The Role of High-Frequency Trading (CFA Level 1): Defining High-Frequency Trading, Market Liquidity Influence, and Simple Visual of HFT Order Flow. Key definitions, formulas, and exam tips.
High-frequency trading (HFT) can seem like it belongs in a sci-fi movie—machines sending in thousands, even millions, of tiny trades at lightning speed, all orchestrated by complex algorithms. But for better or worse, HFT is a major part of modern equity markets. In this section, we’ll discuss what HFT is, how it affects liquidity and market dynamics, and ways investors and portfolio managers adapt to an environment shaped by these ultra-fast traders.
In broad terms, high-frequency trading is a category of algorithmic trading that operates on incredibly short timeframes (often microseconds or nanoseconds). These strategies leverage:
HFT firms often focus on capturing small, short-lived price inefficiencies before they vanish. Their cumulative trading volume can be enormous, even though their average holding periods may last only a few seconds or less. Maybe you’ve heard of stories about entire trading desks losing profitability if they’re only a few milliseconds slower than their competitors. That’s how competitive this space is.
One of the biggest positives often attributed to HFT is its potential to enhance market liquidity. Because HFT algorithms constantly place and cancel orders, there’s a steady supply of buy and sell interest in the order book. In theory, this tightens bid-ask spreads, meaning trades can be executed at more favorable prices.
For equity portfolio managers working with large orders, narrower spreads can help reduce immediate transaction costs when entering or exiting positions. At first glance, that sounds great. But let’s not forget the flip side, which we’ll dive into shortly.
Below is a tiny snapshot of how HFT might slot into the broader trading landscape:
flowchart LR
A["Institutional Investor <br/>(Places Large Order)"] --> B["Broker/Dealer"]
B["Broker/Dealer"] --> C["HFT Firm <br/>(Ultra-Low-Latency)"]
C["HFT Firm <br/>(Ultra-Low-Latency)"] --> D["Exchange"]
In many cases, institutional flows and HFT flows collide in the marketplace, with HFT traders potentially responding to large orders in microseconds.
While HFT may give the market more liquidity on paper, it can also create what some describe as “phantom liquidity.” Orders might be in the market one moment and gone the next, as algorithms rapidly adjust or cancel orders in reaction to shifting prices. This can become especially problematic during times of extreme volatility—like “flash crashes”—when liquidity can suddenly dry up, causing dramatic price moves.
HFT strategies have been accused of front-running. Although traditional front-running involves using insider info on a big pending trade, some HFT approaches rely on spotting a large incoming order and racing ahead of it (usually by detecting partial fills across multiple venues). Michael Lewis, in his book “Flash Boys,” highlighted how certain HFT firms seemingly exploit speed advantages to profit from institutional orders.
Then there’s the infamous 2010 Flash Crash, when major U.S. indices plunged almost 10% in a matter of minutes before recovering just as quickly. Many attributed the confusion in part to HFT algorithms pulling out of the market, leaving dangerously thin liquidity. This event triggered new regulatory measures, such as circuit breakers at the exchanges, designed to pause trading if price movements are excessively large or abrupt.
Regulators worldwide have grappled with how to supervise HFT. Across Europe, MiFID II introduced:
In the United States, the SEC has continually refined market rules and introduced additional controls to handle “market disruptions,” imposing risk checks for firms using automated systems. For instance, many HFT firms must register as broker-dealers, bringing them under the SEC’s oversight with all associated compliance burdens.
HFT calls for significant infrastructure:
Frankly, this is expensive—and the cost is ongoing. Traditional asset managers typically don’t dive into HFT because the opportunity cost might be high, and it’s not necessarily aligned with their strategic, long-term investment horizons. Many institutional asset managers instead rely on more conventional automated strategies and execution algorithms to handle routine rebalancing and reduce market impact (e.g., volume-weighted average price, time-weighted average price, or pegged orders).
Equity portfolio managers often invest for weeks, months, or even years, so they’re not chasing tick-by-tick price movements like HFT traders. Nonetheless, HFT’s existence shapes how everyone else trades. One major focus for the long-term trader is to minimize the “slippage” that can occur when large orders push prices against their best interest.
To minimize these costs, traditional managers use algorithmic trading (often offered by major brokers) that breaks large trades into smaller pieces. These algorithms might:
Dark pools, in particular, can be a refuge for institutional investors wishing to protect their order size. But these pools have also come under scrutiny regarding the fairness of executions, especially if HFT firms gain partial access to that order flow.
Let’s say you want to purchase 100,000 shares of Company X at $50. You create a plan that should, in theory, allow you to buy at that average price over a few hours, but the market moves quickly to $50.10 by the time your order is fully filled, partly because HFT participants detected your flow. That $0.10 difference might sound small, but multiply it by 100,000 shares, and you’ve got a $10,000 opportunity cost—an example of what we call “implementation shortfall.” Algorithmic strategies aim to keep that shortfall as small as possible.
We’ve heard the horror stories: an “algo run amok” that places nonsensical trades or accumulates an unintended massive position. This risk is real for any firm reliant on automated software—though obviously it’s heightened for HFT with its rapid-fire nature. That’s why robust oversight and risk controls are so crucial:
When these measures fail or are not strictly applied, the fallout can be costly—not only from a monetary standpoint but in reputational damage as well.
From a portfolio management standpoint, HFT is a double-edged sword:
As a result, portfolio managers frequently incorporate advanced execution strategies. They consult daily with trading teams to refine how large block orders are handled, deciding whether to place them in the lit markets or route them to dark pools, or even spread them out across multiple exchanges over time.
Though most portfolio managers may never run their own HFT strategies, they can adapt:
Because HFT remains contentious, global regulators continually discuss new rules to manage it more effectively. Potential updates might include:
Portfolio managers—especially those investing cross-border—must keep track of these changes because inconsistent global regulations can affect liquidity, transaction costs, and risk-management strategies.
In short: HFT is here, and it’s not going away anytime soon. Although it can contribute to liquidity and narrow bid-ask spreads, it also raises the risk of sudden market disruptions and creates challenges for large institutional trades. Portfolio managers must be aware of how HFT activity interacts with their own trading objectives. Tools such as algorithmic execution, dark pools, and careful real-time monitoring can mitigate unintended market impacts.
For the CFA exam, keep in mind:
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