“The investment attributes that are used to carve the benchmark into tranches are elements that portfolio managers want to control in order to model a multi-factor portfolio that achieves risk premiums every stage of an economic or equity life cycle,” Radha said, referring to controlled elements like style, regions and volatility.
By building these tranches, managers are better able to deal with the differing natures of economic and equity cycles across countries, design portfolios by overweighting certain groups with certain attributes that are outperforming and underweight those that are falling behind.
The Fama-Macbeth OLS Regression Methodology refers to periodically running FMB regression across rolling time-horizons of fixed spans to determine factors that drive returns of entities chosen in each tranche. Radha describes the FMB regression as simply the set of cross-sectional OLS regressions run over multiple periods spanning the immediate trailing time horizon for a given point in time.
“The regressions allow for periodic observation of the cyclicality of the factors to ascertain which factor regimes are increasing in dominance, losing dominance or emerging,” Radha said. “This aspect of implementing the FMB regressions helps set factor weights of multi-factor portfolios.”
Borealis Global Advisory would then allocate weights to the factors proportional to the magnitude of the t-statistic, or the probability of difference between populations, from the FMB regressions.