Scientific Beta

This paper demonstrates how equity investors can achieve higher factor intensity per unit of tracking error, thereby enhancing the efficiency of their multi-factor strategies. This is a pivotal metric, which we designate as ‘factor efficiency’. By leveraging innovative measures of economic risk, our approach not only targets rewarded factors, but also manages exposures to unrewarded risks, thereby improving overall risk management.

In particular, we seek to minimise the unnecessary tracking error, which can result from implicit exposure to economic risks. At the same time, we maintain robust exposure to rewarded factors and well-diversified portfolios. Just like cholesterol, tracking error can be ‘good’ or ‘bad’. While ‘good’ cholesterol is necessary for the body to function properly, ‘bad’ cholesterol is harmful and should be reduced. Similarly, most tracking error can be considered ‘good’ as it allows deviations from the cap-weighted benchmark to achieve strong exposure to rewarded factors and diversify stock-specific risks. Nevertheless, some tracking error is ‘bad’, as it represents implicit exposure to economic risks that are not rewarded and that have the potential to negatively impact short-term performance. Our novel estimates of stock-level exposures to economic risks enable us to reduce the ‘bad’ tracking error.

Effective management of economic risks is a key source of added value for equity investors. For instance, a portfolio comprising solely oil companies will also exhibit exposure to the consensus-rewarded factors, which will account for a proportion of its return variations. Nevertheless, a significant portion of the tracking error will be attributed to an oil factor. Despite the absence of empirical evidence that the oil factor is rewarded, it can be a source of systematic risk, thereby influencing the return variability of portfolios that have a substantial exposure to oil prices. This exemplifies the broader point that equity portfolio return variations can be influenced by unrewarded economic risks.

Our award-winning research identified a parsimonious set of five economic risk factors, for which we developed reliable estimates of stock-level exposures that are robust to out-of-sample testing, namely short rates, term spread, breakeven inflation, globalisation and supply chain risks. In the initial two sections of the paper, we present evidence to demonstrate the significance of economic factors in portfolio construction. First, we demonstrate that they contribute to the performance variability of factor portfolios. In line with expectations, our findings confirm the importance of rewarded factor portfolios. Notably, their relevance persists despite the recent market turbulence. Nevertheless, the proportion of portfolio return dispersion that remains unexplained by the market and consensus-rewarded factors is not insignificant and may be relevant from an index design perspective. Indeed, our results suggest that up to 50% of this ‘unexplained’ variation is attributable to our menu of economic factors. Moreover, in line with the design of these factors, the effects are more pronounced during episodes of market turbulence, such as the dot-com bubble, the 2016 presidential election, the Covid-19 crisis and the Ukraine war.

Second, we demonstrate that economic factors can be incorporated into risk factor models, which are used to improve the measurement of the covariance matrix for the purpose of designing risk-controlled portfolios. In particular, our findings indicate that minimum tracking error portfolios built with risk factor models that incorporate economic factors exhibit a reduced out-of-sample tracking error. Notably, the incorporation of economic risk factors reduces the average out-of-sample tracking error by 15% in comparison to the naive approach based on the sample covariance matrix. This reduction is more pronounced than the 11% reduction observed when the risk model considers solely the market and rewarded factors.

We proceed to introduce our novel EconRisk weighting scheme, which preserves the advantages of our diversified multi-factor strategy, namely exposure to rewarded factors and diversification, while mitigating economic risks. The EconRisk methodology is applied on a factor-by-factor basis, with each factor sleeve constituting a separate sleeve within the multi-factor strategy. The weights of each individual factor sleeve are allowed to deviate from the diversified multi-strategy in order to minimise economic risks. Deviations are constrained in order to preserve the essential characteristics of each factor sleeve.

The principal advantage of the EconRisk weighting scheme is the enhancement of the ‘factor efficiency’ (factor intensity per unit of tracking error) of our diversified multi-factor strategy. Indeed, by mitigating economic risks, we can eliminate unnecessary deviations relative to the cap-weighted benchmark that are not required to achieve the objective of stronger risk-adjusted performance over the long term, given that economic risks are not rewarded. Indeed, the EconRisk weighting scheme has been demonstrated to enhance factor efficiency by over 7% in both the US and Global universes, a figure that is of significant consequence for investors.

In particular, the reduction in tracking error achieved by EconRisk is considerable and consistent across regions thereby substantiating the robustness of the methodology employed. The EconRisk approach demonstrates an effective balance between robust exposure to rewarded factors, comprehensive diversification and the effective mitigation of unrewarded risk due to implicit exposure to economic factors. Therefore, the management of economic risks via the EconRisk approach represents a significant source of added value for investors seeking diversified multi-factor portfolios.