What moves the markets? Part I

It's a key assertion of the Efficient Markets Hypothesis that markets move because of news and information. When new information becomes available, investors quickly respond by buying and selling to register their views on the implications of that information. This is obviously partially true -- new information, or what seems like information, does impact markets.

Yesterday, for example, US Treasury Secretary Timothy Geithner said publicly that, despite the ominous economic and financial climate, there is “absolutely” no chance that another United States financial institution will fail. (At least that's what the New York Times says he said; they don't give a link to the speech.) That was around 10 a.m. Pretty much immediately (see the figure below) the value of Morgan Stanley stock jumped upwards by about 4% as, presumably, investors piled into this stock, now believing that the government would step in the prevent any possible Morgan Stanley collapse in the near future. A clear case of information driving the market:

Of course, this just one example and one can find further examples, hundreds every day. Information moves markets. Academics in finance have made careers by documenting this fact in so-called "event studies" -- looking at the consequences for stock prices of mergers, for example.

But I'm not sure how widely it is appreciated that the Efficient Markets Hypothesis doesn't only say that information moves markets. It also requires that markets should ONLY move when new information becomes available. If rational investors have already taken all available information into account and settled on their portfolios, then there's no reason to change in the absence of new information. Is this true? The evidence -- and there is quite a lot of it -- suggests very strongly that it is not. Markets move all the time, and sometimes quite violently, even in the total absence of any new information.

This is important because it suggests that markets have rich internal dynamics -- they move on their own without any need for external shocks. Theories which have been developed to model such dynamics give markets with realistic statistical fluctuations, including abrupt rallies or crashes. I'm going to explore some of these models in detail at some point, but I wanted first to explore a little of evidence which really does nail the case against the EMH as an adequate picture of markets "in efficient equilibrium."

Anyone watching markets might guess that they fluctuate rather more strongly than any news or information could possibly explain. But research has made this case in quantitative terms as well, beginning with a famous paper by Robert Shiller back in 1981. If you believe the efficient markets idea, then the value of a stock ought to remain roughly equal to the present value of all the future dividends a stock owner can anticipate getting from it. Or, a little more technically, real stock prices should, in Shiller's words, "equal the present value of rationally expected or optimally forecasted future real dividends discounted by a constant real discount rate." Data he studied suggest this isn't close to being true.

For example, the two figures below from his paper plot the real price P of the S&P Index (with the upward growth trend removed) and of the Dow Jones Index versus the actual discounted value P* of dividends those stocks later paid out. The solid lines for the real prices bounce up and down quite wildly while the "rational" prices based on dividends stay fairly smooth (dividends don't fluctuate so strongly, and calculating P* involves taking a moving average over many years, smoothing fluctuations even further).

These figures show what has come to be known as "excess volatility" -- excess movement in markets over and above what you should expect on the basis of markets moving on information alone.

Further evidence that it's more than information driving markets comes from studies specifically looking for correlations between new events and market movements. On Monday, October 19, 1987, the Dow Jones Industrial Average fell by more than 22% in one day. Given a conspicuous lack of any major news on that day, economists David Cutler, James Poterba and Larry Summers (yes, that Larry Summers) were moved soon after to wonder if this was a one-off weird event or if violent movements in the absence of any plausible news might have been common in history. They found that they are. Their study from 1989 looked at news and price movements in a variety of ways, but the most interesting results concern news on the days of the 50 largest singe day movements since the Second World War. A section of their table below shows the date of the event, how much the market moved, and the principle reasons given in the press for why it moved so much:

Within this list, you find some events that seem to fit the EMH idea of information as the driving force. The market fell 6.62 percent on the day in 1955 on which Eisenhower had a heart attack. The outbreak of the Korean War knocked 5.38% off the market. But for many of the events the press struggled mightily to find any plausible causal news. When markets fell 6.73% on September 3, 1946, the press even admitted that there was "No basic reason for the assault on prices."

[Curiously, I seem to have found what looks like a tiny error in this table. It lists the outbreak of the Korean War (25 June, 1950) as explaining the big movement one day later on June 26, 1950. But then it lists "Korean war continues" as an explanation for a movement on June 19, 1950, five days before the war even started!]

