is reliable across major currencies, and has remained valid for22 years of data. Although this test is crude, and only tests the FRB at one horizon (monthly),despite this we have good evidence to abandon again the efﬁcient market hypothesis.There are a number of explanations that have been put forward to explain the FRB.Table 9.1 Signiﬁcance (t-stats) of forward rate bias regressionvariable bUSD
JPY
JPY
3.9 (P > 99.99%)
DEM/EUR
2.7 (P > 99.3%)
2.7 (P > 99.2%)
GBP
4.1 (P > 99.99%)
3.5 (P > 99.95%)
DEM/EUR
2.7 (P > 99.2%)
9.4.2 Risk premiumThe most common explanation, which is also widely used to explain the excess return ofother asset classes (equities, credit), is that the FRB is a payment to investors for their accepting additional risk. The argument runs: high interest rate currencies are high interest ratebecause they are risky (i.e. volatile and more likely to suffer spot depreciation). Investors recognise this, and demand an expected return premium to hold these currencies – and this is theFRB.The strength of this argument is that it ﬁts in with modern portfolio theory and investors’risk/return trade-off. Its weaknesses, and they are serious weaknesses, are: (1) that currencyrates are not dominated by international investors (trade and current account transactionshave much larger volumes); (2) that in the forward market, half of the transactions are shortpositions not long positions, and for these investors the FRB becomes a cost, not a return;and (3) that investors in each country have different perspectives dependent on their basecurrency, and they will regard their home currency as riskless, even if it is a high interest ratecurrency.
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Box 9.3
Currency Overlay
Currency risk premium
At the core of modern portfolio theory (see Box 5.6) is the concept that investment riskis rewarded. Investors will only hold more risky investments if they expect to receive, onaverage, a higher return from these. Does the same hold for currencies?On the face of it, the market assigns a ‘risk’ rating to each currency in the form of theinterest rate (which in turn implies a forward discount or premium in the currency). ‘Risky’currencies (say the South African rand) have high interest rates; safe currencies (say theJapanese yen) have low interest rates.This is a pleasing description, but does it stand up to inspection?10- Year data, USD base14%Volatility vs. USD
JPY
12%
CHF
SEKEUR
10%
GBP
8%6%−5%
−4%
−3%
−2%
−1%
0%
1%
2%
Interest difference vs. USD
We need to establish what ‘risky’ means for currencies. For investments, the most commonmeasure is volatility of return. But if we apply this to currencies, from what perspectivedo we measure volatility? Suppose we take the US dollar as the base, we can graph therelationship between historical currency volatility historical average interest rates. Thegraph above makes it plain that if we compare 10-year volatility and 10-year averageinterest rates among ﬁve major currencies, we ﬁnd a negative relationship, rather than apositive one. The same relationship holds for 5-year and 20-year data. We will clearly haveto abandon the naive risk premium theory.However, we may also have to abandon the concept of volatility as the relevant measureof risk. It may be that the yen is volatile for a US investor, but the dollar is, by the samemeasure, volatile for the Japanese investor. On the basis of the graph above, the yen hada negative premium, but therefore by deﬁnition the dollar had a positive premium for theJapanese investor. Since all currency relationships can be inverted, we are going to get zeronet risk premiums if all currency bases are taken into account. We are now left without riskpremium theory for currencies.However, one ﬁnal possibility is that high interest rates are seen by the market as aproxy for risky (i.e. potentially weak) currencies. The risk premium argument could bereinterpreted as claiming that high interest currencies are more likely to have positivecurrency surprise (i.e. the high interest rates on average more than compensate for theexpected spot devaluation). This is a subset of the forward rate bias theory, which is discussedelsewhere (see Box 9.2).In summary, the expression ‘currency risk premium’ should be used with care!
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9.4.3 Monetary policy and inﬂationA different explanation lies in the relationship between monetary policy and inﬂation. It runs asfollows. It is governments, not markets, that set short-term domestic interest rates. They do thisin pursuit of their monetary policy objectives – generally the control of inﬂation. Forward ratesin currencies reﬂect these short-term interest rate differentials, and are therefore politicallycontrolled.Governments who maintain higher-than-average interest rates in a particular period will doso to ﬁght inﬂation and inﬂation expectations. To reduce both of these requires higher-thanequilibrium real interest rates (i.e. after adjustment for inﬂation expectations). This will tend tocreate a forward rate bias, since the subsequent movement of the spot rate will reﬂect the actual(lower) inﬂation. The same effect will also happen in reverse – countries pursuing low interestrate policies do so to stimulate demand, potentially raising inﬂation above expectations.The weakness in this explanation is that the relationship between short-term interest rates,spot rate movements and inﬂation is highly unreliable, and a poor basis for a stable phenomenonlike the FRB.
