Disclaimer: Views in this blog do not promote, and are not directly connected to any Legal & General Investment Management (LGIM) product or service. Views are from a range of LGIM investment professionals and do not necessarily reflect the views of LGIM. For investment professionals only.

Our ARP philosophy: simple, liquid, transparent and diversifying

If you’re looking to harvest an alternative risk premium (ARP), we believe you need to make sure you understand that risk and access it without undue cost.

 

In this mini-series of ARP blogs, we have so far delved into what they are and how they have performed recently.

We have touched on some of our principles already but will use this blog to add to these and elaborate further. Our overarching philosophy is simple: you’re only harvesting a reward for taking a certain risk, so make sure you understand the risk, target risks that are different from what you already own, and then harvest them without incurring undue costs.

When selecting ARP strategies, then, we generally look for simplicity, liquidity, transparency and diversification relative to other return streams in a multi-asset portfolio. Let’s take each of these in turn.

Simplicity

Risk assets tend to move up and down together, like a rising tide lifts all boats. The timing of the tide for different ARPs is not the same, but we believe ebb-and-flow dynamics will make similar ARPs rise and fall together as well. The key is thus to have exposure to the risk factor, in our view, more so than the exact implementation.

We therefore prefer a simpler approach over a more complex one, as this should allow the use of more liquid and cheaper-to-trade derivatives and reduce data-mining and over-optimisation concerns.

It’s a bit like the 80/20 rule, whereby we try to capture 80% of the risk premium and leave the last hard-to-get 20% to others. We think the first 80% is sufficient to obtain the diversification benefit we’re looking for; greater specialist quant power may be needed to harvest the remaining 20% of available ARP returns, but this is highly likely to increase the complexity and cost of the ARP strategy exponentially, making successful harvesting ever more elusive.

Our team runs a predominantly macro-fundamental investment process. But in a world where economics, behavioural finance, data and technology have become intertwined, quantitative analysis has an important role to play too.

Many members of our team have a quantitative background so are well equipped for researching and developing ARP strategies. We even think not having dedicated quant researchers comes with advantages too. A fully quantitative analysis could result in tunnel vision, finding statistical artefacts in the rear-view mirror without a good fundamental understanding of why it should be a repeatable return stream. My colleague Tim will explore this more in a forthcoming blog on data mining.

Liquidity

ARPs are, at their core, a set of quantitative rules to target and maintain exposure to certain factor risks. The strategies dynamically adjust to keep exposure to the risk factors fairly constant. This involves trading weekly or monthly for some slower-moving strategies and even intraday for higher-frequency strategies.

In any case, turnover tends to be higher than for most traditional long-only strategies. Trading in liquid derivatives and keeping trading costs low are, for us, a necessary condition for harvesting the risk premium. We use a rule of thumb that trading costs cannot be higher than a third of the potential risk premium (after applying a significant haircut to any historical Sharpe ratio).

Transparency

When discussing potential new ARP strategies, we debate in length why we should be rewarded for running the strategy. Which risk are we insuring against, which structural flow are we hedging, or which behavioural bias is causing the inefficiency?

To understand and assess the risk, we need full transparency in the ARP strategy. We don’t exclude strategies from external providers but would need a full look-through to be able to understand all the strategy’s intricacies. Similarly, we’ll provide the required transparency to our clients on the strategies that we are running.

Diversification

We’re wary of ARP strategies that may look different on the tin, but that basically provide more of the same of what we already own. Positive co-behaviour with other parts of the portfolio, in particular during episodes of market stress, is a no-go for us. A good example is selling equity volatility, with the tail risk of such a strategy typically coinciding with an equity market drawdown.

For strategies that have made the cut, our starting point is to implement a signal as is, so that ARPs can complement other return sources based more on judgment in the portfolio. We do allow ourselves to sense-check the signals before implementation, however, as signals could have been caused by one-off factors and be vulnerable to structural breaks in the data.

As a macro-fundamental team with a more discretionary investment process, we’re geared towards making these types of assessments, so better to make use of it! Of course, we need to strike the right balance, and experience shows we have implemented the actual signals more than 95% of the time.

Investors can decide to select market beta, ARPs and tactical asset allocation from specialist providers. This building-block approach provides similar diversification benefits, but in our view still needs some form of active management. Combining different return streams is like an asset-allocation decision, requiring active decision-making to allocate risk budgets and maintenance as the portfolio is in constant flux. Risk allocations to ARPs can be kept stable, but we don’t believe a passive approach is suitable. When ARPs are part of a wider integrated portfolio, the management of return streams – and single ARP strategies within the ARP return stream – comes naturally as part of the overall holistic management.

Having outlined our ARP philosophy in this blog, the final blog in this mini-series will address modelling risk when relying on back-testing, data mining and potential over-optimisation.

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