What is UBP’s approach to protection strategies? How do you implement them?
A key driver when designing a protection strategy is about striking the right balance between minimising the cost of holding a hedge while maximising its ability to react when stress arises.
Based on our experience of running live mandates, we believe that one size does not fit all, i.e. a single hedging strategy may struggle to capture the majority of the sequences occurring during a sell-off. This is why we have been running two complementary strategies since 2012. The first strategy is designed to protect against standard market corrections and answers the question of minimising the cost of holding the hedge; the second strategy is designed to protect against more extreme markets corrections, and answers the question of the reactivity of the hedge.
A robust, final implementation would then consist of combining both sub-strategies and adding this blend as an overlay to the existing long equities portfolio.
Away from protection strategies, do you see other topics raising interest with the clients you are meeting?
Our team is also focusing on investment themes. Here, we want to think thematically, i.e. identify megatrends that carry significant long-term growth opportunities for investors; however, access to these may currently be suboptimal.
Megatrends are those powerful and disruptive forces that are increasingly reshaping how certain industries have been traditionally run. With this in mind, the artificial intelligence (AI) theme represents an attractive avenue for us to explore. Indeed, we believe that AI is one of those structural shifts with irreversible consequences for the global economy and society. However, there are currently only a few investment vehicles offering a liquid exposure to it, and most of the time the AI allocation will be diluted by other themes, such as robotics.
This is why we decided to offer simple access to AI that would be pure while remaining liquid and cost-efficient.
What is the investment process?
The first thing to do was to analyse and explain in simple terms the greater “AI trade”. When you decide to look beyond buzzwords like “machine learning” or “natural language processing” that may add confusion to the discussion, you quickly realise that the key driver of AI development is data.
Simply put, data are to AI what food is to humans: the quantity and the quality of the data you feed your machine will determine how “intelligent” it will be.
Looking at today’s status of the data ecosystem is quite instructive. 90% of the data currently available have only been generated over the past two years, and that quantity is projected to double every two years, meaning we are in a truly exponential growth phase. However, when looking at the quality of these data, you realise that only 20% is currently being structured – in other words made usable – and, more interestingly, only 1% of it is being analysed.
To us, this vast untapped resource represents the key hidden value to be monetised in the AI trade, i.e. the entire process of transforming data from “volume” to “value”. As data is sometimes described as “the world’s new oil”, our portfolio invests in companies involved in the “refining” process of the data: companies that will store data, structure it and ultimately analyse it.
Providing private investors with quick and easy access to the theme was essential, therefore we wrapped the strategy as an open-ended certificate that offers daily liquidity and an ISIN code. We started promoting the strategy in Switzerland first, positioning the certificate as a long-term satellite investment in a typical equity allocation. Interest from private investors there has been strong, and we believe that, going forward, we may have traction in the region based on the meetings we are currently having.
Are there other themes you are exploring?
The “smart data” theme is another one where we see value for private investors but there is still very limited access to it. In a sense, it’s an extension of what we discussed earlier, as its final objective is to create investment strategies using AI. So, for example, instead of selecting stocks based on traditional fundamental factors like earnings growth, you could select stocks based on AI techniques that would, for example, create a sentiment index based on website activities.
Global Head of Cross Asset Solutions