Cutler and colleagues ultimately concluded that the arrival of news or information could only explain about one half of the actual observed variation in stock prices. In other words, the EMH leaves out of the picture something which is roughly of equal importance as investors' response to new information.

More recently in 2000, economist Ray Fair of Yale University undertook a similar study which found quite similar conclusions. His abstract explains what he found quite succinctly:

Tick data on the S&P 500 futures contract and newswire searches are used to match events to large five minute stock price changes. 58 events that led to large stock price changes are identified between 1982 and 1999, 41 of which are directly or indirectly related to monetary policy. Many large five minute stock price changes have no events associated with them.

 All in all, not a lot of evidence supporting the EMH view on the exclusive role of information in driving markets. Admittedly, these studies all have a semi-qualitative character based on history, linear regressions and other fairly crude techniques. Still, they make a fairly convincing case.

In the past few years, some physicists have taken this all a bit further using modern news feeds. More on that in the second part of this post. The conclusion doesn't change, however -- the markets appear to have a rich world of internal dynamics even in the absence of any new information arriving from outside.

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Why game theory is often useless...

Economic theory relies very heavily on the notion of equilibrium. This is true in any model for competitive equilibrium -- exploring how exchange can in principle lead to an optimal allocation of resources -- or more generally in the context of game theory, which explores stable Nash equilibria in strategic games.

One thing physicists find wholly unsatisfying about equilibrium in either case is economists' near total neglect of the crucial problem of whether the agents in such models might ever plausibly find an equilibrium. You can assume perfectly rational agents and prove the existence of an equilibrium, but this may be an irrelevant mathematical exercise. Realistic agents with finite reasoning powers might never be able to learn their way to such a solution.

More likely, at least in many cases, is that less-than-perfectly rational agents, even if they're quite clever at learning, may never find their way to a neat Nash equilibrium solution, but instead go on changing and adapting and responding to one another in a way that leads to ongoing chaos. Naively, this would seem especially likely in any situation -- think financial markets, or any economy as a whole -- in which the number of possible strategies is enormous and it is simply impossible to "solve the problem" of what to do through perfect rational reflection (no one plays chess by working out the Nash equilibrium).

A brilliant illustration of this insight comes in a new paper by Tobias Galla and Doyne Farmer. This is the first study I've seen (though there may well be others) which addresses this matter of the relevance of equilibrium in complex, high-dimensional games in a  generic way. The conclusion is as important as it is intuitively reasonable:

Here we show that if the players use a standard approach to learning, for complicated games there is a large parameter regime in which one should expect complex dynamics. By this we mean that the players never converge to a fixed strategy. Instead their strategies continually vary as each player responds to past conditions and attempts to do better than the other players. The trajectories in the strategy space display high-dimensional chaos, suggesting that for most intents and purposes the behavior is essentially random, and the future evolution is inherently unpredictable.

In other words, in games of sufficient complexity, the insights coming from equilibrium analyses just don't tell you much. If the agents learn in a plausible way, they never find any equilibrium at all, and the evolution of strategic behaviours simply carries on indefinitely. The system remains out of equilibrium.

A little more detail. Their basic approach is to consider general two player games between, say, Alice and Bob. Let each of the two players have N possible strategies to choose from. The payoff matrices for any such game are NxN matrice (one for each player) giving the payoffs they get for each pair of strategies being played. The cute idea in this analysis is to choose the game randomly by selecting the elements of the payoff matrices for both Alice and Bob from a normal distribution centered on zero. The authors simply choose a game and simulate play as the two players learn through experience -- playing strategies from their repertoire of N possibilities more frequently if those strategies give good results.

