Smart Beta Research Articles

Is Smart Beta Really Smart?

There is a popular new investment strategy in portfolio management called smart beta. With a catchy title and a promise of improved portfolio performance, the strategy has already attracted hundreds of billions of dollars and is growing by leaps and bounds. Unfortunately, according to the author smart beta portfolios do not consistently outperform and when they do produce appealing results, they flunk the risk test.

Betting on 'Dumb Volatility' with 'Smart Beta'

It is possible that some investors make the mistake of buying high and selling low. This may create which might allow some smart beta strategies to exploit the behavior gap by buying low and selling high. Live data suggests 1) the value-added from exploiting dumb volatility has been about 2-4% per year, 2) dumb volatility strategy risk-adjusted-returns have been similar, 3) there could be a volatility frontier offering more return from dumb in exchange for more and 4) some dumb volatility strategies have achieved Warren Buffett-like value-added. A six factor model shows no evidence that traditional factors, such as size and value, drove the dumb volatility return. Going forward, the ability of a strategy to absorb capital will be an important economic moat.

Is Smart Beta Still Smart after Taxes?

In this article, the authors focus on the after-tax performance of several smart-beta strategies. These strategies have higher turnover than the capitalization-weighted index, which leads to a greater tax on returns. Despite this drag, most strategies retain a long-term excess return. The authors also test the effectiveness of tax-managed versions of the strategies. They observe that, relative to most active managers, smart-beta strategies have lower turnover, greater breadth, and are less concerned with stock selection. By allowing a tracking-error risk budget versus the original strategy, tax management can reduce much of the tax impact. <b>TOPICS:</b>Manager selection, passive strategies, statistical methods

INVITED EDITORIAL COMMENT

So-called smart-beta strategies are swiftly gaining market share, with some estimating they will reach $6 trillion in assets within the next five years.[1][1] Smart beta aims to outperform the capitalization-weighted market through alternative weighting methods that emphasize factors such as size,

Many Risks, One (Optimal) Portfolio

This study investigates how to obtain a portfolio which would provide above average returns while remaining robust to most risk exposures. Emphasis is placed on risk management, given our perspective (shared by many other practitioners), that retaining above average portfolio performance in current market environment depends strongly on having an effective risk management process. We rely on a comprehensive survey of the literature to describe stylized facts of market returns and main categories of asset allocation methodologies, including Modern Portfolio Theory, Black-Litterman model, factor-based and risk-based strategies. Furthermore, we present both criticisms and defenses of strategies, together with potential issues identified by practitioners and corresponding solutions (if they do exist).We outline recent enhancements to various types of portfolio strategies, and analyze how to incorporate (in the asset allocation framework) constraints, regularization, personal views, stylized features of empirical market data, and forward information given by financial options market data.More prominence is given to strategies (risk parity, risk factors, factor investing, smart beta, dynamic, etc.) that were shown to deliver better portfolio performance in terms of returns, diversification, risk, etc. We also discuss a wide ranging collection of performance measures proposed in the literature for quantifying portfolio return, risk and diversification, identify which such measures are most popular with practitioners, and which corresponding strategies have best results (as shown in the literature).Since a major topic of this study is managing risks, we provide details on the types of risk that portfolios may be exposed to, on approaches and strategies to handle such exposures, with highlighting of tail risk management. Portfolio insurance is also discussed.We also describe practical aspects needed for a successful portfolio management, including robust estimation of covariances, correlations and model parameters, numerical optimization methods, key questions and issues identified by practitioners, Monte Carlo simulation, comprehensive testing framework, stress testing, available software implementations (usually in R), etc.To summarize, the study analyzes all ingredients that are required, in our opinion, to deliver portfolios with above average performances and resilient to most risks, and concentrates on the strategies which have emerged as frontrunners in the last few years, both in the literature and in the market.

Save the Trading Costs: Simple and Intuitive Rule for Smart Beta Strategy

We propose a practical method of portfolio construction for long only fund such as smart beta strategy. We can save trading costs in portfolio rebalance by a simple and intuitive rule between portfolio weight and score.The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2522817

Momentum, Markowitz, and Smart Beta: A Tactical, Analytical and Practical Look at Modern Portfolio Theory

In this paper we will try to improve on the Modern Portfolio Theory (MPT) as developed by Markowitz (1952). As a first step, we combine the MPT model with generalized momentum (see Keller 2012) in order to arrive at a “tactical” MPT. In our second step, we will use the single index model (Elton, 1976) to arrive at an analytical solution for a long-only maximum Sharpe allocation. We will call this the MAA model, for Modern Asset Allocation. In our third step, we use shrinkage estimators in our formula for asset returns, volatilities and correlations to arrive at practical allocations. In addition, as a special cases, we arrive at EW (Equal Weight), Minimum Variance (MV), Maximum Diversification (MD) and (naive) Risk Parity (RP) submodels of MAA. These EW, MV, MD and RP models are sometimes called “smart-beta” models. We illustrate all these different models on three universes consisting of respectively 10 and 35 global ETFs, and 104 US stocks/bonds, with daily data from Jan. 1998 – Dec. 2013 (16 years), monthly rebalanced. We show that all these models beat the simple EW model consistently on various return /risk criteria, with the general MAA model (with return momentum) also beats nearly all of the “smart beta” models.

