Market fragmentation is driven by the fact that different participants aim to solve different trading problems, and that there is an inherent tenson between the competition among orders and the competition among markets. Most of the analysis in this seriesare derived from the textbook Trading & Exchanges by Larry Harris.
Overview
Financial markets today are marked by a bewildering array of trading venues, execution models, and regulatory mandates—all designed to balance competing needs for liquidity, efficiency, and fairness. In this post, we examine the forces driving market fragmentation and the resulting tension between competition among orders (the heart of price discovery) and competition among markets (the proliferation of alternative trading venues). Drawing on Larry Harris’s Trading & Exchanges and real-world practices—from best-execution obligations and payment-for-order-flow to dark pools and internalization—we’ll explore how different participant types (noise traders, information traders, and liquidity providers) interact across a fragmented landscape. Along the way, we’ll unpack:
- Best Execution requirements and the role of the NBBO
- Payment for Order Flow and the trade-offs between retail price improvement and broader market efficiency
- The anticompetitive externalities of preferencing and internalization
- How dark pools and order crossings reshape liquidity for large blocks
- The persistent friction between delivering low-cost execution for utility traders and preserving tight, information-rich order books for informed participants
By the end, you’ll understand why fragmentation is both a natural consequence of diverse trading needs and a regulatory dilemma: too little competition among venues stifles innovation, while too much undermines the very price discovery that makes markets socially valuable.
Best Execution
Brokers have a responsibility to provide best execution for their clients, i.e., to execute orders quickly at the best available prices. However, most execution traders know that sourcing liquidity can sometimes take time, and as such there is an inherent tension between the price at which an order can be executed and how quickly it can be filled. FINRA Rule 5310(a)(1) requires broker-dealers to “use reasonable diligence to ascertain the best market for the subject security and buy or sell in such market so that the resultant price to the customer is as favorable as possible under prevailing market conditions.”
The NBBO (National Best Bid and Offer), part of the National Market System (Reg NMS), is used to determine best execution. The NBBO displays the most favorable bid and ask prices for NYSE- and Nasdaq-listed securities. When brokers secure prices better than the NBBO for investors, it’s called “price improvement” (Reg NMS Rule 600(b)(92)). However, the price assigned to the customer by the broker can be any price at or between the NBBO displayed by the SIP (Securities Information Processor, i.e., the consolidated feed) during a one-second interval (Reg NMS Rule 600(b)(92)) for market orders.
Payment for Order Flow
Payment for Order Flow (PFOF) is a practice whereby a broker receives compensation from a third party—typically a market maker—for directing customer orders to that market maker for execution; this is also called preferencing. Compensation can take the form of monetary payments or other benefits. The practice was pioneered in the 1990s by Bernard Madoff, who at the time had a stellar reputation but was later convicted of serious financial crimes. When a broker‐dealer fills a customer’s order itself, this is called internalization. While FINRA permits brokers to internalize and preference orders, these arrangements must not compromise the broker’s obligation to achieve best execution (FINRA Rule 5310).
PFOF creates a clear conflict of interest for brokers. While brokers must provide best execution for their clients’ trades, PFOF arrangements give them a financial incentive to route orders to those market makers that pay for the flow. This can lead brokers to prioritize their own profits over their clients’ interests. The conflict exists because the market maker offering the highest payment may not offer the best execution or price improvement. Indeed, it may be easier for a market maker to pay a broker if it doesn’t deliver the best possible fill. The fact that a market maker pays for order flow suggests—but does not guarantee—that it could provide better prices. Brokers are supposed to review market‐maker fills and demand greater price improvement, but doing so may reduce the payments they receive.
The SEC has implemented regulations to address concerns around PFOF. The current framework aims to increase transparency and protect retail investors by mandating specific disclosure requirements for brokers and market centers. In 2005, the SEC introduced Reg NMS, which, among other provisions:
- Requires market centers, including market makers, to publish monthly reports on their trade‐execution quality (Rule 605).
- Mandates broker‐dealers to provide quarterly reports on their order‐routing practices (Rule 606(a)).
- Obligates broker‐dealers to disclose routing practices and relationships with execution venues upon customer request (Rule 606(b)).
- Stipulates that broker‐dealers inform new customers about any PFOF arrangements and the potential for improvement beyond the NBBO (Rule 607).
Collectively, these rules ensure that substantial information about order routing, PFOF arrangements, and execution quality is available to market participants. This transparency enables investors to assess whether PFOF affects execution quality and helps them make informed decisions about their brokers and trading strategies.
Liquidity Improvement with Internalization and Preferencing
Matt Levine of Bloomberg has written in favor of PFOF generally—and Robinhood Markets in particular—arguing that by segregating informed order flow from noisy (i.e., retail) order flow, market makers can offer price improvement because they face less adverse selection and are more likely to receive balanced two‐way flow. As Levine explains:
“[The] risk of being a market maker on the public stock exchanges: Sometimes you sell 100 shares to a small retail investor and it’s random noise; other times you sell 100 shares to Fidelity and you get run over. But if a market maker can guarantee that it will only interact with retail customers—if it can filter out big orders from institutional investors—then its risk of adverse selection goes way down. The way the market maker does this is by paying retail brokers to send it their order flow, and promising those brokers that it will execute their orders better than the public markets would. It can offer a tighter spread than the public markets—and have money left over to pay the retail brokers—because it doesn’t have to worry about adverse selection. If the retail broker is, say, one designed to let young people day-trade for free on their phones, then those orders are probably particularly valuable, because they are probably particularly random.”
