High-Frequency Trading (HFT)
A trading method that uses advanced algorithms to automatically analyze multiple markets and execute orders based on market conditions
What Is High-Frequency Trading (HFT)?
High-frequency trading is a new-age trading method that uses advanced algorithms to automatically analyze multiple markets and execute orders based on market conditions.
High-frequency traders can conduct trades in a matter of microseconds, demonstrating the remarkable speed of execution. This high speed is achieved by:
- Transmitting data over fiber optic cable
- Microwave frequency broadcast
- Via co-location at exchange server sites
Speed of order execution is critical in high-frequency trading, and large investment banks and hedge funds employ this trading method.
Distance or the length of the physical lines (usually fiber-optic) transport data is the most significant determinant of latency. Financial institutions have invested billions of dollars in building infrastructure to reduce latency in the data feed.
Apart from that, the turnover ratio measures how frequently an asset or other portfolio's holdings are replaced in a given period. Turnover ratios can differ based on:
- Type of fund
- Its investment objective
- Portfolio manager's investing style
Funds managed by high-frequency traders have high turnover ratios. For example, a fund managed by these firms has stock or assets changed in a matter of seconds.
Additionally, the Order-to-trade ratios (OTRs) describe the relationship between orders/quotes entered into, modified, and deleted and transactions executed. High-order trade ratios are an essential feature of this type of trading.
Key Takeaways
- High-Frequency Trading (HFT) employs sophisticated algorithms and rapid data transmission methods, executing trades in microseconds and enabling lightning-fast transactions in financial markets.
- HFT allows traders to profit from small price fluctuations, boosts market liquidity, and reduces trading costs significantly by eliminating the need for human intervention.
- Despite its speed, HFT involves minimal profits per trade, which can lead to potential losses. It also poses risks of market instability and unethical practices like spoofing, challenging fair market practices, and investor confidence.
- HFT employs various strategies, including Index Arbitrage, Volatility Arbitrage, Merger Arbitrage, and Long-Short Equity trading, each tailored to exploit specific market conditions.
- HFT can lead to market manipulation, disadvantaging small investors and sometimes contributing to market instability. Regulatory challenges persist due to the fine line between legal and illegal practices, demanding continuous scrutiny and adaptation of regulations in this complex trading landscape.
Advantages of High-Frequency Trading
Let's take a look at some of the advantages this trading provides:
1. Profitable Swings
High-frequency and large-volume securities trading enable traders to profit from even little price swings. In addition, it allows institutions to profit handsomely from bid-ask spreads.
2. Market Analysis
Trading algorithms can analyze markets and exchanges, potentially uncovering new trading opportunities, such as arbitraging minor price variations between the sale of the same asset. This effectiveness varies based on the quality of the algorithms and market conditions.
3. Liquidity Boost
HFT enthusiasts argue that high-frequency trading may improve market liquidity due to faster deals and increased trade volume, potentially promoting market competition. However, this claim is a matter of ongoing debate and varies based on market conditions.
4. Cost Efficiency
Algorithm trading eliminates the need for paid human traders, lowering the cost of trading. In addition, because it is based on many orders, it aids in price discovery and formation.
Risks of High-Frequency Trading
The high-frequency traders pose different risks while using this technique. Some of the risks involved are:
1. Minimal Profits and Potential Losses
High-frequency trading involves rapid trading with minimal profits per trade, leading to potential losses due to the high trading volumes and quick pace.
2. Complex Algorithms and Market Instability
The strategy's reliance on complex algorithms can result in market instability and contribute to "ghost liquidity" (the difference between measured liquidity and tradeable liquidity), raising concerns about fair market practices and investor confidence.
3. Short Holding Periods and High Risk-Reward Ratio
High-frequency traders rarely retain their portfolios overnight, only invest a small amount of money, and only hold their positions for a short period before liquidating them. As a result, the Sharpe ratio, or risk-reward ratio, is relatively high.
The ratio is significantly higher than that of a traditional long-term investor. A high-frequency trader may only make a fraction of a penny profit, which is all they need to make gains throughout the day, but it also increases the risk of a considerable loss.
HFT is associated with market manipulation risks, including practices like spoofing, where traders exploit speed advantages for illegal activities, misleading other market participants.
5. Technological Vulnerabilities
The competitive race for speed also leads to substantial investments in cutting-edge technology, making firms susceptible to significant financial setbacks if their technology fails.
High-frequency trading strategies
HFT strategies refer to a set of rapid and automated techniques employed in financial markets to capitalize on fleeting price differentials and market inefficiencies. These strategies encompass a variety of approaches, such as:
The index arbitrage strategy exploits price differences between two or more market indices to generate profits. There are many ways to do this, depending on where the discrepancy arises.
Arbitrage may involve the same index traded on two different exchanges or include indexes whose typical relative values temporarily diverge from their standard.
Index arbitrage is the basis of program trading, where computers monitor millisecond changes between various stocks and execute buy or sell orders automatically to exploit discrepancies that shouldn't exist.
Because the chances are often brief and razor-thin, large financial institutions prefer to adopt a high-speed, algorithmic trading procedure.
2. Volatility arbitrage
The difference between the predicted future price volatility of an asset, such as a stock, and the implied volatility of options based on that asset is what volatility arbitrage is all about.
Consider a trader who believes a stock option is underpriced because the implied volatility is too low. In that situation, they could profit from the prognosis by buying a long-margin call and selling a short position in the underlying stock.
The option price will rise if the stock price does not move and the trader is correct about implied volatility growing.
