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 ability to attribute portfolio risk and performance to key factors, such as overall market exposure, rates, sectors, and quality, is an essential tool for helping portfolio managers to understand their risk and interpret their results. A parsimonious factor risk model can also support advanced portfolio construction goals, such as minimizing benchmark tracking error or realizing factor exposure tilts.
Risk model providers often commonly report the average R2 value of the asset returns model. Some models, such as statistical models, will consistently have greater R2 values than others. However, strong explanatory power from a returns model does not necessarily translate into an accurate risk model.