Markets can be — and usually are — too active, and too volatile.
This is an idea which goes back to Keynes, if not earlier. Stiglitz says that in the specific area of international capital flows, “there is now a broad consensus that unfettered markets are welfare decreasing” — and certainly you won’t get much argument on that front from, say, Iceland, or Malaysia, or even Spain. As Stiglitz explains:
When countries do not impose capital controls and allow exchange rates to vary freely, this can give rise to high levels of exchange rate volatility. The consequence can be high levels of economic volatility, imposing great costs on workers and firms throughout the economy. Even if they can lay off some of the risk, there is a cost to doing so. The very existence of this volatility affects the structure of the economy and overall economic performance.
The question is: does the same logic, that traders seeking profit can ultimately cause more harm than good, apply equally to high-frequency trading, and other domestic markets? Stiglitz says yes: there’s every reason to believe that it does.
HFT is a negative-sum game.
In the algobot vs algobot world of HFT, the game is to capture profits which would otherwise have gone to someone else. Michael Lewis’s complaint is that if there weren’t any algobots at all, then those profits would have gone to real-money investors, rather than high-frequency traders, and that the algorithms are taking advantage of unfair levels of market access to rip off the rest of the participants in the stock market. But even if you’re agnostic about whether trade profits go to investors or robots, there are undeniably real-world costs to HFT — costs like drilling through Pennsylvania mountains. As a result, the net effect of the algorithms is negative: they reduce profits, for everybody, rather than increasing them.
In theory, HFT could bring with it societal benefits which more than offset all the costs involved. In practice, however, that seems unlikely. To see why, we’ll have to look at the two areas where such benefits might be found.
HFT does not improve price discovery.
Price discovery is the idea that markets create value by putting a price on certain assets. When a company’s securities rise in price, that company finds it easier to raise funds at cheaper rates. That way, capital flows to the places where it can be put to best use. Without the price-discovery mechanism of markets, society would waste more money than it does.
But is faster price discovery better than slower price discovery? Let’s say good news comes out about a company, and its share price moves as a result — does it matter how fast it moves? Is any particular purpose served to seeing the price move within a fraction of a millisecond, rather than over the course of, say, half a minute? It’s hard to think of a societal benefit to faster price discovery which is remotely commensurate with the costs involved in delivering those faster price moves.
What’s more, faster price discovery is generally associated with higher volatility, and higher volatility is in general a bad thing, from the point of view of the total benefit that an economy gets from markets.
HFT sends the rewards of price discovery to the wrong people.
Markets reward people who find out information about the real economy. Armed with that information, they can buy certain securities, sell other securities, and make money. But if robots are front-running the people with the information, says Stiglitz, then the robots “can be thought of as stealing the information rents that otherwise would have gone to those who had invested in information” — with the result that “the market will become less informative”. Prices do a very good job of reflect ignorant flows, but will do a relatively bad job of reflecting underlying fundamentals.
HFT reduces the incentive to find important information.
The less money that you can make by trading the markets, the less incentive you have to obtain the kind of information which would make you money and increase the stock of knowledge about the world. Right now, the stock market has never been better at reacting to information about short-term orders and flows. There’s a good example in Michael Lewis’s book: the president of a big hedge fund uses his online brokerage account to put in an order to buy a small ETF — and immediately the price on the Bloomberg terminal jumps, before he even hits “execute”. The price of stocks is ultra-sensitive to information about orders and flows. But that doesn’t mean the price of stocks does a great job of reflecting everything the world knows, or could theoretically find out, about any given company. Indeed, if investors think they’re just going to end up getting front-run by robots, they’re going to be less likely to do the hard and thankless work of finding out that information. As Stiglitz puts it: “HFT discourages the acquisition of information which would make the market more informative in a relevant sense.”
HFT increases the amount of information in the markets, but decreases the amount of usefulinformation in the markets.
If markets produce a transparent view of all the bids and offers on a certain security at a certain time, that’s valuable information — both for investors and for the economy as a whole. But with the advent of HFT, they don’t. Instead, much of the activity in the stock market happens in dark pools, or never reaches any exchange at all. Today, the markets are overwhelmed with quote-stuffing. Orders are mostly fake, designed to trick rival robots, rather than being real attempts to buy or sell investments. The work involved in trying to understand what is really going on, behind all the noise, “is socially wasteful”, says Stiglitz — and results in a harmful “loss of confidence in markets”.
HFT does not improve the important type of liquidity.
If you’re a small retail investor, you have access to more stock market liquidity than ever. Whatever stock you want to buy or sell, you can do so immediately, at the best market price. But that’s not the kind of liquidity which is most valuable, societally speaking. That kind of liquidity is what you see when market makers step in with relatively patient balance sheets, willing to take a position off somebody else’s book and wait until they can find a counterparty to whom they can willingly offset it. Those market makers may or may not have been important in the past, but they’re certainly few and far between today.
HFT also reduces natural liquidity.
Let’s say I do a lot of homework on a stock, and I determine that it’s a good buy at $35 per share. So I put in a large order at $35 per share. If the stock ever drops to that price, I’ll be willing to buy there. I’m providing natural liquidity to the market at the $35 level. In the age of HFT, however, it’s silly to just post a big order and keep it there, since it’s likely that your entire order will be filled — within a blink of an eye, much faster than you can react — if and only if some information comes out which would be likely to change your fair-value calculation. As a result, you only place your order for a tiny fraction of a second yourself. And in turn, the market becomes less liquid.
It’s important to distinguish between socially useful markets and socially useless ones.
In general, just because somebody is winning and somebody else is losing, doesn’t mean that society as a whole is benefiting in any way. Stiglitz demonstrates this by talking about an umbrella:
If there is one umbrella, and there is a 50/50 chance of rain, if neither of us has any information, the price will reflect that risk. One of us will get the umbrella. If it rains, that person will be the winner. If it does not, the other person will be the winner. Ex ante, each has the same expected utility. If, now, one person finds out whether it’s going to rain, then he is always the winner: he gets the umbrella if and only if it rains. If the other person does not fully understand what is going on, he is always the loser. There is a large redistributive effect associated with the information (in particular, with the information asymmetry), but no real social benefit. And if it cost anything to gather the information, then there is a net social cost.
HFT is socially useless; indeed, most of finance does more harm than good.
As finance has taken over a greater and greater share of the economy, growth rates have slowed, volatility has risen, we’ve had a massive global financial crisis, and far too much talented human capital has found itself sucked into the financial sector rather than the real economy. Insofar as people are making massive amounts of money through short-term trading, or avoiding losses attributable to short-term volatility, those people are not making money by creating long-term value. And, says Stiglitz, “successful growth has to be based on long term investments”.
So let’s do something about it.
HFT shouldn’t be banned, but it should be discouraged. The tax system can help: a small tax on transactions, or on orders, would reduce HFT sharply. “A plausible case can be made for tapping the brakes,” concludes Stiglitz. “Less active markets can not only be safer markets, they can better serve the societal functions that they are intended to serve.”