Jennifer Bender

Foundations of Factor Investing

Factor investing is based on the existence of factors that have earned a premium over long periods, reflect exposure to systematic risk, and are grounded in the academic literature. Early financial theory established that for stocks, exposure to the market was a significant driver of returns (e.g., the CAPM). Later, researchers like Barr Rosenberg, Eugene Fama and Kenneth French extended the CAPM to include certain systematic factors that also were important in explaining returns. Tilts towards these factors such as Value, Low Size, and Momentum historically produced excess long-term returns and there were strong theoretical foundations behind these factors. Until now, passive investing has focused on capturing market beta through market capitalization weighted indexes. The only way institutional investors could get access to factors was through active management. Indexation is opening a new way for factor investing today by allowing investors to access factors through passive vehicles that replicate factor indexes. MSCI Factor Indexes provide access to six solidly grounded factors — Value, Low Size, Low Volatility, High Yield, Quality and Momentum. These indexes have historically earned excess returns over market capitalization weighted indexes and experienced higher Sharpe Ratios. This paper is the first in a three-paper series focusing on factor investing.

Can the Whole Be More Than the Sum of the Parts? Bottom-Up versus Top-Down Multifactor Portfolio Construction

Rules-based factor portfolios combining multiple factors have become increasingly popular in recent years. One often-asked question concerning portfolio construction is whether combining individual factor portfolios is equivalent to building a bottom-up multifactor portfolio. The latter approach has theoretical merit, because each security’s weight in the portfolio will depend on how well it ranks on multiple factors simultaneously. The former approach, combining single-factor portfolios, may miss cross-sectional interaction between the factors at the security level. The authors analyze the magnitude of these effects and find that these interaction effects can in fact have a significant impact on portfolio performance. Both intuition and empirical evidence favor bottom-up multifactor portfolio construction.

Tilted and Anti-Tilted Portfolios:A Coherent Framework for Advanced Beta Portfolio Construction

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.

Can Alpha Be Captured by Risk Premia

This article explores the roles of risk premia strategies in institutional equity portfolios, not only as potential replacements for existing passive beta investments, but for certain active mandates as well. The authors quantify the degree to which active equity manager returns (alpha) can be captured by using long-only factor portfolios, as reflected by the MSCI Risk Premia indices. Using 10 years of historical data from January 2002 to March 2012, the authors find that risk premia can account for a substantial portion of alpha: as much as 80%. They also propose a portfolio construction framework for incorporating active managers who deliver the highest alpha, once risk premia are accounted for.

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.

Manipulating Correlations Through Latent Drivers

The ability to manipulate correlation matrices is useful for a number of applications in finance, including stress testing and portfolio construction. We outline a flexible framework that enables managers to manipulate correlations based on their beliefs and preserve the positive semi-definiteness of the matrix. The framework links the returns for which correlations are captured in the matrix through unobservable factors or drivers, called latent drivers. Changes in correlations can then be introduced through these drivers. Users can specify levels of exposure to a driver based on their prior beliefs. The framework can be applied to any correlation matrix - asset, factor, or asset class.

3 – Peering under the Hood of Rules-Based Portfolio Construction: The Impact of Security Selection and Weighting Decisions

Abstract: Rules-based non-market cap-weighted index-based investing, also known as smart beta, advanced beta, factor investing and risk premia investing among its many names, has generated a lot of research over the past several years. As detailed, the concept of passively managed portfolios (PMF portfolios) has been around since the 1980s. Its foundations are decades long starting with Rosenberg and Marathe and Ross. PMF investing can generally be viewed as a way for investors to capture key sources of return (factors) through a rules-based cost-efficient index. Well-known factors are those such as value, size and momentum but our framework here can, in theory, apply to any targeted source of return or investment theme. Bender et al. provide a review of the foundations of factor investing.

Demystifying Equal Weighting

The idea of accessing risk premia through the use of index-based funds and ETFs has been gaining momentum in recent years. MSCI Risk Premia Indices aim to reflect well-known equity premia to stock characteristics such as value, size, or momentum. Among the risk premia indices, equally-weighted indices are some of the oldest and most well-known. In this paper, we revisit the rationale behind equal weighting and profile their recent performance.

Refining Portfolio Construction When Alphas and Risk Factors are Misaligned

The misalignment of alpha and risk factors may result in inadvertent and unwanted bets that may hamper performance. Lee and Stefek (2008) show that better aligning risk factors with alpha factors may improve the information ratio of optimized portfolios. They propose four ways of modifying a risk model to reduce misalignment. Here, we discuss one way to mitigate these problems by modifying the optimization process, itself. The objective function is modified to include a penalty term on the residual alpha. In our examples, the method proposed helps to mitigate the mismatch between alpha and risk by assigning a suitable penalty to the residual alpha.

The Promises and Pitfalls of Factor Timing

The potential to dynamically allocate across factors, or factor timing, has been an area of academic and practitioner research for decades. In this article, the authors revisit the promises of factor timing, documenting the historical linkages between equity factor performance and different groupings of predictors: sentiment, valuation, trend, economic conditions, and financial conditions. The authors highlight that different predictors are more relevant for certain horizons, so the horizon is critical in factor timing. They also argue there are significant pitfalls with factor timing as well. The difficulty of timing factors has been well documented, given the uncertainty of exogenous elements affecting their behavior and the complexity of the underlying relationships. Most importantly, the underlying causal links are time varying. In addition, these relationships are observed with the benefit of hindsight and thus suffer from the age-old problem of data mining. Despite these caveats, the authors believe that at the margin it is possible to time certain elements that can add value and improve outcomes.