In high-frequency trading (HFT), trades are executed at high speeds, and a large number of transactions are executed in a short time frame. Special computers are used to execute trades quickly, and due to its complexity, it is usually used by large institutional investors like hedge funds and investment banks.
In HFT, complex algorithms analyse individual stocks to spot emerging trends in milliseconds. If the analysis finds a trigger, hundreds of buy orders will be sent out in seconds.
Algorithmic trading involves using pre-programmed trading instructions to execute trading orders quickly in the financial market. Traders and investors use trading software to feed instructions based on time, volume, and price. As soon as the set instructions trigger in the market, the trading software executes the investor's orders.
The main purpose of algorithmic trading is to execute a large number of high-volume trades that would otherwise be impossible for humans to execute. This trading is commonly used by mutual funds, hedge funds, insurance companies, banks, etc. Algorithmic trading allows investors to make more trades in less time without being affected by human emotions.
It is fairly simple to understand how HFT works. The more trades investors do, the higher their profits. For someone engaged in HFT, even a fluctuation of ₹1 or ₹2 makes the trade profitable. A quantitative model determines all portfolio allocation decisions. Owners feed models specific information, and their success depends on their ability to process huge amounts of data, which is impossible for human investors. High-frequency traders compete by executing the most trades in the shortest amount of time. Those who succeed in achieving that objective make the most money.
The following are some HFT strategies:
Market Making: It's a company or investor who buys and sells shares at a publicly quoted price. By using predetermined HFT strategies to place limit orders to sell or buy, many high-frequency trading firms use market making as an effective strategy. These firms do this to earn the bid-ask spread and make money.
Quote Stuffing: It involves buying and selling a lot of orders fast to create confusion in the market. Due to this confusion, the trading volume rises, giving high-frequency traders profitable trading opportunities that they use to start multiple trades.
Tick Trading: In tick trading, powerful computers watch the flow of quotes, and the market information embedded in the market data. In tick trading, one needs to look for when HFT traders are starting to place huge orders.
Statistical Arbitrage: It's a way to identify price differences between securities on different exchanges or markets. Statistical arbitrage is used in liquid markets like bonds, equities, currencies, futures, etc. A HFT strategy can also include traditional arbitrage strategies like interest rate parity.
HFT has the following advantages:
Quick Profits: By executing a lot of trades, high-frequency traders can make quick profits. Even if there are small price fluctuations, investors can make hefty profits using HFT strategies through the bid-ask spreads.
Increased Opportunities: High-frequency trading involves powerful computers and software that can scan and analyse multiple markets simultaneously. As a result, investors can find arbitrage opportunities and profit by buying on one exchange and quickly selling on the other.
Enhances Liquidity: HFT enhances liquidity in the market. By increasing competition and trade volume, HFT results in a decline in bid-ask spreads, resulting in more efficient prices. Additionally, as liquidity increases, the market becomes more transparent and flexible, making it less risky for other investors.
Human Error Is Reduced: Due to the absence of human interference, HFT is more effective than traditional trading. When trading, humans are prone to making mistakes or entering or exiting at the wrong time. Moreover, humans are not capable of executing such a high volume of orders at such a rapid pace.
HFT trading has a few downsides. Some of these are:
Lack of Regulation: Since high-frequency trading involves complex algorithms and software, it is difficult to monitor and regulate. Scholars and finance professionals disagree about HFT, making it a controversial topic.
Replacement: In general, HFT is criticised because it has replaced many brokers and dealers with software and algorithms. At most times, a person's intellect is required to make profits when investing, which is why it is considered to be a flawed process. Also, a comprehensive trading strategy cannot be based solely on data and information.
One-Sided Profits: High-frequency trading is not possible for retail investors due to their lack of infrastructure. Due to this, only large companies with the required infrastructure can profit using the strategy, and retail investors lose out. Hence, the liquidity that arises is called 'ghost liquidity'.
Market Volatility: HFT can contribute to excessive market volatility. Algorithms can trigger massive buy or sell orders within milliseconds, which may result in sudden price swings and flash crashes, unsettling the broader market.
Technological Failures: HFT systems rely on advanced software and infrastructure. Any glitch, coding error, or hardware failure can lead to instant and large-scale financial losses.
Amplified Market Volatility: The rapid pace of trade execution can exacerbate market swings, especially during periods of uncertainty, increasing the chances of flash crashes.
Over-Optimisation Risks: Algorithms may be optimised for historical data but may fail under live market conditions due to changing patterns or unforeseen scenarios.
Front-Running Concerns: HFT firms may exploit millisecond-level advantages to act ahead of other investors, raising questions about market fairness and integrity.
Regulatory Uncertainty: With growing scrutiny, regulatory changes could impose restrictions or compliance costs on HFT operations, impacting profitability.
Neglect of Long-Term Strategy: The focus on speed over substance can sideline sound investment principles, contributing to short-termism and potential systemic risks.
High Infrastructure Costs: Maintaining ultra-low latency systems is expensive, limiting access to large firms and excluding smaller players.
HFT raises ethical concerns and questions about its impact on market integrity. While it adds liquidity, critics argue that the liquidity is often shallow and fleeting, which disappears in volatile times. HFT firms may also gain unfair advantages through access to privileged infrastructure, undermining the principle of a level playing field. The focus on speed over value can distort true price discovery and promote short-termism in markets. Additionally, aggressive strategies like quote stuffing or spoofing can manipulate prices and mislead other investors, leading to trust issues and potential long-term harm to market efficiency.
The high-frequency trading process involves executing trades at high speeds and completing a large number of transactions quickly. A special computer is used to execute trades quickly in HFT. Because of its complexity, it is usually used by institutional investors like hedge funds and banks. Using complex algorithms, high-frequency traders spot emerging trends in milliseconds. To navigate the complexity of trading, investors can get assistance from reputable platforms and brokers that are known for their expertise and valuable guidance.
Yes, high-frequency trading is legal in India and is regulated by the Securities and Exchange Board of India (SEBI). However, SEBI closely monitors such activity due to concerns around market manipulation, unfair access, and systemic risk. Firms must follow stringent rules related to co-location services, latency, and order-to-trade ratios.
Regulators like SEBI use surveillance systems that monitor order patterns, execution speeds, and trader behaviour. In India, there are also requirements like order-to-trade ratio limits, and periodic audits of algorithmic systems by exchanges or independent agencies.
Realistically, no. Retail investors typically lack access to the sophisticated infrastructure, high-speed data feeds, and co-location servers that are essential for HFT. The costs and technical complexity act as high entry barriers.
Latency refers to the delay between the moment market data is generated and when it is acted upon. In HFT, even microseconds of delay can affect profitability, so firms invest heavily in low-latency networks and servers located physically closer to exchange data centers.
Moving average (MA), Exponential moving average (EMA), Stochastic oscillator, and Moving average convergence divergence (MACD) are the best indicators for high-frequency trading.
This article is for informational purposes only and does not constitute financial advice. It is not produced by the desk of the Kotak Securities Research Team, nor is it a report published by the Kotak Securities Research Team. The information presented is compiled from several secondary sources available on the internet and may change over time. Investors should conduct their own research and consult with financial professionals before making any investment decisions. Read the full disclaimer here.
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