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“The internet is a near perfect vehicle for turning crowds from independent evaluators into mobs.”

— Cliff Asness

The least-efficient market hypothesis

Eugene Fama won the Nobel Prize for his 1965 paper on the Efficient Markets Hypothesis, in which he asserted that stock prices typically reflected all publicly available information.

I can assure they did not — at least, not perfectly and not immediately.

I’m qualified to say that because I started as an equities trader decades after Fama’s paper, in 1994 (that’s me in the photo above). And when new information became available back then, it was so slow to be reflected in prices you had ample time to read the headline on a Reuters screen, make a phone call to the trading floor and profitably buy or sell before prices reflected the new information.

Often, you’d have to sit there for a while and wonder if you had misread the headline because the trade wasn’t working — and then, usually, it did.

You could also buy index futures and take a few leisurely minutes to sell the underlying stocks for a four- or five-percentage point arbitrage.

You could buy government bond futures at one price in Frankfurt and immediately sell them at a higher price in London — or sell an Italian stock short in London and immediately buy it at a lower price in Milan.

Perhaps most amazingly, you could short a stock just before an index rebalance that had been announced months earlier and buy it back a few percentage points lower when index funds all sold at the appointed time.

Those opportunities didn’t happen super frequently (it wasn’t that easy back then), but here’s my point in recounting them: People had to do those trades.

To whatever extent markets reflected publicly available information, a real human had to make it happen.

Being one of those humans was fun while it lasted, but —for me at least — it didn’t last long.

Algorithms were introduced to equity markets in the early aughts and trading became a decreasingly fun job thereafter, right up until it was hardly a job at all.

Technology made equity markets so efficient that brokers and traders at investment banks no longer had much of anything to do — trading desks that employed 100 people in 2000 would probably employ less than five in 2024 (if any at all).

Now, however, Cliff Asness says that technology is making markets less efficient.

In his recent paper, The Less-Efficient Market Hypothesis, the hedge fund founder (and one-time research assistant to Eugene Fama) notes that “over the past 30+ years markets have become less informationally efficient in the relative pricing of common stocks.”

Perhaps counterintuitively, he attributes this mostly to technology: “Gamified 24/7 trading on your phone and social media in particular are the biggest culprits.” 

This, he says, has caused the wisdom of crowds to be replaced with the folly of mobs: “Has there ever been a better vehicle for turning a wise, independent crowd into a coordinated, clueless, even dangerous mob than social media?” 

In other words, technology is now making people distort stock prices: Social media group-think, the overconfidence that comes with ubiquitous data and gamified trading is causing a prolonged, irrational mispricing of the most expensive stocks relative to the most inexpensive ones.

This does not, unfortunately, mean that investment banks will be restaffing their trading desks — the mispricings are mostly happening over “medium time horizons” and exploiting those types of long-duration inefficiencies is not easy. 

Nor should it be.

"It's not supposed to be easy,” Charlie Munger once said of investing. “Anyone who finds it easy is stupid."

Still, though, the less-efficient market hypothesis is good news for any investors or traders who are willing to look stupid for a while — because less efficient markets should mean higher returns for those who can wait them out. 

The least-efficient market

Now let’s do crypto. 

If “social media” is distorting stock prices, you can imagine what it must be doing to crypto prices because crypto lives on social media. 

If “the overconfidence that comes when people think all the world’s data is at their fingertips” is distorting stock prices, you can imagine how overconfident traders must be in informationally-overloaded crypto, where everything happens transparently onchain (in theory).

And if “gamified, fake-free, instant, 24/7 trading” is causing irrational behavior in stocks, crypto — which is all of those things but much more so — must be acting much more irrationally. 

Perhaps most importantly, people like Cliff Asness don’t trade crypto, so there are fewer professionals to offset the retail madness.

Asness’s fund, AQR, does actually trade some crypto, but only in small size (by his standards) and only based on price momentum — he doesn’t try to buy cheap tokens and sell expensive ones because he doesn’t believe that terms like “cheap” and “expensive” are applicable in crypto.

If, like me, you think they are, you might also think that irrational crypto prices should present an opportunity — perhaps an even bigger one than in equities.

But it won’t be easy, because crypto can probably stay irrational longer than you can stay solvent.

— Byron Gilliam

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Watch or listen to Bell Curve on YouTube, Spotify or Apple.