Multifactor Portfolio Research Articles

A Multifactor Perspective on Volatility‐Managed Portfolios

ABSTRACTMoreira and Muir question the existence of a strong risk‐return trade‐off by showing that investors can improve performance by reducing exposure to risk factors when their volatility is high. However, Cederburg et al. show that these strategies fail out‐of‐sample, and Barroso and Detzel show they do not survive transaction costs. We propose a conditional multifactor portfolio that outperforms its unconditional counterpart even out‐of‐sample and net of costs. Moreover, we show that factor risk prices generally decrease with market volatility. Our results demonstrate that the breakdown of the risk‐return trade‐off is more puzzling than previously thought.

Measuring Sector Cyclicality: A Factor-Based Approach

Equity sectors are often ascribed static economic classifications that fail to consider their dynamic and time-varying fundamental characteristics across business cycles. Leveraging research findings on factor cyclicality, the authors propose a simple and practical methodology to categorize sectors as cyclical or defensive by estimating their sensitivity to a cyclical multifactor portfolio. Their results reveal that although some sectors have exhibited persistent cyclical or defensive features, most sectors have experienced noteworthy changes over time. Using a predictive business cycle regime framework, they document the effectiveness of this dynamic factor-based sector classification approach and provide examples of sector rotation strategies that have historically generated attractive excess returns, outperforming static alternatives. Their results are consistent with a factor investing paradigm, recognizing that factors are important drivers of portfolio risk and performance, and traditional asset classes can be seen as implementation vehicles of intended factor and macro exposures.

Factor investing: a unified view

ABSTRACT A unified view of factor investing is presented. By examining the levels of exposure to a set of factors collectively, we construct enhanced factor portfolios from conventional single-factor portfolios that substantially increase factor risk premia consistently for nearly five decades in the US equity data. Detailed comparison between these and multifactor portfolios is conducted, and we find that a form of the latter delivers superior return performance. In particular, we present the outperformance of the signal-blended multifactor portfolio for various return measures over all factor portfolios considered at a statistical significance level of 1%.

How Valuable Are Target Price Forecasts to Factor Investing?

The informativeness of financial analysts’ stock recommendations to investors has become the subject of much scrutiny and debate. This article investigates the investment value of target price (TP) forecasts under the specific angle of factor investing. It provides directions to quantitative portfolio managers for the integration of signals conveyed by those forecasts in systematic and rule-based investment decisions. Using TP data from 1999 to 2019 in Europe and North America, the author first documents a decline in analyst opinion on the performance of most equity factors and finds that their forecasts are style biased. He shows that a long-only strategy that picks best-ranked stocks according to consensus TP implied expected returns (CTPER) generates substantial alpha within large-cap, low-idiosyncratic-risk, and noncyclical stocks. Embedding the CTPER signal into the momentum factor helps materially improve its risk and return profile. Those findings support the idea of adequately using TP forecasts in the design of top-down multifactor portfolio construction frameworks.

Continuous innovation in factor credit strategies

There is an large body of academic literature that documents the existence of factor premiums that enable investors to construct portolios that generate better risk-adjusted returns than passive investing. To ensure that such factor-based investment strategies continue to deliver on their objectives, continuous innovation is required, for example by further enhancing factor definitions and investment processes and investigating alternative sources of data. In this article we show two examples of recent innovations in the field of factor investing for corporate bonds.In the first study, we document how factors can be successfully applied to hard-currency emerging market credits. Size, low-risk, value and momentum factor portfolios outperform the emerging market credit universe on a risk-adjusted basis, for example shown by higher Sharpe ratios. A multi-factor portfolio moreover benefits from diversification between the individual factors and obtains the highest information ratio. The factor portfolios benefit from bottom-up allocations to countries as well as from bond selection within these countries. Analysing the break-even transaction costs shows that these factors remain attractive after taking transaction costs into account. While generic factors offer a good starting point, more complete risk-assessments limit the exposure to unrewarded risk. In the second study, we extend previous research that showed that the text from annual and quarterly corporate reports filed with the Securities and Exchange Commission can be used to predict equity returns and volatility. We examine whether the text in these reports is informative for corporate bond returns and volatility as well. Particularly, we test if bonds of ‘non-changers’, companies with few changes in their reports over time, outperform bonds of ‘changers’. In a large sample of corporate bonds over a twenty year period we find that results for equities do carry over to corporate bonds: in both the investment grade and high yield sub-samples non-changers outperform changers by more than 50bps annually.