Merger arbitrage is an investment bank strategy that includes buying and selling the stock of two merging companies simultaneously at the same time to generate "riskless" gains. Because the deal's completion is unclear, the target company's shares often sell for a discounted cost than the acquisition price.
Merger arbitrageurs are concerned with the likelihood of the deal being authorized and its time to complete. Merger arbitrage entails considerable risk because there's a chance the trade won't be authorized.
4. Long-Short Equity
Long-short equity is a strategy where investors establish long positions in undervalued businesses, expecting their value to rise while simultaneously selling short shares of overvalued companies, anticipating their decline.
Hedge funds often deploy long-short equity with specific allocation ratios, such as a 100/20 strategy, indicating the percentage of assets under management allocated to long and short positions, respectively.
The "pair trade" variation involves balancing long positions in one company with short positions in another stock within a similar sector to leverage relative market movements.
Popular High-Frequency Trading firms
The forerunners were founded in the 1990s. 16 of the 22 early high-frequency trading firms are still active.
Knight Capital was one of the early HF firms that went bust, notably losing $460 million due to a faulty trading algorithm. This highlights the risks involved in this kind of trading.
Start-ups in this sector had a tough time in the next decade. Among the 21 start-ups founded between 2000 and 2010, only 13 remain today.
All of the firms that followed after Veterans were founded in 2010. They are all still operating because of the experiences their founders gained from their former companies during the growth and lean years.
The algorithmic trading business has benefited immensely from the surge in volatility and volumes in 2020. Firms have reported record quarters across the board, and the industry has generally avoided scandals and public attention.
These are a few of the most famous high-frequency trading firms:
Jobs and roles at High-Frequency Trading Firms
The high-frequency trader life is exciting and rewarding. These firms attract talent from all fields, including economics and engineering. A Ph.D. in computer science or math is a requirement for quantitative analysts (quants) in developed countries.
For developing economies, a CS, math, or engineering degree or an MBA in finance from a college of repute will suffice to be a quant.
Roles in the HFT industry:
- Quant Analyst / Model Developer: Strong programming knowledge is a must. One must familiarize oneself with quant tools such as R, Matlab, and Python with an edge in probability and statistics.
- Strategy Developer: You would be expected to code or modify existing strategies as a Strategy Developer. It will be your responsibility to code the strategy into the execution platform with the help of a quant.
- High-frequency traders: Traders must be quick problem solvers and good at handling problems. These traders should be familiar with technical analysis and chart reading. As an HF trader, your job is very competitive in that you must constantly improve your method. Even when it's lucrative, it can also be disheartening.
- Networks/System Administrators: They are required to maintain a system that receives terabytes of data in a fraction of a second. A network administrator is required to deal with latency systems with extreme precision.
High-Frequency Traders Salary
The high-frequency traders aim to trade many times daily, earning small amounts for each trade. These professionals receive an average of $4,517 in extra cash compensation, falling within a range of $3,388 to $6,324.
The typical annual salary for individuals in the field of High-Frequency Trading in the United States is approximately $67,783.
Due to the meritocratic approach of HFT firms, projects are often given a significant amount of autonomy.
Quant roles often involve extended hours, such as 80-90 hours per week. However, for HF traders, the fast-paced growth, intellectual stimulation, and compensation outweigh the negative effects of the workload.
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HFT Ethics and Market Impact
HFT is like any other technological advancement; there are two sides to the coin. These traders ostensibly do not intend to deceive. Instead, this advanced trading aims to provide a competitive edge to its users.
These methods may impact:
- Small investors
- Large trading firms
- Brokers
- Other market participants
Is this trading beneficial to the majority? Well, that depends on the perspective. We will examine a few adverse effects of algorithmic trading on the market.
1. Market manipulation
If traders participate in market manipulation, algorithmic trading can give them an unfair edge. In addition, trading orders can manipulate the market to a trader's benefit. Trillium Capital is a notorious firm engaged in market manipulations.
2. Victimization of Small Investors
Another criticism is that it is unjust to small investors. Because they lack the resources to do so, small investors do not compete on an equal footing. One could argue that a lack of resources, such as high-capacity computers and informational imbalances, produces unfairness.
3. The Cascading Effect
The best example of the cascading impact occurred on May 6, 2010, with the "flash crash." In this case, a succession of worldwide events caused investors to be concerned about equities markets. This uneasiness played a role in the steep drop that day.
The Greek fiscal issue initially caused a market drop early in the afternoon. By executing short trades, other traders' bets on the market caused it to continue to fall.
The wave of movement activated AI traders that can track this type of activity, resulting in a subsequent sell-off. The enormous amount of orders overburdened the exchange systems.
The difficulty authorities have in identifying the line between lawful and illegal conduct in these ultra-speed trades was demonstrated by a 2014 Securities and Exchange Commission (SEC) settlement against Athena Capital Research.
According to the SEC's ruling, Athena's algorithmic techniques grew more predatory, ensuring the company traded attractive stock imbalances after the conclusion of each trading day.
Athena agreed to pay the $1 million fine and refrain from committing or causing further violations of the securities laws without admitting or disputing the findings.
In recent years, high-frequency trading has come under fire for increasing market susceptibility to illegal activity. The United States Court of Appeals for the Seventh Circuit upheld the ruling in 2017 against high-frequency traders for spoofing.
Academics and legislators disagree on whether it should be regulated more stringently than other types of trading and, if so, what those restrictions should be. Still, criminal law has rarely been used to restrict high-frequency trading.
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