Whereof One Cannot Speak, Thereof One Must Be Silent
By Scott Sumner
We talk too much. I probably talk way too much. Humans like to explain everything, even things that cannot be explained.
Over at TheMoneyIllusion I did a post trying to rebut the bubble view of NASDAQ, circa 1999-2000. Lots of people bought tech stocks, mostly during periods of time when NASDAQ was around 3500 to 4000. (It briefly spiked to just over 5000.) Now it’s over 7200. Yes, that’s not a good rate of return for a period of 18 years, but it’s not obviously horrible (especially if you include reinvested dividends, and especially for those who didn’t buy at the absolute peak, which lasted very briefly.) In the comment section, Matthew Waters asked:
Saying NASDAQ had an OK value in 2000 brings up the same question as the 1987 crash: how could both the 2000 and 2002 NASDAQ prices be efficient? For that matter, how could both 2002 and 2003 NASDAQ prices be efficient? End of 2002: NASDAQ was at $1,300. End of 2003: NASDAQ was at $2,000.
Maybe bona fide news on future cash flows accounted for a 75% drop followed by a 50% increase within three years. I doubt it. For its part, Bitcoin has such swings in WEEKS rather than years.
Is there anything that could falsify the EMH? It should be dramatically reformulated from “prices reflect all available information.” It would be far stronger and more predictive to say “it’s very difficult to beat the market on a 6 month or 1 year timeline.”
This is a very good question, and one that reaches into the field of epistemology. What can we know about the world? Which I would rephrase as “What information is useful”?
To make my views easier to see, let’s start with an analogy. Suppose I have been playing a certain roulette wheel in Vegas and believe it is tilted toward the red numbers, with relatively few blacks showing up. So the casino offers a test, 10 spins of the wheel. In this test, the number of reds and blacks is pretty even, but I notice another interesting pattern in the numbers:
Notice anything odd? Nine of the ten numbers fall in the right side column of the number grid on a roulette table:
How likely is that!
I claim the roulette wheel is fixed, biased towards numbers divisible by three. In fact, this pattern is no more unlikely than any other pattern. Weird things happen all the time in casinos. Indeed the first time I ever walked into a casino (Surfer’s Paradise, Queensland, 1991) I won my first 12 hands of blackjack, before losing the 13th. How likely is that!
I hope you see the problem here. It’s not kosher to ex post make up a theory to fit the data. (I’m not accusing Matthew of that—we’ll get to his excellent question later.) Yes, much of social science is done exactly this way, but that doesn’t make it right. Indeed data mining (aka P-hacking) helps to explain why people don’t believe social science research, unless they already found the hypothesis to be plausible before being presented with the regression results.
Matthew is right that the EMH doesn’t do a good job of explaining the 1987 stock market crash, or the 2000-02 tech stock crash. It’s hard to find fundamentals that would justify such a dramatic shift in prices over a short period of time. (Actually much harder for the 1987 crash than the tech stock declines, which took considerably longer.) So how do I defend the EMH? Two points:
1. The EMH is very useful to me in all sorts of ways. It’s also consistent with a lot of research on the wisdom of crowds, and basic economic ideas such as competitive rates of return in competitive markets with free entry. It’s got a lot going for it. Because of the EMH, I’ve invested in index funds, and also engaged in buy and hold of stocks (not day trading). I ignored Shiller’s 2011 comments on overvalued stocks. My 401k has done very well as a result. It also helped me during my research on the Great Depression, when I found that market responses to policy shocks were much more perceptive that expert opinion, even the expert opinion of Friedman and Schwartz.
2. The EMH cannot explain certain puzzling facts. (Matthews right about that). And on these points we should just keep our mouths shut.
But people cannot leave well enough alone, they want to explain everything. So they don’t keep their mouths shut; they develop alternative anti-EMH theories, such as the bubble theory of asset prices. And this is where they get into trouble.
I have many posts that talk about the way that cognitive illusions bias people toward believing that bubbles exist. One of my themes is that the debate over the existence of bubbles is meaningless, unless it has useful information. And (as we will see) it does not. If not useful, a bubble theory is just a sort of insult directed at the market, calling the market “irrational.”
Now bubble theories might be useful. For instance, if bubbles exist and can be spotted in real time, it could provide useful investment advice. Or point to the need for regulation. But if not useful, then they are pretty meaningless.
If you are going to claim that NASDAQ was “obviously” wildly overpriced in 1999 and 2000, you had better be confident of that claim. If 18 years later it no longer looks so obvious, then you can’t say, “well then NASDAQ was obviously wildly underpriced in 2002, and so the EMH is still wrong”. “It was a negative bubble.” The claim back in 2002 was that the whole world was obviously crazy in 2000, and that a mania had taken hold. The view was that now (in 2002) we had come to our senses, and the NASDAQ was back to appropriate levels. So are we now to believe that in 2018 we now know that people were actually rational in 2000, and that a wild mania of depression had obviously gripped the country in 2002, causing tech stocks like Amazon and Apple to be wildly undervalued? If all of this is so obvious, why do we keep having to change the story? When will we reach a point where we can look at the world dispassionately? (I say never.)
What you should say is that moves in the stock market are often larger than we’d expect, based on our knowledge of the fundamentals. We simply don’t know why that is the case, and bubble theories don’t help. Perhaps the value of stocks to the public is highly sensitive to things that we don’t understand very well.
Robert Shiller has one of the best anti-EMH bubble theories. He looks at historical patterns for things like P/E ratios, and then correlates that data with the performance of the stock market over the next few decades. Unfortunately, while his theory looks good on paper, apparently it is not very useful, as he seems to frequently give questionable investment calls. (I.e. calling stocks overvalued in 1996 and 2011.)
But I actually have a lot of sympathy for Shiller. When I looked at the market in 2011, it wasn’t obvious to me that it was over or undervalued. And as I look at the market today, at a dramatically higher level, it’s still not obvious to me as to whether it is over or undervalued. For some reason, it seems extremely hard (for me) to figure out what stocks should be worth. I wish I could explain why, but I can’t. (And don’t get me started on bitcoin, which is even more inscrutable).
When looking at investment puzzles, we are faced with two choices:
1. Develop anti-EMH theories of anomalies such as bubbles.
2. Keep silent, and acknowledge our ignorance.
It’s more useful to keep silent—saves wear and tear on the vocal cords.
PS. The EMH can be “falsified”, i.e. found not useful, when anti-EMH theories are found to be useful. For instance, if mutual funds that are based on bubble theories fairly consistently outperform index funds, that would falsify the EMH.
The EMH cannot be “falsified” by engaging in data mining. Finance profs make the mistake of looking for market patterns inconsistent with the EMH, when they ought to be looking for evidence that others have found market patterns that are inconsistent with the EMH.
In another post I used the following analogy. If I were looking for evidence that lead could be turned into gold at low cost, I would not study theories of alchemy, I’d look for evidence (in global gold output data) that someone else had recently made the breakthrough.