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“How To Get Inside Knowledge On FX Market Dynamics” 1/ Some Background – Is there any certainty in FX Markets? There is no certainty in financial forecasting, in particular the large and liquid FX capital markets, other than prognosticators will continue to make extreme comments that reflect the fear, uncertainty and doubt inherent in FX markets. Let’s consider first of all market commentary on the uncertainty of market directions. 1.1 As general comments on the chaotic and complex movements of markets, consider…

• J.P. Morgan, the turn-of-the-century financier whose name still echoes through American finance in J.P. Morgan and Morgan Stanley, had a stock answer when asked him what the market would do: "It will fluctuate." [Ref 1]

• Lloyd Bentsen, former U.S. Treasury Secretary, also has a stock answer when asked what financial

markets will do: "If I knew, I wouldn't be standing here. I'd be calling from my yacht." [Ref 1] Nevertheless in long (secular)bull markets as they approach their peak there usually is a vocal and vociferous group of market authorities assuring investors and the public that the old rules of thumb are no longer valid, that rising “trends” will continue or at least hold their investors value…

• The most famous of these predictions came just before the market crash of 1929, when Yale professor Irving Fisher reassured investors that prices had attained a "permanently high plateau." [Ref 1]

1.2 But when market sentiment turns bearish, or enters periods of uncertain cyclical movement, many simply give up their previous claims for their predictive capability, several such comments came out of market authorities and commentators after the recent end of the U.S. dollar secular bear market currency strength, in its major decline from October 2004 to Feb 2005…

• Alan Greenspan, the Federal Reserve chairman, said in 2002: "There may be more forecasting of exchange rates with less success than almost any other variable." … and in 2004 "Forecasting exchange rates has a success rate no better than that of forecasting the outcome of a coin toss” [Ref 2]

• Mervyn King, Governor of the Bank of England, said in 2004: "I have no idea where exchange rates will

go in the future and I have no intention of ever starting to forecast exchange rates. That's a mug's game." [Ref 3]

• Chris Giles, FT’s Economics Editor said in December 2004 “ So here are two early new year resolutions for

currency analysts. … When the dollar goes down, they should say: "At the old price there were more sellers than buyers." When asked where it will go next, they should say: "I haven't the foggiest." … Forecasting daily, weekly or monthly exchange rate movements is a known unknown. Studies have shown that the best forecast of today's exchange rate is yesterday's level” [Ref 3]

From 1.1 and 1.2 we can make a hypothesis:-

Being 100% Certain about a Future Market Value = False [Rule No 1]

It seems that we can be fairly confident that being certain about any long term market prediction is unsafe. In point of fact many traders might build a contrarian trading strategy that factors a certain amount of hedge trading in the opposite direction when advice begins to manifest itself that Secular Bull or Bear runs are likely to continue despite some emerging evidence to the contrary. 1.3 Though once financial markets become more settled again these same market authorities and commentators do make their opinion on market direction known and frequently they do move markets accordingly, as trader’s factor the “explanations” these statements make into their market “risk” positions…

• Alan Greenspan, the Federal Reserve Chairman said in Feb 2005: “Arguably, however, it has been economic characteristics special to the United States that have permitted our current account deficit to be

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driven ever higher, in an environment of greater international capital mobility. In particular, the dramatic increase in underlying growth of U.S. productivity over the past decade lifted real rates of return on dollar investments. These higher rates, in turn, appeared to be the principal cause of the notable rise in the exchange rate of the U.S. dollar in the late 1990s.” [Ref 4]

The implication in the statement above is that FX rates are at least partly explained (i.e., are predictable), if you have the “right” macro-economic model. In this case U.S. productivity was the “principal” explanatory variable, and if this variable was not seriously undermined in 2004, then neither should be the value of the U.S. dollar. In the late part of 2004 Economists had been “over-concerned” over the “serviceability” of U.S. trade deficits apparently. As FX Economists thus re-considered their “fundamentals” and turned their sentiment around through the first two quarters of 2005. At that time several market commentators, e.g., from Reuters to independent “Bloggers”, reflected that this specific statement did in fact “move the dollar” [see Ref 5]. See figure 1 to see EURUSD price movements around this time.

