Smart Beta Strategies Research Articles

Integrating ESG Analysis into Smart Beta Strategies

Smart beta strategy is an increasingly frequent approach to investment analysis for portfolio selection and optimization and it can be combined with environmental, social, and governance (ESG) considerations. In order to verify the impact of the integration between ESG and smart beta analysis, first we apply a portfolio rebalancing based on ESG scores on securities selected according to different smart beta strategies (ex-post ESG rebalancing approach). Secondly, we apply different smart beta approaches to sustainable portfolios, screened according to the issuers’ ESG scores (ex-ante ESG screening approach). We find that ESG rebalancing and screening are able to impact both on return and risk statistics, but with a different level of efficiency for each smart beta strategy. ESG rebalancing proves to be particularly efficient when it is applied to a “Value” portfolio. On the other hand, when smart beta is applied to ESG-screened portfolios, “Growth” is the strategy which shows the highest increase in risk-adjusted performance, particularly in the US. Minimum volatility proves to be the most efficient smart beta strategy for sustainable portfolios. In general, the increase in the level of sustainability does not deteriorate the risk-adjusted performances of most smart beta strategies.

Is Smart Beta Still Smart under the Lens of the Diversification Return?

The diversification return is known to explain a significant portion of portfolio growth rates. Separately, results from backtesting have created significant interest in smart beta portfolio strategies. Extant research shows that smart beta strategies outperform the passive value-weighted benchmark due to their implicit risk factor tilts, such as value and size. This article reexamines the performance of a subset of smart beta strategies, known as fundamental indexing, focusing on the diversification return. The authors demonstrate that the outperformance of these strategies is completely explained by the diversification returns embedded in the portfolio rebalancing inherent in all such strategies. Efficient factor tilts explain none of the outperformance. TOPICS:Factors, risk premia, factor-based models, performance measurement Key Findings • The article confirms past results that fundamental indexes outperform the market-cap-weighted index. • Importantly, the article shows that all outperformance is due to the diversification return of Fernholz and Shay and Booth and Fama. • Standard explanations for outperformance (factor tilts) do not appear to hold up in factor models after accounting for the diversification return.

A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with smart beta strategies emerging as a trend among institutional investors. Whilst they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To manage short term risk (cyclicality and underperformance), we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We use a variety of portfolio construction techniques to test our smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return-oriented portfolios (up to 50% of return in excess of market adjusted by relative risk annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60% of return in excess of market adjusted by relative risk annually) show model performance improvement with respect to portfolios built using full feature HMMs.

Maximizing an equity portfolio excess growth rate: a new form of smart beta strategy?

It has been claimed that, for dynamic investment strategies, the simple act of rebalancing a portfolio can be a source of additional performance, sometimes referred to as the volatility pumping effect or the diversification bonus because volatility and diversification turn out to be key drivers of the portfolio performance. Stochastic portfolio theory suggests that the portfolio excess growth rate, defined as the difference between the portfolio expected growth rate and the weighted-average expected growth rate of the assets in the portfolio, is an important component of this additional performance (see Fernholz [Stochastic Portfolio Theory, 2002 (Springer)]). In this context, one might wonder whether maximizing a portfolio excess growth rate would lead to an improvement in the portfolio performance or risk-adjusted performance. This paper provides a thorough empirical analysis of the maximization of an equity portfolio excess growth rate in a portfolio construction context for individual stocks. In out-of-sample empirical tests conducted on individual stocks from 4 different regions (US, UK, Eurozone and Japan), we find that portfolios that maximize the excess growth rate are characterized by a strong negative exposure to the low volatility factor and a higher than 1 exposure to the market factor, implying that such portfolios are attractive alternatives to competing smart portfolios in markets where the low volatility anomaly does not hold (e.g. in the UK, or in rising interest rate scenarios) or in bull market environments.

