Information Liquidity Markets and Real-Time Data Exchange Systems

1. Introduction

Information liquidity markets are markets in which information can be rapidly generated, exchanged, aggregated, priced, and redistributed among market participants. Real-time data exchange systems are the technological infrastructures that make this possible—for example, APIs, financial-data feeds, advertising exchanges, trading platforms, cloud systems, data brokers, logistics platforms, and algorithmic marketplaces.

From a competition-law perspective, the central issue is that data can function simultaneously as:

• an input;
• a competitive advantage;
• a transaction object;
• a coordination mechanism;
• a switching-cost mechanism;
• an essential or strategically important resource; and
• an instrument through which algorithms can rapidly observe and react to competitors.

The competitive significance of information therefore depends not merely upon how much data exists, but upon who can access it, how quickly it can be accessed, whether it is interoperable, whether rivals can obtain equivalent data, and whether the system facilitates independent or coordinated decision-making.

2. Meaning of Information Liquidity

Information liquidity describes the ease, speed, reliability and cost with which commercially relevant information can move between participants in a market.

A highly liquid information market may have:

1. instantaneous data transmission;
2. standardized data formats;
3. interoperable APIs;
4. numerous data suppliers and buyers;
5. low switching costs;
6. transparent pricing;
7. continuous updating; and
8. multiple alternative information sources.

A low-liquidity information market may instead involve:

• proprietary databases;
• closed APIs;
• exclusive data agreements;
• delayed access;
• incompatible formats;
• high data-portability costs;
• restrictive licensing;
• data silos; and
• dependence upon one intermediary.

Thus, information liquidity is not synonymous with information abundance.

A market may contain enormous quantities of data but remain competitively illiquid if one dominant undertaking controls the infrastructure through which that information can be accessed.

3. Real-Time Data Exchange Systems

Real-time data exchange systems generally operate through a chain:

Data generation → collection → processing → aggregation → exchange → algorithmic analysis → commercial decision → feedback

For example:

Consumers → platform → behavioural data → analytics system → advertisers → real-time bidding → targeted advertising

or:

Banks → transaction data → financial-data intermediary → API → fintech providers → consumer services

The competitive concern becomes particularly important when the same undertaking controls several stages of this chain.

4. Competition-Law Dimensions

A. Data as a Competitive Input

A dominant platform may possess data that competitors cannot readily replicate.

The relevant question is not simply whether the data is valuable, but whether:

• competitors require it;
• equivalent data can reasonably be obtained elsewhere;
• duplication is economically feasible;
• the data is sufficiently accurate;
• the data is updated sufficiently quickly; and
• access can occur on commercially reasonable terms.

This connects information liquidity with essential-facility and refusal-to-deal theories.

B. Real-Time Data as a Speed Advantage

Real-time access can create a substantial competitive advantage.

Suppose Platform A receives market information every second while competitors receive the same information after 30 minutes.

Even if both ultimately receive identical information, Platform A possesses a temporal information advantage.

In digital markets:

Data quality + data quantity + data velocity = potentially significant competitive advantage.

This is particularly relevant to:

• algorithmic pricing;
• financial markets;
• online advertising;
• ride-hailing;
• logistics;
• energy markets;
• insurance;
• retail platforms; and
• dynamic procurement.

C. Information Exchange Between Competitors

Real-time information systems can also create Article 101 TFEU / competition-law concerns where competitors exchange competitively sensitive information.

Information may concern:

• prices;
• discounts;
• output;
• capacity;
• customers;
• inventory;
• future strategies;
• bidding intentions; or
• production plans.

The danger is heightened where the information exchange permits competitors to reduce uncertainty about one another's future conduct.

The classical competition-law principle is that competition requires firms to determine their commercial policies independently.

5. Information Exchanges and Algorithmic Coordination

Modern systems can transform traditional information exchange.

Historically:

Competitor A sends information → Competitor B receives information.

Modern systems may instead involve:

Competitor A → shared algorithm/data intermediary → Competitor B

or:

Competitor A → algorithm → market signal → Competitor B's algorithm → automated response.

This creates difficult questions concerning:

• attribution;
• intent;
• knowledge;
• algorithmic autonomy;
• intermediary responsibility;
• tacit coordination; and
• hub-and-spoke arrangements.

An intermediary may therefore become competitively important even though it does not itself sell the underlying product.

