Algorithmic trading employs logic backed by computing power to produce trading profits. It largely began as block trading, which was used by institutional traders to sell off large blocks of stock when the market declined. Many of the algorithmic strategies are more complex versions of trading strategies employed by individual traders. However, algorithmic trading now exhibits a level of control over the markets like nothing that's ever been seen before. During the height of the tech craze in 1999, traders were generating 1,000 quotes per second. As of September 2013, that figure has risen to 2 million quotes per second.
One of the most common applications of algorithmic trading is high-frequency trading, which uses algorithms and statistics to execute trading strategies in extremely rapid succession. By early 2011, high-frequency trading accounted for five of every 10 trades on U.S. exchanges. High-frequency trading generates profits via volume, scratching out tiny profits for each trade but executing millions of trades in a trading day. For example, many stocks are traded on multiple exchanges. A trader can program an algorithm to search the market for tiny price discrepancies in the price of the same stock trading on two exchanges. The algorithm will trigger buy orders on whichever exchange has the lower-priced stock, simultaneously selling the stock on the other exchange. This is an example of arbitrage trading.
Algorithmic trading is largely the domain of institutional traders, although small investors highly proficient in programming are increasingly using algorithms to trade. Lacking the capital and computing power held by larger traders, individual algorithmic traders are able to execute similar strategies on a smaller scale, etching out profits averaging between $500 and $1,000 per day. This is thought to be a less risky progression from the day trading that was so popular during the 1990s.
Gaming the System
There is concern that high-frequency traders are making the investment universe a more dangerous place for individual investors. Because so many high-frequency traders are exploiting tiny price imbalances, bringing prices back into equilibrium, human traders who previously engaged in manual arbitrage trading have fewer opportunities. Also, the strategies employed by some algorithmic traders are leaving a bad taste in the mouths of many investors. One such strategy is “penny hopping,” which involves outbidding human investors by a penny to jump ahead in a buying queue. And 90 percent of orders generated by high-frequency traders are canceled on purpose, but the exchanges offer lucrative rebate deals to these traders to encourage this type of volume.
Besides the affronts to individual traders, some have real concerns about the effect algorithmic traders are having on the market. As of September 2013, between 90 and 95 percent of all quotes were generated by high-frequency traders. On May 6, 2010, the Dow Jones Industrial Average declined by almost 1,000 points in minutes -- an event dubbed the “flash crash” -- because algorithmic traders were issuing mass sell orders to exploit the downturn in the market and thus perpetuating it. A trading glitch caused Knight Trading to lose $440 million in a matter of hours; the company was forced to sell itself to stave off bankruptcy in the ensuing months. BATS Global Markets suffered embarrassment when trading glitches resulted in the botching of its own initial public offering on its own exchange.
Video of the Day
- Forbes: High Frequency Trading -- Is It a Dark Force Against Ordinary Human Traders and Investors?
- Wall Street Journal: Does High-Speed Trading Hurt the Small Investor?
- Huffington Post: Critics Weigh in on High-Frequency Trading on Flash Crash Anniversary
- Bloomberg: Knight Trading Loss Said to Be Linked to Dormant Software
- Bloomberg Businessweek: SEC Once Slowed by Data Gap to Report High-Speed Trader Research
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