4 Major Risks of Algorithmic High-Frequency Trading in Today's Markets

Thomas J Catalano, a CFP and Registered Investment Adviser in South Carolina, founded his financial advisory firm in 2018. His extensive expertise covers investments, retirement, insurance, and comprehensive financial planning.

What Is Algorithmic High-Frequency Trading (HFT)?

Algorithmic trading, often called "algo trading," uses computer algorithms—predefined sets of instructions—to execute large trades of stocks or other financial assets efficiently, aiming to reduce the market impact of these transactions. This approach breaks down big trades into smaller portions to avoid significant price fluctuations.

The advantages of algorithmic trading are clear: it ensures optimal trade execution by minimizing human error and allows trading across multiple markets and asset classes more efficiently than manual trading.

Key Insights

• Algorithmic trading employs computer algorithms to handle large blocks of stocks or assets while minimizing market disruption.
• Trades are executed based on specific criteria and divided into smaller segments to prevent drastic price changes.
• The main benefits include best execution of trades and enhanced efficiency compared to human traders.
• High-frequency trading (HFT) is a subset that places thousands of orders at extremely rapid speeds.
• While both algorithmic trading and HFT have improved liquidity and pricing accuracy, they also introduce risks, particularly the amplification of systemic risk.

Understanding Algorithmic High-Frequency Trading

High-frequency trading elevates algorithmic trading by executing thousands of trades within fractions of a second. The objective is to earn small profits from price differences of the same asset across different markets. Unlike traditional long-term investing, HFT thrives on rapid arbitrage and market-making within very short timeframes.

The rise of algorithmic trading and HFT is driven by technological advances, the increasing complexity of financial instruments, and the ongoing pursuit of greater trade execution efficiency and lower costs.

Although these trading methods enhance market liquidity and price consistency, they also carry inherent risks that must be carefully managed.

The Greatest Threat: Amplification of Systemic Risk

A key concern with algorithmic HFT is its potential to escalate systemic risk within the financial system. A 2011 report from the International Organization of Securities Commissions (IOSCO) highlighted how interconnected markets allow algorithms to rapidly spread shocks, intensifying systemic vulnerabilities. The May 2010 Flash Crash exemplifies this risk.

The Flash Crash saw major U.S. equity indices plunge 5% to 6% and rebound within minutes, with the Dow Jones dropping nearly 1,000 points intraday—its largest point decline at that time.

During this event, numerous stocks and ETFs experienced extreme price swings, with some trades executed at prices far removed from their true value, causing widespread market disruption.

Fast Fact

The lightning-fast pace of algorithmic HFT means a single malfunctioning algorithm can incur millions in losses within moments.

This sudden volatility unsettled investors, especially as it followed a period of market recovery from historic lows.

Did "Spoofing" Trigger the Flash Crash?

The cause of the Flash Crash was initially attributed to a large $4.1 billion program trade by a mutual fund trader. However, in 2015, authorities charged Navinder Singh Sarao, a London-based day trader, with market manipulation contributing to the crash.

Sarao allegedly employed "spoofing," placing large fake orders on the E-mini S&P 500 futures contract that were canceled before execution. These deceptive orders misled other traders about genuine market demand, influencing their trading decisions.

For instance, a spoofer might place large sell orders at prices slightly off-market to push prices down, then cancel those orders and buy at the lower price before reversing the process to profit. This repetitive tactic manipulates market prices unfairly.

While debate continues on whether one trader alone caused the Flash Crash, the event underscores how algorithmic HFT can magnify systemic risks.

Why Does Algorithmic High-Frequency Trading Amplify Systemic Risk?

Heightened Market Volatility

Due to the prevalence of algorithmic HFT, many algorithms are designed to outpace competitors by instantly reacting to market changes. During turbulent periods, algorithms may widen bid-ask spreads or halt trading to avoid risk, reducing liquidity and increasing volatility.

Market Ripple Effects

Global market integration means a crisis in one asset class or market can cascade across others. Historical examples include the U.S. housing market crash triggering a worldwide recession and the 2015-2016 Chinese stock market and oil price crashes impacting global equities.

Investor Uncertainty

Algorithmic HFT contributes to exaggerated volatility, fueling short-term investor uncertainty and potentially undermining long-term consumer confidence. Sudden market drops often leave investors searching for explanations, while large traders reduce positions to limit risk, further pressuring prices.

Important

This cycle of volatility can activate stop-loss orders, creating a downward spiral that damages consumer confidence and signals recession risks.

Additional Risks of Algorithmic High-Frequency Trading

Faulty Algorithms

The rapid pace of HFT means a single flawed algorithm can generate massive losses quickly. Knight Capital’s 2012 incident, where a faulty algorithm caused $440 million in losses within 45 minutes, is a stark example.

The algorithm executed erroneous trades, buying stocks at higher prices and selling them at lower ones, which competitors exploited, nearly bankrupting the firm.

Substantial Investor Losses

Volatility spikes caused by algorithmic HFT can trigger stop-loss orders, forcing investors to sell at unfavorable prices. If prices rebound quickly, investors suffer unnecessary losses and missed opportunities.

Most trades during events like the Flash Crash stood, meaning many investors incurred real losses despite temporary market disruptions.

For example, during the Flash Crash, an investor with a $500,000 portfolio and 5% stop-loss orders could have lost approximately $25,000.

Similarly, the NYSE canceled trades during the Knight incident when prices deviated significantly, following its "Clearly Erroneous Execution" rules.

Decline in Market Confidence

Investor trust hinges on market integrity. Recurring episodes of abnormal volatility, such as the Flash Crash, Facebook’s troubled IPO, and Nasdaq’s trading halt, can erode confidence and drive cautious investors away.

Measures to Mitigate Algorithmic High-Frequency Trading Risks

In response to incidents like the Flash Crash and Knight Capital’s losses, exchanges and regulators have introduced safeguards. Nasdaq implemented a "kill switch" in 2014 to halt trading if risk thresholds are exceeded, supplementing existing controls within HFT firms.

Circuit breakers, established after the 1987 crash, pause trading during sharp market declines to prevent panic selling. Updated SEC rules enable halts when the S&P 500 drops 7%, 13%, or 20% within a trading day.

In 2021, the Commodity Futures Trading Commission mandated pre-trade risk controls for algorithmic trading in derivatives, though the requirement for source code disclosure was removed amid controversy.

Conclusion

Algorithmic high-frequency trading carries significant risks, most notably its capacity to magnify systemic financial vulnerabilities. By intensifying market volatility, it can trigger ripple effects across global markets and fuel investor uncertainty. Persistent episodes of unusual market behavior may ultimately undermine confidence in market fairness and stability.

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