high-frequency trading

High-frequency trading – Losing ground?

For a time, it looked as if high-frequency trading would take over the market completely. In 2010, high-frequency trades made up over 60 percent of U.S. equity volume. But the trend may be waning. According to a Bloomberg article describing the rise and fall of high-frequency trading, “In 2009, high-frequency traders moved about 3.25 billion shares a day. In 2012, it was 1.6 billion a day.” At the same time, average profits fell from “about a tenth of a penny per share to a twentieth of a penny.”

In high-frequency trading, powerful computers use complex algorithms to analyze markets and execute super fast trades, usually in large volumes. High-frequency trading requires advanced trading infrastructure like powerful computers with high-end hardware costing huge amount of money and cutting into profits. And with increasing competition, success is not guaranteed. This article looks at why traders are moving away from high-frequency trading and what alternatives strategies they are now using.

Why High-Frequency Trading is Losing Ground

A high-frequency trading program costs enormous amount of money to establish and maintain. The powerful computer hardware and software needs frequent and costly upgrades that eat into profits. Markets are highly dynamic, and replicating everything into computer programs is impossible. The success rate in high-frequency trading is low, due to errors in underlying algorithms which are implemented.

The world of high-frequency trading also includes ultra high-frequency trading. Ultra high-frequency traders pay for access to an exchange that shows price quotes a bit earlier than the rest of the market. This extra time advantage leads the other market participants operating at a disadvantage. The situation has led to claims of unfair practices and growing opposition to high-frequency trading.

High-frequency trading regulations are also getting stricter by the day. In 2013, Italy was the first country to introduce a special tax on high-frequency trading and this was closely followed by a similar tax in France.

The high-frequency trading marketplace has also become very crowded. Individuals and professionals are pitting their smart algorithms against each other. Participants even deploy high-frequency trading algorithms to detect and outbid other algorithms. The net result is of high-speed programs fighting against each other, squeezing wafer-thin profits even more.

Due to above-mentioned factors of increased infrastructure and execution costs, new taxes and increased regulations, high-frequency trading profits are shrinking. Former high-frequency traders are moving towards alternatives trading strategies.

Emerging Alternatives to High-Frequency Trading

Firms are moving towards operationally efficient, lower-cost trading strategies that do not trigger greater regulation.

  • Momentum Trading: The age-old technical analysis indicator based on momentum identification is one of the popular alternatives to high-frequency trading. Momentum trading involves sensing the direction of price moves that are expected to continue for some time (anywhere from a few minutes to a few months). Once the computer algorithm senses a direction, the traders places one or multiple staggered trades with large-sized orders. Due to the large quantity of orders, even small differential price moves result in handsome profits over time. Since positions based on momentum trading need to be held onto for some time, rapid trading within milliseconds or microseconds is not necessary. This saves enormously on infrastructure costs. (See more in Introduction To Momentum Trading and Information and Advice on Momentum Trading.)
  • Automated News-Based Trading: News drives the market. Exchanges, news agencies and data vendors make a lot of money selling dedicated news feeds to traders (see related How To Trade The News). Automated trades based on automatic analysis of news items has been gaining momentum. Computer programs are now able to read news items and take instant trading actions in response. For example, assume ABC Inc. stock is trading at $25.4 per share when the following hypothetical news items comes in: ABC Inc. declares dividend of 20 cents per share with ex-date September 5, 2015. As a result, the stock price will shoot up by the same amount of the dividend (20 cents) to around $25.60. The computer program identifies keywords like dividend, the amount of the dividend and the date and places an instant trade order. It should be programmed to purchase ABC stocks only to the limited (expected) price hike of $25.60. This news-based strategy can work better than high-frequency trades as those orders are to be sent in split second, mostly on open market price quotes and may get executed at unfavorable prices. Beyond dividends, news-based automated trading is programmed for project bidding results, company quarterly results, other corporate actions like stock splits and changes in forex rates for companies having high foreign exposure. (For more, see How To Trade Forex On News Releases.)
  • Social Media Feed-Based Trading: Scanning real-time social media feeds from known sources and trusted market participants is another emerging trend in automated trading. It involves predictive analysis of social media content to make trading decisions and place trade orders. For example, assume Paul is a reputed market maker for three known stocks. His dedicated social media feed contains real-time tips for his three stocks. Market participants, who trust Paul for his trading acumen, can pay to subscribe to his private real-time feed. His updates are fed into computer algorithms which analyze and interpret them for content and even for the tone used in the language of the update. Along with Paul, there can be several other trusted participants, who share tips on a particular stock. The algorithm aggregates all the updates from different trusted sources, analyzes them for trading decisions and finally places the trade automatically. Combining social media feed analysis with other inputs like news analysis and quarterly results, can lead to a complex, but reliable way to sense the mood of the market on a particular stock’s movement. Such predictive analysis is very popular for short-term intraday trading.
  • Firmware Development Model: Speed is essential for success in high-frequency trading. Speed depends on the available network and computer configuration (hardware), and on the processing power of applications (software). A new concept is to integrate the hardware and software to form firmware, which reduces the processing and decision making speed of algorithms drastically. Such customized firmware is integrated into the hardware and is programmed for rapid trading based on identified signals. This solves the problem of time delays and dependency when a computer system must run many different applications. Such slow downs have become a bottleneck in traditional high-frequency trading.