With N = 50, the results show clearly that many games do not ever settle into any kind of stable behaviour. Rather, no equilibrium is ever found. The typical dynamics is reflected in the figure below, which shows the difference in payoffs to the two players (Alice's - Bob's) over time. Even though the two agents work hard to learn the optimal strategies, the complexity of the game prevents their success, and the game shows no signs whatsoever of settling down:

As the authors note, this kind of rich, complex, ongoing dynamics looks quite similar to what one sees in real systems such as financial markets (the time series above exhibits clustered volatility, as do market fluctuations). There are periods of relative calm punctured by bouts of extreme volatility. Yet there's nothing intervening here -- no "shocks" to the system -- which would create these changes. It all comes from perfectly natural internal dynamics. And this is in a game with N = 50 strategies. It seems likely things will only grow more chaotic and less likely to settle down if N is larger than 50, as in the real world, or if the number of players grows beyond two.

Hence, I see this as a rather profound demonstration of the likely irrelevance of equilibrium analyses coming from game theory to complex real world settings. Dynamics really matters and cannot be theorized out of existence, however hard economists may try. As the paper concludes:

Our results suggest that under many circumstances it is more useful to abandon the tools of classic game theory in favor of those of dynamical systems. It also suggests that many behaviors that have attracted considerable interest, such as clustered volatility in nancial markets, may simply be specific examples of a highly generic phenomenon, and should be expected to occur in a wide variety of different situations.

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Efficiency versus stability

UPDATED BELOW

I had an opinion piece published today in Bloomberg Views looking at the relationship between market efficiency and stability, a topic which hasn't received much attention in the economics literature until recently. The point of the essay was to explore two distinct recent studies which suggest that adding more derivative instruments to markets tends to make them less stable, even if they do push markets toward the ideal of market completeness and efficiency.

I wanted to make available here some further technical information on the two studies I mentioned, but as publication arrived very quickly and I've been pressed with other deadlines I haven't yet managed to write the post as I wanted. However, I can at least offer some information with the idea of updating it very shortly (later today, Thursday 25 August).

I've given some extensive discussion of the first study I mentioned, by economists William Brock, Cars Hommes and Florian Wagener, in an earlier post.

The second study by Matteo Marsili is quite technical and relies for parts of its analysis on ideas and techniques imported from physics. I will tomorrow try to give some simplified discussion of the gist of this argument. What makes this particularly fascinating is that it works fully within the confines of standard general equilibrium models, and examines how market stability should evolve as the market approaches the ideal of market completeness. Agents are assumed to be fully rational, there are no problems with asymmetric information, etc. Even here, however, Marsili finds that the equilibrium becomes more and more unstable as the ideal is approached. Efficient markets are also unstable markets.

UPDATE

Marsili's argument is one he has been developing in a series of papers (with various co-authors) over several years. This paper from last year offers what is perhaps the most concise argument. It looks at a market with informed (fundamentalist) traders and non-informed (noise) traders, and shows, first, that the market becomes efficient as the number of informed traders grows. They are assumed in the model to have different kinds of private information about market outcomes, and the market becomes efficient, roughly speaking, once there are enough traders to cover the space of outcomes so all private information gets aggregated into market prices. The paper then introduces a non-informed trader -- a chartist or trend follower -- and shows that this trader has a maximum impact on the market precisely at the point at which it becomes efficient. The conclusion is very much against standard economic thinking:

[The results suggest} that information efficiency might be a necessary condition for bubble phenomena - induced by the behavior of non-informed traders...

Another paper from two years ago approaches the problem from a slightly different angle. This study looks explicitly at how the proliferation of financial instruments (derivatives) provides more means for diversifying and sharing risks and takes the market to an efficient state. However, it finds that this state is what physicists refer to as a "critical state", which is a state characterized by extreme (essentially infinite) susceptibility to small disturbances. Any small noise stirs up huge fluctuations. Again, efficiency trails instability in its wake. As the paper asserts:

This suggests that the hypothesis of Arbitrage Pricing Theory (the notion that arbitrage works to keep market in an efficient state) may not be compatible with a stable market dynamics.

This paper also makes the important point that market stability really ought to be thought of as a public good because well functioning markets do help everyone. But like most public goods, private individuals acting in their own interests will not likely provide it.

