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Blogs review: High frequency trading

What’s at stake: Michael Lewis’ new book “Flash Boys: A Wall Street Revolt” has unleashed a huge controversy about the economic benefits and costs of high frequency trading (HFT). For the author, this new breed of traders use sophisticated algorithms and fast computers to effectively front-run trades, a practice that is illegal if performed by humans but which remains legal if performed by computers.

By: Date: April 7, 2014 Topic: Finance & Financial Regulation

What’s at stake: Michael Lewis’ new book “Flash Boys: A Wall Street Revolt” has unleashed a huge controversy about the economic benefits and costs of high frequency trading (HFT). For the author, this new breed of traders use sophisticated algorithms and fast computers to effectively front-run trades, a practice that is illegal if performed by humans but which remains legal if performed by computers.

The emergence of HFTs

Justin Fox writes that while the rest of the stock market world was still operating in terms of minutes and seconds, the HFTers (led by two Chicago-based firms, hedge-fund giant Citadel and upstart GETCO, now called KCG) found a whole new world of profit in the milliseconds and microseconds between when orders were placed and filled. Thanks to their early success, and the regulatory push that had broken the NYSE/Nasdaq duopoly into a scrum of competing exchanges, the HFTers were then able to get exchanges and brokers to craft all sorts of new ways for them to make money. Eric Hunsader explains that Regulation NMS issued in 2007 by the SEC took x amount of available stock at one place, and made it one tenth of that x at ten different places.

Wonkblog writes that sophisticated and expensive computers allow high-frequency traders to take advantage of minuscule differences in price among the many exchanges where securities are bought and sold. Some firms pay to place their computers on the site of a stock exchange to be sure their access to price data is as fast as possible, a practice known as colocation; others will use technology to obscure their trading intentions for a few crucial thousandths of a second.

Felix Salmon writes that the scale of the HFT problem — and the amount of money being made by the HFT industry — is in sharp decline: there was big money to be made once upon a time, but nowadays it’s not really there anymore. Lewis’ book appears to be an exposé not of high-frequency trading as it exists today, but rather of high-frequency trading as it existed during its brief heyday circa 2008.

Front running and dark pools

Dean Baker writes that Michael Lewis’ basic story is that a new breed of traders can use sophisticated algorithms and super fast computers to effectively front-run trades. This allows them to make large amounts of money by essentially skimming off the margins. By selling ahead of a big trade, they will push down the price that trader receives for their stock by a fraction of a percent. Similarly, by buying ahead of a big trade, they will also raise the price paid for that trade by a fraction of a percent. Since these trades are essentially a sure bet (they know that a big sell order or a big buy order is coming), the profits can be enormous.

Michael Lewis writes that it used to be that when trading screens showed 10,000 shares of Intel offered at $22 a share, it meant that one could buy 10,000 shares of Intel for $22 a share by only pushing a button. By the spring of 2007, however, when one pushed the button to complete a trade, the offerings would all disappear, and the stock would pop higher. What happened was that HFTs were taking advantage of the different time it took orders to travel a trading desk in the World Financial Center to the various exchanges.

The Economist writes that the HFTs’ trading edge comes from two different sources. When an investor presses the button to deal, that signal is sent to a broker or bank, who in turn is supposed to search the many different stock exchanges for the best price. But because of the time taken for trading signals to be sent down the wire, those orders arrive at different stock exchanges at separate times. The HFTs were sitting in wait, and used their advantage to exploit the time differences. The second edge comes from the existence of “dark pools” — trading venues set up, usually by banks, which were designed to give investors anonymity. But banks, says Mr Lewis, have been allowing HFTs access to those pools in return for a fee, allowing them to prey on unsuspecting investors.

Who ends up being harmed by HFTs?

Craig Pirrong writes that although this has been framed as evil computer geniuses taking money from small investors, this isn’t at all the case. If anyone benefits from the tightening of spreads, especially for small trade sizes, it is small investors. Instead, the battle is mainly part of the struggle between large institutional investors and HFT. Large traders want to conceal their trading intentions to avoid price impact. Other traders from time immemorial have attempted to determine those trading intentions, and profit by trading before and against the institutional traders.  Nowadays, some HFT traders attempt to sniff out institutional orders, and profit from that information.  Information about order flow is the lifeblood of those who make markets.

Matthew Philips writes that the idea that retail investors are losing out to sophisticated speed traders is an old claim in the debate over HFT, and it’s pretty much been discredited. Speed traders aren’t competing against the ETrade guy, they’re competing with each other to fill the ETrade guy’s order. Felix Salmon writes that small investors are helped by HFT: they get filled immediately, at NBBO. (NBBO is National Best Bid/Offer: basically, the very best price in the market.) It’s big investors who get hurt by HFT: because they need more stock than is immediately available, the algobots can try to front-run their trades.

The distinction between non-public information and inside information

Craig Pirrong writes that the FBI is investigating whether HFT trades on “non-public information”.  Well, “non-public information” is not necessarily “inside information” which is illegal to trade on:  inside information typically relates to that obtained from someone with a fiduciary duty to shareholders. Indeed, ferreting out non-public information contributes to price discovery: raising the risk of prosecution for trading on information obtained through research or other means, but which is not obtained from someone with a fiduciary relationship to a company, is a dangerous slippery slope that could severely interfere with the operation of the market. If firms trade on the basis of such information that can be obtained for a price that not everyone is willing to pay, and that is deemed illegal, how would trading on the basis of what’s on a Bloomberg terminal be any different?

Dean Baker writes that, like insider trading, HFTs is rewarding people for doing nothing productive. Many analysts may carefully study weather patterns to get an estimate of the size of the wheat crop and then either buy or sell wheat based on what they have learned about the about this year’s crop relative to the generally held view. In principle, we can view the rewards for this activity as being warranted since they are effectively providing information to the market with the their trades. If they recognize an abundant wheat crop will lead to lower prices, their sales of wheat will cause the price to fall before it would otherwise, thereby allowing the markets to adjust more quickly. The gains to the economy may not in all cases be equal to the private gains to these traders, but at least they are providing some service. By contrast, the front-running high speed trader, like the inside trader, is providing no information to the market. They are causing the price of stocks to adjust milliseconds more quickly than would otherwise be the case. It is implausible that this can provide any benefit to the economy.

Transaction costs and systemic risk

Charles Jones writes that the key question is whether HFT improves liquidity and reduces transaction costs, and economic theory identifies several ways that HFT could affect liquidity. The main positive is that HFT can intermediate trades at lower cost. Those lower costs from automation can be passed on to investors in the form of narrower bid-ask spreads and smaller commissions. The potential negative is that the speed of HFT could put other market participants at a disadvantage. The resulting adverse selection could reduce market quality. There is also the potential for an unproductive arms race among HFT firms racing to be fastest.

Tom Lin writes that the emergence of cyborg finance has borne two new systemic risks: one related to connectivity, “too linked to fail”, and the other related to speed, “too fast to save.”


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