6. Information Liquidity and Market Power

A company may acquire market power through control over the information infrastructure rather than through control over the final product.

Indicators may include:

Data concentration

A single undertaking controls a disproportionate quantity of commercially useful information.

Data velocity

It obtains information faster than competitors.

Data exclusivity

Rivals cannot obtain equivalent information.

Data interoperability

Competitors cannot easily connect to the information system.

Data portability

Users face difficulties transferring their information elsewhere.

Data feedback loops

More users generate more information, which improves the service, attracting additional users.

This produces a potentially self-reinforcing cycle:

Users → Data → Better algorithms → Better service → More users → More data

7. Information Liquidity and Network Effects

Real-time information systems can generate network effects.

More participants generate more data.

More data improves:

• prediction;
• matching;
• search;
• pricing;
• fraud detection;
• advertising;
• recommendation;
• logistics; and
• personalization.

Improved functionality attracts more participants, producing additional data.

This can generate:

Data → Scale → Algorithmic improvement → More users → More data

Competition authorities may therefore need to examine whether data accumulation creates structural entry barriers.

8. Information Bottlenecks

An information bottleneck occurs where a particular intermediary becomes difficult to bypass.

Examples include:

• dominant API providers;
• payment-data intermediaries;
• app-store analytics;
• advertising exchanges;
• financial-market data providers;
• cloud-data platforms;
• dominant logistics exchanges; and
• industry-standard data repositories.

A bottleneck becomes particularly important where competitors cannot economically reproduce the information or infrastructure.

9. Relevant Case Laws

1. United States v. Terminal Railroad Association of St. Louis (1912)

The U.S. Supreme Court examined control over a critical railroad terminal infrastructure.

Although the case predates digital information markets, its importance lies in the principle that control over an indispensable infrastructure can create competitive exclusion when rivals cannot reasonably access an alternative.

Relevance

The case provides an early foundation for analysing information infrastructures as potential competitive bottlenecks.

Where a real-time data exchange platform becomes indispensable to downstream competitors, questions analogous to access to essential infrastructure may arise.

2. Associated Press v. United States (1945)

The U.S. Supreme Court considered restrictions imposed by the Associated Press concerning access to news supplied through its information network.

The Court recognized the competitive significance of controlling an important information-distribution system.

Relevance

This is particularly important for modern information liquidity markets because it demonstrates that control over information dissemination itself can have competition-law consequences.

Modern parallels may include:

• financial-information networks;
• digital advertising data;
• news-data aggregation;
• market intelligence platforms; and
• proprietary real-time information feeds.

3. United States v. Microsoft Corp. (2001)

The Microsoft litigation concerned Microsoft's conduct relating to the Windows operating-system ecosystem and Internet browsers.

The case demonstrated how a dominant firm can use control over a technological platform to disadvantage competing products.

Relevance

For real-time data systems, the broader lesson is that control over an important technological layer can be used to restrict downstream competition.

A dominant API or platform operator might similarly manipulate:

• access conditions;
• technical interoperability;
• default settings;
• data availability; or
• integration permissions.

4. European Commission — Google Shopping (2017)

The European Commission found that Google had abused its dominant position by giving preferential treatment to its own comparison-shopping service in search results.

Relevance

The case illustrates the importance of information access and ranking infrastructure.

A platform controlling the informational gateway through which consumers discover competing services can influence competitive visibility.

In a real-time data environment, similar concerns could arise where a platform:

• privileges its own data products;
• restricts competitors' access to commercially important information;
• manipulates data visibility; or
• uses information gathered from rivals to strengthen its own downstream service.

5. Google Android — European Commission (2018)

The European Commission examined Google's contractual restrictions concerning Android devices and the distribution of applications and search services.

Relevance

The case illustrates how control over an ecosystem can influence access to users and complementary markets.

In information-liquidity markets, an analogous problem may arise when control over a technological ecosystem permits a firm to determine:

• who receives data;
• which APIs are available;
• what information can be exported;
• which applications obtain privileged access; and
• whether competing services can interoperate.

6. Slovak Telekom v European Commission (2021)

The Court of Justice considered exclusionary conduct involving access to telecommunications infrastructure.

The judgment is important to the broader analysis of access to infrastructure controlled by a dominant undertaking.

Relevance

Real-time data exchange systems may become infrastructure-like when competitors depend upon them for market participation.

