Are Markets Informationally Efficient?

A key assumption in many mainstream macroeconomic models (both formal and informal) is the Efficient Market Hypothesis. Very simply, this is the belief that markets are informationally efficient — that they reflect information with little (or no) delay, leaving few (or no) arbitrage opportunities.

So the real question here is what information do markets (and by markets, I mean free markets where market participants are free to pay and receive any negotiated value for an asset) really reflect? When we see the price of an asset or asset class fluctuating, what does this movement signify? Is it fluctuation in the fundamental value of the asset? Is it just a fluctuation in the market’s perception of an asset? Is it some combination of these two factors? Or is it just random noise? Fundamentally, markets are composed of a series of transactions, each between a bidder and a seller. Each transaction in itself reflects a discrete set of information — specifically, what the bidder and the seller are willing to pay for, and take for that specific asset. This in turn is typically (although not always!) influenced by a some or all of the following: what others are willing to pay and accept for an asset presently, the use-value of an asset, notions of fundamental value (price-over-earnings, stock-to-flow, EBITDA, cashflow, etc), notions of momentum and what others may be willing to pay and accept for an asset in the future (trendlines, gut feelings, “hot stock tips”, etc).

An intriguing addendum to this is that the automation of trading (high-frequency trading) has created bidders and sellers who are acting on the instructions of algorithms. As these instructions are programmed by humans — usually automating some form of technical analysis — the only real difference is that of (extreme) speed. The beliefs reflected in high-frequency trading reflect the underlying algorithmic instructions programmed by the humans who created the algorithm.

Ultimately, whenever we purchase an asset for the purpose of speculation or investment (and even use-value — prices can change, and the price we paid last week or last year could end up looking very expensive, or very cheap) we are taking a guess as to whether the current bid or sell value is worth it. Each agent makes their guess based on a different set of data and expectations. What the prices in markets signify is the operation of this mechanism — different agents evaluating information and making guesses about the future.

Let’s consider the example of Bitcoin, the price of which is currently soaring. Some choose to buy Bitcoins based on momentum, or their liking of the cryptography, or Bitcoin’s inherent deflationary bias or some other positive belief. This is a speculation that the price may continue to climb. Some may choose to buy bitcoins based on their use-value, as an anonymous, decentralised currency that can be used to buy a wide array of things. Holders of Bitcoins may be motivated to sell by the fact that the price has risen since they bought or mined their coins, or by the belief that bitcoin is “in a bubble”, or some other negative belief.

What the market reflects is the net weight of different opinions and resultant human actions. If those who are motivated to buy outweigh those who are motivated to sell, the price  rises and vice versa. This means that the beliefs of big players in a particular market can have strange and disproportionate effects. Consider the effect of the Hunt Brothers’ attempts to coin the silver market in 1980. The price of silver rose from $11 an ounce in September 1979 to almost $50 an ounce in January 1980, as the Hunt Brothers bought more and more. The market was very efficient at reflecting the fact that the Hunt brothers were willing to buy more than the market could supply at lower prices. And once the Hunt Brothers faced margin calls, the market quickly adjusted to reflect the fact that they were now selling instead of buying, and prices fell.

That’s what (transparent) markets are guaranteed to reflect — bidders and sellers, supply and demand. This information is still useful to firms trying to gauge what, and how much to produce.  Everything else — the information that bidders and sellers are acting upon — is not necessarily reflected in market activity. Very often, bidders and sellers are brought to the market by new information regarding a large number of things — price changes, earnings, business decisions, technologies and inventions, macroeconomic data, etc — but there is no systematic or reliable way to predict what humans will respond to, or how they will respond. Human psychology and human action in this sense is totally unstable and nonlinear — consider the recent contrast in market reaction to earnings data from Apple and Google. This instability is an alternative explanation for why consistently beating the market is indeed very difficult, as the Efficient Market Hypothesis implies.

And prices do not even reflect an aggregation of sentiment toward an asset or asset class — they only reflect the sentiment of those who are involved in the market, in proportion to their level of buying and selling activity. This means that the opinions of big players who buy or sell a lot, are reflected many times more than those of small players who buy or sell a little. And irrationality can create a feedback loop — if stock prices are rising, and macroeconomic fundamentals are weak, many market participants may initially be sceptical. Yet as more participants pile into the stock market purely for reasons of sustained upward momentum, more and more participants may begin to suspend their disbelief, if only to not miss out on a profit opportunity. This is one mechanism (of infinitely many) through which price bubbles can form.

Yet accurately reflecting supply and demand is not the same thing as informational efficiency. Empirical data show that arbitrage opportunities are widely exploitable and exploited even in modern marketsOne of the largest forms of high frequency trading is of course statistical arbitrage. This reality should probably be a final nail in the coffin of the idea that markets reflect anything more than the actions of bidders and sellers. Unfortunately, very many models rest on the assumption of informational efficiency in markets, meaning that this approach is very unlikely to die out any time soon.

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How to Predict the Future

I’ve spent most of today reading.

The modern world appears divided into two camps (no — not those who believe the world can be divided into two camps, and those who don’t): those who believe that man has mastered nature, and those who know better.

The first camp share two chief delusions:

  1. That human beings — through the use of simulations, models, mathematics and other predictive techniques — can routinely make accurate predictions about the future.
  2. That these predictions should be deployed — usually via the power of government — to advance society.

The first supposition has been shown to be largely wrong, both empirically (predictions from models and simulations routinely miss — sometimes by wild margins, as they did during the 2008 meltdown) and rationally (economies are nonlinear systems where the output is not proportional to the input, and therefore chaos will always skew predictions). The second supposition is problematic, because it is dependent upon the first one being true.

The problem is that human beings want to predict the future — and accurately. Businesses want to be able to know what products will be selling in six months, six years or sixty years, so they can make money from it. Children want to know what field to study at college, so that they can get a paying job. Governments want to know what interest rates will be in three months, six months or 10 years, so they can decide how much to borrow. Scientists want to know what amounts of greenhouse gases will be emitted over the next half century, so they can attempt to model climate change.

And most significantly, those in power want the (often-undeserved) authority granted by a “window on the future “.

The best way to come-to-terms with this problem is to treat it as an advantage and not as a disadvantage. It is inevitable that there will be some forms of mathematical, statistical and predictive modelling, just as it is inevitable that philosophers and historians will write predictive literary works. Some will be wrong, and some will be right. The key is that it must be safe to be wrong. Societies, communities, individuals and organisations should plan for the future based not on the idea that the future is predictable, but based around the fact that the future is uncertain, and fundamentally difficult to predict. This means that everything needs leeway to break.

Some crucial examples:

  1. An international financial system which is torn down via a default-cascade through default of one bank or one nation is not robust to bad predictions.
  2. A banking system which is torn down through debt-deflation during a credit contraction is not robust to bad predictions.
  3. A lifestyle in which one bad prediction leads to serious illness or injury is not robust to bad predictions.
  4. An organisation  or family whose wealth (or health) can be destroyed by uncontrollable externalities is not robust to bad predictions.
  5. A nation dependent on the import of credit, resource and goods from hostile nations is not robust to bad predictions.
This just means that economists, writers, historians, bureaucrats and just about anyone who claims that they need to predict the future (that’s all of us, occasionally) needs to frequently and honestly ask themselves the question: what happens if I am wrong?