T. Rowe Price Program for Charitable Giving

The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different basis assets, indicating that volatility is not an independent risk, but that it is related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors, specifically, the better long-term performance of the asset classes bonds and real estate as contrasted with materials, precious metals, and the healthcare industry. Our methodology is applicable to any long–short anomaly but we focus on the low-volatility anomaly since it is formed explicitly on the risk characteristic rather than on embedded risks of other anomalies. The AMF model outperforms the Fama–French 5-factor model significantly both in-sample and out-of-sample.

That investors should diversify their portfolios is a core principle of modern finance. Yet there are some periods in which diversification is undesirable. When the portfolio’s main growth engine performs well, investors prefer the opposite of diversification. An ideal complement to the growth engine would provide diversification when it performs poorly and unification when it performs well. Numerous studies have presented evidence of asymmetric correlations between assets. Unfortunately, this asymmetry is often of the undesirable variety: It is characterized by downside unification and upside diversification. In other words, diversification often disappears when it is most needed. In this article, the authors highlight a fundamental flaw in the way some prior studies have measured correlation asymmetry. Because they estimate downside correlations from subsamples in which both assets perform poorly, they ignore instances of successful diversification (i.e., periods in which one asset’s gains offset the other’s losses). The authors propose instead that investors measure what matters: the degree to which a given asset diversifies the main growth engine when it underperforms. This approach yields starkly different conclusions, particularly for asset pairs with low full-sample correlation. The authors review correlation mathematics, highlight the flaw in prior studies, motivate the correct approach, and present an empirical analysis of correlation asymmetry across major asset classes. <b>TOPICS:</b>Portfolio theory, portfolio construction, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ There is strong empirical evidence that asset class correlations are asymmetric, which poses complications in portfolio construction. ▪ Investors prefer diversification when a portfolio’s main growth engine performs poorly and unification when it performs well. ▪ To measure correlation asymmetry caused by nonnormality, investors must adjust for changes in correlation that arise mathematically when part of a sample is excluded. ▪ Unlike prior research, investors should condition correlations on the performance of a single asset, not two assets.

Abstract Euclid, the Wide Field Infrared Survey Telescope, and the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) will make emission-line selected galaxies the largest observed constituent in the z &gt; 1 universe. However, we only have a limited understanding of the physical properties of galaxies selected via their Lyα or rest-frame optical emission lines. To address this problem, we present the basic properties of ∼2000 All-Wavelength Extended Groth Strip International Survey, Cosmological Evolution Survey, Great Observatories Origins Deep Survey-North, Great Observatories Origins Deep Survey-South, and Ultra Deep Survey galaxies identified in the redshift range 1.90 &lt; z &lt; 2.35 via their [O ii], Hβ, and [O iii] emission lines in the 3D-Hubble Space Telescope survey. For these z ∼ 2 galaxies, [O iii] is generally much brighter than [O ii] and Hβ, with typical rest-frame equivalent widths of several hundred Angstroms. Moreover, these strong emission-line systems span an extremely wide range of stellar mass (∼3 dex), star formation rate (∼2 dex), and [O iii] luminosity (∼2 dex). Comparing the distributions of these properties to those of continuum-selected galaxies, we find that emission-line galaxies have systematically lower stellar masses and lower optical/UV dust attenuations. These measurements lay the groundwork for an extensive comparison between these rest-frame optical emission-line galaxies and Lyα emitters identified in the HETDEX survey.

A wide body of academic literature suggests that macro factors can be significant drivers of asset returns. And among practitioners, statements such as “stocks make money in expansions and tend to lose money in recessions” are often held as self-evident. However, there is little published on how to use these factors to inform investment decisions. From a practitioner’s perspective, the authors show how to build dashboards to integrate macro factors into a broader discretionary tactical asset allocation process. Importantly, their goal is not to design stand-alone systematic trading strategies based on macro factors. Rather the authors believe that investors should build macro factor dashboards to introduce discipline into their asset allocation process, in combination with other inputs, such as valuations.