High-frequency trading (HFT) is a professional form of algorithmic trading. Firms use extremely fast market-data access and automated order systems to run short-term strategies, trade frequently within a day, and end the day close to flat. In U.S. electronic markets, the best-documented finding is that HFT, particularly market-making activity, is associated with tighter spreads, better liquidity, and lower transaction costs, mostly under normal market conditions. The same body of work identifies conditional liquidity, possible amplification of price moves during stress, and trade-offs in a fragmented market structure.
This article explains how these systems operate, what the cited reports do and do not establish, and which market-design questions remain open. It is an account of an institutional activity, not a guide to trading.
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What high-frequency trading means
The Securities and Exchange Commission’s 2020 Report to Congress on Algorithmic Trading treats HFT as a subcategory of algorithmic trading. It generally refers to professional traders that use extremely fast data access and processing to execute short-term strategies. HFT firms tend to trade frequently within a day and avoid carrying positions overnight.
HFT is not a settled regulatory category. In 2010 testimony about the May 6, 2010 market plunge, the SEC said HFT has no settled definition and may cover several strategies, not only passive market making. Study results depend on how a given paper identifies HFT activity, so check that definition before comparing findings. In the same period, then-SEC Chairman Mary L. Schapiro called HFT “one of the most significant market structure developments in recent years.”
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The features commonly attributed to HFT include:
- high-speed computer programs that generate, route, and execute orders;
- exchange colocation and individual data feeds, used to reduce latency (the delay between a market event and the system’s response);
- short position horizons;
- many orders canceled shortly after submission;
- ending the day near flat rather than carrying large unhedged positions.
How an HFT system operates
The features above form a rough operating sequence. The steps below are a simplified sketch built from those characteristics, not a description of any one firm’s software.
- Receive market data quickly. Firms place equipment in or near an exchange’s data center (colocation) and subscribe to individual data feeds to shorten the delay before they can react.
- Decide and generate orders. Automated programs produce buy and sell orders from the strategy’s logic.
- Route and execute. Orders are sent to one or more trading venues. Many are canceled shortly after submission, so the order book changes constantly.
- Hold positions briefly. Positions are short-horizon, and the firm aims for a position near flat at the end of the day rather than large unhedged exposure into the next session.
Three strategy families, and why they must be separated
Regulatory literature describes uses that are economically different. Treating HFT as one block hides the key distinction: some strategies supply liquidity to the market, while others take it.
Passive market making and liquidity supply
Market makers post buy and sell quotes and aim to trade with incoming orders. This is the function most associated with HFT’s liquidity benefits, and the SEC’s 2020 review reports that spreads often narrow where it is present. It is also the function where the quote-quality concerns discussed below apply.
Short-horizon responses to price discrepancies
Some strategies react when the prices of related instruments diverge. Austin Gerig’s 2015 SEC staff paper models this activity as a force that synchronizes prices in related securities. That mechanism can make prices more consistent, but it can also spread an incorrect relationship between securities.
Liquidity-demanding strategies that react to information
These strategies take liquidity by trading quickly against resting orders when new information appears. The SEC’s 2020 review notes that some such activity can pick off stale orders, raising adverse-selection costs for the participants who posted those orders.
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Benefits: what the evidence supports, and under what conditions
The benefits case rests mainly on the SEC’s 2020 synthesis, which states that most academic studies find algorithmic trading and HFT have improved market quality and helped reduce transaction costs. Results are not uniform, and they depend on how HFT is identified and on technology and regulation that keep changing.
Liquidity, spreads and transaction costs
The SEC’s 2020 report states there is ample evidence that, under normal market conditions, algorithmic trading and HFT improve liquidity and price efficiency and reduce short-term volatility. Market-making HFT often improves liquidity and reduces spreads, while strategies that pick off stale orders can increase adverse-selection costs.
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Depth also appears in a historical figure. In his May 11, 2015 statement, SEC Commissioner Luis A. Aguilar quoted one study: “Displayed market depth for the median stock has, according to one study, grown nearly 300% in the past eight years.” That describes a past period as of 2015. It is not a current measurement, and the statement does not establish HFT as its cause.
Price synchronization across related securities
Gerig’s SEC Division of Economic and Risk Analysis staff working paper, High-Frequency Trading Synchronizes Prices in Financial Markets, was published January 21, 2015. Its model finds that synchronization can make prices more accurate and reduce transaction costs. This is a model-based analysis of one mechanism, not a settled consensus, and the paper pairs its benefit with a warning about error propagation, covered in the risks section.
Competition across venues
Aguilar’s statement also offers a wider frame. Competition among venues and orders, he said, has been associated with lower trading costs, quicker executions, and better execution quality, but the decentralized structure also brings costs and complexity. Such gains reflect competition in market structure as a whole and should not be credited to HFT alone.
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Risks and limits
The risks below are mechanisms and concerns documented in these sources. They describe what can happen and what the evidence has and has not shown, not a universal outcome.
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Liquidity can be conditional
The Congressional Research Service’s 2025 report on high-frequency trading summarizes concerns that HFT quotes may be small, fleeting, or canceled quickly. It also notes that these firms lack the affirmative market-making obligations attached to certain traditional market makers. Some observers call the result “phantom liquidity.” That term is a criticism, not a description of every HFT quote. The concern is that displayed quotes may not be there when they are most needed.
