Multifactor Portfolios Research Articles

Application of Multi-Factor Financial Models in Asset Pricing

This paper explores the theoretical underpinnings and practical applications of multi-factor models in stock portfolio management. It outlines the theoretical frameworks of multi-factor asset pricing models such as APT theory, Fama-French three-factor, and five-factor models, analyzing how various risk factors influence asset returns. The study then discusses methods for identifying core risk factors empirically, constructing portfolios, optimizing weight allocations, and evaluating investment performance. Empirical testing using data from the CSI 300 and CSI 800 indices confirms that optimized multi-factor portfolios achieve significant excess returns while managing risk effectively, showcasing the practical utility of this quantitative investment framework in the Chinese A-share market.

Volatility Managed Multi-Factor Portfolios

Volatility Managed Multi-Factor Portfolios

Forecasting Long-Horizon Factor Volatility

This article investigates forecasts of long-term volatility for the fast-growing field of long–short factor strategies in an extensive in-sample and out-of-sample framework. The author follows previous work by empirically comparing various forecast configurations to provide guidance for academics and practitioners on how to form accurate predictions of future volatility for various established factors. The set spans 21 factor return time series over multiple asset classes, factor styles, and a long historical data period. Both in-sample and out-of-sample results suggest monotonically increasing forecast accuracy for longer historical lookback periods, longer forecasting windows, and more-sophisticated models (considering short-term volatility clustering and external predictors motivated by the asset-pricing literature), while the findings appear less pronounced in a real-time setting than observed in-sample. Moreover, investors engaging in carry-styled factor strategies and multifactor portfolios (rather than single factors) achieve more-reliable forecasts, on average, as confirmed by the out-of-sample analysis.

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%.

Cross-Asset Skew

The authors find that realized skewness is a pervasive negative predictor of returns across commodities, government bonds, equity indexes, and currencies, hence providing supporting evidence that investors exhibit skewness preferences. Portfolios in these asset classes with long positions on the most negatively (or least positively) skewed assets and short positions on the least negatively (or most positively) skewed assets generate on average a Sharpe ratio of 0.36 between 1990 and 2017. The authors find little evidence of a common risk driver, with a cross-asset skewness portfolio delivering a Sharpe ratio of 0.73. The patterns are not driven by reversal dynamics and are not subsumed by value, momentum, or carry factors or other macroeconomic and liquidity risks; consequently, mean–variance efficient multifactor portfolios assign a positive weight to skewness-based market-neutral portfolios. Their results remain robust to different measures of skewness and across subsamples.

When it rains, it pours: Multifactor asset management in good and bad times

AbstractWe examine the profitability of multifactor portfolios on the US stock market. Using passive sector investing as the benchmark, we assess the performances of factor‐based asset management strategies in good and bad times. When short selling is unrestricted, factor investing outperforms sector investing in all respects. For long‐only portfolios, our results reveal a trade‐off between the risk premia associated with factors and the diversification potential of sectors. Multifactor investing tends to be more profitable than the benchmark during good times but less attractive during bad times, when diversification is needed the most.

Momentum-Managed Equity Factors

Managed portfolios that exploit positive first-order autocorrelation in monthly excess returns of equity factor portfolios produce large gains in Sharpe ratios. We document this finding for factor portfolios formed on the broad market, size, value, momentum, investment, profitability, and volatility. The value-added induced by factor management via short-term momentum is a robust empirical phenomenon that survives transaction costs and carries over to multi-factor portfolios. The novel strategy established in this work compares favorably to well-known timing strategies that employ e.g. factor volatility or factor valuation. For the majority of factors, our strategies appear successful especially in recessions and times of crisis.

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.

Factor investing: alpha concentration versus diversification

Despite extensive research support, the role of diversification in current factor investing strategies remains neglected. This paper investigates whether well-designed multifactor portfolios should not only be based on firm characteristics, but should also include portfolio diversification effects. While the alpha concentration approach mainly considers factor-specific firm characteristics, the diversified approach utilizes covariance estimators in addition to firm characteristics to account for portfolio diversification. The corresponding out-of-sample results show that including an efficient covariance estimator improves the performance of long-only multifactor portfolios compared to the pure alpha concentration approach. A particular advantage of diversified factor investing strategies can be identified in the significant increase in exposure to the low-volatility factor represented by firm characteristics with high informational content. No significant performance differences are observed for long-short portfolios where the factor exposures of the alpha concentration and diversification approaches are similar with respect to the low-volatility factor.

Tactical factor allocation for multifactor portfolios

ABSTRACT Factor investing comprises selecting the set of factors and the right amount of each factor for investment. While past research provides a better guidance on which factors to invest, deciding the right factor allocation has remained relatively elusive. Motivated by the superior performance provided by the equal-weighted strategy compared to the more sophisticated ones, we choose the former as the baseline or the strategic allocation scheme for factor investing and investigate if improvements can be established. Specifically, exploiting the momentum property exhibited by the factors, we propose a simple tactical factor allocation scheme for the equity-based multifactor portfolios. Empirical results reveal that the proposed tactical factor allocation outperforms the strategic one persistently over time and pervasively across different markets.