Finally, the paper I discussed in the Bloomberg article is from last year and analyses a model set up specifically so as to include the finance sector. It is very much akin to standard general equilibrium models, and includes essentially two components:

1. There are investors who aim to take their current wealth and preserve it (or make it grow) into the future. They do this by investing in various instruments provided by a sector of financial firms. These investors are assumed to be rational and have full information and they invest their wealth optimally over the set of possible investments.

2. There are financial firms who create the investment instruments and take on risks in supplying them. They also act optimally, and they hedge their risks by trading between themselves. Again, the firms are rational and have full information.

Marsili then studies what happens to this world of investors and financial firms optimally making decisions as the number of different financial instruments grows. The first result confirms expectations -- the financial firms are ever more successful in hedging their risks and they can provide the financial instruments more cheaply. Investors can therefore invest more effectively. The market becomes efficient.

But there are also two unexpected consequences. As Marsili describes them,

As markets approach completeness, however, two "unintended consequences" also arise: equilibrium portfolios develop a marked susceptibility to idiosynchratic shocks and/or parameter uncertainty and hedging engenders divergent trading volumes in the interbank market. Combining these, suggests an inverse relation between financial stability and the size of the financial sector...

In other words, the character of the optimum portfolios for both the investors and the financial firms becomes hugely sensitive to tiny shocks to the economy. As the efficient state is approached, these agents have to work ever harder to adjust their holdings to remain in the optimal condition. The market only remains efficient through an ever faster and more vigorous churning of investment positions. This shows up in the hedging done by the financial firms, where the volume of trading required to remain optimally hedged actually becomes infinite as the market reaches efficiency.

All three of these papers show much the same thing -- efficiency bringing instability along with it. But this latter paper may be the most interesting as it shows directly how the size of the financial sector also naturally explodes as this efficient-unstable regime is approached. The effect sounds suspiciously like what has happened in the past 30 years or so with massive growth in the financial industries in most developed nations.

What I find really remarkable, however, is that all of this comes from the very models that economists have been using for a long time to make arguments about market efficiency. Why did it take a physicist to look at what happens to stability at the same point? This seems bizarre indeed.

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What's Efficient About the Efficient Markets Hypothesis?

The infamous Efficient Markets Hypothesis (EMH) has been the subject of rancorous and unresolved debate for decades. It's often used to assert that markets don't need regulation or oversight because they have a remarkable power to get prices just about right (stocks, bonds and other assets have their correct "fundamental values"), and so never get too much out of balance. Somehow the idea still gets lots of attention even after the recent crisis. Financial Times columnist Tom Harford recently suggested that the EMH gets some things right (markets are "mostly efficient") even if it is also supports unjustified faith in market stability. In a talk, economist George Akerlof took on the question of whether the EMH can be seen to have caused the crisis, and concludes that yes, it could, although there are plenty of other causes as well.

Others have defended the EMH as being unfairly maligned. Jeremy Siegel, for example, argues that the EMH actually doesn't imply anything about prices being right, and insists that, recent dramatic evidence to the contrary, "our economy is inherently more stable" than it was before -- precisely because of modern financial engineering and the wondrous ability of markets to aggregate information into prices. Robert Lucas asserted much the same thing in The Economist, as did Alan Greenspan in the Financial Times. Lucas asserted his view (equivalent to the EMH) that the market really does know best:

The main lesson we should take away from the EMH for policy making purposes is the futility of trying to deal with crises and recessions by finding central bankers and regulators who can identify and puncture bubbles. If these people exist, we will not be able to afford them.

That debate over the EMH persists half century after it was first stated seems to reflect tremendous confusion and disagreement over what the hypothesis actually asserts. As Andrew Lo and Doyne Farmer noted in a paper from a decade ago, it's not actually a well-defined hypothesis that would permit clear and objective testing:

One of the reasons for this state of affairs is the fact that the EMH, by itself, is not a well posed and empirically refutable hypothesis. To make it operational, one must specify additional structure: e.g., investors’ preferences, information structure, etc. But then a test of the EMH becomes a test of several auxiliary hypotheses as well, and a rejection of such a joint hypothesis tells us little about which aspect of the joint hypothesis is inconsistent with the data.

