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The alpha and beta of managed FX

Jon Stein

AlphaMetrix Investment Advisors LLC

Q3 2006

 

As one finds in any asset class, the foreign exchange world has fostered a variety of distinct styles and strategies. This article will attempt to make some sense of the style classes available within the FX realm today.

More importantly, we will also explore what value can be added by understanding the differences among these styles. Being armed with a good understanding of a manager’s style gives the portfolio manager a clear advantage, one that can help overcome the drawbacks of optimisation. Mean variance-based and other portfolio optimisers are very helpful tools, but they are blind to styles and the market environments that favour one style over another. (Even some naive style blending can add value to the portfolio construction process.)

Even more compelling is the possibility of timing allocations to one style versus another given the recent research into risk aversion and other filters, some of which we will survey at the end of this article.Such timing processes are impossible to apply to managers without an understanding of their styles.

We sampled 14 different FX managers (from the universe of more than 120 viable candidates) representing five broad style classes (see Figure 1), and labeled them accordingly: systematic momentum managers (‘SMO’, 4 managers), macro discretionary managers (‘MD’, 3 managers), short-term managers (‘ST’, 3 managers), model-driven managers (‘MS’, 2 managers), an options (gamma) trader (‘GT’), and a carry / yield trader (‘CS’). The selection process was largely random, and while we preferred the ‘slightly above average’ manager in each case, in more limited style classes the top flyer may have been one of the few choices just by virtue of survival.

Figure 1: Five broad style classes of FX manager

Systematic momentum / trend following
SMO
Medium- to longer-term trend followers and managers that rely on pattern recognition or some price-based system.
Macro discretionary
MD
Discretionary managers relying on a variant of factors, including price and economic and monetary inputs.
Model-driven or systematic macro
MS
Managers that trade based on some model or systematic process but relying on factors other than price, including economic and monetary inputs.
Short-term
ST
Managers having an average trade duration of less than 3 days, typically based on price movement.
Options (gamma) trading
GT
Options traders (note: naked option sellers, covered put sellers, covered call sellers are typically not included).
FX carry and yield trading
CS
Managers whose prime goal is appreciation from the interest income and not the rise or fall of the underlying currencies.

 

The labels were applied simply by assessing our impressions of the manager’s programme and performance – much as any portfolio manager or tracking service does. (We also took an informal poll of our analyst staff.) Obviously, selecting and allocating to managers based on such a priori, subjective assumptions alone would be fraught with several dangers. Not only does one assume that the label actually captures what the manager is doing, but that the label will govern how the strategy correlates to other strategies (e.g. that a macro discretionary manager will always be a good complement to a systematic momentum manager). That said, such labels do serve as a quick reference check and give analysts a good starting point but they should not be widely utilised without further validation.

A priori labels, or qualitative assumptions, allow an analyst to intuitively organise managers. But this practice cannot be complete without validating these assumptions utilising statistical style mapping as empirical evidence. To make this easy, we admittedly refrained from a more thorough style mapping process (typically involving multiple factor regression1) in favor of a more simple method of observing correlation to benchmarks. In short, we wanted to know what styles correlated highly with the benchmark and which ones offered diversification away from it.

What is the beta in FX?
 

The case has already been made that an actively managed FX portfolio is an appropriate source of alpha for a traditional stock or bond, or even hedge fund, portfolio. But looking at the asset class alone, there exists an identifiable beta and alpha within foreign exchange itself.

And therein lies the threshold question: What is the benchmark for FX traders?

In the stock market, suitable benchmarks have always been markets-based, typically a broad market index of companies, to which a company, investment manager, or mutual fund can gauge its relationship or co-dependency, or ‘beta’2. If we applied this to foreign exchange, we would construct a benchmark that simply averages the returns of all of the major currencies in the world. But this would be unrepresentative and even misleading. Not only do some currencies rise at the expense of other currencies, but few industry players outside of long-term hedgers take an active (as opposed to incidental) ‘buy and hold’ approach to currencies.