Adverse selection and the cost of speed
When a fast participant picks off a stale order, the resting order that was not updated in time is filled at a price the market has already moved past. The SEC review identifies this as a source of higher adverse-selection costs. Competition for queue priority and speed also carries costs that do not necessarily appear as liquidity benefits, so a gain in speed for one firm is not automatically a gain for the market.
Stress amplification and error propagation
The SEC’s 2020 literature review says evidence, including evidence associated with the May 6, 2010 Flash Crash, indicates that HFT may exacerbate price movements in some high-volatility or stressed periods. Gerig’s model adds a mechanism: without safeguards, localized errors can propagate through linked securities during stress, and HFT may enforce incorrect relationships among securities. Neither source supports reducing a complex market event to a single HFT cause.
Fragmentation and transparency
Aguilar’s 2015 statement explains that splitting trading across many venues can make liquidity harder to locate, add complexity, increase vulnerability to disruption, and make order routing less transparent. He also notes that studies of fragmentation produced mixed findings and have methodological limits.
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Why cause is hard to prove
HFT is difficult to isolate in public data. Market structure, regulation, decimalization (the move to decimal price increments), and computing technology changed together over the same years, so a correlation between HFT growth and spread improvements does not show that HFT caused them. The CRS report explicitly cautions against reading correlation as cause. Readers should treat the effects described here as findings from particular studies, not as settled causal effects.
How effects differ by market condition and segment
The sources do not describe one uniform HFT effect. The table compares what the cited reports say under normal conditions and under stress.
| Measure | Normal market conditions | Stressed or high-volatility periods |
|---|---|---|
| Liquidity | Improved by market-making HFT, per the SEC’s 2020 review; CRS notes quotes may be small or quickly canceled | Not separately measured in the cited sources; the quick-cancellation concern is not limited to calm markets |
| Spreads and transaction costs | Market-making HFT often narrows spreads; most academic studies find reduced transaction costs (SEC, 2020) | Not stated in the cited sources |
| Price efficiency | Improved, per the SEC’s 2020 review | Not stated in the cited sources |
| Short-term volatility | Reduced, per the SEC’s 2020 review | HFT may exacerbate price movements, per the SEC’s 2020 review, including evidence associated with the May 6, 2010 Flash Crash |
| Error propagation | Not stated in the cited sources | Localized errors can propagate without safeguards, per Gerig’s model (January 21, 2015) |
Effects may also differ by segment. Large-cap and smaller-cap stocks, displayed and non-displayed trading venues, and different strategy types may not experience the same effects, so a finding for one segment should not be assumed for another. When you read a claim about HFT, check:
- which strategy is described: market making, price-discrepancy trading, or information-driven liquidity taking;
- which securities and which venues the study covers;
- whether the period was normal trading or stress;
- which definition of HFT the study used, and in what year;
- whether the claim is a correlation or a causal finding.
How much of U.S. trading is high frequency?
The sources cited here do not state a current official figure for the U.S. HFT share. The two numbers in circulation come from different years, markets, and definitions.
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| Source | Date | Scope | Reported figure |
|---|---|---|---|
| SEC testimony on the May 6 market plunge | 2010 | Equity markets | “Estimates of HFT volume in the equity markets vary widely, though they often are 50 percent of total volume or higher.” |
| Austin Gerig, SEC staff working paper | January 21, 2015 | U.S. and Europe, over the paper’s period | “In the US and Europe, it now accounts for nearly one-half of all trades.” |
| Current official U.S. share | Not applicable | U.S. markets | Not stated in the SEC’s 2020 report or the CRS 2025 summary |
Two cautions apply. The 2010 figures are estimates of equity volume that varied widely across sources. The 2015 figure combines the U.S. and Europe and should not be restated as a U.S.-only share. Neither describes today’s market.
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Does HFT help or hurt ordinary investors?
The sources measure market quality, including spreads, depth, transaction costs, price efficiency, volatility, and resilience. They do not measure the returns of individual investors. Lower transaction costs and tighter spreads can reach ordinary investors indirectly through the venues where they trade, but none of the cited sources quantifies the net effect on a given investor’s results.
Aguilar’s framing is useful here. He wrote that “no market structure is optimal for all market participants,” and that “The interests of investors and issuers should come first.” Those statements call for judging market design by its effects on investors, not by the speed of the firms involved.
HFT is not a retail product category. Its defining inputs, exchange colocation, individual data feeds, and low-latency infrastructure, are institutional arrangements. A faster consumer computer, a generic trading platform, or a home internet connection does not supply them, and buying hardware does not make a retail account a high-frequency participant. For individual investors, the practical question is how the market they use is designed, not how to replicate these systems.
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The cited sources point to design questions rather than investment opportunities, and they make no forecast of future returns or products. Four questions recur.
Quick Recap
- Keeping competition and dependable liquidity together. Competition has been associated with lower costs and better execution. The open question is how to keep those gains while the liquidity fast quotes provide remains dependable.
- Monitoring by segment and venue. Aguilar argues for granular monitoring of dark trading and warns against complacency. Monitoring that separates securities, venues, and market conditions would test whether the findings above hold in each setting.
- Safeguards against propagated errors. Gerig’s paper emphasizes safeguards against stress propagation. Which safeguards limit error propagation across linked securities, while preserving the price alignment his model links to lower costs, remains an open design question.
- Measurement that can separate causes. Separating HFT from the regulatory and technological changes that happened alongside it requires data that identify the activity directly. Without that data, the benefits and risks above will stay contested.
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