Written by: Jinsong Park
Algorithmic Trading (AT) has become a key method in modern financial markets, which rely on programmed computation algorithms to execute trades with fast speed and efficiency. High-Frequency Trading (HFT), a subset of AT, operates at an even faster pace, executing large volumes of trades in extremely short intervals (The New Titans of Wall Street). Although AT and HFT have introduced benefits such as narrowed bid-ask spreads and lower transaction costs, they have also spurred debates regarding market fairness, liquidity fragility, and potential volatility spikes. This journal will explore the key features of AT and HFT, focusing on their impact on market dynamics with broader financial stability and liquidity.
In stable periods, HFT can provide a steady stream of orders that reduce spreads and facilitate rapid execution for diverse market participants. Despite these liquidity advantages, episodes of severe stress expose the current limits of HFT. A firm’s decision to scale back trading can create a sudden liquidity gap, as notably observed during the 2010 Flash Crash. During this event of the Flash Crash, the Dow Jones Industrial Average plummeted nearly 1,000 points (about 9%) within minutes where it only rebounded almost as quickly.
This liquidity fragility raises for market stability, since withdrawals compound the existing volatility and undermine investor confidence. This withdrawal magnified downward momentum, triggering automated stop-loss orders and further algorithmic selling, thus exacerbating short-term price swings.One of the central concerns about HFT is that high-speed algorithms can exacerbate short-term volatility. By reacting to rapidly changing market signals immediately, multiple algorithms generate sharp price swings that lead to short-term volatility. The Flash Crash of 2010 once more underscores how even minor triggers can spark dramatic collapses or surges when algorithmic trades cascade through interconnected markets.
HFT operates within highly interconnected global markets, systemic risk can spread quickly if sell-offs are triggered all together. The sheer volume of automated trades can overwhelm traditional safeguards, raising questions about the resilience of market infrastructure. Additionally, critics argue that HFT firms benefit from privileged data access, leading to accusations of market manipulation and undermining public trust (The New Titans of Wall Street). Regulators therefore face persistent challenges in designing frameworks robust enough to guard against flash crashes, manipulative behaviors, and other unintended consequences. Due to the certain risks, regulators have a tough job trying to create rules that prevent market crashes and manipulative behaviors.
Regulatory bodies have attempted to rein in the potential downsides of HFT through measures such as minimum resting times for orders, circuit breakers, and enhanced reporting requirements. However, firms continue to escalate their technological arms race, investing in faster data transmission channels and advanced analytical models (Financial Times, 2024). Policymakers must balance the need to encourage innovation and competition against the imperative to maintain market stability, particularly as HFT expands into fixed-income markets like Eurozone bonds.
By contrast, human-centered or manual trading, Warren Buffett’s long-term, value-oriented approach do often avoids short-term speculation and places greater emphasis on fundamental analysis (Buffett). Human intuition, as argued by Buffett and others, can offer insights into management quality, changing consumer preferences, and broader economic trends that might be difficult to encode into a computational algorithm (Cunningham 2019). These strategies generally reduce the likelihood of rapid position reversals that can destabilize markets, thus fostering a potentially more resilient form of investment.
Recent years have witnessed major HFT participants, such as Citadel Securities, seeking to become “material players” in Eurozone bond markets (Finance Time, 2024). This expansion illustrates how algorithmic firms are reaching beyond traditional equity markets and extending their influence to a wider range of asset classes. Meanwhile, the integration of artificial intelligence in trading systems has accelerated, promising improved data-processing capabilities but also raising new concerns about volatility and unpredictability (Reuters, 2024).
Algorithmic Trading and High-Frequency Trading will continue to advance its technology in the finance world and become the behavior of global financial markets. While these methods offer notable benefits such as improved liquidity, reduced transaction costs, and greater efficiency; they also carry risks tied to volatility amplification and the sudden disappearance of liquidity. Human-centered approaches, centered on fundamental analysis on one’s intuition and long-term investment by seeding undervalued companies which can provide a stabilizing counterbalance to the automation and rapid turnover that characterize much of modern trading. As HFT expands into new territories, AI-driven strategies become more prevalent. There should be ongoing monitoring and thoughtful policy interventions remain essential to preserving both stability and confidence in global markets to prevent potential risk of algorithmic trading.
Works Cited
“Citadel Securities Aims to Become ‘Material Player’ in Eurozone Bond Trading.” Financial Times, 15 Mar. 2024.
“Flash Crash of May 6, 2010: Events and Analyses.” U.S. Securities and Exchange Commission, 2010.
“For Markets, AI Efficiency May Bring Volatility.” Reuters, 17 Oct. 2024.
“The New Titans of Wall Street.” Wall Street Journal, n.d.
Buffett, Warren E. “2023 Letter to Shareholders.” Berkshire Hathaway, 25 Feb. 2023.
Cunningham, Lawrence A., editor. The Essays of Warren Buffett: Lessons for Corporate
America. 5th ed., Carolina Academic Press, 2019.
Guess, Michael. “Are There Specific Indicators Best Suited for Day Trading?” Medium, InsiderFinance Wire, 6 Sept. 2023, wire.insiderfinance.io/are-there-specific-indicators-best-suited-for-day-trading-bd5abcc1647f.