Figure 1 EURUSD – I Year Actual Chart by eFXSysTM

It is not my point that market authorities and commentators are inconsistent or misleading, e.g., Alan Greenspan in his earlier quoted remarks [Ref 2] qualified his comments in detail and are broadly consistent with his later comments mentioned above [Ref 4]. The key point is rather that Financial markets are very influenced by human sentiment, which is capricious, since the choice of the “right” explanatory variable (e.g., Deficit or Productivity figures), and the failure to place comments in the full context of a market situation or commentary leads to simplistic understandings. It is understandable though in that Traders want simple directions and advice, since explaining the “detail” of complex & chaotic phenomenon can lead to no practical market insight as to whether to place a “buy or sell” trades. If we are to get more direction we need to look at mathematical models that offer us more reliable predictive power. We need to develop models that address these issues of human sentiment if we have hope of understanding FX market dynamics, they will need to model the reality of complex and chaotic phenomena. 2/ Modeling the Chaos & Complexity of Financial Markets 2.1 Statistics is at the core of much of financial modeling. It is used to circumvent the unfortunate fact that we are challenged in capturing the complexity of the markets. Financial markets involve thousands of agents (“Traders”) whose rules and reasons (“heuristics”) for doing things are partly hidden from us or based on dynamic circumstances that are difficult to track.

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Furthermore the businesses implicit in the instruments traded in the markets are themselves at another level of complexity and chaos that we can’t hope to fully capture. Thus conventional wisdom based mathematical analysis has contented itself with gross characterizations of market forces and attributed a large part of the events we see to non-deterministic random noise. In general many quantitative financial models involve characterizing market forces as random variables with certain statistical distributions and many of the interactions between these variables are often assumed to be non existent or of a certain rigid form so that the analysis is tractable. A commonly held belief about financial markets is that they are “efficient” which is often taken to mean that predictability cannot be profitably exploited in a trading heuristic on the basis of publicly available information once the proper discounting for risk is done. A narrower statement of this basic belief is the Random Walk Hypothesis which proposes that the best prediction for future values of a series is the last observed value. If these hypotheses are true it is easy to see that statistical modeling will not easily yield useful predictive results. However the premise of the efficient market simplifying hypotheses is suspect to say the least. Traders are, ultimately, intelligent (and emotional) agents whose specific motivations on individual trades, whilst may be obscure, are nevertheless seen occasionally to manifest in predictable manners to certain external influence, e.g., consensus actions taken on basis of fear, uncertainty and greed - the so called “Herd instinct” or Avalanche effect. Individual Traders in financial markets are also subject to learning processes and intelligent action that is purposefully directed, e.g., Risk Management/Profit Taking. Financial markets thus may indeed have deep non-linear information layers or patterns that can be filtered from the “noise”, though these “signals” may be intermittent and difficult to detect. The fact that markets are not always 100% efficient is now well established, but is there enough “signal”, and do we have enough “intelligence” to detect it and exploit it? This is the key issue. For some big investors this is not in question.

• Warren Buffet: Has made several comments in this area, such as; [Ref 6]

o “I'd be a bum in the street with a tin cup if the markets were always efficient” o “Investing in a market where people believe in efficiency is like playing bridge with someone who

has been told it doesn't do any good to look at the cards.” o “The professors who taught Efficient Market Theory said that someone throwing darts at the stock

tables could select stock portfolio having prospects just as good as one selected by the brightest, most hard-working securities analyst. Observing correctly that the market was frequently efficient, they went on to conclude incorrectly that it was always efficient”

