As alternative investment managers continue their never-ending search for alpha it is clear that for most the priority is in generating the smoothest returns possible with the lowest possible volatility. In our business - currency - this is problematic as we operate in a universe of limited choice in terms of instruments: 10 developed currencies and approximately twice that many emerging currencies (with varying liquidity). Just as a fixed income manager will be able to generate smoother returns and lower volatility by managing a portfolio of 100 bonds versus a 10-bond portfolio, currency managers who are able to create diversification in their portfolios will be able to provide their investors with better information ratios over time than those are more concentrated. Once such diversification exists, be it over multiple currency pairs, time horizons, trading styles or all of the above, the next frontier becomes how to allocate effectively.
The first step in constructing an effective trading strategy is making certain that it effectively exploits all sources of return available in its given market. Foreign exchange is unique in that despite its great liquidity it is far from the most efficient market in the world. Whether this is due to the often-remarked upon “lack of non-profit participants” or because currency has never been considered an asset in the ‘buy and hold’ sense is a topic in its own right. Whatever the reason, the existence of a base return - or beta - in currency can be illustrated through the historical performance of naïve rules-based strategies which proxy the returns available by simply exploiting the principal inefficiencies in the FX market. The two most widely-followed strategies are directional trading and carry trading and a third exists in the volatility space – ie exploiting various mispricings between implied and realised volatility.
If simple rules-based systems can generate positive returns then it should follow that an intelligent, experienced and disciplined manager should be able to add alpha to the beta described above. The three main forms of such alpha can be summed up as: 1) good model design, 2) intelligent portfolio construction, and 3) sound risk management. What has become clear to us as model-designers is that allocation is front and centre in the success of all three of the above alpha components.

Among the first quantitative techniques used to design directional trading models was systematic trend-following. Relying on any number of related signals such as momentum, moving averages, trendline analysis, and so on, trend-following relies primarily on a ‘pairs-based’ approach to portfolio construction. It assumes serial correlation, that what went up yesterday will rise again today and tomorrow. The problem with this approach is that, although certain assumptions regarding future performance can be made taking past performance into account (such as “JPY sees more trends vs the USD whereas GBP/USD tends to mean-revert, resulting in choppier and more difficult dynamics for trend-following”), the process of creating a pairs-based model and running it on a discrete number of currency pairs involves an essentially naïve and static allocation of risk capital. Furthermore, as trend-following is a ‘long-volatility’ strategy one of its main weaknesses is the ‘lumpiness’ of its returns – that is, it has several instances of strong return in a year separated by long periods of flat or negative performance. Increasingly, in order to smooth this lumpiness and optimally deploy risk capital assigned to trend-following, managers are turning to optimisers which dynamically allocate capital to currency pairs where risk-adjusted return expectations are highest.
The second-most popular technique used to generate currency alpha is yield-based, or carry, trading. Here once again, naïve strategies were originally used to capture this beta, which is based on the observed phenomenon that over time currencies with high yields tend to outperform those with low-yields. Capturing this beta should therefore be as easy as constructing a portfolio of long positions in high-yielding currencies financed by short positions in low-yielding currencies. While this approach has historically generated positive returns – especially in recent years – the risk has been in the ‘tails’, the infrequent but significant losses which can occur when risk appetite (which typically corresponds to positive performance for yield-based strategies like FX carry) turns to risk aversion. Overcoming this hurdle is what separating beta capture from alpha generation is all about – the point at which managers become smarter about their risk allocation.
The third beta available is probably the least-exploited, as it is only in the past several years that systematic strategies operating in the volatility space have become practicable. These strategies are based on a number of inefficiencies observed in the past, the principal one being that implied volatilities embedded in currency options tend to be more expensive than realised volatility. It would then follow that even a naïve options-selling system would do well over time – and such is the case. Once again, however, there is a price to pay – which is the ‘short volatility’ risk profile of the strategy. This tends to look the opposite of the ‘long volatility’ trend-following risk profile, which makes it a good complement to any systematic trend-following strategy – but nevertheless it can be improved on by dynamic allocation.
Once a series of individual trading systems has been developed the next step is putting them together in a portfolio with the goal of capitalising on the benefits of diversification and low correlation between models. Even the most basic portfolio combining systems based on the three ‘betas’ above will probably outperform any single strategy on a risk-adjusted basis, given the low correlation between the three approaches. In the case of many professional currency managers this is only the beginning, however – as a more complex portfolio using multiple systems will probably produce a superior risk-adjusted return than one using only two or three systems.
In the case of our firm, our investment process has evolved over the years to the point at which the very complex investment decision tree below depicts our most sophisticated currency/fixed income strategy – the Global Financial Markets (GFM)
Programme.

Once the portfolio becomes this complex it is clear that a manager is no longer operating at the level of a basic FX trader but rather in a way more similar to that of a fund of funds manager – ie with a focus not only on having good strategies but also on getting the allocation between strategies right. This ‘allocation alpha’ becomes more significant with the increasing complexity of the underlying portfolio, to the point at which it can become a more important contributor to the overall return than any one underlying strategy. A correspondingly more intense discipline is therefore required in order to effectively allocate risk capital between various strategies.
So the goal becomes outperforming a benchmark ‘naïve allocation’ – easily said, but how to go about it? Just as dynamic allocation was shown above to be a key element in improving the returns of underlying trading models, so also can it be an important element in improving the portfolio construction process.
As is the case with underlying models, the key to this is in understanding the drivers of performance in the strategies making up a portfolio – in other words, under what circumstances they are expected to outperform and when they are more likely
to underperform. We have grouped our strategies into those that are essentially long volatility in nature and are therefore more likely to outperform during ‘divergent’ periods in the market, versus those that are either short- or neutral-volatility or more likely to outperform during ‘convergent’ periods.
Using such an approach, it will quickly become evident that the strategic allocation process can, and should, be considered to be a significant source of return. Of course, it can also be a significant source of risk as well; but as trading strategies become increasingly sophisticated and widely available it will be dynamic allocation - and the skill with which it is executed - that will separate the men from the boys in the game of currency.
Daniel Szor is managing director of FX Concepts, based in New York

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