The trading stack

Every trade touches a chain of infrastructure — data feeds, execution venues, clearing, settlement, risk systems — and each link charges a toll. AI increases both the volume flowing through that chain and the value of the data it generates.

Historical context: electronic trading transformed markets over two decades, concentrating value in exchanges, data providers, and execution platforms with network effects. Those franchises proved extraordinarily durable, compounding through every market cycle.

The structural driver is that AI is data-hungry and finance is data-rich. Quantitative strategies consume more market data, alternative data becomes a licensed product, and model-driven risk management raises demand for granular, historical, high-quality feeds.

The technology layer spans market-data infrastructure, execution algorithms, portfolio and risk analytics, post-trade automation, and the compliance surveillance systems regulators effectively require — all of which are being rebuilt with machine learning at their core.

Adoption economics favour incumbents: exchanges and data providers monetize volume and connectivity, so client AI adoption raises their revenue without a new product sale. Analytics vendors sell into budgets that expand when volatility rises.

The beneficiaries include exchanges and clearing houses, market-data and index providers, execution and portfolio software vendors, post-trade infrastructure firms, and the alternative-data companies licensing novel signals.

The value chain runs from data generation through distribution and analytics to execution and settlement. Index and benchmark ownership is the highest-margin position in the chain, since fund assets track them contractually.

The overlooked layer includes smaller market-data and analytics vendors, trade-surveillance and compliance specialists, financial-messaging and connectivity providers, and the fund-administration and middle-office outsourcers absorbing back-office work.

Competitive dynamics are oligopolistic in data and exchanges, with regulators periodically scrutinizing data pricing. Consolidation continues as data, analytics, and index businesses converge into single platforms.

Risks: regulatory action on market-data fees is a persistent threat to the highest-margin revenue; trading volumes are cyclical; passive-investing fee compression pressures asset managers who are the end customers; and open-source analytics erode some vendor moats.

What to watch: market-data revenue growth versus volume growth, index and benchmark asset flows, alternative-data licensing deals, and regulatory reviews of data pricing. The research follows the toll booths rather than the traders.