A more appropriate model is found in the hedge fund and managed futures arenas, where indices are based on managers and not markets3. Accordingly, we have selected the widely utilised Parker FX Index as the representative benchmark for the asset class. The Parker FX Index is comprised of both systematic and discretionary managers. (The index is comprised of two subcomponents – the Parker Systematic Index and the Parker Discretionary Index – although by asset-weighting the managers, the overall index appears to reflect more of the systematic component.)
In addition, we also tracked correlations against three ‘non-beta’ indices: the Parker Discretionary FX Index (a subcomponent of the broader index), the Passive Carry Strategy Index, and an index comprised of an average of a database of short-term FX managers. Like any manager-based index, these are guilty of all the typical drawbacks including a skew toward the more heavily marketed programs, survivor bias, etc. But since the purpose of the benchmark is for style analysis and not extolling the profitability of the asset class, these are acceptable evils.

Using the simple correlation of monthly returns over a 36-month period, we measured the correlation of monthly returns of the various managers against the Parker FX Index (see Figure 2), as well as the three other indices named above. Generally speaking, we deemed any manager with a correlation of 60 or more to be ‘beta’, and less than 40 to be ‘alpha’. In addition to correlation we also displayed the actual mathematical alpha and beta that the manager had to the benchmarks in question.


Figure 2: Managers vs benchmarks
Alpha and beta of managed FX - figure 2 

Beta strategies in FX
 

As shown in Figure 2, similar to the managed futures universe, trend following, model-driven and longer-term momentum strategies appear to be most represented by the industry benchmark. Of these, the core strategy is trend following, represented by the label SMO (systematic momentum). (There are several rationales for this, including the fact that systematic managers have higher capacity and therefore more assets under management.) This is a rules-based strategy applied across an array of currencies, with the overall objective of capturing the upside (right) tail of the returns distribution, accepting the fact that the strategy will lose on more trades than it will win, while trying to limit the downside (left) tail as much as possible.

This SMO programs are also some of the most volatile and more likely than others to suffer prolonged periods of non-performance or drawdown. Some FX veterans may take issue with this strategy as the representative, or the highest Beta, of their asset class. But as far as the Parker FX Index serves as the benchmark, the numbers speak for themselves.

However, our labeling is far from perfect if we let the benchmark completely rule the day. Other longer-term managers with a model-driven component – those represented by the moniker ‘MS’ (macro systematic or model-driven) – did not make it into the beta club. One discretionary manager did.

One way to evaluate the relative performance of these managers is to see how they performed in both up and down periods of the industry benchmark (see Figure 3). If a manager went up more than the benchmark when the benchmark rose, the manager plots above the horizontal line in the graph. If a manager fell more than the benchmark when the benchmark was down, it appears to the left of the vertical line. If the manager fell less than the index it is plotted to the right of the vertical line. In this analysis, the upper right quadrant is the most desirable.

Figure 3: Up capture/down capture vs FX benchmark
Alpah and beta of managed FX - figure 3 

Alpha strategies in FX
 

If medium- to longer-term systematic strategies populate the beta camp, it is rather intuitive which strategies will not. The macro-discretionary managers (MD), short-term traders (ST), carry and yield trader (CY), and options trader (GT) generally did not track the industry benchmark.

So while firmly in the alpha camp, we now ask the question whether we can take a step further and create specialised style indices – ‘benchmarks for the non-benchmarkable’ – in the interest of style mapping within alpha. This is the reason we included the other benchmarks. (Perhaps these comparisons can assist in discerning whether or not one’s favorite FX carry manager is actually a day trader in disguise.)

The Passive Carry Strategy Index does pick up the one carry strategy (CY 1) in the bunch, but it also correlates highly to one of the supposed trend followers (SMO 3). This was disappointing, until we learned that this manager actually had been generating some shorter-term interest rate-driven trades in addition to trend trading.


Another observation about these other ‘alpha’ benchmarks is that the one index constructed from passive rules, rather than managers’ returns, seemed to be the most helpful. Our home-cooked Short-Term Index was more a diverse stew than a true style benchmark, most likely due to the diversity of shorter-term strategies. (This may inspire us to explore some form of passive rules-based index here - e.g. rules approximating opening range breakouts, etc – although even it may encounter the same problem.)