Making Your Smart Beta Even Smarter

In a continually evolving investment universe, strategies that “begin at the beginning” with smart beta—such as GDP-weighted index strategies for bond investments and fundamental indexing strategies for equity investments—may offer the potential for better outcomes than traditional active and passive approaches. And investors can build upon the inherent benefits of smart beta by incorporating actively managed bond strategies within a smart beta solution, whether in a global bond portfolio informed by forward-looking fundamental views or in an absolute-return-oriented bond portfolio as a return generator in an index-plus approach. In either case, smart beta and active bond management can form a compelling combination. <b>TOPICS:</b>Passive strategies, fixed income and structured finance, fundamental equity analysis

Expect More from Your Smart Beta

“Smart beta” is a term that has prompted much discussion and debate. Recent commentary has run the gamut, from calling smart beta “a better mousetrap” to alluding to its “dark side.” It is important for investors and advisors alike to fully understand investment strategies deemed “smart” how they might fit into portfolios, given the current investment landscape. <b>TOPICS:</b>Analysis of individual factors/risk premia, portfolio construction, passive strategies

The Latest Wave in Advanced Beta: Combining Value,Low Volatility, and Quality

The latest wave in advanced beta, also known as smart beta, factor investing, and risk premia investing, among other names, has focused on combining multiple factors in one portfolio. One of the more widely discussed combinations has been Value, Low Volatility, and Quality, which results in a portfolio with lower-than-average valuation and return volatility, and higher-than-average quality (measured by metrics like profitability, earnings variability, and leverage). In this article, the authors discuss why this combination has been of interest and summarize the key considerations for investing in such a strategy. The intuition behind the three factors as well as the empirical evidence has provided support for the combination. Moreover, the three-factor portfolio attempts to take advantage of potential diversification benefits over time, which dampens the well-known challenge of cyclicality in advanced beta strategies, a key hurdle in implementation.

The Smart Beta Indexing Puzzle

In this article, the authors consider smart beta indexing, which is an alternative to capitalization-weighted (CW) indexing. In particular, the authors focus on risk-based (RB) indexing, the aim of which is to capture the equity risk premium more effectively. To achieve this, portfolios are built that are more diversified and less volatile than CW portfolios. However, RB portfolios are less liquid than CW portfolios by construction. Moreover, they also present two risks in terms of passive management: tracking difference risk and tracking error risk. This article examines the trade-off relationships that smart beta investors have to puzzle out among diversification, volatility, liquidity, and tracking error. The authors also define the return components of smart beta indexes.

Smart Beta ETFs: A Bird’s-EyeView of the Market and Analysis of Its Performance Trends

A new category of exchange-traded funds, which can arguably be referred to as “smart beta ETFs,” has lately issued a meaningful challenge to traditional, market-value-oriented ETFs. Although the concept seems new, a review of the relevant academic literature shows that most of these new ETFs find their roots in traditional ETFs. After having divided smart beta ETFs into three distinct categories, a thorough review shows that their share of the overall ETF market is rapidly expanding. In particular, factor and fundamental smart beta ETFs have established themselves as the dominant duo relative to low-volatility ETFs. But in terms of risk-adjusted performance, this study shows that investors in smart beta ETFs have, on average, not been appropriately compensated for the risks they have taken in comparison to investors who put their money into more traditional, cap-weighted ETFs.

Value Investing: Smart Beta versusStyle Indexes

The active shares of traditional value style indexes are dominated by industry bets. They also capture less than the entire value premium; because they weight constituents on the basis of capitalization, they tend to hold large positions in overpriced stocks and small positions in underpriced (i.e., value) stocks. Smart beta strategies, in comparison, are better diversified, and they systematically buy low and sell high by periodically rebalancing to non-price-related target weights. In addition to exploiting mean reversion in prices, smart beta strategies profit from mean reversion in the value premium by effectively implementing a dollar cost averaging program. <b>TOPICS:</b>Mutual funds/passive investing/indexing, analysis of individual factors/risk premia, portfolio construction

Smart Beta: Building Low-VolatilityPortfolios of ETFs

Risk-reduction strategies have gained attention in recent times. Among these, low-volatility strategies have enjoyed significant inflows, making them one of the most sought-after smart beta strategies. The so-called low-vol anomaly (empirical outperformance of low-volatility equities versus their higher-volatility peers) has been well documented over the past 10 years in academia as well as among market participants. This article demonstrates how to implement a minimum variance portfolio using country/sector exchange-traded funds (ETFs). The analysis shows that a minimum-variance methodology based on allocations to country and sector ETFs may allow for the capture of a significant portion of the low-vol risk premia on developed markets as well as emerging markets. <b>TOPICS:</b>Exchange-traded funds and applications, analysis of individual factors/risk premia, emerging

Recent Trends in Equity PortfolioConstruction Analytics

Portfolio analytics is critical for identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring portfolio risk and performance. Analytics-based portfolio management lets investment managers filter information quickly, take advantage of statistical arbitrage opportunities, and deal with inefficiencies, such as transaction costs incurred during trading and tax consequences of investment decisions. This article reviews some widely used approaches to portfolio analytics, discussing new trends in metrics, modeling approaches, and portfolio analytics system design. Topics include stock screening, text analytics, traditional and new uses for factor models, investment methodologies of recent interest (such as smart beta), new visualization and analytics features available from vendors and open source software, and cloud-based solutions for data management and analysis.

