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on a drop in demand for gold from Asia, the rise and fall of the popularity of gold as a diversification commodity, contraction of bank lending in the US so that no inflation appears, and safe haven inflows to the dollar. One thing that watching the FX market will teach you is that ideology is a poor guide to trading success.

      The FX market is not what you think

      Chances are the realities of the FX market are not what you might think. In this book, we point out that:

       FX is driven more by pure speculation and global investment flows than by economic facts and ideas.

        FX players thumb their noses at the efficient market hypothesis and the concept of rational expectations.

        FX has a disconnect puzzle in which factors that should move the market do not, such as the three decades of persistent trade surpluses in Japan and deficits in the US. The exchange rate does not work as an equilibrating factor, as economists insist it should.

        While we cannot forecast exchange rates systematically, we can trade FX systematically.

        FX does not have a benchmark rate of return that traders or investors must try to match and surpass.

        FX traders are vastly more disciplined than traders in other sectors, in part because they use technical analysis.

        We do not have volume statistics in FX, except in the most delayed and roundabout forms.

      In summary, the FX market is endlessly fascinating, not least because figuring out some of its puzzles and perversities leads to profound insights into the human heart and mind, albeit sometimes all you get is the same old insight that the profit motive always rules in markets and it doesn’t pay to attribute mystical properties to mere prices.

      Chapter 1 – The Matrix Concept

      “I do not believe such a quality as chance exists. Every incident that happens must be a link in a chain.”

      Benjamin Franklin

      What is the matrix?

      The FX Matrix refers to a grid format of the multiple factors and players in the FX market and the way they interact. The term matrix is borrowed from random matrix theory and we use the matrix concept as a metaphor to help you avoid reaching or accepting oversimplified explanations of why the market behaves the way it does.

      In random matrix theory, the maths is truly advanced. Graduate students, hedge funds and governments devise models of complex dynamic systems. Most of us can’t get past page one of their articles and books because of the daunting calculations, but the metaphor is helpful to get a general grasp of the idea. In finance, random matrix theory was borrowed from physics and used to do things like remove idiosyncratic noise from correlation studies in designing optimum portfolios, leading to better estimates of component risk. The factor modelling includes weighting endogenous variables, exogenous variables and unobserved factors, and measuring their vectors.

      Most relevant to the FX market today is estimating effects like sovereign risk contagion. Central bankers, including the Federal Reserve, are avid practitioners. ^[1] As the European Monetary Union (EMU) grapples with bank capital adequacy and sovereign credit issues, it’s a pretty good assumption that European economists are using matrix theory, too.

      The 2008 failure of Lehman Brothers (considered a local behaviour) jumped the boundaries of its own (large) matrix and became a universal factor independent of the pre-existing probability distributions of the other matrices. In the vernacular, a falling tide lowers all boats. But we want to know whether the factors involved in the Lehman failure (including the behaviour of the US government) were random noise to the FX market, or an exogenous factor (out of left field), or maybe an endogenous (inherent) factor in the FX matrix. Some correlations are, after all, just coincidence. Millions of random correlations exist in the financial world. We want to know how much weight to give Big Financial Institution Failure and Government Refusal to Intervene in the FX matrix.

      Lehman Brothers declared bankruptcy on 15 September 2008. Before then, the rumour mill was already active with word of the bankruptcy. We heard of European banks closing lines to Lehman several months before the final collapse. Lehman wasn’t the only factor in the FX market, but consider the trajectory of the dollar index. It had bottomed in March 2008 (at 70.698) and put in a second low in July (71.314) but then rose to 80.375 by 11 September. Over the next week, encompassing the Lehman debacle, the dollar index fell to what turned out to be an intermediate low of 75.891 on 22 September. The index then rose to a high of 88.463 by late November. The dollar’s rise was a surprise to those FX players accustomed to selling the currency of a country in trouble. The dollar’s use as a safe-haven trumped the negativity of Lehman’s bankruptcy and therefore gave the safe-haven status more weight in the matrix.

      The dollar index was already on the upswing when Lehman went bankrupt and the sell-off on the actual bankruptcy news was very short-lived; only one week. Smart FX analysts were detecting that overall financial market risk aversion was in play over the summer of 2008 and the dollar kept rising. The Lehman bankruptcy in hindsight was an exogenous shock, mostly because it was inconsistent with our assumptions about how the US government behaves and how the financial sector had behaved in the past. Up until the very last minute, some observers expected a bailout like the one of Long-Term Capital in 1998, and a return to risk positioning. But once the news was digested, the FX market returned to its previous mode of shunning risk. At that time (and up to the S&P downgrade of the US sovereign rating in August 2011), to be risk averse was to buy the safe-haven dollar.

      Why the matrix is useful

      The Lehman case is an example of a factor from a relatively distant corner of the FX matrix wending its way to FX prices themselves via interbank liquidity and interest rates, coupled with a major change in perception of the banking sector and US government – the Establishment. We would normally not expect an exogenous variable like the bankruptcy of a single financial institution to have such broad-reaching effects. It remains a puzzle why the rumours of the bankruptcy and then the event itself caused such an exaggerated reaction among international investors, sending them rushing to the safe-haven dollar.

      The Lehman bankruptcy marks a dividing line in FX history between a time when price determinants encompassed an already wide range of factors to a new period in which price determinants range even more widely and reach into even more unexpected corners. This is why the concept of risk appetite and risk aversion is so useful – today, just about any exogenous variable has the potential to fly over the standard cause-and-effect factors and land on FX.

      In the next sections of this chapter, we see examples of the new power of exogenous variables in FX. Pre-Lehman, for example, a popular uprising in an emerging market seldom had any effect on FX prices. But in 2011, regime change in North Africa had a pro-dollar effect, but in limited and varying ways. The effect of the Egyptian change differed from the effect of the change in Libya, in part because of Libya’s role as an oil producer and the presence of foreign military forces. The addition of geopolitical events to the universe of FX determinants through the medium of risk appetite/risk aversion has made the FX world a vastly more complicated place in just a few years.

      We now have to enter into consideration issues to which we used to give little thought, such as what will happen to oil prices if and when the current Venezuelan leader Chavez leaves office? We can suppose that the oil market will respond but we do not know whether a resulting rise or fall in oil prices will be correlated with the dollar – or the euro. The matrix helps us to make sense of all these interconnecting strands and thus of the FX market.

      The primitive matrix

      When an economist sits down to map out a hierarchy of fundamental factors that determine exchange rates, he may come up with something like Table

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