Factor tracking: A new smart beta strategy that outperforms naïve diversification

This paper proposes a factor tracking strategy that minimizes the variance of the difference in returns between a target portfolio and return-driving factors. We establish and solve single- and multi-factor tracking models, and analyze the properties of the optimal portfolios. We find that the optimal factor tracking portfolios consist of factor mimicking portfolios and the minimum-variance portfolio determined in the traditional mean-variance model. Moreover, we derive the condition under which the multi-factor tracking portfolio is a linear convex combination of the corresponding single-factor tracking portfolios. Using various datasets from the US market, we show that the factor tracking portfolios outperform the equally-weighted portfolio, measured by both risk and Sharpe ratio. The superior performance of the factor tracking portfolios are attributed to these portfolios’ better upside participation and downside protection.

ESG Investing: From Sin Stocks to Smart Beta

Research on socially responsible investment in equity markets initially focused on sin stocks. Since then, the availability of data has been extended substantially and now covers environmental, social, and governance (ESG) criteria. Using ESG scores of firms belonging to the MSCI World universe, the authors measure the impact of score-based exclusion on both otherwise passive investment and smart beta strategies. They find that exclusion leads to improved scores of initially standard portfolios without deterioration of the risk-adjusted performance. Smart beta strategies exhibit a similar pattern, often in a more pronounced way. Moreover, the results demonstrate that exclusion also implies regional and sectoral tilts as well as (possibly undesirable) risk exposures of the portfolios. TOPICS:Portfolio theory, portfolio construction, ESG investing Key Findings • The authors show that environmental, social, and governance (ESG) screening can substantially improve ESG scores for both otherwise passive and smart beta portfolios without reducing risk-adjusted returns. • Starting from initially passive multicountry portfolios, ESG screening may lead to substantial regional tilts, such as overweighting Europe and underweighting the US and emerging countries or sectoral bets, for instance in favor of information technology and against financial and energy stocks. • Although the broad conclusion of improved ESG profile without affecting risk-adjusted performance also holds for smart beta portfolios, aggressive exclusion of ESG low-scoring firms may lead to some reduction in exposure to targeted factors.

How Smart Is the Real Estate Smart Beta? Evidence from Optimal Style Factor Strategies for REITs

This paper has a twofold objective. First, we contribute to the stream of literature that investigates whether traditional asset pricing factors show any predictive power for the cross-section of Real Estate Investment Trust (REIT) returns. In particular, we investigate the existence of a premium associated to the Value, Size, Momentum, Investment, and Profitability factors over the period 1993-2018. We find support for all the pricing factors but for the Profitability one. Second, we investigate whether a set of smart beta strategies, based on the combination of the identified factors, may outperform similar allocation techniques that do not exploit factors. We find that all the proposed factor-based strategies display a higher risk-adjusted out-of-sample performance than a simple buy-and-hold investment in the real estate market (proxied by the FTSE NAREIT All REITs Index). In addition, we find that when factor-based strategies are implemented, REIT-only portfolios display risk-adjusted performances comparable to those of diversified portfolios that include equity, bond, and commodities.

Analysis of the Performance Between Smart Beta Strategies With Active and Passive Managed ETF

This research will discuss the origins of the smart beta and its subsequent trends by explaining its primary characteristics and theoretical background. To address the primary research question, we will conduct a comparative analysis of Smart beta ETFs, actively managed ETFs, and passive ETFs in order to quantify the outperformance of Smart beta strategies. Our research group will be completed by many ETF instruments, with eight distinct ETF funds representing each investment strategy being compared to their respective selected benchmark. The Sharpe, Sortino, and Jensen ratios are used to calculate the overall risk-adjusted returns. We came to two distinct conclusions: US large-capitalization equity Smart Beta funds do not outperform active or passive strategies in terms of risk adjusted returns. Despite this, and contrary to academic research, emerging markets equity smart beta funds had significantly larger excess returns than other types of investment funds.

