Many institutional investors develop proprietary return forecasting models, but use third-party/alternative models such as the MSCI Global Equity Total Market Model to measure risk and transaction costs. While there may be a significant overlap between the factors used in alpha and risk models, at times they may be misaligned. For managers who optimize their portfolios, the optimizer will tend to amplify the component of alpha that is not aligned with the risk model; this may lead to...

Many institutional investors develop proprietary return forecasting models, but use third-party/alternative models to measure risk and transaction costs.

Heading into 2016, MSCI examined 12 stress points globally to be used in quantifying the effect on portfolios of a range of shifts in markets, liquidity and the macroeconomy. These stress points include the prospect of additional interest-rate hikes by the Federal Reserve, weakness in the eurozone and a deceleration in Chinese economic growth.

The “quant meltdown” of 2007 and the subsequent global financial crisis highlighted the risks of crowded investment strategies. The recent growth of “smart beta” indexes and their use in ETFs has added to concerns about crowding. In this Research Spotlight, we explore the risks posed by crowded strategies and explain how the MSCI Crowding Scorecard enables asset managers to assess these risks as they exist in today’s markets. The Scorecard employs four metrics...

AUTHORS: Mehmet K. Bayraktar, Stuart Doole, Altaf Kassam, Stan RadchenkoThe “quant meltdown” of August 2007 and the subsequent unfolding of the global financial crisis highlighted the risks of crowded investment strategies. The rapid growth of smart beta indexes and their use in ETFs has added to the need for scrutiny. In this Research Insight, we propose a set of four key metrics (our “Crowding Scorecard”) for monitoring and detecting the crowding risk of any...

Systematic Equity Strategies, when represented as factors in risk models, allow investment managers to better monitor the sources of risk and return in equity portfolios. We believe that they also improve forecast accuracy and help construction of portfolios that tilt towards (or away from) these strategies, which are rules-based or computer-based implementations.

January saw the return of volatility to the U.S. equity market. A confluence of factors led to this uncertainty: Investors were faced with the influence of a stronger dollar and the effect of lower oil prices on corporate earnings growth.

Actively managed large cap mutual funds have significantly underperformed their benchmarks from March 1 through April 30 this year, which has raised questions among investors since this happened during a flat stock market with volatility levels at historically low levels. In this paper, we demonstrate the significance of this recent performance, measure mutual fund tilts on investment styles and analyze the impact of these styles by calculating contributions of tilts to the performance of...

In "Market Spin-cycle: Uncovering Style and Sector Rotation in a Flat Market", we investigated the sector and investment style rotations in the US market from March 1st through May 8th, 2014. In particular, we identified declining performance in growth-oriented styles associated with risk-taking behavior, such as Beta, Growth, and Momentum, with improving performance in Value, Profitability, and Size suggesting rotation in investment styles.In this follow-up paper, we extend this...

March and April of this year saw one of the worst periods of active performance over the past 10 years for actively managed portfolios. And this happened, despite a flat stock market and historically low volatility levels.

This Model Insight summarizes the methodology and empirical results for the fourth-generation Barra South Africa Equity Model (ZAE4). This paper includes extensive information on factor structure, commentary on the performance of select factors, an analysis of the explanatory power of the model, and an examination of its effectiveness in portfolio construction using minimum volatility and index tracking portfolios. It also includes a side-by-side comparison of the forecasting accuracy and...

Recent U.S. equity market performance, driven by a significant decline in glamour (high-growth, high-momentum) names, has attracted a lot of attention from market analysts. By now it is well publicized that, despite the range-bound performance of the US equity market since March, there have been significant differences among the performance of various sectors of the economy.We contribute to this discussion by focusing on what we view as a rotation in investment styles. This rotation has been...

This Research Insight, second in a series, introduces the Seasonality factor in our equity models; this factor was identified as part of MSCI's Systematic Equity Strategies (SES) research program. Seasonal behavior of stock returns is widely discussed in finance literature. The most prominent is the "January Effect," where prices tend to rise during January after stock sell-offs in December. In this paper, we examine how the SES Seasonality factor identifies seasonal pricing patterns for US...

This Model Insight provides empirical results for the new Barra Emerging Markets Model, including detailed information on the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a thorough side-by-side comparison of the forecasting accuracy of the new Emerging Markets Model and the Global Equity Model (GEM3).

In this Research Insight, we introduce the Prospect factor. Systematic implementation of Prospect theory may be thought of as a contrarian investment strategy that takes long positions in stocks with poor historical performance and short positions in stocks with historical good performance. We find that the Prospect factor is significant in explaining risk and return characteristics of Japanese and US securities. The Prospect factor was identified as part of our Systematic Equity Strategies...

This Model Insight provides empirical results for the new Barra US Small Cap Model, including detailed information on the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a thorough side-by-side comparison of the forecasting accuracy of the new model and its predecessor.

This Model Insight explains MSCI's motivation for building the Barra US Sector Equity Models, describes the methodology and factor structure, and provides empirical results for the US Sector model family, which includes 10 individual sector models and a fully integrated model.

This Model Insight provides empirical results for the new Barra Korea Equity Model (KRE3), including detailed information about the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a side-by-side comparison of the forecasting accuracy of the KRE3 Model and the KRE2 Model, its predecessor.

In this Research Insight, we introduce “Systematic Equity Strategies” (SES), which refers to a rules-based implementation of investment strategies and anomalies.  Our research finds that SES, when used as factors in risk models, can help predict both expected and abnormal stock returns, thus improving forecast accuracy. Some Systematic Equity Strategies may lead to crowding risk as large pools of capital pursue shared strategies; by using SES factors, investors can monitor...

In an introductory paper, we explained Systematic Equity Strategies (SES) and how they can be used as factors in a risk model.  In this paper, we use data from the Japan equity markets to define seven new SES factors and study their empirical behavior.  Our findings illustrate the important role that these factors play in portfolio construction and risk management. Our study also shows problems associated with omitting these factors from a risk model, and explain why models that...

This Model Insight provides empirical results for the new Barra Japan Equity Model (JPE4), including detailed information on the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a thorough side-by-side comparison of the forecasting accuracy of the JPE4 Model and the JPE3 Model, its predecessor.