Finance & Economics
Quantitative Finance
Discover volatility scaling laws, correlation structures, and regime transition equations from market data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Quantitative Finance teams still struggle to explain what is happening
Financial markets generate massive volumes of high-frequency data: tick-level prices, order flow, bid-ask dynamics, and cross-asset correlations. These systems are complex and adaptive, exhibiting regime transitions, non-stationary volatility, heavy-tailed returns, and feedback effects between participants. Despite decades of quantitative research, the governing laws of market microstructure and price dynamics remain incompletely characterized, with most models relying on distributional assumptions that empirical data consistently violates.
Geometric Brownian motion, GARCH volatility, and factor models impose parametric assumptions that fail to capture market complexity. Regime changes invalidate calibrated parameters without warning. Correlation structures break down during stress periods, exactly when accurate risk assessment matters most. Machine learning improves predictive power but produces opaque models that trading desks and risk managers cannot interpret, validate, or explain to regulators.
The CDE Approach
How CDE closes the explanation gap in quantitative finance
CDE takes market data (tick-level prices, order flow statistics, volatility surfaces, cross-asset time-series) and discovers governing dynamics of financial markets. Its discovery modes identify volatility scaling laws, correlation structure dynamics, and regime transition equations without imposing distributional assumptions. It captures non-stationary dynamics and structural breaks that parametric models miss, providing governing equations grounded in empirical evidence.
Regime classification identifies structural breaks automatically (volatility regime transitions, correlation shifts, liquidity state changes) enabling trading and risk systems to adapt before calibrated models fail. Causal mode separates genuine drivers of market behavior from spurious correlations, critical where confounded relationships lead to costly allocation errors. The Evidence Ledger provides the audit trail compliance functions require for model validation.
Discovery Engine
How CDE applies here
Symbolic discovery identifies closed-form volatility scaling laws, mean-reversion dynamics, and microstructure equations that trading desks can implement and risk teams can validate. Causal mode maps relationships between market variables, separating genuine drivers from noise and identifying shock transmission pathways across assets and geographies. Regime classification enables dynamic model selection that adapts to changing conditions rather than relying on static calibrations.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Volatility scaling laws
- Correlation structure dynamics
- Regime transition equations
- Market microstructure models
- Mean-reversion governing laws
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For quantitative finance, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
Get started
Put CDE on a real quantitative finance problem
Whether you are exploring quantitative finance data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.