Several philosophies and academic approaches exist to build portfolios. The choice of the theoretical framework is the first challenge that an asset allocator may face. Making an absolute choice in a fast moving environment due to continuous financial innovation, frequent market regime changes and volatile relative performances is not an easy task to accomplish.

Will show the latest studies’ results comparing forecasts based on factor models to those using more traditional past returns time series models and highlight key decision triggers for an informed choice.

What do recent studies tell us ?

Widely covered by academic research for several decades already starting with the famous Fama and French (1988, 2002), factor investing has continued to play an increasingly important role in academic research. Financial innovation has also seized this opportunity to propose diverse and varied investment strategies and products such as Smart Beta ETFs using new factors and increasingly complex quantitative methods.

Recent studies such as the one by Tessaromatis and Sakkas (Forecasting the Long-Term Equity Premium for Asset Allocation, Financial Analysts Journal, 2022) conducted on the U.S. equity market and those of 11 other developed countries continue to support the idea that predictive models based on a factorial approach provide statistically significant outperformance over the long term compared to portfolio models built on the basis of the best-known models that are based on historical portfolio returns.

Long-term country equity premium forecasts based on a cross-sectional global factor model (CS-GFM), where factors represent compensation for risks proxied by valuation and financial variables are superior, statistically and economically, to forecasts based on time-series prediction models commonly used in academia and practice. CS-GFM equity premium forecasts produce significant utility gains compared to long-term asset allocation strategies based on eighteen commonly used prediction models, consistently across the US and eleven developed equity markets.

While factor strategies (including Smart Beta) have traditionally been sold as stand-alone products to complement a reference portfolio  portfolio construction, some studies such as the paper of Stefano Cavaglia; John Hua Fan; Zhenping Wang (Portable Beta and Total Portfolio Management, Financial Analysts Journal,2022) tend to show that the integration of ARP (Alternative Risk Primea) strategies in the construction of a core portfolio based on an approach tending to diversify factor exposures significantly improves the sharpness ratio and thus the risk/return trade-off of a portfolio.

 This study shows how ARP overlays can complement decentralized investment management models and benefit plan constituents.  ARP can be integrated with a reference portfolio to achieve optimal total portfolio outcomes.  A factor diversifying overlay reduces the risk of the reference portfolio and captures a welfare enhancing diversification premium. The relaxation of the risk budget enhances the fund Sharpe ratios through strategic factor tilts and by levering existing asset class or active management exposures.

So there seems to be a growing consensus on the value of capturing the premiums of some factors over the long run, but what about their relative short-term performance compared to a benchmark?

Which factors showed better resilience in H1 this year and why?

The analysis of the relative performance of the factors compared to their benchmarks shows different results in H1 of this year depending on the geographical area selected and this is mainly due to central Bank monetary policy divergence and in particular between developed countries, which have experienced rate hikes, and China which has had a more accommodating monetary policy. The quality factor also underperformed significantly, while the value and minimum volatility strategies outperformed significantly.

Sector is King

Source : Morningstar, L’Allocataire, MSCI, data as of 31/8/2022

Value’s low exposure to tech and its overexposure to energy and utilities were its main performance drivers in H1. Conversely, Quality and Growth factors’ overexposure to tech and their underexposure to the energy sector weighed on their relative performance, making them the most negative contributors to the MSCI World index. Quality factor, which is supposed to protect portfolios thanks to the balance sheet strength of the companies it comprises, did not play its expected role. This clearly demonstrates that, in the short term and during a market downturn, a sector approach is more relevant, or at least it remains the most important one in driving the markets and the factors.