Practical Applications of Is Smart Beta Still Smart After Taxes?

Most investment processes completely ignore taxes, which is particularly alarming given the tax increases that took effect in 2013, asserts <b>Paul Bouchey</b>, Managing Director of Research at <b>Parametric Portfolio Associates</b>. Bouchey focused on the after-tax performance of several smart beta strategies when he addressed delegates at the <b>67th CFA Institute Annual Conference</b> in Seattle. In an exclusive interview for this <b><i>Practical Applications</i></b> report, Bouchey said smart beta strategies are gaining in popularity among high-net-worth individuals and institutional investors, leading to a plethora of new products. And, they inherently incur higher turnover than portfolios following capitalization-weighted indices, resulting in a greater tax impact that can reduce the value added on an after-tax basis.

The Latest Wave in Advanced Beta: Combining Value, Low Volatility, and Quality

The latest wave in advanced beta, also known as smart beta, factor investing, and risk premia investing, among other names, has focused on combining multiple factors in one portfolio. One of the more widely discussed combinations has been Value, Low Volatility, and Quality, which results in a portfolio with lower than average valuation and return volatility, and higher than average quality (measured by metrics like profitability, earnings variability, and leverage). In this paper, we discuss why this combination has been of interest and summarize the key considerations for investing in such a strategy. Both the intuition behind the three factors as well as the empirical evidence has have provided support for the combination. Moreover, the three-factor portfolio takes advantage of strong diversification benefits over time which dampens the well-known challenge of cyclicality in advanced beta strategies, a key hurdle in implementation.

Socially Responsible Investment Performance: Impacts of Weighting by Capitalization

Risk-based allocation strategies, also known as Smart Beta allocation, define the weights of assets in portfolios as functions of the individual and common asset risk. In this paper we focus on the Minimum Variance (MV), Maximum Diversification (MD), Equal Risk Contribution (ERC) and Equal-Weight (EW) risk-based allocation strategies. The popularity of risk-based strategy is commonly justified by their good record of out-performing the cap-weighted (CW) allocation strategy. Because of the low-volatility profile of risk-based allocation this is especially true when crises occur. From March 15, 2002 to May 1, 2012 we investigate how using a Socially Responsible Investment universe impacts performance of risk-based allocation strategies. We use different measures of performance, included risk-adjusted one (multi-factor models), and we propose to disentangle the effect of using a SRI universe from the effect of using risk-based allocations. SRI universe only contains firms that have good environmental, social and governance performance. This kind of filtering is increasingly popular among institutional investors. On the estimation period, using European stocks, we find that the use of the SRI universe improves performance of risk-based allocations. However this improvement is not uniform among all the risk-based allocation strategies.

Smart Beta 2.0

Alternative equity indexes are likely to outperform traditional cap-weighted indexes over the long term, research results show that such smart beta strategies are exposed to several types of risk, including systematic risk (e.g., factor tilts), specific risk (related to the assumptions and inputs of a strategy), and relative risk (i.e., the risk of potentially severe underperformance) compared to cap-weighted indexes that can last for extended periods of time. Smart beta can play an important role in institutional investors’ allocations, but only at the price of implementing a genuine risk-management process. This article discusses a new approach to smart beta investing (Smart Beta 2.0) that not only deviates from the default solution of using market capitalization as the sole criterion for weighting and constituent selection, but also analyzes and manages the risks of such deviations.

From Passive to Active—The Development of the ETP Industry

Exchange Traded Products (ETPs) are garnering massive interest around the world, and continue to capture a greater portion of fund inflows. ETPs have now garnered over $2 trillion in assets globally, across a variety of asset classes and strategies. Though initially passive, ETPs are now also “active,” both in the strategies/indexes they employ and in terms of their usage, with some ETPs trading their entire Assets Under Management (AUM) on a single day. Much of recent ETP innovation has been focused on alternative indexes, which include smart beta and “alpha”-styles indexes. These indexes seek to generate superior risk-adjusted returns compared to standard market capitalisation-weighted indexes, or offer low correlation to traditional market indexes. As new entrants continue to innovate, ETPs will continue to target niche areas, such as new geographies, asset classes, portfolio-indexes, active ETPs, and more strategic research-led indexes. The result is that indexes associated with more tactical or active investing will find their way into ETP-format, and that the lines between passive and active may appear blurred.