Factors with Style

The authors document significant spreads in style factors—value, size, quality, momentum, and low volatility—in each of the style box categories. This is also true even for the value and small size factors, which are reflected in the original definition of the style box framework. Some single factors stay within a given style box, like quality in Blend, while other factors drift across style boxes, like momentum and even the size factor! They build multifactor portfolios within each style box, thus giving access to five style factors that can stay within a style box category, and the portfolios had information ratios of about 0.8 over June 2003 to December 2020. <b>TOPICS:</b>Factor-based models, portfolio construction, performance measurement, risk management <b>Key Findings</b> ▪ Although the traditional style box provides categories based on size and value, there is still substantial factor dispersion within each of the style boxes. This may not be so surprising for the quality, momentum, and low-volatility factors that are not directly captured in the style box framework, but it is also true for the size and value factors themselves. ▪ Active style box managers within the large-cap US equity space have demonstrated dispersion in the value and size factors in line with their benchmarks, but the median active manager tends not to exhibit significant quality, momentum, and low-volatility factor exposures. The range for these three factors is also relatively narrow, compared with size and value. ▪ Optimal combinations of the five factors—momentum, value, quality, size, and low volatility—can be constructed within each style box, allowing investors to harvest all five, and not just two, factor premiums while remaining in the same style box category. In historical data, these five-factor combinations resulted in information ratios of above 0.8, which fall in the top decile of above-benchmark performance relative to active managers in the respective peer group, while exhibiting lower relative active risk.

Is smart beta investing profitable? evidence from the Nordic stock market

ABSTRACT This study examines the profitability of the mixing and integrating approach for constructing multi-factor smart beta portfolios. While most studies explore this issue in a U.S. market setting, this is the first study that exclusively focus on the Nordic equity market, which exhibits some unique and stylized features as recently highlighted in the literature. Our findings indicate first strong evidence for return variations for sorting stocks on value-, momentum-, and ex-ante beta-signals. Surprisingly, variations in payoffs are not only small stock phenomena in the Nordic equity markets. While the current literature does not yet agree on a consensus, our study supports the literature documenting the superiority of the integrating approach. Our results challenge the efficient market hypothesis in a market environment offering a high-level of information-flow-efficiency.

Factors with Style

We document significant spreads in style factors — value, size, quality, momentum, and low volatility — in each of the style box categories. This is also true even for the value and small size factors, which are reflected in the original definition of the style box framework. Some single factors stay within a given style box, like quality in Core, while other factors drift across style boxes, like momentum and even the size factor! We build multifactor portfolios within each style box, giving access to five style factors that can stay within a style category which have exhibited information ratios over 1.0 over June 2003 to March 2019.

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.

Cross-Asset Skew

We find that realized skewness is a significant indicator of returns across a range of assets from different asset classes, namely commodities, government bonds, equity indices and currencies. Taking on skewness risk is broadly compensated within, but more substantially across asset classes. Portfolios in these four asset classes with long positions on most negatively (or least positively) skewed assets and short positions on least negatively (or most positively) skewed assets generate on average a Sharpe ratio of 0.35 between 1990 and 2017. We find little evidence of a common risk driver among these portfolios, to the extent that their combination benefits substantially from diversification, delivering a Sharpe ratio of 0.72. The patterns are not subsumed by other known factors that drive returns, such as value, momentum or carry factors and, consequently, mean-variance efficient multi-factor portfolios assign a positive weight to skewness. Our results remain robust to different measures of skewness and across sub-samples.

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

Size Factor in Multifactor Portfolios: Does the Size Factor Still Have Its Place in Multifactor Portfolios?

The finance literature has established a size effect: stocks with small market capitalization outperform larger stocks over the long term. The size factor is included in asset-pricing models because of its explanatory power for cross-sectional differences in equity returns. However, recent studies recommend removing size from the factor menu, given its relatively weak performance. Instead of looking at the stand-alone performance, we account for cross-factor correlation to assess the impact of excluding the size factor. We consider three tests. First, we measure the impact on model fit of asset-pricing models. Second, we assess whether the size premium remains intact when accounting for implicit exposures to other factors. Third, we evaluate the impact of the size factor on the performance of optimal multifactor portfolios. Our results suggest that the size factor improves model fit, delivers a significant positive premium in the presence of other factors, and contributes positively to the performance of multifactor portfolios. Omitting the size factor has substantial cost to investors, which often exceeds that of omitting other popular factors. <b>TOPICS:</b>Factor-based models, style investing <b>Key Findings</b> • The size factor carries a significant premium after adjusting for implicit exposure to other factors. • Optimal factor portfolios allocate to the size factor even if the return assumption is extremely conservative. • The size factor improves diversification due to its low correlation with other factors and different exposure to macroeconomic conditions.

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 can also be found within liquid subsamples of the market.

Momentum-Managed Equity Factors

Managed portfolios that exploit positive first-order autocorrelation in monthly excess returns of equity factor portfolios produce large alphas and gains in Sharpe ratios. We document this finding for factor portfolios formed on the broad market, size, value, momentum, investment, prof- itability, and volatility. The value-added induced by factor management via short-term momentum is a robust empirical phenomenon that survives transaction costs and carries over to multi-factor portfolios. The novel strategy established in this work compares favorably to well-known timing strategies that employ e.g. factor volatility or factor valuation. For the majority of factors, our strategies appear successful especially in recessions and times of crisis.