9.4.4 Nominal rate illusionAn explanation which does not suffer from the same shortcoming as the two above is thenominal rate illusion argument, which runs as follows. Markets are dominated by the behaviourof their players, and therefore by their perceptions and expectations. Currency markets arereported and traded in spot rate terms, and ‘currency surprise’ is a little known concept.However, all proﬁts and losses in the forward market in currency arise out of currency surprise,which, to recap, is the result of currencies not ending up where their forward rate predicts theywill. This dissonance between proﬁts and losses actually experienced (currency surprise), andthe direction of the market and its ‘pressure points’ as actually reported and understood, meansthat FRB can ﬂourish. How is it that this is not arbitraged away by the force of the marketrecognising the proﬁt opportunity?Currencies are not principally investment instruments, and the vast majority of players inthe currency markets are not ‘investing’ in currencies. This means that not just the perception,but the reality, is different for different players.Currency exchange rates have always attracted the attention of central banks. They areoften charged with defending particular (spot) exchange rates, and will ‘smooth’ extreme(spot) movements by intervention. They are not proﬁt centres, and therefore they have apolitical imperative to perpetuate the ‘nominal price’ illusion by their behaviour. A signiﬁcantproportion of market participants are corporations, and many of these will have target exchangerates expressed in spot terms, and the movement of the spot rate will be a critical determinantof their currency transaction behaviour. This emphasis on spot rates is extremely widespread.Major commodities are priced around the world by conversion of their ‘core’ currency (mainlydollars) into the users’ currency at the spot rate. When a German oil company is buyingRotterdam crude in dollars, it is the spot euro/$ that determines the oil price in euros – andultimately the price of petrol at the pumps in Germany and the rest of Europe.Similarly, when a company has multiple production facilities in different currency blocs,it is the spot rate that is the determinant of the lowest cost producer. To a great extent forcorporations, the nominal price illusion is not an illusion – nominal prices are hard reality.Corporations inhabit the ‘current account’ world, and they have to respond to immediate
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opportunities and threats arising from exchange rate movements, not to notional accumulationof ‘currency surprise’ (which calculations are irrelevant).But the scale and importance of the ‘irrelevant’ calculations is signiﬁcant. As an example,the yen was on a downward path in 1995 (from a high of 82 yen/$), and in October crossedthe 101 yen/$ level and continued down (i.e. the number rising). It reached a low of 144 inJuly 1998, and then promptly recovered its poise, to rise all the way to 101 in December1999. It then turned round and fell again. It looks at face value as though the 101 in 1999was the ‘same level’ as the 101 in 1995. From the point of view of the ‘exchange rate charts’,the market-watchers, the competitiveness of Japanese goods, the price of oil in yen and theopportunities for exporters to sell to Japan, this is true. But the JPY/USD currency surprise indexwhich was 101 in October 1995 was 126 in December 1999. So some 25% of yen weaknessin the currency surprise was concealed in the nominal price illusion, and any market playerwho maintained a short position in yen throughout this period (exploiting the forward ratebias) would have made a 25% cumulative proﬁt, as opposed to nothing measured by the spotrate.9.4.5 Other inefﬁcienciesI have demonstrated the evidence for, and some possible explanations for, two well-establishedand widely accepted inefﬁciencies. There are more inefﬁciencies, although perhaps none asreadily demonstrable and as transparent as trends and the forward rate bias.Active currency overlay managers have successfully exploited more inefﬁciencies than justtrends and the forward rate bias over the past 10 years, and have as a manager group addedvalue (see Chapter 11). This observation itself is unusual and needs elaborating.
