Perhaps the assumption that markets are still anchored by predictable economic policies needs to be challenged. For the first time since the fall of the Berlin Wall, political volatility – once background noise – is potentially reshaping investment risk at a structural level.
Despite authorities taking their foot off the environmental, social and governance pedal and recent volatility in equities and bonds, the benefits of Artificial Intelligence and improved investor transparency are just beginning.
… as value fares better in crowding markets
While quantitative managers, on average, had a rough trot during 2018, including the popular alternative risk premia strategy managers, new work indicates that certain styles will be more resilient in the current market climate, performing better in the event of investor ‘crowding’.
A study, by Nick Baltas, head of R&D for systematic trading strategies at Goldman Sachs, called ‘The Impact of Crowding in Alternative Risk Premia Investing’Â shows that “divergence premia”, such as momentum, are more likely to underperform following crowded periods. “Convergence premia”, such as value, show signs of outperformance as they transition into phases of lager investor flows. The results of the study were published in the June, 2019, edition of the Financial Analysts Journal of the UK.
The findings suggest that crowding is not always a catalyst for underperformance and should not be treated as such by investors. The results have direct implications for risk management and multifactor portfolio construction, Baltas says. Investors should consider how to risk-manage strategies with divergence dynamics, especially when those strategies experience net flows.
“Volatility targeting is a well-studied systematic way that has been shown to improve risk-adjusted returns of divergent strategies, such as momentum and betting against beta (Barroso and Santa-Clara 2015; Moreira and Muir 2017; Barroso and Maio 2018). In unreported results, I found that divergence premia exhibit higher volatility following crowded periods (in agreement with Lou and Polk 2014), which justifies the use of a volatility-targeting mechanism.
“Conversely, applying volatility targeting to convergent strategies might not improve risk-adjusted returns. In unreported results, I found that convergent premia exhibit lower volatility following crowded periods. In their analysis of the impact of volatility targeting on equity factors, Moreira and Muir (2017) found that the Fama and French (1993) HML factor (high book-to-market value minus low book-to- market value) does not benefit statistically strongly from volatility targeting.”
Putting it together, he says, the findings have practical implications for investors who build multi-factor portfolios. Popular risk-based schemes, such as risk parity, appear to be more suitable for portfolios of divergent strategies than for portfolios of convergent strategies.
– G.B.
