The Foreign Exchange Matrix. Barbara Rockefeller

that affect it can in theory be checked off to reach an understanding of what affects that rate. Note that the factor list is incomplete – we could add dozens of other fundamentals such as labour market flexibility, tax rates, etc.

      Table 1.1 – fundamental factors

      But if an economics-minded trader tries to apply incoming data to real-time trading, very quickly he discovers that economic fundamentals are sadly insufficient to explain FX price action – you also need the institutional context, as in the Lehman case and European sovereign risk contagion in 2009-2011. So now we need the mindset of political economy (as it was named in the 19th century). Table 1.2 shows how this grid might look.

      Table 1.2 – institutional factors

      For example, the European Central Bank (ECB) famously has a single mandate – to maintain price stability – whereas the US Federal Reserve has an additional mandate, to promote employment. In addition, as the issuer of the reserve currency, the US has a unique responsibility to lend to other central banks in time of crisis. Meanwhile, the euro is a currency without a state, which circles around to public deficits and to several factors in the economic factors spreadsheet, like the terms of trade.

      Just when the analyst is trying to figure out how to combine the two sets of factors, he realises something else is missing, and it’s big – the goals and attitudes of the FX market players themselves. In this grid (Table 1.3) it’s the players that are on the y-axis rather than the currencies.

      Table 1.3 – market player factor

      In short, the fundamentals matrix introduced first doesn’t include all the factors you need to analyse the FX market, and by the time you add institutional factors and market player factors, you need a three-dimensional matrix more like a Rubik’s cube than a spreadsheet.

      No sooner can you fix on a matrix like the one above than something will come along and show its inadequacy, if only because financial markets evolve. China opened a gold and precious metals commodity futures exchange in 2011, and is exploring reserve diversification not only in other currencies, especially the euro, but also in the form of bigger equity investments at the expense of US Treasury and Agency paper. European peripherals may default on sovereign debt, an event that has never happened in quite the same way throughout all history because we never before had a currency union quite like the EMU. So even if you build a system for analysing the FX market, you have to be prepared for it to be out of date, or for a factor you didn’t think to take account of to occur, quite soon.

      As for two-dimensional charts that appear to illustrate that Factor X is highly correlated with Currency Outcome Y and must be causative, this is often misleading. Two-dimensional charts are deceptive – correlation is not always causation, and worse, they result in emphasis on outcomes instead of processes. The salient question in FX analysis is in fact: how do traders rank various multiple variables and events to determine whether they want to buy or sell a currency?

      Charts in two dimensions lack explanatory power and analytical depth – they don’t enable us to answer the question just posed. Worse, they are static. It would be nice to be able to display interactive, dynamic effects, but that would take video, and even then it’s not clear we could see cause-and-effect clearly.

      We can try another matrix. Along one axis are the players in the FX market and along the other axis are the factors that influence FX prices. The grid should be imagined like a standard spreadsheet so that a big change in one cell is transmitted to other cells.

      For example, we know that not only current interest rate differentials influence FX traders and investors, but also interest rate expectations. When a big change in expectations arises, as when Federal Reserve Chairman Bernanke announced in August 2010 that a second round of quantitative easing was pending, it dominates the market. When news of this magnitude is released we can grey out all the other variables on the matrix until bigger news about a key factor comes along. Because the graphic process is not a dynamic one, we need to prepare additional matrices and throw away the old ones as conditions change.

      As a practical matter, you could combine and simplify to produce a master matrix that looks something like the one shown in Table 1.4.

      Table 1.4 – combined master matrix

      The two Xs mark the primary factors for just two types of FX market participants, the position trader and the high-frequency trader. The position trader, which includes the deep pockets of sovereign wealth funds and hedge funds (as well as hedgers from the equity and bond markets), will probably analyse the market using trend-following technicals, while high-frequency traders will use short-term breakout and momentum technicals. They may be on opposite sides of the same trade. Fund managers, whether of money market instruments, stocks or bonds, may have their antennae tuned to GDP growth and capital flows, while the carry trader cares about only thing; the stability of interest rate differentials.

      The matrix process

      We are still missing an important component, namely the weights to attach to each player and his favoured factor, and the vectors by which the factor invades another player’s consciousness. In the set of matrices shown below, we start out with an allocation of the importance of various factors to professional bank traders. We chose bank traders as the leading edge of FX price determination – these are the players who cause directional breakouts based on their interpretation of factors. Don’t forget that the professional bank traders are informed by the trading decisions of hedge funds and others who have a global worldview and make value judgments; the bank trader is not going to fight the tape if he sees sentiment shifting in a particular direction.

      Let’s say that over the summer of 2010, professional traders attributed a weight of 50% to interest rates and expected interest rates (Period 1 in the tables below). We can quibble over the weights, but let’s take 50% for the minute.

      Period 1 – June 2010 to end-August 2010

      Then Ben Bernanke pre-announced QE2 at the Kansas City Fed summit in Jackson Hole, Wyoming in August 2010. The focus immediately shifted to interest rate expectations, which now get a much higher weight in Period 2. The other players, seeing the dollar fall on the pros building a wider differential against the dollar, increased their weight to that factor, too. The weight attributed by the pro bank traders bled into the other players’ evaluations of factors.

      Period 2 – Quantitative Easing 2: end-August 2010 to end-December 2010

      In Period 3, Eurostat released data showing that inflation was over the ECB cap of 2%, raising the spectre of the ECB tightening rates faster than the US. The interest rate expectation factor thus got stronger, the dollar fell more, and because the FX market is reactive, all the other players imputed a higher weight to this factor, too.

      Period 3 – euro zone inflation rises: January 2011

      At end-January 2011, Tunisia experienced rioting and the government fell (Period 4). Civil unrest broke out in Egypt, and in a single day, geopolitical factors became the dominant factor. Oil rose, returning the commodity correlation story to the fore.

      Period 4 – revolution in Egypt, 28 January 2011