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In this article, the author discusses current structural research and investment trends that are shaping the future of factor investing. Specifically, the author focuses on three emerging trends: the ongoing evolution of traditional factor models and strategies, recent innovation in data sources and modeling techniques, and the potential disruption from integrating factor strategies into the asset allocation process. Key Findings ▪ Factor models and strategies evolve gradually over time, reflecting the evolution of capital markets and advances in academic theory, data availability, empirical research, and investment practice. ▪ Current innovation efforts in factor investing focus on exploiting new unstructured data sources, applying new data science modeling techniques, and integrating sustainability and climate factors. ▪ Innovation in factor investing is reshaping the traditional asset allocation paradigm by creating new opportunities for investors to express active investment views through a factor allocation process.

Factors are the underlying forces that drive portfolio risk and performance over different investment horizons. In this article, first the author reviews the theoretical foundations and practical applications of factor models. The author then discusses the integration of factors into different types of investment strategies. He concludes with a discussion of how factor models and strategies enable investors to manage total portfolio risk and capture risk premiums through the asset allocation process. TOPICS:Factor-based models, portfolio construction, risk management, performance measurement Key Findings ▪ Factor models empower investors to understand and manage the sources of portfolio risk. ▪ Factor strategies reflect factors, offering investors the ability to capture factor premiums. ▪ In factor allocation, factors replace asset classes as the drivers of asset allocation decisions.

Many studies have focused on the relationship between companies with strong environmental, social, and governance (ESG) characteristics and corporate financial performance. However, these have often struggled to show that positive correlations—when produced—can in fact explain the behavior. The authors of this article provide a link between ESG information and the valuation and performance of companies, by examining three transmission channels within a standard discounted cash flow model—which they call the cash-flow channel, the idiosyncratic risk channel, and the valuation channel. They tested each of these transmission channels using Morgan Stanley Capital International ESG Ratings data and financial variables. This showed that companies’ ESG information was transmitted to their valuation and performance, both through their systematic risk profile (lower costs of capital and higher valuations) and their idiosyncratic risk profile (higher profitability and lower exposures to tail risk). The research suggests that changes in a company’s ESG characteristics may be a useful financial indicator. ESG ratings may also be suitable for integration into policy benchmarks and financial analyses. TOPICS: ESG investing, security analysis and valuation, risk management

There has been a wide range of research in academia and the asset management industry about the financial benefits of ESG investing. However, the question of how to achieve consistency when integrating ESG has not obtained the same level of attention. As a result, ESG integration currently is often applied inconsistently and incompletely across asset owners’ portfolios. The authors of this article focus on how asset owners can implement ESG integration through index-based allocations to portfolios that seek to replicate ESG indexes. Index-based approaches offer consistency, transparency, and replicability and are generally cost-effective. Over a seven-year study period, global and regional versions of the MSCI ESG Leaders Indexes (as proxies for regional allocations) resulted in significant variations in their respective ESG profiles and performance, but in all instances, there was a clear reduction in all key risk measures.

The authors examine how a factor allocation can be integrated into an asset owner’s existing roster of active managers. Using a risk-budgeting framework, they have several findings. (1) Asset owners who wish to maintain their existing roster of active managers and incorporate factor views may consider a top-down factor implementation, funded entirely from the core passive allocation. This approach distributed most of the active risk to active managers. (2) Asset owners who wish to preserve their existing roster of active managers and incorporate high-conviction factor views may consider an allocation between active management and a bottom-up factor implementation. This approach more evenly distributed the risk budget to active management and factors and partially funded the factor allocation from active management. (3) Asset owners who pursue a barbell strategy between core passive allocations and concentrated active managers could implement a low-volatility factor allocation, which may lower the total risk of the equity program, releasing active risk budget that can be deployed to active managers.

