Abstract: 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.

Abstract: 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,

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: On the basis of simulated backtests, many portfolios including so-called smart beta and other factor products often boast impressive track records. However, given the additional trading that occurs, can such advertised performance truly be realized once transaction costs are taken into account? One might expect these products to make a concerted effort to manage liquidity. However, explicit efforts to manage liquidity in existing smart beta and factor ETF and index products have been relatively modest. A new set of rules are provided that are independent of the notional value of the portfolio that can be used to better manage the liquidity of investment portfolio.

Abstract: Inter-temporal risk parity is a strategy that rebalances risky assets and cash in order to target a constant level of ex-ante risk over time. When applied to equities and compared to a buy-and-hold portfolio it is known to improve the Sharpe ratio and reduce drawdowns. We apply inter-temporal risk parity strategies to factor investing, namely value and momentum investing in equities, government bonds and foreign exchange. Value and momentum factors generate a premium which is traditionally captured by dollar-neutral long-short portfolios rebalanced every month to take into account changes in stock factor exposures and keep leverage constant. An inter-temporal risk parity strategy re-balances the portfolio to the level of leverage required to target a constant ex-ante risk over time. Value and momentum risk-adjusted premiums increase, sometimes significantly, when an inter-temporal risk parity strategy is applied. Volatility clustering and fat tails are behind this improvement of risk-adjusted premiums. Drawdowns are, however, not smoothed when applying the strategy to factor investing. The benefits of the inter-temporal risk parity strategy are more important for equity and foreign-exchange factors, with the strongest volatility clustering and fat tails. For government bond factors, with little volatility clustering, the benefits of the strategy appear less significant.

Abstract: Size, momentum, volatility and value have all been shown to be partly responsible for explaining equity returns over the long run but they do not seem to fully capture the returns of some companies. This has therefore given credence to the idea that a fifth factor – quality – exists and, when combined with other risk factors, acts as a good diversifier in investment portfolios. There isn’t much agreement on what ‘quality’ is or how it should be measured. Some simply equate it to profitability and others, believing it to be a multi-faceted concept, use more complex measures (e.g. the Piotroski’s F score). Regardless of the approach taken, the aim of any quality measure should help estimate a company’s future profitability and understand its source of risk. Broadly speaking, high-quality companies should generate higher revenue and enjoy more stable growth than the average company. This is why we believe that any good quality measure should take into account profitability generation, earnings quality and financial robustness.

Abstract: 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.

Abstract: The authors propose a robust optimisation approach to construct realistic constrained multi-strategy portfolios that starts with the identification of different sources of excess returns and the risk-budgeting exercise to optimally combine them They show how systematic factor strategies can be combined with judgemental strategies and how bottom-up-based strategies for stock picking can be combined with top-down sector or country allocation strategies The approach is fully transparent for both unconstrained and constrained portfolios In particular, it is shown that constrained portfolios retain the exposures to systematic risks factors in the unconstrained target solution as much as possible, and that specific risk takes the toll of portfolio constraints A realistic back-tested example combining four different well-known factor strategies – value, momentum, low risk and size – demonstrates the robustness and transparency of the approach The advantages of the approach over the alternative process based on selecting and investing in a mix of different index funds implementing off-the-shelf active strategies is highlighted The authors find their approach particularly suited for institutional investors interested in fully controlling the active risk budget allocation to factor strategies in their portfolios while fully understanding the final allocation in their constrained portfolios

Abstract: 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.

Abstract: The active shares of traditional value style indices are dominated by industry bets. They also capture less than the entire value premium because, weighting 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.

Abstract: 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

Abstract: There has been significant evidence on the forecasting ability of Regime switching regression models. Smart beta or alternative beta indices are gaining wide popularity among investment community. Smart beta indices constructed with either fundamental or scientific weighting schemes are proven to outperform cap-weighted portfolios in the long run. At the same time, smart beta indices have significant exposure to risk factors such as size, value, momentum and volatility. The risk factors exhibit different behaviour in different regimes. In this research we examine and present evidence of the presence of regimes in smart beta indices. We also examine the possibility of adding value to a portfolio by switching between regime dependent portfolios of smart beta indices exploiting factor exposures.

Abstract: 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.