9.5 A SUCCESSFUL UNIVERSE?How can a universe of managers add value versus an objective benchmark? In a marketwhere the universe of managers in a survey constitutes all the managers in the market, thenit is logically impossible for the universe to outperform the index (which is the objectivebenchmark), since the universe being tested is itself the complete constituents. Since managersincur costs and indices don’t, then managers as a group will not just not outperform, theymust underperform. All the available studies of active management that I have seen, where theactive manager universe is a signiﬁcant proportion of the market (which is most equity andbond markets), illustrate the reality of this ﬁnding: that active managers as a class do not (andcannot) add value.The best analogy for this comes from sport. While the absolute skill in the UK SoccerPremiership6 is undoubtedly very high, the inescapable fact is that the average ﬁnal position ofthe teams in this league is 10.5. The interesting question then becomes ‘are there any individualteams which have a consistent record of success’ (i.e. consistently higher than 10.5th place)? Inthe active manager context, this is a question about individual management ﬁrms (or possiblyeven individuals within ﬁrms) – not about the universe. The average score of the universe isalways 10.5.76
The top UK soccer league, which has a ﬁxed membership of 20 teams.The analogy can neatly be extended to survivor bias as well. Since the lowest three teams are relegated each season, the average5-year premiership position of teams currently in the premiership must be a lower (i.e. better) number than 10.5 (and cannot be higher)as some teams will have less than a full 5-year record.7
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What is different about currency? From the foregoing sections on the structure of the currency markets it is clear that the active money available to exploit the persistent inefﬁciencyidentiﬁed above, and any others, is inadequate to fully eliminate them. On very rough guesstimates based on the volume of cross-border assets, and the constraints on asset managers, Iestimate that the total of long-term active money directly available for this purpose is less than1% of FX market turnover.8With this very small active, proﬁt-seeking component, it is entirely consistent with logic thatan identiﬁable sector of active managers (currency overlay managers), who all exploit one ormore persistent inefﬁciencies, can outperform an objective benchmark or market index. Thecorollary of this is that the remaining 99% non-proﬁt-seeking sector must lose money againstthe same benchmark. Why don’t they notice or mind?They don’t notice for three reasons. Firstly, the amounts involved are tiny, and get ‘lost inthe wash’. Secondly, many participants have to trade in the FX market to cover internationalpayments, so they do not have the choice to withdraw even if they thought they were losing anunidentiﬁably small amount in their trading activities. Finally, the measurement of performancein FX is fundamentally dependent on the customers’ perception of the benchmark, and theirbase currency. This means two participants can simultaneously think they are making money.Section 9.5.1 gives a simple example of this phenomenon.9.5.1 An example of different perspectives in the FX marketCustomer 1 buys $1m dollars with euro from the bank, and simultaneously another customer(customer 2) sells $1m to the bank for euro. The bank charges 0.05% difference between thetwo prices, and it is proﬁtable (and happy). Suppose the market rates were (in $/EUR) 0.9000and 0.9004 respectively. Let us suppose the rate then moves to 0.9500 over the next few weeks.Customer 1 is a euro-based importer, and is delighted that his imports for next month andsubsequently will be cheaper. He plans to reduce his prices (which will stimulate demand) andsimultaneously increase proﬁts because of the 5% input price reduction. He may take moreforward currency cover at these levels to lock these new rates in. Customer 2, a US-basedinvestor, congratulates himself on the timing of his unhedged euro investment – it has alreadygone up 5% in value. In these complex and varied circumstances, it is easy to see that verysmall and unidentiﬁable amounts of ‘losses’ will get lost.
9.6 SUMMARY ON EVIDENCE FOR INEFFICIENCY9.6.1 Weak form efﬁciencyI have presented here two independent pieces of evidence that give very strong support forthe contention that currency markets are not even weakly efﬁcient. Both of these pieces ofevidence refute the weak form of the efﬁciency model, namely that prices are not predictablefrom historical prices or price patterns.If weak form efﬁciency is refuted, does this mean that semi-strong and strong form efﬁciencyare refuted as well? This is an interesting question, to my knowledge not examined widely inthe literature.8Currency overlay = $110bn; cross-border portfolio assets = $1.5trn; 10% asset manager hedging leeway = $150bn. Hedgefunds = $100bn of risk. Total active = $360bn. T/O 3× value p.a. Therefore long-term active turnover = $1.08trn p.a. Total FXturnover = 260 × $1.2trn × 41% (customer proportion) = $128trn p.a. Therefore active proportion = 1.08/128 = 0.84%. All 2000estimated values, various sources.