The Effect of Market Regimes on the Performance of Market Capitalization-Weighted and Smart Beta Shariah-Compliant Equity Portfolios

Shariah-compliant investment guidelines, while explicit on screening criteria for stock selection, are silent on the weighting methods to be used in the construction of Shariah-compliant equity portfolios. The market capitalization-weighted strategy and smart beta strategies (fundamental value-weighted, equal-weighted, and low-risk weighted strategies) exhibit different risk and return characteristics. This paper investigates whether the choice of weighting strategy affects the performance of Shariah-compliant equity portfolios under different market conditions. The sample consists of active constituent data from S&P 500 for the period 1986-2016. By utilizing the Markov-regime switching model, it is possible to categorize market returns into two regimes — high regimes for bullish market conditions and low regimes for bearish market conditions. The empirical results indicate a significant difference between the performances of Shariah-compliant equity portfolios in high and low regimes following different weighting strategies. The evidence suggests that market capitalization and fundamental value-weighted strategies perform better during market rallies. On the other hand, a low-risk strategy can be used as a hedge during periods of maximum drawdown and is associated with relatively lower value-at-risk and expected shortfalls as compared to alternative strategies.

Dealing with Drift Uncertainty: A Bayesian Learning Approach

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain level of confidence, we focus on drift uncertainty in this paper. Building on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and provide a simple and practical procedure to implement optimal strategy. To illustrate the value added of using the optimal Bayesian learning strategy, we compare it with an optimal nonlearning strategy that keeps the drift constant at all times. In order to emphasize the prevalence of the Bayesian learning strategy above the nonlearning one in different situations, we experiment three different investment universes: indices of various asset classes, currencies and smart beta strategies.

Does the application of smart beta strategies enhance portfolio performance? The case of Islamic equity investments

Traditionally, passive portfolios are structured using an easy to implement market capitalization method albeit highly skewed towards large cap stocks. The introduction of smart beta strategies has allowed passive investors to structure equity portfolios using alternative strategies such as fundamental-weighting, equal-weighting, and low-risk weighting strategies. This paper investigates whether constrained portfolios such as Shariah-compliant equity portfolios (SCEPs) can benefit by adopting smart beta strategies. The sample consists of equities from the USA, Canada, Australia, Europe, Middle East, Indonesia, and Malaysia for the period January 2003 to December 2016. The empirical findings suggest that smart beta SCEPs outperform not only conventional market capitalization weighted portfolios but also SCEPs following a market capitalization-weighted strategy. Higher risk-adjusted returns and lower drawdown as a result of following smart beta strategies highlights the importance of considering smart beta portfolio weighting strategies for passive investors. The supremacy of smart beta strategies indicate the value proposition for investors and fund managers alike. We also found that geographical location affects the performance of smart beta SCEPs; countries with a Muslim majority report higher cardinality and lower drawdowns. The results remained robust with alternative Shariah screening guidelines and empirical estimation methodology.

Smart Beta Strategies based on the Quality Indices

Smart Beta Strategies based on the Quality Indices

Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending

Long-only multifactor strategies may be constructed by combining individual-factor portfolios (portfolio blending) or by combining individual-factor signals into a composite signal to construct the portfolio (signal blending). To compare these two approaches, we present a framework for building exposure-matched portfolios. In empirical tests on global equity markets, we find that, generally, portfolio blending generates higher information ratios for low-to-moderate levels of tracking error. At high levels of tracking error, signal blending delivers better risk-adjusted performance. These results generally hold for various factor combinations, and they have important practical implications for investors considering the implementation of multifactor smart-beta strategies.

Industry Rotation and Time-Varying Sensitivity by VIX

In this article, the authors, one of whom was a doctoral student of Stephen A. Ross, investigate the cross section of industry returns and the effect of uncertainty as proxied by the VIX index. They find that different moments of the distribution of cross-sectional returns for industries are significantly correlated to both the change in VIX and the level of VIX. Furthermore, the size effect diminishes after controlling for the industry effect. The sensitivity to the change in VIX is time varying. Different VIX regimes and different events can alter industry sensitivities. Non-stationarity of the sensitivity may also introduce complexity on factor or smart beta strategies, which tilt toward a favorable risk premium factor. Finally, the authors propose a general approach for industry rotation.

