The impact of computing technologies on financial markets includes security, data transfer, accessibility, and enhanced strategies.
Investors deploy many approaches, from short-term trades to long-term methods. High-frequency trading has become popular in recent years due to advancements in processing power and internet connectivity.
What Is High-Frequency Trading?
High-frequency trading (HFT) refers to using powerful computer programs to execute large numbers of orders via online trading. Manal trading is limited by the number of orders that traders can place within a specified time, but HFT allows traders to place far more orders within fractions of a second with complex algorithms.
The HFT strategy exploits market inefficiencies, discrepancies, and quick but small price changes to generate profit. It relies on the concept of “faster execution” to gain small profits that add up to significant amounts.
HFT is widely used for scalping, arbitrage, and momentum trading, where speed is crucial and the price of assets changes quickly. The three key characteristics of HFT are trading at high speeds, a large number of transactions executed, and short-term investment horizons.
The complex and intricate process involves computer algorithms that analyze large data sets and make trading decisions 30 times faster than humans can blink; the average HFT program can execute hundreds of trades in just under ten seconds.
Requirements for High-Frequency Trading
There are six key requirements for HFT. Let’s take a look at each one:
- An HFT broker: Not all brokers allow HFT due to regulatory compliance and other reasons. HFT broker provides backend infrastructure that will enable traders to deploy HFT algorithms.
- A fast computer with powerful processors: HFT programs are complex and require powerful processors to work efficiently.
- Low latency infrastructure: HFT relies on high-speed networks, data centers, servers, cloud platforms, etc., to achieve microsecond speed, which is critical for performance.
- An HFT algorithm: A computer program that analyzes markets and executes trades based on preset parameters or instructions.
- Technical expertise: The process of deploying HFT is complex and requires a level of technical expertise in algorithmic trading, low-latency systems, and quantitative finance.
- Market data feed: HFT relies on extensive data sets to gain actionable information and execute profitable trades.
Cybersecurity, backtesting, optimization, and risk management are also critical when deploying HFT algorithms.
How High-Frequency Trading Algorithms Influence Market?
Market microstructure (with reference to financial trading) is how prices form and deliver, how traders execute orders, and how information is disseminated. Microstructure is crucial for price discovery, trade execution, algorithmic trading, and risk management. There are six key components of market microstructure: order book (asks and bids), order types, liquidity, HFT, dark pools, and trader behavior.
HFT influences microstructure primarily by increasing competition. HFT executes trades at high speeds, increasing market volume and execution. This results in tighter ask-bid spreads (which may be eventually eliminated) and added liquidity. HFT algorithms act as market makers, especially in arbitrage trading, where they buy low, sell high, and can influence volatility.
HFT also improves price discovery, allowing traders to react to call price changes faster than non-HFT traders. This ability to rapidly process and respond to new information may have far-reaching effects — such as influencing market direction since institutional investors are the major HFT users. Market defragmentation may also occur due to HFT, as they seek price levels with favorable conditions, thereby decreasing transparency.
Short-term investors have more use of HFT than long-term swing traders, but it could also impact long-term trading if it enters a cycle of feedback loop for correlated trading. This could cause disruptions in the market and amplify price movements.
Advantages of High-Frequency Trading HFT has five major advantages:
- Speed and ease of execution: Trades are executed quickly and placed easily.
- The large volume of transactions: HFT algorithms can repeatedly open and
close positions with large volumes.
- Absence of human errors/factors: Algorithmic trading removes human factors
from trading and ensures strict data-driven decisions.
- Improved market liquidity: HFT provides liquidity in markets with repeated
execution.
- Tighter bid-ask spreads: Spreads are stricter and often removed after using HFT
for some time.
Cost reduction and innovation are other benefits of HFT in financial markets.
Criticisms and Challenges
For all of its advantages and benefits, HFT also poses some challenges that draw criticism today.
- More beneficial to institutional traders: HFT is considered more beneficial to institutional traders than all traders. This disparity may reflect on various aspects of the market.
- Ephemeral liquidity: Although HFT trading adds liquidity to markets, they are considered temporary liquidity, which vanishes within seconds and cannot be utilized
- by other non-HFT traders: Trading platforms often rely on liquidity providers, such as the supplemental liquidity providers (SLPs) used by the New York Stock Exchange (NYSE).
- Complex algorithms: Retail traders may not have access to such technologies due to the requirement for complex algorithms. HFT traders can easily push out traditional traders from financial markets.
Case Studies: Examples of High-Frequency Trading
Although HFT was launched in 1999, automated trading at high speeds began in 1976 with the NYSE’s DOT program.
The introduction of ECN (electronic communications networks) in the late 1990s improved the process and eventually led to the SEC (Securities and Exchange Commission) authorizing algorithmic trading in 1998.
HFT is credited with surviving the 2008 global economic recession triggered by the collapse of the housing market; HFT generated revenue of $15 to $20 billion. In 2012, a trading firm, Knight Capital, lost 40% of its value after accidentally trading stocks worth $7 billion through HFT.
Financial institutions with massive trading capital can leverage HFT to take advantage of the lag in updating prices between market sessions, such as the New York and London sessions and the latest market news.
Future Trends of High-Frequency Trading
The integration of artificial intelligence (AI) and machine learning (ML) will enhance the performance of HFT programs, which are automated trading robots. This will take the process to new levels and reduce the associated risks.
Cloud computing and microservices architecture will also play an important role, providing scalability and flexibility to accommodate market changes. Modern investors can also expect governments to propose regulatory frameworks to address new developments in HFT.
Ultimately, if you’re trading online, it’s worth keeping up to date with upgraded processes and government regulations so you can maintain your edge in a constantly evolving market.
