate factors-based approaches with particular investment objectives. For this and other reasons, smart beta solutions in fixed income are likely to take a different shape than in equities. In this article, we discuss the concept of smart beta in fixed income. The first part describes the drawbacks of traditional benchmarks and analyzes the main factors driving fixed income returns. The second part presents a few examples of smart beta strategies providing exposure to the analyzed risk factors while addressing some of the benchmark issues.

Abstract: The reasons for outperformance in smart beta portfolios remains a mystery. We extend previous literature on the link between portfolio performance and macroeconomic factors by exploring the response of a low beta portfolio to interest rate movements. The implications for fund managers heavily invested in low-risk strategies where the immediate risk lies in the future rise in interest rates are worth considering. In particular, low beta funds appear to go up when interest rates fall more than when interest rates rise. We focus on the case of US equity investment based on the capital asset pricing model (CAPM). We find that the anomaly is partially explained by interest sign changes due to macroeconomic events, and observe heterogeneous impacts for low and high beta portfolios.

Abstract: Alternatives to traditional cap-weighted indexes have been growing in popularity as investors strive to add diversification using a transparent, mechanical, and low-cost approach. Alternative index strategies, sometimes called smart beta, promise enhanced return, often with lower risk and greater diversification power. We examine a number of such strategies and find that many feature persistent tilts (especially to value and small-cap stocks), which account for the majority of their risk and return characteristics. Because alternative index strategies reweight stocks from the same universe, diversification with standard benchmarks has been very weak. Investors can use alternative index strategies to capture factor tilts instead of relying on active management, or they can reallocate assets out of cap-weighted index strategies to a middle ground between traditional passive and active. Although past performance results have been strong, there is no reason to expect future outperformance from these strategies unless the embedded tilts continue to be rewarded. For those who really believe in factor tilts (such as value and small cap), risk-premia strategies—which can invest across multiple asset classes and permit short selling— are far more compelling than single-asset-class, long-only alternative index strategies.

Abstract: The past several years have witnessed the introduction of hundreds of so-called “smart beta” equity indices. These indices provide exposure to risk factors, such as value or low volatility, in order to seek excess return and/or risk reduction compared to cap-weighted indices. Although the set of risk factors that these indices target is relatively small, construction methodologies and historical performance have varied significantly, even among those indices seeking exposure to exactly the same factors. In this article, we introduce a simple metric we call the factor efficiency ratio that gauges the amount of active risk an index product derives from intentional, desired factor exposure versus active risk stemming from unintended or undesired bets. This ratio is a measure of how efficiently an index targets a group of intended factors and we demonstrate the strong relationship between efficiency and risk-adjusted returns. In doing so, we also highlight several potential problems with the design of existing smart beta indices.

Abstract: We investigate whether structurally hedging the currency risk of global equity products benefits long-term investors. Based on a 35 year back-test of 3 smart beta strategies from 6 currency perspectives, our answer is a qualified “yes”. Currency hedging was effective in reducing risk and generally improved medium to long-term Sharpe ratios, albeit at a small cost to average returns. It may not be the proverbial free lunch, but does appear a value meal from the risk-adjusted perspective that is most relevant in an asset allocation context. The most effective hedging strategy and the resultant benefits vary by investor domicile, the nature of the equity holdings, and over time. The benefits were strongest for defensive (low-volatility, non-cyclical) equity portfolios for investors from safe-haven currency zones, and least pronounced for cyclical equities held by investors using pro-cyclical currencies. Particularly since the Global Financial Crisis, being smart about how much of a portfolio’s currency exposures to hedge has been the key to avoiding perverse impacts.

Abstract: There is business intelligence in “big data” that can be utilized to improve performance in both actively and passively managed portfolios. But extracting that business intelligence from big data has a number of challenges, including the processing scale implied in the name itself. This article is intended to be a primer for investment professionals seeking to learn more about how to meaningfully utilize the brand-name citation metrics that are available from big data. It shows how to use those metrics as either a new form of fundamental data for tactically or quantitatively managed active portfolios, or as alternate selection and weighting strategies for tracking tolerant “smart beta” applications.

Abstract: Transparent rules-based index-tracking portfolios that employ alternative weighting schemes have grown rapidly in the last decade, especially within equities. These passively managed factor portfolios can be constructed in many ways, ranging from relatively simple rules-based approaches that specify weights as a function of factor characteristics to more complex optimization-based ways. Both single factor and multiple factor portfolios can be constructed. In the latter case, one often-asked question is whether it is better to combine individual factor portfolios or build a multi-factor portfolio from the security-level. Here, we show that a bottom-up approach to multi-factor portfolio construction can produce superior results than a combination of individual single factor portfolios, at least for well-known factors such as Value, Quality, Low Volatility, and Momentum. Because the bottom-up approach assigns weights to securities on multiple factor dimensions simultaneously, it accounts for cross-sectional interaction effects in a way that combining single factor portfolios does not.

Abstract: "Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart is smart beta investing can be empirically examined by testing the performance of those factors that underlie smart beta portfolios. We use a long time-series of data and show that the value, momentum, low volatility and quality factors all generate positive abnormal returns in the Australian equity market. Rather than ranking these factors based on relative performance, we argue that the optimal approach to smart beta investing is to diversify across these factors, given the low correlations between factor returns. Our results provide important implications for the Australian funds management industry. First, while this study does not examine the specific strategies applied by smart beta fund managers, the evidence presented provides a justification for the application of smart beta as a low cost alternative to active investment. Second, given evidence that multiple factors explain equity returns, multi-factor models should be used to measure active portfolio manager performance in order to distinguish pure alpha from abnormal returns generated due to smart beta exposure.

Abstract: We discuss the extension of rules-based factor portfolios to a long-short framework. Advanced beta (or smart beta) involves capturing well-known factors in simple rules-based ways. Long-short factor portfolios have historically provided compelling performance even after costs are accounted for. Not all factors are created equal--some factors are more compelling than others in terms of their historical returns and volatility when expressed in a long-short framework. Importantly, the choice to construct portfolios as dollar neutral versus beta neutral has a significant impact on the historical returns. Factors such as Low Volatility and Quality for instance are much less compelling when captured in a dollar neutral framework versus a beta neutral framework. Most importantly, there is a tradeoff between the returns of the portfolios and the cost of running the portfolio. We find there are ways to reduce turnover to an acceptable level, such that the returns of the factor portfolios are still worthwhile.