Discover how high-frequency trading affects market liquidity, the role of advanced technology in modern equity markets, and key regulatory considerations and best practices for HFT.
High-frequency trading (HFT) has transformed the modern equity market landscape—not just in the sense of speed, but also in terms of the strategies used to discover market inefficiencies and exploit even the tiniest price discrepancies. If you’ll allow me to share a quick story, I remember sitting in on a roundtable discussion with HFT developers a few years ago—one even joked that, in the race to reduce latency, he was more worried about the time it takes for electrons to travel through fiber cables than anything else. That’s how high-stakes and precise this business can be. And it sure sounds thrilling, right? But with that thrill comes complexity, risk, and the potential for regulatory headaches.
HFT simultaneously offers liquidity and, if you want to be dramatic about it, the possibility of market instability. Put simply, HFT participants frequently operate as market makers by posting bids and asks, only to change or cancel them in fractions of a second. Well-executed HFT can narrow spreads and improve price discovery. In less ideal scenarios—like the infamous “Flash Crash” of 2010—there’s a risk that large volumes of orders get canceled so quickly that liquidity effectively vanishes, leaving typical investors scrambling.
In this article, we’ll explore the nuts and bolts of high-frequency trading, with a special focus on market liquidity, risk considerations, regulatory updates, and the practical implications for equity investors. We’ll keep it a bit casual, so you shouldn’t expect to be reading dense legalese every other paragraph. At the same time, because you’re studying for the CFA exam, we’ll keep the content thorough, tying real-world examples back to fundamental concepts that might show up on your test.
High-frequency trading generally involves placing thousands (or even millions) of orders across various markets and instruments in an effort to capture minuscule but consistent profits. Below are some of its most important components:
Algorithmic trading is broader than HFT. Think of it as the umbrella under which HFT sits. Algorithmic trading involves using computerized strategies to execute orders based on pre-set rules—these rules might concern timing, price, volume, or even more complex features such as technical indicators or real-time news. HFT simply ratchets this up a thousand notches and focuses on very short holding periods, ultra-high speed, and massive order volumes.
Latency means delay. The shorter the delay, the faster you can react to price movements. HFT participants spend enormous resources on:
• Optimizing hardware.
• Acquiring premium data feeds.
• Co-location (placing servers physically close to exchange servers to reduce signal travel time).
It’s almost funny how people will measure time in microseconds, or even nanoseconds, to see who can get that teeny advantage. Many times, the edge is no more than a fraction of a millisecond.
HFT strategies rely heavily on real-time market data. Traders subscribe to direct exchange data feeds—bypassing potential data “bottlenecks” from third-party aggregators. Some HFT firms even purchase fiber routes that literally try to follow the Earth’s curvature in the shortest possible path. The reason is simple: If you can know the best bid or ask price a microsecond ahead of your competitor, you can submit trades at a more favorable price.
High-frequency traders are often liquidity suppliers. They post thousands of orders at both the bid and ask sides. Because they continuously revise these orders, the result is typically narrower bid-ask spreads. Narrower spreads, in turn, mean lower transaction costs for other market participants. Everything sounds wonderful, right?
Many market makers today employ HFT strategies to facilitate efficient order matching. For large institutions, transacting in markets populated by HFT can serve as a double-edged sword, but under typical conditions, it offers:
• Deep order books, as HFT participants place an enormous quantity of small orders.
• Tighter spreads, which reduces the overall cost of trading.
• Rapid price discovery, especially useful in times of normal or moderate volatility.
If an investor like you or me wants to buy shares of an actively traded stock, the presence of HFT typically ensures we can do it at a near-instantaneous price that is closer to “fair value” (whatever that might be at the moment).
Let’s cut to the chase: One of the major criticisms is that HFT firms can withdraw their liquidity in a heartbeat. If an algorithm detects an unusual or adverse event, it can cancel or retract orders in microseconds, effectively causing a “liquidity vacuum.” That’s precisely what happened during the Flash Crash on May 6, 2010. The official story goes that a massive trade in the futures market triggered certain algorithms to start selling aggressively. Then, as prices fell, many HFT algorithms simply stopped providing liquidity and canceled their resting orders. This caused prices to plunge rapidly—some big-name stocks briefly traded at a penny (!)—before everything snapped back.
The overall effect:
• Extreme intraday volatility.
• Rapid dislocation of prices from underlying fundamentals.
• A potential loss of confidence in the market’s stability.
In a way, the presence of HFT can be a boon or a bane, and the line between the two can be minuscule. If we’re discussing “typical” conditions, it’s incredibly helpful for liquidity. But under extreme stress, it can be a different story.
Regulators and exchanges worldwide are grappling with how to manage HFT. There’s nothing inherently illegal about using fast, data-driven strategies, but certain practices have been flagged as manipulative or detrimental to market integrity.
When you have a bunch of algorithms chasing each other’s trades, especially if they all incorporate similar triggers—well, you might see herd-like behavior, only in microseconds. This can cause volatility to spike out of nowhere. For big institutional traders, it’s a real concern because sudden price swings can disrupt portfolio rebalancing or even trigger margin calls.
We can’t ignore that HFT, if used nefariously, can facilitate manipulative techniques such as:
• Quote-stuffing: Submitting a massive number of orders and cancellations in quick succession, bogging down exchange systems, and creating illusions of volume.