Factor Investing in Emerging Market Credits

We examine factors in a novel dataset on the cross-section of emerging market hard currency corporate bonds. We find that the size, low-risk, value, and momentum factors predict future excess returns. Single-factor and multi-factor portfolios obtain economically and statistically significant premiums. Further, alphas remain significant after controlling for exposures to developed market credit factors. The factor portfolios benefit from bottom-up allocations to countries, sectors, ratings, and maturity segments, as well as from bond selection within these segments. Higher risk-adjusted returns of factor portfolios also can be found within liquid subsamples of the market. <b>TOPICS:</b>Fixed income and structured finance, emerging markets, analysis of individual factors/risk premia, portfolio construction <b>Key Findings</b> ▪ We examine factors in the cross-section of emerging market hard currency corporate bonds and find that the size, value, momentum, and low-risk factors predict future excess returns. ▪ Factor portfolios yield significant alphas in the Capital Asset Pricing Model, and a multi-factor portfolio that allocates equally to the four factors shows even stronger results, due to the low pairwise correlations among the individual factors. ▪ Alphas remain significant versus developed market credit factors, and the results hold within countries, sectors, ratings, maturities, and liquid subsamples.

Bottom-up versus top-down factor investing: an alpha forecasting perspective

In a recent discussion about efficient ways to combine multiple firm characteristics into a multifactor portfolio, a distinction was made between the bottom-up and top-down approach. Both approaches integrate characteristics with equal weights and ignore interaction effects from differences in informational content and correlations between the firm characteristics. The authors complement the bottom-up approach for the missing interaction effects by implementing a linear alpha forecasting framework. Bottom-up versus top-down factor investing is typically discussed using the assumption that all characteristics are equally priced, but the pricing impact of different firm characteristics can vary tremendously. The alpha forecasting perspective provides a theoretical motivation for factor investing and helps to compare the bottom-up and top-down approach with regard to the difference of informational content and interaction effects between firm characteristics. Taking into account the difference in informational content between firm characteristics leads to significant performance improvement in factor models with a high concentration of informational content. Equally weighted characteristics result in related performance irrespective of whether the bottom-up or top-down approach is applied.

Risk Management and the Optimal Combination of Equity Market Factors

Managing the intertemporal risk of optimally constructed multifactor portfolios adds to performance. The increases in Sharpe ratios are in addition to the utility that investors gain from controlling how much active risk they are exposed to over time. We derive a simple closed-form formula for security weights in optimal multifactor portfolios with an active-risk target. We test the risk control of five well-known factors—value, momentum, small size, low beta, and profitability—and the optimal multifactor portfolio. Our empirical research was carried out on the large-capitalization US equity market for 1966 through 2019. We conclude that for the equity market, more active factors are better than fewer if each subportfolio is “pure” as to factor, anchored to the benchmark, and combined on the basis of forecastable risks. Our portfolio construction methodology allows for transparent performance attribution and replication of the process in other markets and time periods.

Dynamic Risk Management of Equity Market Factors

Managing the intertemporal risk of optimally constructed multifactor portfolios adds to performance. The increases in Sharpe ratios are in addition to the utility that investors gain from controlling how much active risk they are exposed to over time. We derive a simple closed-form formula for security weights in optimal multifactor portfolios with an active-risk target. We test the risk control of five well-known factors—value, momentum, small size, low beta, and profitability—and the optimal multifactor portfolio. Our empirical research was carried out on the large-capitalization US equity market for 1966 through 2019. We conclude that for the equity market, more active factors are better than fewer if each factor subportfolio is “pure” as to factor, anchored to the benchmark, and combined on the basis of forecastable risks. Our portfolio construction methodology allows for transparent performance attribution and replication of the process in other markets and time periods.