This study aims at assessing the differences induced in the Global Horizontal Irradiance (GHI) predictions by the mesoscale atmospheric Weather Research and Forecasting (WRF) model when using different shortwave radiation. Model predictions are compared with GHI measurements at 12 stations of the Hellenic Network of Solar Energy (HNSE) for January, April, July and October 2013. The shortwave radiation schemes that were evaluated are: the Dudhia, the updated Rapid Radiative Transfer Model (RRTMG), the updated Goddard and the Goddard Fluid Dynamics Laboratory (GFDL) schemes. All schemes perform better under cloudless conditions due to limited ability of the WRF model to simulate cloudy conditions. The Dudhia scheme performs best with mean relative difference of 2.2 ± 15% for clear-skies, while the differences for the other schemes range between 5 and 12% with similar standard deviations. For all-skies, the model-derived hourly GHI is overestimated for all schemes (∼40–70%). For the daily averages, the model predictions are in better agreement with the measurements, mainly under all-sky conditions, with deviations of about half those of the hourly data and smaller standard deviations. There are strong indications that the differences of the model predictions from the measurements depend on the solar zenith angle and the amount of aerosols at each station.

Practical Applications Summary In Managing Risks Beyond Volatility, from the Fall 2017 issue of the Journal of Index Investing, authors Mehdi Alighanbari, Stuart Doole, and Dimitris Melas, all of MSCI, discuss how well investment strategies that minimize price-volatility risk address other risks not directly targeted by a traditional minimum-volatility strategy, such as concentration, sustainability, and crowding. By applying factor models, the authors examine whether minimum-volatility strategies guard against these other risks, or if they do not, how such strategies could be modified to do so without compromising their fundamental mission of minimizing overall portfolio volatility. Minimum-volatility strategies, by selecting for low-volatility and weakly correlated assets, innately promote diversification. The authors demonstrate that adding a diversification constraint to portfolio management may in fact be counterproductive to a minimum-volatility investment approach. In contrast, mitigation of crowding risk, and, to some extent, sustainability risk, can be enhanced through portfolio-construction constraints without compromising a minimum-volatility strategys fundamental objective. Therefore, a minimum-volatility strategy properly incorporating some methodological constraints into portfolio design can effectively mitigate some risks other than volatility, especially from overvalued assets that may increasingly take over a minimum-volatility portfolio’s composition over time.

This article investigates the commonality in risk factors and sector biases in managed volatility equity strategies (both active and passive). A unique aspect of this article is that it is written from the perspective of a user of the strategies. Unlike all other past studies it considerers several versions of low volatility strategies—from different managers with different investment approaches and risk models. The findings here are therefore more general and not specific to a particular implementation or risk model. We will explore both actively managed volatility strategies as well as an index strategy.

In the past 10 years, ESG considerations have increasingly become integrated into mainstream portfolio management. A large number of long-term institutions such as pension funds, sovereign wealth funds, insurance companies, endowments and foundations have signed up to the UN Principles for Responsible Investment. Many of these institutions either make allocations to segregated ESG mandates or incorporate ESG criteria across their entire portfolio. Asset managers have responded to this increasing demand from asset owners and have started to integrate ESG into their strategies using mechanisms ranging from exclusions based on screening to full integration into the security selection and portfolio construction process.

MSCI Value Weighted Indices are systematic indices that aim to reflect the value premium by employing an alternative weighting scheme that tilts the index towards stocks with lower valuation ratios. In this paper, we review the theoretical aspects of value weighted indices and through empirical studies we discuss the important facets of index construction that underpin the design of MSCI Value Weighted Indices. The MSCI Value Weighted Indices are based on an objective and transparent methodology by which all the constituents of a standard MSCI parent index are re-weighted using four common accounting measures: book value, sales, earnings and cash earnings -- thereby adding a value tilt to the parent MSCI index towards stocks with relatively lower valuation ratios. Based on the empirical results, the MSCI Value Weighted Indices have historically outperformed their respective parent MSCI indices across different regions, reflecting the value premium, with slightly higher risk, low tracking error and modest turnover. The empirical finding on the long term outperformance of MSCI Value Weighted indices is consistent with the long history of published research on the value premium. MSCI Value Weighted Indices are complementary to capitalization weighted indices and could serve as tools in strategic asset allocation to enable investors to gain exposure to market beta with a value tilt.