• Spoofing: Placing large orders without the intention of executing them, so as to mislead the market about supply or demand.
• Layering: Placing multiple orders at different price levels to create a false impression of depth.
In many jurisdictions, spoofing is explicitly illegal and punishable by hefty fines and prison sentences. The U.S. Dodd-Frank Act singled out spoofing as a form of manipulation. Similarly, the European Union’s Markets in Financial Instruments Directive (MiFID II) and Market Abuse Regulation (MAR) address manipulative practices in algorithmic and high-frequency trading.
Some common measures to keep HFT in check include:
• Circuit Breakers: Automated halts in trading if prices move beyond predefined thresholds.
• Limit Up/Limit Down: Rules that prevent trades from occurring outside a specific range around the most recent reference price.
• Fee Structures: Exchanges may alter fees for adding or removing liquidity to discourage excessive canceled orders.
From a regulatory standpoint, authorities also require HFT firms to stress test their algorithms, maintain audit trails, and comply with pre- and post-trade risk checks. The message is clear: If you want to trade in high-speed markets, you’d better be prepared to show you’re doing so in a fair and well-controlled manner.
The speed factor in HFT can’t be stressed enough. Traders co-locate servers within the same data centers as major exchanges. Fiber cables are replaced with even faster mediums—some firms have tested microwave and laser-based communication lines to shave off microseconds. The advantage can be ephemeral: once you have it, your competitors will race to beat you. The arms race is constant and expensive.
Below is a simplified depiction of a typical HFT order flow:
flowchart LR
A["Market Data <br/>Feed"] --> B["Pre-Trade <br/>Risk Checks"]
B --> C["HFT Algorithm"]
C --> D["Server at <br/>Exchange (Co-Located)"]
D --> E["Order Execution <br/>(Matching Engine)"]
E --> F["Order <br/>Confirmation"]
F --> A
To clarify, here’s what happens:
You might be wondering: “How does all of this speed and complexity affect me as an investor, or even a portfolio manager?” Well, in normal market conditions, HFT:
• Reduces spreads.
• Increases order book depth.
• Potentially enhances price discovery.
Where you might see a negative impact is in scenarios involving big trades. A large institutional investor might use a specialized algorithm (not necessarily HFT) to split a big order over time to minimize market impact. However, sophisticated HFTs can sometimes sniff out these “footprints” and trade ahead, a tactic colloquially known as “front-running” (though front-running in a legal sense typically implies a broker misusing client information).
Furthermore, from a portfolio management perspective:
• Transaction cost analysis becomes more complicated. You need to factor in not just commissions and fees but also slippage and the possibility of HFT targeting large orders.
• Risk management needs to account for the possibility of sudden liquidity pullbacks.
• Short-term anomalies or “mini-flash crashes” might create opportunities or hazards, depending on whether your systems can respond as quickly.
One of the most infamous examples was the May 6, 2010 Flash Crash, when the Dow Jones Industrial Average plummeted roughly 1,000 points in minutes, then quickly reversed. Investigators found that a large sell order in E-mini futures, combined with certain HFT algorithms, led to a feedback loop that ultimately drained liquidity. The result was a jaw-dropping but brief market collapse.
Knight Capital lost hundreds of millions of dollars in less than an hour due to a faulty software deployment. A code glitch erroneously sent large orders to the market, demonstrating what can happen when risk controls and systems testing do not catch errors in a high-speed environment.
There have been many high-profile spoofing cases. Regulators in the U.S., UK, and across Europe have levied fines on individuals and firms that placed large orders with no intention of executing them, purely to manipulate market sentiment. This underscores how swiftly authorities have cracked down on nefarious HFT strategies.
In the CFA Program—especially at Level I and Level II—topics like primary vs. secondary markets, order types, and the fundamentals of market microstructure are core. By Level III, you’re looking at portfolio-level insights and advanced risk management. HFT ties together market organization, microstructure, portfolio execution cost analysis, and regulation.
From an exam standpoint, watch for:
• Item sets (vignettes) describing abnormal trading behavior.
• Questions on how liquidity and spreads are affected by technology-driven trading.
• Ethical and professional standards scenarios addressing market manipulation.
• Structured response prompts that ask you to evaluate the effect of HFT on trade execution costs or portfolio volatility.
And remember, whenever an exam question touches on new regulatory reforms or best execution policies, consider how HFT might be impacted or how it might factor into a recommended course of action.
• Be Familiar with Definitions: Know the difference between algorithmic trading and HFT. Understand what spoofing, layering, and quote stuffing look like.
• Connect HFT to Liquidity: The exam might test your ability to argue both sides—how HFT can improve liquidity under normal conditions but also threaten it under stress.
• Think About Regulatory Regimes: Expect references to U.S. and EU frameworks. The CFA Institute Code of Ethics and Standards of Professional Conduct also addresses market manipulation.
• Prepare for Scenario-Based Questions: They might give you a scenario involving a flash crash-like event and ask how a portfolio manager should respond, or how risk controls could mitigate the damage.
• Aldridge, I. (2013). High-Frequency Trading: A Practical Guide. Wiley.
• CFA Institute. (Current Year). “Market Organization and Structure” in CFA Program Curriculum.
• Bank for International Settlements. BIS Papers on High-Frequency Trading: https://www.bis.org
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