A comprehensive study on smart beta strategies in the A-share market

ABSTRACTIn this article, we explore how smart beta strategies are applied in the Chinese A-share market. Specifically, we empirically examine several popular smart beta strategies, including mean-variance optimization, minimum-variance portfolio, equal weighting, risk parity strategy, and fundamental indexation, and we do so using the Shanghai Stock Exchange (SSE) 50 index and SSE sector indices as our comparison benchmarks. We find that all smart beta strategies outperform these benchmarks from year 2006 to year 2015, and that all smart beta strategies outperform the SSE 50 index by an average of 2.57% per year. In turn, these strategies improve the Sharpe Ratio by 46.2% on average.

Practical Applications of Capacity of Smart Beta Strategies from a Transaction Cost Perspective

<h3>Practical Applications Summary</h3> As the smart beta market grows, investors are wondering how big these strategies can get before they start losing efficacy. In <b><i>Capacity of Smart Beta Strategies from a Transaction Cost Perspective</i></b>, published in the Winter 2017 issue of the<b><i> Journal of Index Investing</i></b>, <b>Ronald Ratcliffe</b>, <b>Paolo Miranda</b>, and <b>Andrew Ang</b> set out to tackle one dimension of this debate. Using a transaction cost model that was developed by the <b>BlackRock</b> team for daily use, the article examines the “capacity” of momentum, quality, size, value, minimum volatility, and multifactor strategies. Capacity is defined as the hypothetical breakeven AUM at which the turnover transaction costs of an ETF would exactly offset the historically observed style premium. <b>TOPICS:</b>Factor-based models, performance measurement

Smart Equity Investing: Implementing Risk Optimization Techniques on Strategic Beta Portfolios

This paper investigates the mean-variance and diversification properties of risk-based strategies executed on style or basis portfolios. We show that the performance of these risk strategies is highly sensitive to the sorting procedure used to form the basis assets. Whereas the extant literature provides mixed support for the outperformance of smart beta strategies based on scientific diversification, our designed strategies outperform both the market model and multifactor model. Our testing framework is based on bootstrapped mean-variance spanning tests and shows valid conclusions when controlling for multiple testing, transaction costs, and luck from random basis portfolio construction rules. Economically, our results are supported by diversification-based properties.

Mind the Gap: On the Importance of Understanding and Controlling Market Risk in Smart Beta Strategies

The authors argue that more attention should be paid to market exposure when conducting analyses of smart beta strategies. They point out that most research proposing new multifactor investment methodologies essentially ignores exposure to the market factor, which is the most consensual among all factors and often the most influential factor for a strategy. Overlooking the dominant factor is in stark contrast to the objective of factor investing, which aims to identify and manage the main drivers of risk and return. The authors document that different levels of market beta indeed have a strong impact on the performance and risk of smart beta strategies. The impact is visible in terms of long-term returns, volatility, and the dependence of performance on market conditions. Such effects need to be properly documented to allow investors to make explicit choices in their risk exposures. In the event of a mismatch with investor preferences, it is possible to adjust multifactor strategies to respect target levels of market beta.

Dealing with Drift Uncertainty: A Bayesian Learning Approach

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain level of confidence, we focus on drift uncertainty in this paper. Building on filtering techniques and learning methods, we use a Bayesian learning approach to solve the Markowitz problem and provide a simple and practical procedure to implement optimal strategy. To illustrate the value added of using the optimal Bayesian learning strategy, we compare it with an optimal non-learning strategy that keeps the drift constant at all times. In order to emphasize the prevalence of the Bayesian learning strategy above the non-learning one in different situations, we experiment three different investment universes: indices of various asset classes, currencies and smart beta strategies.

Why your smart beta portfolio might not work

Smart beta, which accounts for rule-based factor-tilting strategies that fall between active and passive investment, has emerged as an alternative to active investment after its major decline since the global financial crisis. In spite of the smart beta's remarkable commercial prosperity, many experts in both industry and academia share some concerns. Some of them believe that the marketing hype might confuse investors while others are concerned about the exposure to unintended risks that smart beta products might bring. In this study, we provide a comprehensive review of diverse perspectives from both practitioners and researchers on smart beta and we perform empirical and theoretical investigations on the efficiency of smart beta (or factor-tilting) strategies as investment building blocks. We find that factor-based investment building blocks may cause inefficiency under the mean-variance framework.