After a difficult 2018, investors are increasingly wary of risk premia, concerned that factors leading to underperformance might be a recurring problem. Imene Moussa, executive director at UBS, clarifies this issue
Risk premia strategies’ below-expected performance last year has raised the question of whether it was an aberration. What was the main reason for underperformance in 2018?
Imene Moussa: The pattern of monthly returns of alternative risk premia (ARP) strategies across assets (equities, credit, rates, commodities, foreign exchange and cross-asset) and styles (carry, trend, value and momentum) suggests that a series of shocks rolled through during the year with a cumulative effect.
These strategies were each affected by a major idiosyncratic event, such as the February market correction, the Italian general election, the natural gas spike and the October market correction. The unexpected effects of this sequence of events were the major cause of overall ARP underperformance in 2018.
For example, in February 2018, while the equity volatility carry strategy was drawing down, the forex carry strategy delivered a good performance – but not good enough to alleviate the extreme equity volatility spike. On the other hand, the forex carry strategy incurred a large loss due to the drawdown in Turkish lira in the summer.
Could better portfolio construction have helped in 2018?
Imene Moussa: To a certain extent. UBS has identified three potential improvements to the basic risk parity algorithm allocation between these ARP strategies, which would have helped reduce underperformance:
1. Implementing a risk budgeting technique in portfolio optimisation. A hierarchical clustering method applied to a combined universe of ARP strategies and traditional assets – such as equities and bonds – provides a framework to classify the strategies into groups. By assigning a risk budget to each group, certain risk-on strategies are de-emphasised to mitigate the left-tail behaviour of the overall portfolio.
2. Reducing the estimation error of the covariance matrix via a shrinkage methodology that uses information from the output of the clustering algorithm.
3. Capping allocations to each strategy based on expected shortfall. By looking at the worst-performing aspect of each strategy’s distribution of returns, UBS can provide intuitive limits to exposure, aiming to prevent any single strategy from negatively affecting portfolio performance in an outsized way.
While these exercises demonstrated positive impacts on performance, the performance for 2018 would still have been negative.
Since then, the enhanced portfolio – although more defensive – has delivered 6.8% in the first quarter of 2019 and performed slightly better than the basic risk parity portfolio while the market was trending up.
What else is UBS using to mitigate drawdown in its clients’ risk premia portfolios?
Imene Moussa: Diversification remains key; a majority of investors have invested in equities-focused risk premia. UBS has been developing new strategies with the power to bring greater diversification to client portfolios. We have launched a series of academically driven and uncorrelated commodity strategies to extract value from skewness, crowding, and the like, leveraging our long track record and strong index presence in the commodity space underpinned by our ownership of the Bloomberg Commodity Index and the Bloomberg Constant Maturity Commodity Index – two of the largest benchmarks.
Other strategies employed to produce a more resilient portfolio are macro-oriented – an increasingly in-demand area.
There is a lot of talk in the ARP industry of ‘next-generation’ ARP – ranging from operational improvements to machine learning. What are your thoughts on these developments?
Imene Moussa: With the development of technology, UBS continues to focus primarily on building a rigorous platform for developing new strategies. For example, operational improvements – such as reliably trading over-the-counter options using prices derived from observable data and options’ delta hedging at multiple fixings intraday – are crucial not only to capture market inefficiencies but also to offer further diversification and tap deeper liquidity.
Artificial intelligence is also in vogue. UBS has already implemented machine learning in strategies such as our quality dividend index – live already for some years – which aims to identify quality names that were likely to continue paying high dividends, using a random forest search algorithm and additional selection techniques. Random forests capture non-linear relationships and factor interaction efficiently. They are also robust to outliers and can handle a very large number of explanatory variables.
We have also developed a multi-asset allocator strategy based on big data, which provides the most up-to-date data ahead of periodic GDP or inflation statistics, typically used in a fundamental allocation approach. In this case, machine learning is used to process and analyse tens of gigabytes of data every day and interpret the macro environment, rather than forecast asset returns directly.
We are currently working on integrating our nowcasting capabilities powered by UBS Evidence Lab in our portfolio construction.
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