This paper examines the recent trend of adding leverage to fixed income allocations of multi-asset class portfolios of large asset owners. We show that the optimality of adding leverage from a volatility-reduction perspective depends on the correlations between bonds and equities, the relative volatility of bonds versus equities, and the weights of the two asset classes in the portfolio. If correlations between bonds and equities are negative, adding leverage could reduce the volatility of a portfolio, especially if the weight in fixed income assets is low, leverage is moderate, and bonds have a low risk relative to equities. Negative correlations also increase the likelihood that adding leverage will improve the risk-return profile of the portfolio. Asset owners considering adding leverage to their fixed income allocation can examine these influences to decide whether negative correlations between bonds and equities, a low ratio of bond to equity volatility, and higher risk-adjusted returns of bonds relative to equities are likely to persist.

MSCI Barra has recently published three research papers that examine the current equity asset allocation practices in the US, UK, and Japan and identify an increasing adoption of a global approach to equity allocation. This paper reviews the current strategic equity allocation policies of European institutional investors. It discusses the evidence that challenges the separation of equity policy portfolio into domestic/international allocations or regional allocations at the strategic level, and the rationale and potential benefits of an integrated global approach to equity allocation.

The market turmoil of 2008 highlighted the importance of risk management to investors in the UK and worldwide. Realized risk levels and risk forecasts from the Barra Europe Equity Model (EUE2L) are both currently at the highest level for the last two decades. We explore the historical diversification effects of an international allocation for UK investors. We illustrate that investing only in the UK market can be considered an active deviation from a global benchmark. A UK domestic strategy has high concentration, leading to high asset-specific risk, and significant style and industry tilts. We show that an international allocation resulted in higher returns and lower risk for a UK investor in the last one, three, five, and ten years. In GBP terms, the MSCI All Country World Investable Market Index (ACWI IMI) - a global index that could be viewed as a proxy for a global portfolio - achieved higher return and lower risk compared to the MSCI UK Index during these periods. The decreases in risk represented by allocations to MSCI ACWI IMI were robust based on four different measures of portfolio risk.

We present alternative methods for constructing factor mimicking portfolios in practice. We illustrate how portfolios with a limited number of assets and relatively low turnover can be used to track pure factor returns. These portfolios provide an effective instrument to support the practice of investment management. We illustrate how they can be used to hedge out unintended factor exposures of a passive benchmark thus facilitating the optimal management of beta exposure. We illustrate how they can be used to hedge out unintended factor exposures of an active strategy thus isolating pure alpha and facilitating the management of alternative sources of alpha.

The NOPEX two concentrated field efforts (CFEs) (June 1994 and April–July 1995) provide high quality data sets for the Boreal environment. The analysis of these data with traditional meteorological and hydrological approaches allow estimations of fluxes of latent and sensible heat, but these flux estimates are not directly comparable due to differences in temporal and spatial scales. The challenge here has been to overcome these difficulties so that the different estimates can be critically compared and evaluated in a systematical way. Five different approaches for the estimation of the regional flux of sensible and/or latent heat over the NOPEX area have been evaluated: (1) Direct aggregation — mixed layer evolution method, (2) Weighted averages of (a) aircraft measurements in the boundary layer and of (b) mast measurements, (3) Numerical models (a) ECOMAG — a distributed hydrological model and (b) MIUU — a mesoscale meteorological model. In general, good agreement was found between the regional estimates of the sensible heat flux, based on the mixed layer evolution method and land use weighted mast measurements. The aircraft measurements were found to be systematically smaller than the land use weighted mast estimates. For the latent heat flux good agreement was found between the regional latent heat flux derived from airplane measurements, land use weighted mast estimates, and the two mesoscale numerical models.