Efficient Markets, as they stand.

From The Ambrosini Critique, a blog I enjoy by a macroeconomics graduate student, I see two defenses of the Efficient Markets Hypothesis:

Armed and Dangerous:

Here it is: Markets don’t seek efficiency because investors aren’t rational. Yeah, well, gas molecules aren’t rational either, but they obey very simple regularities in large numbers. Rational-expectations theory doesn’t actually require individual investors to be rational, it merely predicts that en masse they will behave as if they are rational.

Reality backs this up; if investors weren’t good at rapidly folding information into prices, it would be possible for humans to beat the stock market by applying an algorithm – but, in fact, even the savviest professional money managers notoriously do no better than chance over long periods.

The Money Illusion:

I think about that failure in terms of efficient markets theory….

Whenever I read opinion pieces by almost any macroeconomist— Keynesian, monetarist, new Classical, Austrian, etc, there is almost invariably a point where alarm bells go off. At some point the economist will make an assertion that seems to me to be in conflict with the EMH. And after that point I have trouble taking anything they say seriously. I keep thinking “If you’re so smart . . . ”…

If Fed policy was obviously far off base, and if the policy was inevitably going to lead to a crash, why did so many smart investors ignore those signs? Why couldn’t anyone using your model have gotten rich?

The Efficient Markets Hypothesis (EMH) states that well-informed traders will balance out any noise traders in a market by doing the opposite of their actions. If noise traders bid up a stock beyond its fundamental value, arbitrageurs will short it by the same amount to bring the price back to its fundamental value. Following this, the EMH states, in its weak form, that there is no historical data that should help predict future prices.

I think one way forward for finance is to re-evaluate the EMH, and as such, I’m going to explain some of the problems that currently stand with it. One of the important conceptual moves of behavioral finance is that it allows us to argue that we can end up in situations where prices can not reflecting fundamentals yet there is no profitable arbitrageur move to bring them back into line. I’ll talk about them now.

Noise-Trader Risk

Any risk you take on as an investor you have to be compensated for. Credit Risk, liquidity risk, interest-rate risk, etc. In fact, we use the compensation for an investment asset in order to back-out the ‘implied’ risk. All these risk factors are from the asset itself and are compensated by the asset itself in the rate it is charged.

But what if there are risks that the asset itself can’t compensate for? In the model above, arbitrageurs take the opposite position of noise-traders, and in the long-run they’ll be rich. But what about in the short run? If prices move further away from fundamentals, the arbitrageurs have lost money. If they need to cancel their position in the short-run, and there is a non-zero chance that they will, they have lost money. So even though their is a sure bet of making money, it involves taking some risk.

This risk needs to be compensated for, but it can’t be since it comes from the market or from the asset – there are limits to arbitrage. Since nobody wants to take risks they aren’t compensated for, we should expect the arbitrageur to be hedged. This model is a very mathy way of explaining “the market can stay irrational longer than you can stay solvent” – and it is a very important conceptual point. Matthew Ygelsias was onto this point today, and I think it should become a normal way we think of markets.

Limits to Arbitrage

Any model that believes it is as easy to short a stock as it is to go long a stock is going to have problems. Going short a stock is not easy, or is it costless. There is an asymmetric information problem here – if someone wants to sell you their car, there’s a good chance something is wrong with it. If someone wants to borrow your stock to sell it (go short it), there’s a good chance something is wrong with it. Banks tend to want certain terms and fees that go against the costless model associated with EMH; these fees will be a damper in bringing prices back into fundamental values.

In general, when I interview people, I asked them about “Limits to Arbitrage.” If they can talk for more than a minute, they’ve impressed me. Any MBA can talk about psychology 101 bias; the actual problems within models of risk and market micro structure are where the real game is.

Arbitrageurs Riding the Momentum

The semi-strong EHM quickly falls apart when you look at statistical tests. There is a clear momentum strategy that exists, as well as a mean-reversion strategy that exists as well. Here is the question: since these are well known (and they are), why don’t hedge funds balance them out? One reason might be that it is more profitable to go with the momentum rather than try to balance it out. This strategy by informed arbitrageurs increases, not decreases, noise-trader risk mentioned in #1 above, which creates a more vicious cycle.

These models, from the late 1980s that gave a theory basis for the following statistical work, are a part of the reason Brad Delong is kind of a big deal. Catch him and his co-author in this blog exchange reflect on how they modeled back in the day whatever everyone is rediscovering now.

Behavioral Factors

There are also behavioral factors. People overestimate and underestimate when it comes to certain types of information. Without getting too technical, they also appear to do this when it comes to estimating the volatility of an option, so in situations where one simply can’t point to model misspecification or conditional asset prices. (That paper is fantastic, btw.)

I think there may also be a habitus bias at work as well. We talk about “representative agents” and all that, but trading is done within 100,000 people, who have much of the same education and class dispositions, and within a certain number of large institutions, using the same models, facing the same rewards and career risks. So problems can multiply. Using a constant volatility in your option pricing models? Blow up in 1987. Giving convex rewards to concave payouts? If a firm has poor earnings, and lays off 2,000 people, how should you adjust their valuation? And so on.

Mind you, this is just the theory. Empirically, as dsquared says, Andrew Lo killed the EMH: “There are measurable and robust momentum effects, under and over reactions and lead/lag relationships between large and small cap stocks.” So there’s plenty of data to back this, but how will the theory proceed? That remains to be seen.

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One Response to Efficient Markets, as they stand.

  1. Pingback: alephblog » Blog Archive » One Dozen Notes on our Current Situation in the Markets

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