Felix Salmon has a really good article on the increasing mechanization of Wall Street.
Over the past decade, algorithmic trading has overtaken the industry. From the single desk of a startup hedge fund to the gilded halls of Goldman Sachs, computer code is now responsible for most of the activity on Wall Street. (By some estimates, computer-aided high-frequency trading now accounts for about 70 percent of total trade volume.) Increasingly, the market’s ups and downs are determined not by traders competing to see who has the best information or sharpest business mind but by algorithms feverishly scanning for faint signals of potential profit.The stock market is a casino where the game is betting on the fortunes of companies. I don't go to casinos either, but at least at a real casino you know exactly how the house is taking you. Here we've got a system that seems designed to fleece the small, non-mathematician investor. Salmon adds:
Algorithms have become so ingrained in our financial system that the markets could not operate without them. At the most basic level, computers help prospective buyers and sellers of stocks find one another—without the bother of screaming middlemen or their commissions. High-frequency traders, sometimes called flash traders, buy and sell thousands of shares every second, executing deals so quickly, and on such a massive scale, that they can win or lose a fortune if the price of a stock fluctuates by even a few cents. Other algorithms are slower but more sophisticated, analyzing earning statements, stock performance, and newsfeeds to find attractive investments that others may have missed. The result is a system that is more efficient, faster, and smarter than any human.
It is also harder to understand, predict, and regulate. Algorithms, like most human traders, tend to follow a fairly simple set of rules. But they also respond instantly to ever-shifting market conditions, taking into account thousands or millions of data points every second. And each trade produces new data points, creating a kind of conversation in which machines respond in rapid-fire succession to one another’s actions. At its best, this system represents an efficient and intelligent capital allocation machine, a market ruled by precision and mathematics rather than emotion and fallible judgment.
But at its worst, it is an inscrutable and uncontrollable feedback loop. Individually, these algorithms may be easy to control but when they interact they can create unexpected behaviors—a conversation that can overwhelm the system it was built to navigate. On May 6, 2010, the Dow Jones Industrial Average inexplicably experienced a series of drops that came to be known as the flash crash, at one point shedding some 573 points in five minutes. Less than five months later, Progress Energy, a North Carolina utility, watched helplessly as its share price fell 90 percent. Also in late September, Apple shares dropped nearly 4 percent in just 30 seconds, before recovering a few minutes later. [...]
Some built algorithms to perform the familiar function of discovering, buying, and selling individual stocks (a practice known as proprietary, or “prop,” trading). Others devised algorithms to help brokers execute large trades—massive buy or sell orders that take a while to go through and that become vulnerable to price manipulation if other traders sniff them out before they’re completed. These algorithms break up and optimize those orders to conceal them from the rest of the market. (This, confusingly enough, is known as algorithmic trading.) Still others are used to crack those codes, to discover the massive orders that other quants are trying to conceal. (This is called predatory trading.)
The result is a universe of competing lines of code, each of them trying to outsmart and one-up the other. “We often discuss it in terms of The Hunt for Red October, like submarine warfare,” says Dan Mathisson, head of Advanced Execution Services at Credit Suisse. “There are predatory traders out there that are constantly probing in the dark, trying to detect the presence of a big submarine coming through. And the job of the algorithmic trader is to make that submarine as stealth as possible.”
Firstly, there are millions of individual investors doing diligent homework on companies and trying to invest intelligently in the stock market. When they finally arrive at a conclusion and the time comes to buy or sell, their collective decisions are known politely as “retail order flow,” and less politely as “dumb money”; high-frequency trading shops make lots of money by paying for the privilege of filling those orders and taking the opposite side of those trades.It's worth remembering that pretty much all of this algorithmic trading is pure casino—completely valueless from a social utility point of view. These MIT whiz kids trying to outsmart each other has absolutely nothing to do with the larger economy or the underlying reality of the companies being traded. One wonders if this amount of high-grade math skill being slurped up by Wall Street to do at best nothing of value, or at worst active harm to the economy (in the form of arcane financial crises) might be a social problem. Yglesias speculates that this might be the death knell of the stock market system:
It’s possible that one individual investor—Mr Iyer himself, perhaps—can beat the odds and make more money on his own than he would do simply investing in an index fund. If he does, then it might be due to luck, and it might be due to skill. But if I know nothing about Mr Iyer except for the fact that he’s a retail investor looking at corporate fundamentals, I wouldn’t give him much of a chance of beating the market. Fundamentals-based investing is (still) a very crowded trade, and most people who try it fail—they get picked off by faster, smarter, more sophisticated players in the market.
As best I can tell, the trends point in the direction of smart potential retail investors realizing they don’t want to take their life savings to the casino, leading to a stock market that’s ever-more-dominated by suckers and algorithms. That, in turn, means entrepreneurs will be ever-less-inclined to turn ownership of their firms over to the market. That creates a demand for more innovative ways to let larger groups of people invest in private firms, which should further drain the public market of “smart” money. And at some point the era of the publicly traded firm’s hegemony may come to look like an aberration.