What Is High-Frequency Trading (HFT)?
For a time, it looked as if high-frequency trading (HFT) would take over the market completely. According to global investment firm Franklin Templeton in 2019, HFT has accounted for approximately "half of U.S. stock market trading volume on an annual basis since the global financial crisis (GFC) a decade ago."
This may signal a stabilizing rate of high-frequency trading software after its peak usage in 2009, when high-frequency traders moved about 3.25 billion shares a day. In 2012, it was 1.6 billion a day, according to Bloomberg. At the same time, average profits fell from “about a tenth of a penny per share to a twentieth of a penny,” the report noted.
Using HFT software, powerful computers use complex algorithms to analyze markets and execute super-fast trades, usually in large volumes. HFT requires advanced trading infrastructure like powerful computers with high-end hardware costing huge amounts of money and cutting into profits. And with increasing competition, success is not guaranteed. This article looks at why traders are moving away from HFT and what alternatives strategies they are now using.
- The usage of high-frequency trading software (HFT) has accounted for about half of U.S. stock market trading volume in the past decade, signaling a potential maxing out of its growth.
- Over time, the popularity of HFT software has grown due to its low rate of errors; however, the software is expensive and the marketplace has become very crowded as well.
- In its place, many alternatives to HFT have emerged, including trading strategies based on momentum, news, and social media.
Why High-Frequency Trading (HFT) Is Losing Ground
An HFT program costs a lot of money to establish and maintain. The powerful computer hardware and software need frequent and costly upgrades that eat into profits. Markets are highly dynamic, and replicating everything into computer programs is impossible. The success rate in HFT is low due to errors in underlying algorithms.
The world of HFT 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 to operate at a disadvantage. The situation has led to claims of unfair practices and growing opposition to HFT.
HFT 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 HFT marketplace has also become very crowded. Individuals and professionals are pitting their smartest algorithms against each other. Participants even deploy HFT 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 the 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 toward alternative trading strategies.
Alternatives to High-Frequency Trading (HFT)
Firms are moving toward operationally efficient, lower-cost trading strategies that do not trigger greater regulation.
The age-old technical analysis indicator based on momentum identification is one of the popular alternatives to HFT. 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 place one or multiple staggered trades with large-sized orders. Due to a large number 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.
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. Automated trades based on automatic analysis of news items have been gaining momentum. Computer programs are now able to read news items and take instant trading actions in response.
For example, assume company ABC's stock is trading at $25.40 per share when the following hypothetical news items come in: ABC declares dividend of 20 cents per share with ex-date Sept. 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 HFTs 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 programed for project bidding results, company quarterly results, other corporate actions like stock splits and changes in forex rates for companies having high foreign exposure.
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 that 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 slowdowns have become a bottleneck in traditional high-frequency trading.
The Bottom Line
Too many developments by too many participants lead to an overcrowded marketplace. It limits opportunities and increases the cost of operations. Such trends are leading to the decline of high-frequency trading. However, traders are finding alternatives to HFT. Some are reverting to traditional trading concepts, low-frequency trading applications, and others are taking advantage of new analysis tools and technology.