Practical Applications of Trade-Off in Multifactor Smart Beta Investing: Factor Premium and Implementation Cost

<h3>Practical Applications Summary</h3> In <b>Trade-Off in Multifactor Smart Beta Investing: <i>Factor Premium and Implementation Cost</i></b>, from the 2019 Quantitative Special Issue of <b><i>The Journal of Portfolio Management</i>, Feifei Li</b> and <b>Joseph (Yoseop) Shim</b> (both of <b>Research Affiliates</b>) investigate the effects of implementation costs on multifactor portfolio construction. They examine the performance impact of rebalancing multifactor portfolios by looking at portfolio characteristics such as volume, tilt, turnover, and turnover concentration. They find that multifactor portfolios that included all six style factors under consideration—value, low beta, profitability, investment, momentum, and size—yielded the highest information ratios, both before and after trading costs, with a very small negative impact on Sharpe ratios compared with multifactor portfolios composed of fewer factors. The authors also determine that a uniform concentration level of 25% for all factors provides the greatest performance in the presence of implementation costs. Overall, they suggest investors pursue multifactor portfolios composed of the top 25% of stocks in each of the six style factors. <b>TOPICS:</b>Analysis of individual factors/risk premia, portfolio construction, VAR and use of alternative risk measures of trading risk

Constructing inverse factor volatility portfolios: A risk-based asset allocation for factor investing

In this study, we investigate risk-based asset allocation approaches for factor investing strategies by constructing a multifactor portfolio based on the inverse weighting method. We propose the inverse factor volatility (IFV) strategy, which is the simplified variant of a factor risk parity, assuming constant factor correlation. In IFV portfolio construction, the portfolio's weights are determined by using scaled inverse factor volatility treated as a proxy for a targeted exposure in the optimization. Based on daily stock and index returns on global markets from 2002 to the end of 2017, we implemented the empirical analysis of IFV portfolios among three stock markets: Japan, Euro, and the US. The results obtained reveal that the IFV portfolios significantly outperformed market capitalization weighted portfolios by successfully acquiring factor risk premiums.

Alpha forecasting in factor investing: discriminating between the informational content of firm characteristics

This paper applies a linear alpha forecasting framework to enhance commonly used factor investing strategies by taking into account the informational content and interaction effects of selected firm characteristics. To demonstrate conditions under which it is beneficial to deviate from equally weighted characteristics, we evaluate a comprehensive number of factor portfolios. We consider four single-factor portfolios with 14 different firm characteristics in total and a multifactor portfolio where all factors are included. Empirically, the strategies are analyzed with the S&P 500, the Stoxx Europe 600 and the Nikkei 225 index. In addition, we also examine the strategies’ performance in a simulation experiment and investigate the properties of the information coefficient estimates as a measure of the informational content. The empirical results are consistent with the simulation results, which reveal that the overall portfolio performance can be improved in well-defined factor models with a high dispersion among the mean information coefficients of the firm characteristics. In contrast, the naive combination shows a comparable or better performance in factor models with a small dispersion in informational content between firm characteristics.

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 multi-factor portfolio. The latter has theoretical merit since each security’s weight in the portfolio will depend on how well it ranks on multiple factors simultaneously. The former approach may miss cross-sectional interaction effects between how factors express themselves at the security level. The authors analyze the magnitude of these effects here and find that these interaction effects can in fact have a significant impact on portfolio performance. Both intuition and empirical evidence favor bottom-up multi-factor portfolio construction.

When Factor Timing Makes Sense

In this paper we investigate when it makes sense for portfolio managers to implement factor timing in their quantitative investing, that is we outline necessary circumstances under which the benefits of factor timing (measured by the improvement in Sharpe ratio and the skewness of the returns) outweighs the challenges associated with development and implementation of factor timing. In particular, we mathematically show that factor timing for a single strategy does not yield substantial improvements unless either (1) the Sharpe ratio of the strategy is orders of magnitude different across states, or (2) the signal used for factor timing can accurately predict when the strategy will deliver negative returns (and the portfolio manager is willing to go short the strategy at that time). On the other hand, using simulation, we provide evidence that for a multi-factor portfolio (containing more than 10 factors) allocating risk based on instantaneous correlations between the factors at the beginning of each time period improves performance above the passive approach of allocating risk based on the long-term factor correlations.

Multifactor Portfolio Construction by Factor Risk Parity Strategies: An Empirical Comparison of Global Stock Markets

Since the financial crisis in 2008, factor investing has attracted the attention of investment managers and asset owners. A factor-based investment portfolio can enjoy effective diversification and acquire factor risk premiums at lower cost than active managers can. In equity investments, “smart beta” investing has been established as a common investment style and the multifactor strategy has become the most popular in recent years. This study makes an additional contribution to empirical research for risk-based asset allocation in multifactor investing. We verified the effectiveness of the factor risk parity (FRP) strategy by constructing active equity FRP portfolios for four stock markets, Japan, the United States, United Kingdom, and Euro countries. For the Japanese market, four factors considered to be effective in the market were adopted for constructing FRP portfolios. For the other markets, we constructed FRP portfolios by equalizing factor risk contributions between the cyclical and defensive factors. We found that the FRP strategy is a prospective method for capturing factor risk premiums, since it can adequately allocate risk to factors that contribute positive returns in the active FRP portfolios.