Abstract: Methods of getting factor premium by constructing a factor portfolio are called factor tilts. In this study, we propose a simple method to build a factor portfolio from alpha factor (score) and market beta. Simple investing rule "alpha minus beta" is a good method to build a factor portfolio which is efficient in absolute risk return space.

Abstract: 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.

Abstract: The authors combine characteristics-based stock screening using carbon emissions with four different weighting schemes to investigate if those techniques generate risk adjusted out-performance and quantifiable environmental impact. The results show that environmental impact and risk-adjusted out-performance can both be obtained using a carbon screened universe of stocks and non-market-capitalization weighting schemes. This has implications for investment managers who seek to reduce their portfolio carbon in a fiduciary compliant manner.

Abstract: We got a simple and intuitive rule for smart beta strategy by solving a quadratic problem with long only constraint and under existence of proportional trading costs. We can see two lines in optimal solution. Smart beta portfolio can save portfolio turnover (e.g. trading costs) by omitting trade against stocks between the two lines. We proposed portfolio construction rule for smart beta strategy in previous paper ("Save the Trading Costs:Simple and Intuitive Rule for Smart Beta Strategy"). This paper is technical appendix for previous paper.The paper "Save the Trading Costs: Simple and Intuitive Rule for Smart Beta Strategy" to which these Appendices apply is available at the following URL: http://ssrn.com/abstract=2453380

Abstract: 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.

Abstract: 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.

Abstract: “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.

Abstract: Equal-weight indices have two clear advantages: They are easy to understand, and they generally outperform cap-weight indices over the long term. Their drawbacks are less apparent. They have higher turnover due to rebalancing than other smart beta strategies, and that turnover includes buying and selling lower-liquidity stocks. Our market impact model demonstrates that, as global assets under management increase, implementation costs tend to rise faster in equal-weight than in fundamentally weighted strategies. This article summarizes what we have learned about the relative performance of equal-weight indices before and after implementation costs.

Abstract: We analyze five popular smart beta indices with a simple two risk-factor framework. Our analysis shows that majority of the return variations of these five smart beta indices can be explained by S&P 500 and Barclays Treasury index. We also demonstrate that the diversification effect is limited by including the smart beta indices in an equity index portfolio with a metric called implied breath.

Abstract: Risk models play a key role in quantitative equity management. While risk models are generally good predictors of ex-post portfolio volatility we are painfully aware that, at times, these models are subject to significant misspecification. For this reason we cannot view risk model predictions as point estimates but must view them instead as confidence intervals, within which the true measure of risk should lie. In this paper we demonstrate that for any given risk model misspecification, the width of a portfolio’s confidence interval is a positive function of its active share. In other words, the higher a portfolio’s active share the less confidence we have in its risk measurements. Monte Carlo simulation shows that these confidence intervals grow nonlinearly with active share.

Abstract: Smart beta corporate bond indices potentially offer a better risk/return profile than traditional market-value-weighted benchmarks. The simulated fundamentally weighted index described in this paper tilts toward less risky issuers, and periodically rebalancing to target weights provides a source of excess return by taking advantage of mean reversion in bond spreads.

Abstract: Indices providing cost-efficient passive exposure to factors such as value, growth, momentum, small size or low volatility, and other non-cap-weighted indices such as high dividend yield, high quality, high and low beta or equal-weighted indices, are well known and widely used in practice as portfolio diversifiers and return enhancers. These indices are produced as alternatives to cap-weighted portfolios, which are known for several shortcomings and inefficiencies. Factor-oriented portfolios are further highlighted because of the inability of the CAPM framework to explain portfolio variation in such portfolios. The frequent use of such indices as benchmarks or asset allocation plans for both institutional and retail investment products command a study of their marketing promise – to provide exposure to factors beyond the market. Factor tilts are also created implicitly in equal-weighted indices – even though the construction methodology of the index holds no promise of factor exposure stability. In light of evidence of the time variability in market betas in the CAPM model (across regimes or in reaction to market information), I study factor exposure instability across bull and bear markets in the United States since the early 2000s using a parsimonious, transparent, easily interpreted and well specified regression model, and a parameter stability test based on regression coefficients of determination and standard errors in the estimates. I also aim to provide indication of the economic importance of these instabilities. I find strong evidence for regime variability of factor exposures in a broad selection of well established equity indices, which often translates into substantial uncontrolled risk and performance fluctuations.