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9.6.2 Semi-strong form efﬁciencyThis form of efﬁciency states that all public non-price information is already in the price, butprivate information is not. To refute this form requires evidence that there is public non-priceinformation not in the price. This in turn requires evidence that non-price-based models (i.e.fundamental models) can be constructed which give us predictions about future prices in whichwe can have (statistical) conﬁdence. So this boils down to the question ‘do fundamental modelsor forecasting work?’. We will explore this question further in Chapter 10.9.6.3 Strong form efﬁciencyIn the mainstream developed currency markets (which includes probably 15 currencies, andtherefore 105 pairs [=(15 × 14)/2]), there is virtually no availability (and little concept) ofprivate information. Currencies are not like equities or other corporate securities: they are not‘owned and controlled’ by any one individual or group of individuals. Whereas a disappointingtrading statement, composed by the CEO and CFO of a quoted company, will almost certainlycause a fall in the price of the company’s share price, a piece of ‘bad news’ about an economymay not necessarily cause a fall in the exchange rate. For example, ‘weak demand’ may beinterpreted by the markets as reducing the possibility of a rate rise (probably bad for theexchange rate), or as reducing the demand for imports (probably good for the exchange rate).In addition, the length of time that price-sensitive information remains in the private domain isvery limited. Most statistics are published very quickly after they emerge from compilation –often within 24 hours.This combined with the paucity of such information remaining in the private domain makesstrong form efﬁciency in the currency markets hard either to prove or disprove – in fact probablyirrelevant.9.6.4 Transactional efﬁciencyAs mentioned above, all of this makes no statement about the transactional efﬁciency orotherwise of the currency market. The evidence presented elsewhere in this book is stronglysupportive of the argument that the currency market is extremely efﬁcient from the perspectiveof transactions and liquidity. The evidence presented shows that the depth of liquidity is thedeepest, and the cost of transactions is the lowest, of any of the global markets. This low costof transactions does not sit at all uncomfortably with the informational inefﬁciencies identiﬁedin this chapter. In fact, as one consultant specialising in the currency market says: ‘wouldn’tyou expect active managers to be able to make money if over 90% of assets were indexed,transactions costs were 5 bp or less, there were no taxes, duties, or other frictional costs, anddaily turnover was over $1trn per day?’
10Active Currency Overlay – ManagementStylesIn this chapter I will look at two main issues – how active currency managers come to design theactive processes that they do, and what those processes are. To use an architectural analogy,this is the same thing as ﬁrstly looking at how architectural styles developed (the cultural,technological and artistic background) and therefore why an architect designed in the way thathe did. Secondly, again using the architectural analogy, what the styles are (i.e. a descriptionof classical, gothic, modernist, etc.).
10.1 THE PROBLEMThe problem that all active currency managers (or potential active currency managers) face ishow to use information available today to predict with a measure of success (at least a measureBox 10.1
Cross hedging
Cross hedging is where a currency overlay manager undertakes a forward contract or hedgebetween two currencies, neither of which is the home currency of the investor. Such activityeither passes risk from one currency to another, or creates new currency risk; it does not byitself hedge or reduce it.Under what circumstances is cross hedging likely to be used? Firstly, there are circumstances in which the owned (i.e. the target of the underlying investment) currency isrelatively illiquid and expensive to trade, but closely associated, either formally or informally, with another foreign currency. An example for a European investor might be a LatinAmerican currency, with the US dollar being the associated currency. It would be quitepossible to passively hedge the target currency back to US dollars (i.e. cross hedge), andthen run an active overlay between the US dollar and the home currency.Secondly, the overlay manager may wish to exercise more freedom to choose the currencypairs on which he is likely to add value. With the client’s agreement, he may release therestrictions on (i) the client’s base currency always being one half of the traded currency pair;and (ii) the weights of the active bets being determined by the underlying asset allocation.In theory cross hedging could have any number of legs. (Currency A hedged to B; B hedgedto C; C hedged to D; D hedged to home currency, etc.). However, active cross hedging is aform of gearing. It allows more active bets on the same asset base; it will engender a highertracking error, higher expected value added and a higher potential downside.There are few circumstances in which cross hedging would be a valid strategy for passive hedging. Only if the currency market was quoting irrationally (bid/offer spreads forcurrencies A:C being wider than the sum of bid/offer spreads for currencies A:B and B:C)would passive hedging have a ﬁnancial incentive.
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Currency Overlay
more than random) what will happen to exchange rates in the future. This is a problem simplystated, but extremely difﬁcult to solve. I should state here that there will be a group of readerswho will believe that the problem does not have a solution. Such a belief will be founded intheir own experience, and based perhaps also on what they see as inconclusive evidence to thecontrary.My goal is to present the relevant information clearly and with insight to both sceptics andbelievers. I will also declare my beliefs so that the reader can gauge the position from which Iam speaking:The evidence from over 20 years’ experience of analysing and trading in the foreign exchangemarkets has convinced me that highly sophisticated managers can consistently beat the market (andtherefore add value) over time. However, doing so is difﬁcult (much more difﬁcult than it mightappear), requires time and patience as well as expertise, and will be a great deal more anticipatedthan achieved by those managers who try.
