During turbulent risk environments, it is imperative that fundamental portfolio managers learn to understand factor exposures to know what is driving their portfolios’ returns. By avoiding the performance drag that results from certain risk bets they can deliver higher alpha. Fundamental managers can leverage quantitative tools to help identify and reduce the impact of those unintended bets, while still maintaining their investment views and goals.
The STOXX Factor Index suite is comprised of five single-factor indices and a multifactor index engineered to deliver the excess returns associated with each factor using a diversified index of securities with carefully managed exposure, liquidity and risk characteristics.
2019 was a remarkable year, with benchmarks around the world climbing to new records, while volatility plunged. Both emerging and developed markets shared in the overperformance, with all components of risk falling for both markets. However, style factors saw mixed results, with few reporting outsized returns for the quarter or year.
Many quant managers are having a tough go of it this year. While one might blame factors in general, their returns do not tell the whole story (or even the bulk of the story). We think one of the major culprits in the US is that a number of factors worked better on the short side and among small-cap names, but even that does not explain all of the underperformance.
In this paper, we take a look at how minimum variance performed vis-à-vis its core market counterpart during nine recent geopolitical risk events. The nature of these events is that they tend to push correlations towards 1.0. This may pose a problem for minimum-variance portfolios, as they are constructed by leveraging the covariance matrix in order to build portfolios with strong systematic hedge.
In this paper, we use the backtester capabilities of the Axioma Portfolio Optimizer to investigate the maximum capacity of a smart beta strategy, based on the Profitability factor in the Asia ex-Japan equity market.
Minimum variance strategies have gained significant traction especially since the global financial crisis. They aim at reducing or minimizing variance, i.e. the square of volatility as measured by standard deviation, or, in this case, price fluctuations of portfolio prices around their mean.
When we refer to factor returns, we mean the return to a long-short portfolio with unit exposure to the factor in question, and no exposure to any other model factor. The portfolio encompasses the model’s investment universe, is rebalanced daily, and has hundreds or thousands of small positions.