The Global Risks Alliance (GRA)
Capital markets are being reshaped by algorithmic and AI-driven trading, by tokenised securities and fund units, and by stablecoins and other digital assets that link to traditional finance. These changes can lower costs and speed settlement; they also introduce new forms of correlated behaviour, operational dependency and legal uncertainty. This page sets out how those risks develop and propagate, what is measured, and who decides on the rules.
GRA · Established · service level R0
Algorithmic trading is trading in which computer programs decide order timing, price or quantity with limited human intervention, now increasingly using AI. With tokenised securities and stablecoins it reshapes capital markets, lowering costs and speeding settlement while adding correlated automated behaviour and new dependencies.
How a failure at one financial centre can spread through settlement links. Design links, not real dependencies.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Digital markets · Bond market · Session 2
vs largest two members' default, illustrative
| Settlement fails | 37 | ▲ 3 | Normal |
| Margin calls intraday | 8 | ▲ 4 | Elevated |
| Surveillance alerts | 129 | ▼ 10 | Triage |
AI and tokenization risks. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Trading algorithms and AI models built on similar data and objectives can react to the same signals in the same way. Correlated responses can drain liquidity and produce sharp intraday price moves that reverse once the trigger passes.
Firms that rely on a few AI model providers, data vendors and cloud platforms share their failure modes. A defective update, an outage or a cyber incident at one provider can affect many firms at once.
Recording securities on distributed ledgers can enable simultaneous delivery and payment and longer settlement hours. It also raises questions of legal finality, the settlement asset used, interoperability with existing systems and responsibility when code fails.
Stablecoins hold reserves in bank deposits, short-term government debt and money market instruments. A run on a large stablecoin could force rapid sales of those reserves, carrying stress from digital asset markets into short-term funding markets.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Circuit breaker | A rule that halts or limits trading after a large price move. |
| Tokenised security | A security whose ownership is recorded as a token on a distributed ledger. |
| Order-to-trade ratio | The number of orders sent relative to trades executed, a measure of automated activity. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Share of order messages from algorithmic trading | share of message traffic | quarterly | Venue and regulator market structure data | OBSERVED |
| Order cancellation to trade ratio | ratio | daily | Exchange market structure metrics | OBSERVED |
| Volatility halts and circuit breaker triggers | count per period | monthly | Exchange halt and circuit breaker records | OBSERVED |
| Tokenised securities and fund units outstanding | currency units | monthly | Issuer disclosures and public ledgers | REPORTED |
| Stablecoin circulating supply | currency units by stablecoin | daily | Public blockchain ledgers | OBSERVED |
| Stablecoin reserve composition | share of reserves by asset type | monthly | Issuer reserve attestations | DECLARED |
| Firms depending on each critical third-party technology provider | count of firms per provider | annual | Supervisory registers and surveys | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| SEC market structure metrics (MIDAS) | Order, cancellation, trade and quote metrics for US equity markets | Public domain, US government work |
| ESMA reports on algorithmic trading and crypto-asset markets | Analysis of algorithmic trading, AI use and crypto-asset markets in the EU | Free, reuse with attribution under the ESMA legal notice |
| IOSCO reports on artificial intelligence in capital markets | Use cases, risks and regulatory approaches to AI in capital markets | Free public reports, IOSCO copyright |
| FSB reports on crypto-assets and stablecoins | Monitoring and policy recommendations for crypto-asset activities and stablecoin arrangements | Free, FSB terms of use |
| Public blockchain ledgers | Transaction records, supply and holdings of tokens and stablecoins | Open data, verifiable from any full node |
| ESMA interim MiCA register | Authorised crypto-asset service providers, token issuers and crypto-asset white papers in the EU | Free, reuse with attribution under the ESMA legal notice |
| Stablecoin issuer reserve attestations | Periodic reports on the size and composition of stablecoin reserves | Public, terms per issuer |
Capital Markets Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Securities regulators | Which controls, testing and governance to require for algorithmic and AI-driven trading | Market structure metrics and incident evidence |
| Central banks | Which settlement assets may be used for tokenised securities and whether to provide tokenised central bank money | Settlement design evidence and spillover channels |
| Exchanges and trading venues | How to set circuit breakers, order throttles and kill switches | Halt, price reversal and message traffic evidence |
| Prudential and crypto-asset supervisors | How to authorise and supervise stablecoin issuers and crypto-asset service providers | Reserve composition, redemption and concentration evidence |
Tokenization represents assets as digital tokens on distributed ledgers, potentially allowing faster settlement and programmable transfers. It may also introduce new operational, legal and liquidity risks, for example if settlement becomes atomic and leaves no time to arrange funding, or if the legal status of tokens is unclear.
Crypto-asset markets have shown high volatility, failures of intermediaries and links to traditional finance through stablecoins, funds and bank exposures. AI is increasingly used in trading and risk management; if many firms rely on similar models and data, their behaviour may become more correlated, and model errors may spread.
Studying transactions recorded on public ledgers, using distributed ledgers and digital assets. On-chain data miss off-chain activity.
Tracing links between crypto-asset and traditional markets, with network science and contagion.
Studying correlation and failure modes of AI in markets, informed by payment and financial market technology.
Learning from regulatory sandboxes and tokenization pilots. Small pilots may not reveal system-level effects.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.