What Is High-Frequency Trading(HFT)?
For a while, it seemed like high-frequency trading (HFT) would fully dominate the market. According to global investment company Franklin Templeton, since the global financial crisis (GFC) a decade ago, HFT has accounted for about “half of U.S. stock market trading activity on an annual basis.”
This might indicate a slowing of the use of high-frequency trading software following its peak in 2009, when high-frequency traders traded around 3.25 billion shares per day. According to Bloomberg, it was just 1.6 billion per day in 2012. At the same period, average earnings dropped from “about a tenth of a cent per share to a twentieth of a penny,” according to the research.
Using HFT software, sophisticated computers analyze markets and execute super-fast transactions, often in massive quantities. HFT necessitates complex trading infrastructure, such as powerful computers with high-end technology, which costs a lot of money and reduces returns. And, with rising competition, success is far from certain. This article examines why traders are abandoning HFT and what other tactics they are currently using.
- In the last decade, high-frequency trading software (HFT) has accounted for over half of all stock market trading activity in the United States, indicating that its growth may have peaked.
- Because of its low mistake rate, HFT software has gained in popularity over time; nonetheless, the program is costly, and the market has become quite congested.
- Many alternatives to HFT have arisen in its stead, including trading systems based on momentum, news, and social media.
Why High-Frequency Trading Is Losing Ground
Establishing and maintaining an HFT program is expensive. The sophisticated computer gear and software need regular and expensive updates, which cut into revenues. Markets are very dynamic, and it is hard to replicate everything in computer programs. Due to faults in the underlying algorithms, the success rate in HFT is poor.
HFT encompasses ultra-high-frequency trading as well. Ultra-high-frequency traders pay to get access to an exchange that displays price quotations slightly ahead of the rest of the market. Because of this additional time advantage, other market players are at a disadvantage. The issue has resulted in charges of unfair practices and an increase in hostility to HFT.
HFT laws are likewise becoming more stringent. Italy was the first nation to impose a specific tax on high-frequency trading in 2013, and France quickly followed suit.
The HFT market has also gotten quite saturated. Individuals and professionals are competing with their brightest algorithms. HFT algorithms are even used by participants to identify and outbid other algorithms. As a consequence, high-speed initiatives compete with one another, further compressing wafer-thin earnings.
High-frequency trading earnings are declining as a result of the aforementioned reasons of rising infrastructure and execution costs, additional taxes, and tighter restrictions. Former high-frequency traders are shifting their focus to alternative trading tactics.
Alternatives to High-Frequency Trading
Firms are shifting toward more operationally efficient, lower-cost trading practices that do not need further regulation.
One of the prominent alternatives to HFT is the age-old technical analysis indication based on momentum identification. Momentum trading is detecting the direction of market movements that are predicted to continue for some time (anywhere from a few minutes to a few months).
When the computer system detects a trend, the traders execute one or more staggered transactions with large-sized orders. Because of the enormous volume of orders, even little pricing differences result in substantial gains over time. Because positions based on momentum trading must be held for a period of time, quick trading within milliseconds or microseconds is not required. This saves a lot of money on infrastructure expenditures.
Automated News-Based Trading
The market is driven by news. Exchanges, news organizations, and data providers earn a lot of money offering traders customized news feeds. Automated trading based on automated analysis of news items are gaining popularity. Computer algorithms may now read news stories and conduct immediate trading decisions in response.
Assume ABC’s stock is trading at $25.40 per share when the following hypothetical news items arrive: ABC announces a dividend of 20 cents per share, effective September 5, 2015. As a consequence, the stock price will rise by the dividend amount (20 cents) to roughly $25.60. The computer software recognizes terms such as dividend, dividend amount, and date and makes an immediate trading order. It should be set up to buy ABC stocks only up to the restricted (estimated) price increase of $25.60.
This news-based technique may outperform HFTs since orders are placed in milliseconds, largely based on open market price quotations, and may be executed at disadvantageous prices. Aside from dividends, news-based automated trading is programmed for project bidding outcomes, business quarterly results, other corporate activities such as stock splits, and changes in currency rates for corporations with significant overseas exposure.
Social Media Feed-Based Trading
Another rising trend in automated trading is scanning real-time social media feeds from established sources and verified market players. It entails making trading judgments and placing trade orders based on predictive analysis of social media information.
Assume Paul is a well-known market maker for three well-known equities. His specialized social media account features real-time recommendations for his three equities. Market players who believe in Paul’s trading abilities may pay to subscribe to his exclusive real-time stream. His updates are input into computer algorithms, which assess and understand the substance as well as the tone of the language used in the update. Along with Paul, there may be many more reliable players who offer stock suggestions. The program collects all updates from various credible sources, evaluates them for trading choices, and then executes the deal automatically.
Combining social media feed analysis with additional inputs such as news research and quarterly results may result in a difficult, yet dependable technique to gauge market sentiment on a certain stock’s movement. Predictive analysis of this kind is highly common in short-term intraday trading.
Firmware Development Model
In high-frequency trading, speed is critical for success. The available network and computer setup (hardware), as well as the processing capability of apps, all influence speed (software).A novel idea is to combine hardware and software to build firmware, which substantially decreases the processing and decision-making speed of algorithms.
This customized firmware is built into the hardware and is configured to trade quickly depending on recognised signals. This eliminates the issue of time delays and dependence when a computer system must execute many apps. Slowdowns of this magnitude have created a bottleneck in typical high-frequency trading.
The Bottom Line
An overcrowded marketplace results from too many advancements by too many players. It restricts prospects and raises operating costs. High-frequency trading is declining as a result of such factors. Traders, on the other hand, are looking for alternatives to HFT. Some traders are returning to conventional trading techniques and low-frequency trading applications, while others are using modern analytical tools and technology.
Investopedia does not provide tax, investment, or financial advice. The material is offered without regard for any individual investor’s investing goals, risk tolerance, or financial circumstances, and may not be appropriate for all investors. Investing entails risk, including the possibility of losing money.
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