The legal analysis may therefore involve:

• indispensability;
• foreclosure;
• access conditions;
• duplication;
• objective justification; and
• effects on downstream competition.

7. IMS Health v NDC Health (2004)

The Court of Justice examined refusal to license intellectual-property rights concerning pharmaceutical sales information.

The case is particularly important because it addressed circumstances in which access to an information-based asset could become relevant to Article 102 TFEU.

Relevance

It demonstrates that proprietary information may, under exceptional circumstances, become competitively significant enough for refusal of access to raise abuse-of-dominance concerns.

This is highly relevant to:

• proprietary databases;
• market intelligence;
• industry datasets;
• real-time feeds; and
• specialized commercial information.

8. Bronner v Mediaprint (1998)

The Court of Justice considered whether access to another undertaking's newspaper-delivery system could be compelled under Article 102 TFEU.

The Court adopted a demanding approach to compulsory access.

Relevance

For information exchange systems, Bronner highlights that not every commercially valuable database or infrastructure must be opened to competitors.

A competition authority generally needs to distinguish between:

valuable information
and
information whose denial creates legally significant competitive foreclosure.

10. Consolidated Case-Law Principles

11. Real-Time Data Exchange and Article 101

Information liquidity systems may facilitate prohibited coordination when competitors receive competitively sensitive information.

The risk increases when:

1. information is individualized;
2. information is current;
3. information concerns future conduct;
4. competitors have repeated access;
5. exchange occurs through a common intermediary;
6. algorithms automatically react to the information; and
7. the exchange reduces strategic uncertainty.

For example:

Competitors → shared data platform → real-time price information → algorithms → synchronized price adjustments

may create substantially greater competition concerns than an exchange of old, aggregated market statistics.

12. Article 102 and Information Liquidity

A dominant undertaking may potentially abuse its position by:

Refusing access

Competitors cannot obtain critical information.

Discriminating access

The dominant firm provides superior data/API access to itself or selected partners.

Degrading interoperability

Data can technically be exported but only through inferior interfaces.

Delaying competitors

The dominant firm provides rivals with slower data feeds.

Self-preferencing

The platform uses information from competitors to improve its own competing service.

Data leveraging

Information gathered in one market is used to foreclose competition in another.

Exclusivity

Data suppliers are contractually prevented from supplying competing platforms.

13. Real-Time Data and Dynamic Pricing

One of the most significant modern applications is algorithmic pricing.

Consider:

Retailer A → pricing algorithm → real-time market information

and

Retailer B → pricing algorithm → same information

If both algorithms rapidly observe and respond to market signals, prices may become highly responsive and potentially less competitive.

Competition law must distinguish between:

Independent adaptation

Each company independently observes publicly available information and sets its own price.

and

Coordinated information environment

A common system supplies competitors with strategically sensitive information in a manner that facilitates alignment.

The distinction is fundamental.

14. Public Information vs Commercially Sensitive Information

Not all real-time data exchange is problematic.

Generally, competition concerns are greater where information is:

• non-public;
• individualized;
• recent;
• commercially sensitive;
• forward-looking; and
• sufficiently detailed to predict a rival's conduct.

Conversely, genuinely public, historical and aggregated information is generally less problematic.

However, technological accessibility does not automatically determine legal publicness.

Information may technically be observable online while still being structured, processed, or transmitted in a manner that gives it strategic significance.

15. Data Aggregators as Intermediaries

Data aggregators occupy a particularly important position.

They may collect information from numerous market participants and redistribute it.

This can have two opposite competitive effects.

Pro-competitive effect

Aggregation may:

• reduce search costs;
• improve price discovery;
• reduce information asymmetry;
• enable new entrants;
• improve matching;
• increase market transparency.

Anti-competitive effect

The same system may:

• facilitate collusion;
• expose strategic information;
• disadvantage non-participants;
• create dependency;
• permit discriminatory access; or
• become an information bottleneck.

Therefore, competition law should examine the architecture and effects of the information system, rather than treating data exchange as inherently good or bad.

16. Data Liquidity and Entry Barriers

New entrants may face a threefold disadvantage:

Data disadvantage + latency disadvantage + network disadvantage

An incumbent may possess:

• years of historical data;
• millions of real-time observations;
• sophisticated processing infrastructure;
• established API connections;
• trained algorithms; and
• a large user network.

A new competitor may therefore have a technologically functional product but remain unable to achieve competitive scale.