2.2 Thus our hypothesis is that the dynamical systems comprising the financial markets require more complex and dynamic models than have been tried previously. For instance much statistical modeling and hypothesis testing in the financial markets has traditionally been done with linear models. Partly this has been done for practicality, linear models have the best developed and understood techniques for specification, estimation and testing, and given processing/memory capability of computers in recent past realistic in algorithmic implementation performance constraints. Another possibility for capturing complexity may lie in estimating larger models. The problem with larger models, however, is that the potential for capturing extra complexity does not come for free. Larger models mean more parameters which means either that we need more data to estimate the parameters (an issue of both data availability and computational effectiveness) or we are less certain in our estimates and thus in the overall usefulness of the model. However we have argued that it is possible or even likely that many important relationships in finance are nonlinear, and that no simple transformation can be made to make them practically linear. Furthermore patterns of the past may not persist into the future, in other words Market characteristics or Modality may “evolve”. A more dynamic “learning” non-linear model is required that can handle this inherent complexity and chaotic behavior. Fortunately in recent years there has been an explosion in research and interest in this area. 3/ Complexity & Chaos Theory – Some new insights and emerging economic models 3.1 What have Mathematicians and Financial Theorists, taking into account latest insights into complexity & chaos theory, recently been saying about Predictability and Trends in Financial Markets?…

• John Allen Paulos in his landmark book “A Mathematician Plays the Market” in 2003 argued that whilst most financial theorists doubt that traditional technical analysis will make more money than investing into

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an index linked fund, there is nevertheless “tantalizing evidence for the effectiveness of momentum strategies or short-term trend-following…result in moderate excess returns, having done so over the years, their success is not the result of data mining”. And furthermore… “…they do seem to point to behavioral models and psychological factors are relevant”. [Ref 7]

• Benoit Mandelbrot in his 2004 revolutionary re-evaluation of the standard tools and models of financial

theory “The (mis)Behavior of Markets”, wrote “Conventional financial theory assumes that variation of prices can be modeled by random processes that, in effect, follow the simplest “mild” pattern, as if each uptick or downtick were determined by the toss of a coin…by that standard, real prices misbehave very badly.” Furthermore he writes … “What is an investor to do? Brokers often advise their clients to buy & hold. Focus on the average increases in stock prices they say. Do not try to “time the market”, seeking the golden moment to buy & sell. BUT THIS IS WISHFUL THINKING. What matters is the particular not the average. Some of the most successful investors are those who did, in fact, get the timing right. [Ref 8]

• Stephen R Waite in his 2004 book “Quantum Investing” makes several good points…[Ref 9]

o “Conventional financial theory bears a strong resemblance to Newtonian or Classical Physics. It

describes a hypothetical state where the future is known rather than uncertain. Just as it is in the Quantum world of atoms and subatomic particles, uncertainty is pervasive in the investment world”

o “The essence of investment is “the hidden future”…If the road ahead was always clear, we would readily adjust to what we see and tomorrows stock prices would always equal today’s. In their calmer moments, investors recognize their inability to know what the future holds. In moments of extreme panic or enthusiasm, however, they become remarkably bold in their predictions. During such times, they act as though uncertainty has vanished and the outcome beyond doubt….A switch from doubt to certainty defines major tops and bottoms in the stock market”

o “Complexity theorists have discovered that wild fluctuations and extreme volatility in financial markets – things that cannot be explained by mainstream models – can be easily produced in non-conventional economic models by assuming that heterogeneous (multi-direction) expectations and beliefs suddenly become homogeneous (single-directional), the result is extreme volatility”

o “In the future, computer-based agent models are likely to help researchers better understand how complex, real-world financial markets behave. These models simulate the real world and can generate patterns of stock market booms and busts.”

