The EMH Isn't Testable and That's OK - Part I

After reading this feisty exchange between Stephen Williamson and John Quiggin, I wanted to address an argument Quiggin raises against the Efficient Markets Hypothesis (EMH). I read the EMH chapter in his book, Zombie Economics, first, just to make sure I was really seeing the strongest form of his point and not some watered down blog version. While there is much in the book I disagree with, I think Quiggin attempts to grapple with his opponents’ strongest arguments. And he’s a first rate economist (top 1% according to this ranking).

What is the Efficient Markets Hypothesis?

Quiggin gives this definition, “financial markets are the best possible guide to the value of economic assets and therefore to decisions about investment and production. This requires not only that financial markets make the most efficient possible use of information, but that they are sufficiently well-developed to encompass all economically relevant sources of risk.” I honestly don’t know what that means. What is an economic asset? Are there non-economic assets? “Best possible guide for who”? Encompassing all risk sounds like a notion of market completeness, but that isn’t a requirement of market efficiency. Market completeness is just the notion that any risk I have can be insured. This is obviously not true; for instance, I think I'll get a PhD, but I might not. I could flunk out. I'd love to buy insurance against that possibility, but I can't. Does that mean IBM’s stock isn’t fairly priced?

I’m going to use a simple definition given by Eugene Fama in this podcast and seminal paper, and since Fama is one of the major foils in the book, that seems especially appropriate. EMH says, “prices reflect all available information.” This obviously leads to the question, what is the “available information”? Fama broke the EMH into three forms, in order to try to encompass the types of tests people were doing at the time (he regrets this, btw). The forms are weak – the relevant information is past prices, semi-strong – the relevant information is all publically available data (financial statements, earnings announcements…), or strong all public and private data. Quiggin is mostly talking about the semi-strong form, which is standard.

We just have to cover the “prices reflect” part and this is really the crux of the issue. Quiggin argues that EMH implies that prices generated by markets are “right” (scare quotes in the original). The only distinction Fama would make is that they are the best guess, but not necessarily correct. Otherwise they would agree on this statement by Quiggin, “the value of an asset is determined by the flow of income it generates over the period for which it is held and its disposal value. This stream of payments can be converted into a current value by a discounting procedure: [at the] “right” discount rate.” I like this statement. To know what the price should be, we need to know its future cash flows and we need to know what rate to discount the flows.

Here is the crux of the issue. In order to know if the market prices are right we need some theory to tell us what the cash flows will be and more importantly, how to discount them. But how do we know if our theory is correct? In order to test our theory, we have to assume markets are efficient and see if our theory matches market prices. This is called the joint hypothesis problem and has been taught in finance courses for a long time.

This problem really should have been central to the chapter (as it is in Finance courses), because every one of the reasons Quiggin gives in refutation of the EMH is subject to the joint hypothesis problems. Stock prices are too volatile. How volatile should prices be? We need some theory. Is the theory wrong or EMH? Quiggin places great weight on the recent financial crisis, and after every market crash there is always lots of clamoring against EMH. But what theory says markets can’t crash?

Quiggin argues that EMH has been redefined in response to criticism to make it unfalsifiable. As such it isn’t science, but a new sham hoisted on you by the finance community. In Part II, I’ll argue at the very least, it’s an old sham :-). And that maybe there is some hope.