So what does the EMH assert?

In trying to bring some order to the topic, one useful technique is to identify distinct forms of the hypothesis reflecting different shades of meaning frequently in use. This was originally done in 1970 by Eugene Fama, who introduced a "weak" form, a "semi-strong" form and a "strong" form of the hypothesis. Considering these in turn is useful, and helps to expose a rhetorical trick -- a simple bait and switch -- that defenders of the EMH (such as those mentioned above) often use. One version of the EMH makes an interesting claim -- that markets always work very efficiently (and rapidly) in bringing information to bear on prices which therefore take on accurate values. This (as we'll see below) is clearly false. Another version makes the uninteresting and uncontroversial claim that markets are hard to predict. The rhetorical trick is to mix these two in argument and to defend the interesting one by giving evidence for the uninteresting one. In his Economist article, for example, Lucas cites as evidence for information efficiency the fact that markets are hard to predict, when these are very much not the same thing.

Let's look at this in a little more detail. The Weak form of the EMH merely asserts that asset prices fluctuate in a random way so that there's no information in past prices which can be used to predict future prices. As it is, even this weak form appears to be definitively false if it is taken to apply to all asset prices. In their 1999 book A Non-random Walk Down Wall St, Andrew Lo and Craig MacKinley documented a host of predictable patterns in the movements of stocks and other assets. Many of these patterns disappeared after being discovered -- presumably because some market agents began trading on these strategies -- but there existence for a short time proves that markets have some predictability.

Other studies document the same thing in other ways. The simplest argument for the randomness of market movements is that any patterns that exist should be exploited by market participants to make profits. The trading they do should act to remove these patterns. Is this true? Take a look at Figure 1 below, taken from a paper from 2008 by Doyne Farmer and John Geanakoplos. Back in the 1970s, Farmer and others at a financial firm called The Prediction Company identified numerous market signals they could use to try to predict market movements in the future. The figure shows the correlation between one such trading signal and market prices two weeks in advance, calculated from data over a 23 year period. In 1975, this correlation was as high as 15%, and it was still persisting at a level of roughly 5% as of 2008. This signal -- I don't know what it is, as it is a proprietary signal of The Prediction Company -- has long been giving reliable advance information on market movements.

One might try to argue that this data shows that the pattern is indeed gradually being wiped out, but this is hardly anything like the rapid or "nearly instantaneous" action generally supposed by efficient market enthusiasts. Indeed, there's not much reason to think this pattern will be entirely wiped out for another 50 years.

This persisting memory in price movements can also be analyzed more systematically. Physicist Jean-Philippe Bouchaud and colleagues from the hedge fund Capital Fund management have explored the subtle nature of how new market orders arrive in the market and initiate trades. A market order is a request by an investor to either buy or sell a certain volume of an asset. In the view of the EMH, these orders should arrive in markets at random, driven by the randomness of arriving news. If one piece of news is positive for some stock, influencing someone to place a market buy order, there's no reason to expect that the next piece of news is therefore more likely also to be positive and to trigger another. So there shouldn't be any observed correlation in the times when buy or sell orders enter the market. But there is.

What Bouchaud and colleagues found (originally in 2003, but improved on since then) is that the arrivals of these order are correlated and remain so over very long times -- even over months. This means that the sequence of buy or sell market orders isn't at all just a random signal, but is highly predictable. As Bouchaud writes in a recent and beautifully written review: "Conditional on observing a buy trade now, one can predict with a rate of success a few percent above 1/2 that the sign of the 10,000th trade from now (corresponding to a few days of trading) will be again positive."

Hardly the complete unpredictability claimed by EMH enthusiasts. To look at just one more piece of evidence -- from a very long list of possibilities -- we might take an example discussed recently by Gavyn Davies in the Financial Times. He refers to a study by Andrew Haldane of the Bank of England. As Davies writes,

Andy Haldane conducts the following experiment. He estimates the results of an investment strategy in US equities which is based entirely on the past direction of the stockmarket. If the market rises in the period just ended, the strategy buys stocks for the next period, and vice versa. In other words, the strategy simply extrapolates the recent trend in the market. The result? According to Andy, if you had been wise enough to start this procedure with $1 in 1880, you would have consistently shifted in and out of stocks at the right times, and you would now possess over $50,000. Not bad for a strategy which could have been designed in a kindergarten.