Multi-Factor Portfolios: A New Factor? Limits of the Static Approach

We assess the value added of a multifactor portfolio from a performance-agnostic point of view. First we introduce a broad general definition of factor, that encompasses usual factors like Size or Value, and then we prove that static long–short multifactor strategies (as the equal weighting of factors) are indeed factors according to our definition. This result is new in the literature and states that, by investing in a long–short static multifactor strategy, one is indeed investing into a new (synthetic) factor. Finally we test the strength of such a synthetic factor compared to each single factor by looking at its predictive power. We empirically test the equal-weighting of Value, Size, Momentum and Low Volatility in the US and Europe. Our conclusion is very clear in both regions: the equal-weighting of these four standard factors is a synthetic factor that has no predictive power on stocks’ return, while each of the factors shows clear ability to distinguish among stocks. In other words, the measure that underlies this equal-weighting of factors has zero predictive power on cross-sectional differences in stocks’ returns.

Real Estate: The Case for Investment in Private and Listed Real Estate

Executive Summary. Our analysis of long-term trends points to the outperformance of listed REIT stocks and private equity real estate opportunistic funds compared to more traditional asset. To explain these results, we appeal to Merton (1973) and Fama's (1996) multifactor mean-variance efficient portfolio theory, where the asset weights for these portfolios are those that produce the market portfolio and an extra multifactor-efficient portfolio that performs well in a low equilibrium rate environment. Researchers have found that private and listed real estate performs well as a diversifier in a stock and bond portfolio. However, mean-variance portfolio theory does not provide a unified account of the outperformance of private and listed real estate over the past two and a half decades. A multifactor-minimum variance model is necessary, which introduces the issue of hedging and of tailoring portfolios to mitigate potential vulnerability to a low equilibrium rate environment. We find that investors should add a mimicking portfolio to mitigate their potential vulnerability to a low equilibrium rate environment, and that this portfolio should consist primarily of private and listed real estate.

The maximum bid-ask spread

We examine the return premium associated with a new measure of illiquidity that focuses on extreme points in the distribution of bid-ask spreads. Results show that stocks with larger maximum bid-ask spreads and price impacts command a return premium that is both statistically and economically significant. These results are robust to a series of multifactor portfolio tests and cross-sectional regressions controlling for mean spreads and other observable liquidity metrics. These findings suggest that the distribution of spreads matters when identifying illiquidity return premia due to the multi-faceted nature of liquidity.

Estimating multifactor portfolio credit risk: A variance reduction approach

The importance of credit markets in China and Asia Pacific has been increased significantly in the past decade and international regulation demands high standard in credit risk quantification for financial institutions. Computation for credit risk measures is a challenge problem. Hence this study aims to develop a fast Monte Carlo approach of estimating portfolio credit risk. The method could be applied to estimate the probability of large losses and the expected excess loss above a large threshold of a credit portfolio, which has a dependence structure driven by general factor copula models. Except for the assumption that a global common factor driving the default events of all defaultable obligors exists, the study does not impose any restrictions on the composition of the portfolio (e.g., stochastic recovery rates). Hence, this method can therefore be applied to a wide range of credit risk models. Numerical results demonstrate that the proposed method is efficient under general market conditions. In the high market impact condition, in credit contagion or market collapse environments, the proposed method is even more efficient.

Analyzing the Performance of Multifactor Investment Strategies under a Multiple Testing Framework

Evaluating portfolios based on numerous combinations of factors using the individual backtesting method could suffer from serious data mining bias and lead to spurious significant findings. Accordingly, the authors employ a multiple hypothesis testing method to examine the multifactor portfolio’s performance. Their empirical results show that even after they adjust for the multiple comparisons bias, stock-picking strategies with certain combined firm characteristics could generate significantly better liquidity risk–adjusted returns. In addition, the outperforming multifactor strategies that the authors report are robust to alternative definitions of factors. However, they observe that the number of significantly profitable multifactor portfolios has decreased substantially in the era of increased liquidity and trading activity in the U.S. stock market.