It is worth stating at this point that most active overlay mandates will constrain the activemanager to vary hedge ratios between 0% and 100%, irrespective of the benchmark. It iswithin this constraint that the following discussion takes place.
10.2 MODELLING AND FORECASTINGFaced with the problem above, almost all currency managers turn to historical data, and quantitative modelling, to assist them in unravelling the mysteries of the currency market. If they donot do so, what is their guide? They may have some anecdotal or period-speciﬁc experience ofa particular currency move or moves, but without mathematical support, any views or beliefsthey may have are untested and unveriﬁable. So how do they go about modelling?10.2.1 Modelling – Occam’s razor1Many professionals in currency management, particularly in stand-alone currency overlay, usemathematical models to analyse and exploit historical inefﬁciencies in price movements, andto determine (more or less formally) their active positions for their clients. The advantageof models is that they allow scientiﬁc methodology to be applied to the process of activemanagement. A hypothesis is established – say that currencies have medium-term trends, orthat volatility in a period is positively inﬂuenced by a decay in volatility in previous periods.A model is built to test the hypothesis or hypotheses, and the model is tested on historicaldata. Modern computers and statistical techniques are now very sharp tools for conductingthis analysis, and it is not difﬁcult to get statistically signiﬁcant results out of this process.Statistical signiﬁcance means abandoning the efﬁcient market hypothesis, and accepting thatthe chosen model has explanatory power.However, there are signiﬁcant pitfalls in applying naive modelling processes. The ﬁrst oneis that a competent modeller can get very high levels of explanatory power modelling manydata series.2 Increasing model complexity, and relaxing the ‘intuitive tests’ that good modelssatisfy, can allow explanatory power to rise almost without limit, to the point where information1Occam’s razor states that where there is a choice between two equally valid explanations or approaches to a problem, you shouldchoose the simplest. William of Ockham (Occam) (1285–1349) was a medieval English philosopher. Einstein had a similar view –‘theories should be as simple as possible, but no simpler’.2This also unfortunately applies to data series which have no genuine explanatory variables. It is frequently possible to getsigniﬁcant explanatory variables by modelling randomly generated series with enough different ‘explanatory’ series.
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ratios3 apparently exceed 1. Increasing model complexity increases the historical ﬁt, but theevidence is that it also increases the gap between history and the future: as the explanatorypower increases, the relative predictive power decreases. The intuitive explanation for this isas follows.Historical price patterns in history are the distillation of the almost inﬁnite variety of humanbehaviour, economic conditions and chance. When presented with a price series, and other economic variables (relative interest rates, inﬂation rates, economic data, ﬂow data), a modellerwill design a mathematical model to ‘explain’ the movements. The model may have a variety ofintuitive bases, but can only capture, at best, a tiny subset of the actual forces which ‘really’ applied to the historical price series. There will be a huge amount of unexplained price movement.The temptation will be to try to ‘explain’ the unexplained movement, and modern modellingtechniques will allow the modeller to do this by adding autoregressive terms, non-linear functions, and almost limitless exogenous variables. The explanatory power can only go up whenthis complexity is added. But unless this added complexity is genuinely capturing underlying behaviour, the added complexity is just ‘curve ﬁtting’ – that is it ‘explains’ the historicalmovement by nature of identifying particular correlations and relationships that are supportedonly by the data – not by any ex ante intuitive beliefs. It is only a short step from this to ex postrationalisation of the chosen model, and we have the makings of a very unsuccessful model.In reality, there is a continually varying combination of generators of currency returns whichinclude long-term structural elements, and shorter term factors which will create a great dealof noise if ignored, but by contrast will lead to disappointment and failure if modelled. Theyen hedge fund debacle of 1998 was a classic case of a large amount of money temporarilybehaving in a particular way, and thereby moving the market. Modellers were faced with adilemma (which faces them less obviously all the time): either this behaviour creates a lot of‘errors’ in models which ignored this factor, or it is captured and ‘explained’ by new complexmodel features. Unfortunately, the causative factors for this behaviour, namely a herd play onthe ‘yen carry’, evaporated in a few days in October 1998 with a massive fall-out for the hedgefund players, and a failure of the new ‘explaining’ elements of the model.This feature of model building is very difﬁcult to guard against in inexperienced hands,since enthusiastic exponents will always be looking for ‘best ﬁt’ models, and will be undercommercial pressure to present them with short (or ‘theoretical’) track records. It takes a veryexperienced modeller to reject models with good ﬁt and good ‘stats’ (split-test results, etc.)because he does not believe them to be capturing a stable non-random characteristic of themarket.With all these caveats, is there a role for modelling in active management?10.2.2 Can models work?The answer is yes they can – but it doesn’t mean they all do. Models of physical processesare the life-blood of technological advance, and many model the real world near perfectly.However, as the explanatory power of models decreases with increasing ‘random’ elements,their validity is increasingly hard to be sure about. You only need to think about weatherforecasting to understand some of the problems.3Information ratio = annual value added %/annualised tracking error %. Annualised tracking error is the annualised standarddeviation of the differences between manager performance and benchmark performance. Successful active managers (currency or anyother asset class) tend to deliver live information ratios in the 0–0.5 range. It is rare (although not impossible) to ﬁnd long live trackrecords (which haven’t been ‘cherry picked’) with information ratios higher than this.