Next, Andy tries an alternative strategy based on value. This calculates whether the stockmarket is fundamentally over or undervalued, and buys the market only when value gives a positive signal. The criterion for measuring value is the dividend discount model, first devised by Robert Shiller. If you had been clever enough to devise this measure of value investing in 1880, and had invested $1 at the time, the procedure would have left you with a portfolio now worth the princely sum of 11 cents.

That, according to the weak version of the EMH, shouldn't be possible.

If weakened still further you might salvage some form of the weak hypothesis by saying that "most or many asset prices are difficult to predict," which seems to be true. We might call this the Absurdly Weak form of the EMH, and it seems ridiculous to form such a puffed-up "hypothesis" at all. Does anyone doubt that markets are hard to predict?

But the more serious point with regard to the weak (or absurdly weak) forms of the EMH is that the word "efficient" really has no business being present at all. This word seems to go back to a famous paper by Paul Samuelson, the originator (along with Eugene Fama) of the EMH, who established that prices should fluctuate randomly and be impossible to predict in a market that is "informationally efficient," i.e. in which participants bring all possible information to bear in trying to anticipate the future. If such efficient information processing goes on in the market, then prices will fluctuate randomly. Informational efficiency is what Lucas and others claim the market does, and they take the difficulty of predicting markets as evidence. But it is not, in fact, evidence of anything of the sort.

Think carefully about this. The statement that information efficiency implies random price movements in no way implies the opposite -- that random price movements imply that information is being processed efficiently, although many people seem to want to draw this conclusion. Just suppose (to illustrate the point) that investors in some market make their decisions to buy and sell by flipping coins. Their actions would bring absolutely no information into the market, yet prices would fluctuate randomly and the market would be hard to predict. It would be far better and more honest to call the weak form of the EMH the Random Market Hypothesis or the Market Unpredictability Hypothesis. It is strictly speaking false, as we just noted, although still a useful, crude first approximation. It's about as true as it is to say that water doesn't flow uphill. Yes, mostly, but then, ordinary waves do it at the seaside every day.

So the weak version of the EMH isn't very useful. Perhaps it has some value in dissuading casual investors from thinking it ought to be easy to beat the market, but it's more metaphor than science.

Next up is the "semi-strong" version of the EMH. This asserts that the prices of stocks or other assets (in the market under consideration) reflect all publicly available information, so these assets have the correct values in view of this information.That is, investors quickly pounce on any new information that becomes public, buy or sell accordingly, and the supply and demand in the market works its wonders so prices take their fundamental values (instantaneously, it is often said, or at least very quickly). This version has one big advantage already over the weak form of the EMH -- it actually makes an assertion about information, and so might plausibly say something about the efficiency with which the market absorbs and processes information. However, there are many vague terms here. What do we mean precisely by "public"? How quickly are the prices supposed to reflect the new information? Minutes? Days? Weeks? This isn't specified.

Notice that a hypothesis formulated this way -- as a positive statement that a market always behaves in a certain way -- cannot possibly ever be proven. Evidence that a market works this way today doesn't mean it will tomorrow or did yesterday. Asserting that the hypothesis is true is asserting the truth of an infinite number of propositions -- efficiency for all stocks, for example, and all information at all times. No finite amount of evidence goes any distance whatsoever toward establishing this infinite set of propositions. The only thing that can be tested is whether it is sometimes -- possibly often or even frequently -- demonstrably false that a market is efficient in this sense.

This observation puts into a context an enormous body of studies which purport to give "evidence for" the EMH, going back to Fama's 1970 review. What they all mean is "evidence consistent with" the EMH, but not in any sense "evidence for." In science, you test hypotheses by trying to prove they are wrong, not right, and the most useful hypotheses are those that turn out hardest to find any evidence against. This is very much not the case for the semi-strong EMH.