From 1.2, 2.1, 2.2 and above we can make another hypothesis:-

Being 100% Uncertain about a Future Market Value = False [Rule No 2] It seems that we can be fairly confident that being entirely uncertain, a piori, about any short term market prediction is ignoring opportunity – particularly if we can see risk clearly not being managed efficiently in a market. The timing then of trades can become safer. Trading in these situations when profitable will make the market more efficient. E.g., quite recently in April 2005 the Board of Governors of the Federal Reserve System produced an “International Financial Discussion Paper” that modeled the predictive ability of Order Flow (buyer initiated orders net of seller initiated orders) on Exchange Rate Dynamics [Ref 10]. This paper found that not only is their indeed a statistically significant correlation in the very short term (so called very high frequency trading) but also this evidence is inconsistent with the simple efficient markets view. From such a “guarded and conservative source” this is a very interesting finding, particularly given Mr Greenspan’s earlier remarks in 2002 on the predictability of FX [Ref 2]. Getting insight into Order Flow is a tough issue though. Large trading houses do have this “inside” information, to a limited extent through knowledge of the trades they are managing. The issue becomes one of “can we build a model of Order Flow, disentangling it from very high frequency market data?” To do so we will have to contend with building a view of the financial markets in very short time scales and with a market model that seeks to build a representation of “price” based on discrete and probabilistic views of Order Flow. 4/ Quantum Economic models - eFXSysTM 4. 1 Given the above and from our Hypothesized Rules No 1 & 2, can we find a mathematical model that handles this uncertainty in the small scale structure of the market? In Quantum Physics we have such a mathematical paradigm.

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As a commentator on “Quantum Economics” recently said …[Ref 11]

• François-René Rideau” “In elementary physics, scientists know they cannot pretend they have certainty anymore; actually, uncertainty about which events happen to a particle is deeply rooted in the laws of quantum physics. The best way to express knowledge about a particle is through “wave functions” that describe the probability of its possible states, depending on the infinitely many sequences of interactions in which it may or may not participate. Of course, when it appears that a particle effectively interacted with other particles in an observable way, then this new knowledge corresponds to an update in the wave function that describes the particle (or set of particles); the technical term is that the wave packet collapses. In quantum physics, information has a cost, because you may only extract useful information through interaction, which not only costs you energy, but also modifies the system in ways that void other information.”, …and “If economics has to have any future as a science, it will probably be as Quantum Economics: a science describing human interrelations in terms of uncertain discrete transactions, where information matters, where it has a value and a cost; where it is acquired by interaction, and used by interaction; where it is unknown until the interaction happens, and where it is uncertain when the interaction will happen and what it will be, but where it is certain that the interaction will eventually happen, leading to an inevitable according reduction of the wave packet”

This “transactional view” can be expressed simply in trading terms. We can say we know the value of an asset only at the time it is quoted, and the quote itself is a function of expected future valuations, which is dependent, in part, if the transaction is executed (or not) at that price. Consider the following commentary in this respect…

• George Soros: “The prevailing wisdom holds that markets tend toward equilibrium--i.e., a price at which willing buyers and sellers balance each other out. That may be true of the market in widgets, but it is emphatically not true of financial markets. In financial markets a balance is difficult to reach because financial markets do not deal with known quantities; they try to discount a future that is contingent on how they discount it at present.” [Ref 12]

• John Walker: Founder of Autodesk Inc said: “Saying “The market was up 15 points today'' is

meaninglessness layered on meaninglessness. The market is neither up nor down. The market is a place where discrete transactions occur--a surging organic sea of buyers and sellers with different goals, opinions, and strategies, who momentarily and unpredictably agree to exchange specific assets. We aggregate these transactions into the abstraction of a continuum of price. We aggregate a selection of these abstracted continua into an average price. We then assign meanings to the action of this average, and impute its behavior as being representative of the market.” [Ref 13]

In Quantum Physics we have one interpretation of how to understand the value of a wave function that is based on what is known as the “Many Worlds” view, where the wave function at time t is a probabilistic “composite” of all of the possible wave function values at time t. However how can we get a view of this composite? If we are able to do so we would have genuine “inside” knowledge as to the movement of markets. Sigma Delphi has built eFXSysTM, using the advanced techniques of Artificial Intelligence (“AI”), such techniques are becoming increasingly popular in this domain [see Ref 14, 15]. This eFXSysTM application service is based on the integration of AI techniques with a “Quantum Economics” model, a non-conventional model that simulates market agents as individual “worldviews”. Each worldview is an independent trading simulation that comprises 4 factors;