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Models of ﬁnancial market behaviour are bound to have large unexplained elements, andthey also have a further, logical problem already referred to above. In most ﬁnancial markets(e.g. equities and bonds), if a model is used to observe a market inefﬁciency (i.e. explain aproportion of the returns), and then used to exploit it, the very act of exploiting it will (ifdone in sufﬁciently large volume) change the market’s behaviour and thereby undermine themodel. However ‘reﬂexive’ and ‘clever’ a player – perhaps calculating the extent of a particularmodel’s use and predicting how its use will subsequently affect the markets – nevertheless inall markets in which objective benchmarks can be established, one player’s outperformancewill be another’s underperformance.4 It is quite hard to imagine a model sufﬁciently reﬂexiveand ‘game theory’ orientated to be reliably successful in such an environment. Hence therelatively small proportion of purely quantitative houses in active management of equities andbonds.Box 10.2
Alpha
The expression ‘alpha’ means ‘value added’ or ‘excess return’ over and above the marketreturn available for that asset class. The use of the greek letter α arises from an equation inthe Capital Asset Pricing Model (CAPM) that describes the pricing of an actual portfolioin relation to its riskiness, namely:Portfolio return = α + β(market return) + εwhere ‘return’ is the return in excess of the risk-free rate, β is the elasticity of the portfolioreturn with respect to market return, and ε is a random error term with a zero mean.Put into words, alpha is the additional return in a portfolio over and above the returnexpected from a passively managed portfolio of a certain riskiness. The expression ‘alpha’is often used as shorthand for the ‘skill’ of a manager.The expression ‘alpha’ was popularised by Michael Jensen, then a Chicago PhD student, who studied (in ‘The performance of mutual funds in the period 1945–64’, Journalof Finance, 1968) the performance of actively managed mutual funds compared to theperformance of market indices. He suggested that ‘alpha’ was an effective measure ofstock-picking ability in such mutual managers, although he found a disappointingly smallproportion (a minority) of his subjects did outperform the market index.In the context of currency overlay, ‘alpha’ is universally used to mean the value added ofthe currency manager versus the benchmark. Since there is no concept of ‘beta’ in currencymanagement, alpha is a measure of skill. It does not, however, say anything about the nonsystematic risk that a manager is taking to achieve his alpha – this is commonly measuredby the information ratio.What about active currency models? As described above, there is a very large, active (in thesense that they are dealing), but non-proﬁt-seeking customer sector in the currency market. Ifthis sector behaves in a stable way (because of its sheer size and diversity, and because it doesn’thave a choice about a lot of its deals), then in logic it should be possible to build a model which4This is only true if all the players in the market are represented in performance measurement, and the benchmark representsthe whole relevant universe of investments. Under these conditions, the universe must underperform the benchmark – because theformer have costs and the latter does not. However this does not imply that active management cannot make money – just not all activemanagers.
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exploits the inefﬁciencies which this behaviour throws up without the model being self-limitingor self-destroying. However, stable features are likely to be few and rather general (trendingmarket, forward rate bias), so such a model will have to successfully contend with a great dealof noise. The level of noise may make the process of creating a successful track record quitelong-drawn-out, but if the model is genuinely exploiting a stable