If markets move quickly to absorb new information, then they should settle down and remain inert in the absence of new information. This seems to be very much not the case. Nearly two decades ago, a classic economic study by Lawrence Summers and others found that of the 50 largest single-day price movements since World War II, most happened on days when there was no significant news, and that news in general seemed to account for only about a third of the overall variance in stock returns. A similar study more recently (2002) found much the same thing: "Many large stock price changes have no events associated with them."

But if we leave aside the most dramatic market events, what about price movements over short times during a single day? Here too the evidence rather strongly contradicts the semi-strong EMH. Bouchaud and his colleagues at Capital Fund Management recently used data for high-frequency trading to test the alleged EMH link between news and price movements far more precisely. Their idea was to study possible links between sudden jumps in the prices of stock prices and possible news items appearing in electronic news feeds, which might, for example, announce new information about a company. Without entering into the technical points, they found that most sudden price jumps took place without any conceivably causal news arriving on the feeds. To be sure, the news entering did cause price movements in many cases, but most large movements happened in the absence of such news.

Finally, we can immediately also dismiss -- with the evidence just cited -- the strong version of the EMH which claims that markets rapidly reflect not only all public information, but all private information as well. In such a market insider trading would be impossible, because insider information gives no one an advantage. If I'm a government regulator about to issue a drilling permit to Exxon for a wildly lucrative new oil field, even my personal knowledge won't permit be to profit by buying Exxon stock in advance of announcing my decision. The market, in effect, can read my mind and tell the future. This is clearly ridiculous.

So it appears that the two stronger versions of the EMH -- which make real claims about how the markets process information -- are demonstrably (or obviously ) false. The weak version is also falsified by masses of data -- there are patterns in the market which can be used to make profits. People are doing it all the time.

The one statement close to the EMH which does have empirical support is that market movements are very difficult to predict because prices do move in a highly erratic, essentially random fashion. Markets sometimes and perhaps even frequently process new information fairly quickly and that information gets reflected in prices. But frequently they do not. And frequently markets move even though there appears to be no new information at all -- as if they simply have rich internal dynamics driven by the expectations, fears and hopes of market participants.

All in all, the EMH then doesn't tell us much. Perhaps Emanuel Dermin, a former physicist who has worked on Wall St. as a "quant" for many years, puts it best: you shouldn't take the thing too seriously, he suggests, but only take it to assert that "it's #$&^ing difficult or well-nigh impossible to systematically predict what's going to happen next." But this, of course, has nothing at all to do with "efficiency." Many economists, lured by the desire to prove some kind of efficiency for markets, have gone a lot further, absurdly so, even trying to make a strength of its own ignorance about markets, indeed enshrining its ignorance as if it were a final infallible theory. Dermin again:

The EMH was a kind of jiu-jitsu response on the part of economists to turn weakness into strength. "I can't figure out how things work, so I'll make that a principle." 

In this sense, on the other hand, I have to admit that the word "efficient" fits here after all. Maybe the word is meant to apply to "hypothesis" rather than "markets." Measured for its ability to wrap up a universe of market complexity and rich dynamic possibilities in a sentence or two, giving the illusion of complete and final understanding on which no improvement can be made, the efficient markets hypothesis is indeed remarkably efficient.

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The Queen of England criticizes economic models

It seems the Queen of England visited the London School of Economics a couple of years ago and listened to some discussion of the general equilibrium models used by central banks in their efforts to understand and manage entire economies. According to economist Thomas Lux, she astutely commented on the peculiarity that these models do not even include a financial sector, even though the financial industry has expanded markedly over past decades and clearly now plays an enormous role in any developed economy.

I'm hoping someone can tell me more about this anecdote, perhaps if they were present at the meeting. Lux's comments are listed in his response to an email sent out to various scientists by Dirk Helbing, seeking their views on the primary shortcomings of contemporary economic theory. The responses from those scientists hit on a number of themes - replacing equilibrium models with more general models able to include instabilities, going beyond the representative agent approximation, and so on. And, as the Queen rightly noted, acknowledging that the financial sector exists.

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