This can create a form of data-based entry barrier.

17. Data Portability as a Competition Remedy

Where information liquidity is excessively concentrated, regulators may consider remedies such as:

• data portability;
• interoperability;
• API access;
• standardized formats;
• non-discriminatory access;
• data-sharing obligations;
• separation of data pools;
• transparency obligations; and
• restrictions on exclusive data contracts.

However, compulsory access must balance competition against:

• privacy;
• cybersecurity;
• intellectual property;
• confidentiality;
• commercial incentives; and
• data protection.

The Bronner and IMS Health line of authority demonstrates why compulsory access under traditional abuse-of-dominance doctrine requires careful analysis.

18. Information Liquidity and Privacy

Competition law cannot treat data as an ordinary commodity.

A real-time exchange system may process:

• personal information;
• behavioural information;
• location information;
• financial information;
• health-related information;
• device information; and
• inferred preferences.

Consequently, increasing information liquidity can simultaneously increase competition and privacy risks.

A regulatory framework must therefore consider whether greater data mobility genuinely improves competition or merely redistributes surveillance capabilities among dominant intermediaries.

19. Information Liquidity as a Two-Sided Market

Many information exchange systems are two-sided or multi-sided.

For example:

Data suppliers ↔ exchange platform ↔ data purchasers

The platform may serve:

• consumers;
• advertisers;
• businesses;
• developers;
• data providers; and
• analytics companies.

The platform's competitive position may therefore derive from cross-side network effects.

More data suppliers attract more buyers.

More buyers make participation attractive to data suppliers.

This can create rapid concentration.

20. Key Competition Concerns

The principal competition concerns can be summarized as follows:

1. Data concentration

Too much strategically valuable information is controlled by one undertaking.

2. Data foreclosure

Competitors cannot access important information.

3. Information discrimination

The platform gives itself better information than rivals.

4. Algorithmic coordination

Real-time information exchange facilitates synchronized conduct.

5. Data exclusivity

Contracts prevent rivals from obtaining equivalent data.

6. Interoperability restrictions

Competitors cannot technically connect to the system.

7. Latency discrimination

Competitors receive information more slowly.

8. Data leveraging

Information obtained in one market is used to strengthen dominance elsewhere.

9. Feedback-loop dominance

Data accumulation reinforces an existing market position.

10. Information infrastructure dependency

Market participants become dependent upon a single exchange mechanism.

21. Regulatory Analytical Framework

A competition authority examining an information-liquidity market can ask:

Step 1 — Identify the information

What data is being exchanged?

Step 2 — Determine its competitive sensitivity

Is it price, customer, capacity, inventory, strategic or future information?

Step 3 — Measure velocity

How quickly does information circulate?

Step 4 — Identify control

Who owns or controls the infrastructure?

Step 5 — Assess alternatives

Can competitors obtain equivalent information elsewhere?

Step 6 — Examine interoperability

Can rivals connect to the system?

Step 7 — Examine exclusion

Does access discrimination disadvantage competitors?

Step 8 — Examine coordination

Does information exchange reduce strategic uncertainty?

Step 9 — Examine network effects

Does greater participation increase the platform's informational advantage?

Step 10 — Assess remedies

Would portability, interoperability, access or structural remedies restore effective competition?

22. Conclusion

Information liquidity markets represent a major evolution of competition economics. Information is no longer merely an input supporting commercial decisions; the infrastructure through which information moves can itself become a source of market power.

Real-time exchange creates substantial efficiencies through:

• faster price discovery;
• improved matching;
• reduced transaction costs;
• better forecasting;
• innovation; and
• reduced information asymmetry.

At the same time, excessive concentration can create:

• information bottlenecks;
• exclusionary access restrictions;
• data-driven entry barriers;
• algorithmic coordination;
• self-preferencing;
• discriminatory latency;
• ecosystem dependency; and
• reinforcement of incumbent dominance.

The central competition-law question is therefore not whether information should flow freely, but rather:

Who controls the flow of information, on what terms, at what speed, and with what consequences for independent competitive decision-making?

The Associated Press, IMS Health, Bronner, Microsoft, Google Shopping, Google Android, Slovak Telekom, and Terminal Railroad lines of authority collectively provide useful doctrinal foundations for analysing these emerging information infrastructures, even though most of those cases arose before today's fully real-time algorithmic data ecosystems.