How to take advantage
 

Assuming one can appropriately separate alpha from beta, and even categorise trading styles within these groups, the next question is what to do with this information. Obviously it is sound investment theory that non- or relatively low-correlated strategies together will have the effect of reducing overall portfolio volatility and downside performance. In short, non-correlated volatility is non-additive and, therefore, it can be utilised to decrease portfolio risk while generally preserving upside performance as demonstrated in the following tables. Figure 4 provides the correlation matrix among all the managers. Figure 5 illustrates what happens when we pair the highest correlating manager (SMO 1) with three randomly chosen managers from other styles, and with the three lowest correlating managers (which also happen to be the lowest correlators to the industry benchmark). The key here is that risk is reduced, as demonstrated by the volatility reduction, while upside return is largely preserved.

Figure 4: Correlation matrix
Alpah and beta of managed FX - figure 4 

Figure 5: Volatility reduction by combining managers

Alpah and beta of managed FX - figure 5 

The more exciting possibility – and one that has captured investors’ attention over the last few years – is timing different strategies. Some analysts believe there are indicators or filters that signal when it is time to reduce exposure to one strategy in favor of another4.

One such indicator is the risk aversion index. Studies have confirmed that currency markets tend to lag economic events and developments , but still exhibit substantial reactions to these developments5. In short, FX managers are in a good position to trade shifts in risk aversion since they can see them occurring outside their realm and they can make the most out of them when the reaction plays out.

The concept of risk aversion in FX was first developed as a filter for timing carry trades. The thinking goes like this: assuming there’s a direct relationship between capital flows and risk appetites, cross border flows should increase when capital is eager to leave low-yielding currencies to capture interest rate differentials in higher yielding currencies. In other words, when risk aversion is low (and ‘risk seeking’ is high), high-yielding currencies outperform; when risk aversion is high, low-yielding currencies outperform . Several ‘risk aversion indices’ have since populated the scene – most incorporating some or all of the following factors:
• FX implied volatilities
• FX historical volatilities
• Stock index implied volatilities (VIX)
• Credit spreads
• Emerging market bond spreads
• Swap spreads
• Shape of yield curves

In short, over the past few years, nearly every serious FX carry trader has begun using a risk aversion index of one type or another to actively reduce exposure to the strategy during ‘risk averse’ periods. Many of these have expanded this by deploying other strategies during these periods, including favoring lower-yielding but more stable currencies or certain intraday/short-term trading strategies that appear to thrive in risk averse times. Other research has left FX carry altogether, exploring whether risk seeking environments favor beta strategies such as trend following.

If such filters can help the trader, they should help the portfolio manager as well. Theoretically, allocators could bias their allocation at any point in time to a beta manager or carry strategy, or certain alpha strategies (with the exception of carry).

Conclusion
 

Applying short-hand labels to one’s managers is not harmful, and often provides a good starting point. But these labels are subjective and need to be checked by objective, quantitative-based mapping. There are a myriad of types of quantitative style mapping tools – correlation and beta analysis being the more basic. As soon as this is accomplished, the real challenge lies in how to best take advantage of this knowledge. One thought is using these as a forward-looking dimension of portfolio construction. Another entirely different field of study is whether these strategies can be filtered or timed based on macroeconomic conditions and environments and conditions. There is some evidence that they can be.

Note:
Aleks Kins and Charley Penna of AlphaMetrix Investment Advisors LLC contributed significantly to the research and content of this article.

1 - One example of multiple regression is the least squares method, which attempts to explain a managers returns in terms of an arsenal of indices and benchmarks, resulting in a confidence factor (beta) plus a residual (alpha). Yi = β0 + β1X1i + β2X2i + . . .+ βkXki + ui
2 - Beta can also be described as the correlation multiplied times the ratio of the underlying volatilities of the two sets of data. CorrPM= (volM / volP) βPM
3 - The managed futures industry has begun following several indices comprised of the top players in the industry, typically weighted by assets under management for a particular program. Examples include the S & P Managed Futures Index and the Calyon-Barclay Index.
4 - “Trading Risk Aversion in Currency Markets”, JP Morgan Global FX and Metals Research, McCormick, James, September 29, 1999.
5 - “Introducing the Liquidity and Credit Premia”, JP Morgan Global FX and Metals Research, McCormick, James, August 25, 1999.

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