1. Currency pairs - Tracking pairs comprising key crosses of: USD, EUR, JPY, GBP, CHF, NZD, CAD and AUD 2. Market modality – Modeling a variety of market characteristics/sentiments including; Secular Bear/Bull,

Cyclical Bear/Bull, Range, Trend, etc. 3. Forecast horizon - Forecasting in 1,2,4,8,16 hour “horizons”, each of 15 intervals (e.g., 1 hour horizon Forecast

is comprised of fifteen (15) 4 minute intervals) 4. Forecast method. – We use a combination of Neural Network, Technical Analysis and

Fundamental/News/Judgment Bias algorithms to make biasing judgment on market future direction Further information can be found on this technology in Sigma Delphi’s white paper “Financial Market Modeling using Hybrid Learning Networks & Expert Systems” available on its website www.sigmadelphi.com [Ref 16] The current eFXSysTM application service models 2,520 such worldviews updating every 4 minutes using very high frequency market data input in sub-minute intervals. The results of each worldview can be individually monitored (as shown in Figure 2 below) or the many worldviews can be monitored simultaneously. Examining the current active recommendations of many worldview helps Traders to get insight into circumstances where heterogeneous (multi-direction) expectations and beliefs suddenly become homogeneous (single-directional),

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resulting is extreme volatility in a particular direction. Such circumstances are exploitable by Traders who wish to anticipate market movement profitably. Take a look at the following screenshot from eFXSysTM Alert Composite page (following page). This page display many “worldviews” or simulating many individual trading agents buy, hold, sell transactions. When these actions are taken together as a composite of many trades that comprise a real market, we can make a judgment if the overall movement is heterogeneous or homogeneous. In this example we see many worldviews of the USDJPY currency pair all declaring their interaction “advice” as a homogeneous “sell”. Some four hours later the subsequent screenshot was taken (on following page). A significant movement is noted. Martin Ciupa Managing Director & Founder Sigma Delphi Ltd 15th Sept 2005 References Ref 1: http://www.j-bradford-delong.net/OpEd/slategreenspan3.html Ref 2: http://www.federalreserve.gov/boarddocs/hh/2002/july/testimony.html Ref 3: http://news.ft.com/cms/s/779564ce-39a1-11d9-b822-00000e2511c8.html Ref 4: http://usinfo.state.gov/usinfo/Archive/2005/Feb/04-916926.html Ref 5: http://www.williampolley.com/blog/archives/2005/02/carrying_the_do.html Ref 6: http://www.global-investor.com/quote/2710/Warren-Buffett Ref 7: John Allen Paulos, “A Mathematician Plays the Market”, Penguin Books 2003 Ref 8: Benoit Mandelbrot & R L Hudson, “The (mis)Behaviour of Markets”, Basic Books 2004 Ref 9: Stephen R Waite “Quantum Investing”, Texere, 2004 Ref 10: http://www.federalreserve.gov/pubs/ifdp/2005/830/default.htm Ref 11: http://www.livejournal.com/users/fare/34676.html Ref 12: http://www.geocities.com/ecocorner/intelarea/gs21.html Ref 13: http://www.fourmilab.ch/autofile/www/subsectionstar2_73_4_2.html Ref 14: http://www-psych.stanford.edu/~andreas/Misc/DavidsonNewScience.html Ref 15: http://www-psych.stanford.edu/~andreas/Misc/DavidsonNewTools.html Ref 16: http://www.sigmadelphi.com/article_en.aspx

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Figure 2 eFXSysTM Audit Trail of Past 10 Trades (Trade Worldview EURUSD/Range/SDEFNJTA)

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