Thanks this website
Thanks this website

At the end of last week, I wrote a post on market corrections: “Wait – was that a bubble? BUBBLES.

The basic idea:

  1. I’m not convinced that you can talk about market corrections in relation to historic norms and averages.
  2. Basically, because of two things:
    1. Quantitative Easing – as of two weeks ago, the M2 money supply of the US dollar was sitting at around $11.5 trillion. 5 years ago, it was $8.5 trillion. That is a seismic 36% jump in such a short period of time.
    2. Wealth Inequality – the gap has been steadily growing for decades, and it leapt in the last five years, as most of that increase-in-money-supply recovery went into the investments of the already-rich*.
      *Of course, we’re also suddenly more aware of it now that the formerly-middle and now-mostly-lower classes can’t hide from it with credit.
  3. Those are foundational differences. If you look at the inequality story, the wealthy invest based on their expectation of a consumption demand – but if the middle class is disappearing, then you’re looking at a shrinking consumer base. What else can that imply but settling for lower returns on capital?
  4. Comparing today’s market to recent history is about as meaningful as expecting today’s communication levels to be one a historic par with average communication levels from the past 50 years (bearing in mind that we’ve only had the internet for the last two decades, and the first iPhone was only introduced in 2007). I mean – sure, we’re still human beings with a need to communicate – but our abilities have shifted.
  5. So for those reasons, and a variety of structural ones, I think that we’re headed into an era where the wealthy are going to accept lower returns for each level of risk that they take on.

When Paul Krugman talked about the Great Moderation in the 1990s, maybe he only got his timing wrong.

Anway – this weekend, I spent some time thinking about how other empirical theories in finance might have just become anachronistic.

The first big one that I came up with: stock-trading and the efficient market hypothesis.

How The World Of Trading Has Changed

For most of history, stock exchanges operated as you’d probably imagine: skeezy gentlemen in brown suits and bowler hats shouting at each other across a floor.

But then we invented the computer, and trading started to become electronic.

The first major stock exchange to do so was the London Stock Exchange in 1986. Then the Italian Stock Exchange in 1994, the Toronto Stock Exchange in 1997, and the Tokyo Stock Exchange in 1999. The NASDAQ has been mostly electronic since the 1987 crash – although the other US exchanges (the NYSE, NYMEX, Chicago Merc, the Chicago Board Options Exchange…) took a little longer to get on board. The NYSE only really stepped over to the electronic side in early 2007.

So obviously, with the rise of electronic trading, a whole world of algorithmic possibilities opened up. Instead of waiting for news, interpreting it, trying to decide how it would impact a stock, and then making a decision – you could make some decisions prior to the news event and tell an algorithm how to trade for you.

From there, it was only a quick leap to bandwidth speed being a determinant in the trade. And we witnessed the rise of High-Frequency Traders (HFTs). doing the split-second version of front-running.

This then led to Dark Pools and the fragmentation of the stock exchanges in attempts to assuage the fears of large institutional investors who felt they were being gouged by the HFTs. Which ironically led to even more gouging as the fragmentation permitted yet more speedy front-running.

Estimates of high-frequency trading amount range from between 50% to 70% of all stock trades. Algorithmic trading in general is estimated to be over 80% of all stock trades (and that was back in 2008).

To talk about “efficient market hypothesis” studies from the 80s and 90s, at this point, is madness:

  • You have algorithms that are pre-set to trade based on certain keywords in a news releases.
  • You have algorithms that are pre-set to trade based on the volume of trades in shares immediately after a news release.
  • You have algorithms that are pre-set to bid the share price up or down depending on the direction of the trade in order to earn the bid/ask spread.
  • You have algorithms that are pre-set to re-balance portfolios in response to specific movements in the market in order to match a pre-defined index.
  • You have algorithms that are pre-set to anticipate the re-balancing of those portfolios in order to profit off the trade up or down.
  • You have algorithms that are pre-set to anticipate the anticipation of the re-balancings.

In short: you have a trading environment of algorithms that can cause an infinite swirl of feedback loops – limited only by pre-defined limits in their formulae, their fund size, and a trade’s proximity to the next news release.

What this may mean

For human traders, this means that the short-term movements (and even the longer-term movements) in the market can cause dysfunctional disconnection between a share price and the share’s fundamentals.

And you really have no idea whether this is an appropriate market reaction, or whether this is just a market reaction based on conflicting opinions by the algorithm’s designers as to what the market reaction would be to an infinite array of determining factors.

Also, any move you make to take advantage of a perceived disconnection will likely throw the algorithmic traders into a frenzy, which could spark off even more swirling feedback mechanisms.

So what is one to do in all this chaos?

Accept the fact that “market efficiency” is a total misnomer because it implies that:

  • The market absorbs all information into the price; and
  • It does so correctly (or the absorption is rendered “correct” by the market consensus).

Only, you can’t have that when a piece of new information spawns new information which in turn spawns new information and so on into the dark night.

I can’t help but think that this creates an excellent opportunity for some long-term fundamentals-focused contrarianism.

Until, you know, they create an algorithm for that. If they haven’t already.

Rolling Alpha posts opinions on finance, economics, and the corporate life in general. Follow me on Twitter @RollingAlpha, and on Facebook at