He also addresses the most common objection to PFOF:
“[I]t is bad for investors whose orders aren’t sold to market makers, the institutional investors who instead trade on public stock exchanges. Payment for order flow fragments the markets, takes retail order flow away from the public stock exchanges, widens out spreads on those exchanges, and, by segregating retail and institutional orders, makes institutional execution worse. This objection is probably true! If you’re a hedge-fund manager, you should dislike payment for order flow, because it makes public markets worse for you.”
“[B]y selling its customers’ orders to market makers, Robinhood is actually stealing from two sets of ‘the rich’: rich market makers [who] are paying it directly for the orders, while rich hedge-fund managers are getting worse execution on public stock exchanges so that Robinhood customers can get better executions off those exchanges. Big institutions are paying to subsidize free trades for Robinhood’s customers. It feels pretty Robin-Hood-y!”
This analysis highlights the trade-off between encouraging price efficiency—by reducing costs for informed traders—and providing liquidity to utilitarian traders via order flow segmentation. It’s difficult to quantify how much the broader economy is hurt by reduced price efficiency, but the savings to retail investors are straightforward to measure. For example, Robinhood’s Execution Quality page reports that they save customers approximately $2.77 per 100 shares on average and that executions typically occur within the middle 40% of the NBBO.
Anticompetitive Aspects of Internalization and Preferencing
Dealers and other limit‐order traders are more likely to post aggressive prices when their quotes attract order flow. Internalization and preferencing by brokers therefore divert flow away from those aggressive liquidity providers and reduce incentives to quote aggressively. As a result, the bid–ask spread on central exchanges widens beyond what one would expect from increased adverse selection alone. This effect also distorts the measurement of “price improvement,” which compares PFOF execution quality against the centralized‐exchange NBBO.
This behaviour isn’t unique to financial markets—it also appears in consumer markets. For example, travel portals and retailers often promise to meet or beat any advertised price. While this seems like aggressive competition, it actually discourages others from posting the lowest prices; if price-matching guarantees exist, the cheapest seller won’t attract additional orders. In the long run, consumers would benefit more if sellers simply offered the lowest prices rather than matched the lowest.
The above effect mainly applies to market orders captured by PFOF, but limit orders suffer too. When retail investors submit limit orders to their brokers, the broker can route them to the central market—which often offers standing liquidity‐fee rebates (more on the maker-taker model later). However, these limit orders face severe adverse selection: they’re unlikely to execute against other retail market orders (which are internalized) and are more likely to fill against informed flow. In other words, retail limit orders are less likely to trade when prices will move toward them and more likely to fill just before prices move against them.
Market makers are supposed to compete with other limit-order traders to provide liquidity; yet by engaging in preferencing and internalization, brokers shift bargaining power toward those market makers that pay for flow. Segmentation thus grants liquidity providers a net advantage, raising overall trading costs for all liquidity takers—even though some small retail orders may see significant price improvement.
Theoretical Equilibrium vs Reality
Brokers internalize and preference order flow to segment uninformed trades. In a perfectly competitive market, PFOF ensures that dealers do not earn excess profits from trading against that flow: zero commissions and stock-and-cash bonuses offset any trading spread, so low execution costs trade off against execution quality and net trading costs become independent of best-execution standards.
However, no market is perfectly competitive. High-frequency market-making in equities and equity derivatives isn’t a monopoly, but it is an oligopoly of roughly half a dozen firms handling the vast majority of retail orders—Citadel Securities even proudly advertises ~35% of U.S. retail equity volume. Sophisticated market-making entails high fixed costs (regulatory compliance, technology) and substantial recurring labor costs (traders, software engineers, researchers).
High barriers to entry, coupled with winner-take-all effects (being a nanosecond faster doesn’t just win you some of the best trades—it wins you all of them), allow these firms to entrench their positions. Early adopters of HFT remain the dominant players today. Because superior technology directly translates into competitive advantage and captures all excess profit in each trade, barriers to entry become insurmountable as firms continually invest in faster exchange connections and data feeds.
Today’s major market makers share much in common with tech platform giants—Amazon, Apple, Google, Meta, Microsoft—all of which intermediate transactions and interactions between distinct user groups and extract value from the resulting data and network effects. Market makers likewise intermediate securities trading and leverage ever-larger data sets to refine their models: better service begets greater market share, which begets more data, and so on.
We should therefore expect dealers and brokers with market power to extract excess profits from public traders, the extent of which depends on market competitiveness—and not just the number and sophistication of rivals, but also end-users’ price sensitivity and their ability to assess execution quality. How many retail investors actually review their trade reports? How many run order-routing data through a Jupyter notebook? How many even care?
Dark Pools & Order Crossing
Dark pools and order crossing are alternative trading systems that operate outside traditional public exchanges, providing a mechanism for large institutional investors to execute trades without revealing their intentions to the broader market. These systems work by matching buy and sell orders for securities directly within a private venue, typically at prices based on the midpoint of the public market’s bid–ask spread. For example, if two pension funds want to trade a large block of shares with each other, they can submit their orders to a dark pool, where they will be matched at the market midpoint and executed anonymously.
This approach offers several benefits to clients, including reduced market impact for large trades, potential price improvement, lower trading costs, and anonymity. However, it also carries drawbacks such as a lack of transparency, potential conflicts of interest (dark pools are often run by broker-dealers), fragmented liquidity, and regulatory concerns. While dark pools and order crossing can significantly benefit institutional investors by enabling efficient execution of large trades, the opacity of these systems has raised questions about market fairness and overall efficiency.
Fragmentation & The Competition Among Markets vs Among Orders
At the core of market fragmentation is the diverse nature of market participants: retail investors seek liquidity and ease of execution; institutional investors need to execute large trades with minimal market impact; and broker-dealers seek to profit from uninformed flow while avoiding informed flow. To address these varied needs, broker-dealers have developed segmented order-flow systems, routing different types of orders to different venues. This segmentation—while beneficial for certain uninformed traders—creates challenges for information traders who rely on centralized, efficient order books to capitalize on their informational advantage. As order flow is divided among various alternative trading venues, it becomes more difficult for informed traders to execute their strategies effectively, potentially reducing overall market efficiency as the price-discovery process grows less centralized and transparent.
The proliferation of alternative trading venues can be viewed as a form of competition among markets, offering varied execution options to different trader types. However, this competition among markets inherently conflicts with the competition among orders that is crucial for maintaining efficient central markets. When orders are dispersed across multiple venues, the central market’s ability to aggregate information and determine the most accurate price is diminished.
This situation highlights the fundamental tension between market efficiency and liquidity. While alternative trading venues can enhance liquidity for certain trader segments, they may do so at the expense of overall market efficiency. The central market is most price-efficient when there is robust competition among orders, allowing for the most accurate price discovery. Competition among markets, however, undermines this order-level competition.
Moreover, the efficiency and liquidity of wholesale markets depend heavily on central markets, since a dealer’s only obligation is to offer price improvement to the NBBO—which widens as a result of internalization and preferencing. Similarly, dark-pool orders are often pegged relative to the prices displayed by central markets; their very existence is a product of the positive externality of informative pricing provided by those markets. Over the long run, it is far from clear that high levels of market fragmentation will yield net economic benefits. In the SEC’s proposal for Regulation NMS, this concern is described as follows:
“[T]he competition among multiple markets trading the same stocks can detract from the most vigorous competition among orders in an individual stock, thereby impeding efficient price discovery for orders of all sizes. … To the extent that competition among orders is lessened, the quality of price discovery for all sizes of orders can be compromised. Impaired price discovery could cause market prices to deviate from fundamental values, reduce market depth and liquidity, and create excessive short-term volatility that is harmful to long-term investors and listed companies. … When market prices do not reflect fundamental values, resources will be misallocated within the economy and economic efficiency—as well as market efficiency—will be impaired.”
Technology and the Means to Lessen Market Tension
Blockchain technology directly tackles the market‐structure frictions we’ve discussed—opaque NBBOs, best‐execution trade‐offs, order‐flow segmentation, and dark‐pool opacity—by embedding transparency, programmability, and composability into the core plumbing of trading.
On‐chain order books and AMM pools record every quote, trade, and reserve change in real time, eliminating hidden venues and creating a decentralized “NBBO” that any participant can audit. Permissionless AMMs like Uniswap, Curve, and Balancer democratize market‐making, breaking the oligopoly of high‐frequency dealers. Concentrated‐liquidity innovations (e.g. Uniswap v3) let providers target narrow price ranges, driving spreads tighter and improving capital efficiency—outperforming many dark pools and internalizers; hence at least partially resolving the tension between competition among orders and competition among markets.
At the same time, on‐chain arbitrage bots continuously realign prices across venues, counteracting cumulative noise‐driven mispricings and restoring order‐level competition without costly co‐location or proprietary data feeds (which distort the playing field and represents a waste of human talent). Aggregators such as 1inch, Paraswap, and Matcha solve fragmentation head on by routing trades across dozens of AMMs, on‐chain order books, and centralized venues in a single transaction.
Finally, and most importantly, blockchain’s global, 24×7 operation and lighter upfront regulatory overhead empower rapid innovation in execution models—on‐chain options, perpetual swaps, programmable limit orders—without sacrificing the decentralized knowledge aggregation that Hayek championed.
As standards mature and compliance frameworks evolve, these DeFi primitives promise a marketplace where hundreds of independent liquidity providers compete freely, spreads remain tight, and consumers reap the benefits of transparency, efficiency, and genuine best execution.
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