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Quantitative research for financial institutions.

We build custom models, analytics, and research for banks and investment managers.

Discuss a questionSee how we work

We work with banks and investment managers on problems where how you get the answer matters as much as the answer.

Start with the decision

We begin with what you need to decide, then work out what evidence would settle it.

Show the working

Assumptions, checks, and limits are written down so anyone can follow them.

Make it reviewable

Delivered in a form your risk or investment committee can take apart.

What we do.

We work in four areas. Each one has its own page describing the work we can take on, how we approach it, and examples.

01

Credit

Underwriting, cash-flow analysis, securitized credit, loan and collateral data, valuation.

Credit → 02

Equity

Factor research, structured equity, bespoke indices, portfolio analysis.

Equity → 03

Macro

Nowcasting, economic data, indicators, regimes, cross-asset questions.

Macro → 04

Balance Sheet

Interest-rate risk, deposit behavior, stress testing, liquidity, asset-liability questions.

Balance Sheet →

Made for the people who have to approve it.

The people reading our work are in risk, treasury, ALM, and investment teams, and they have to explain the number to someone else. So every model comes with its assumptions, its checks, and its limits written down.

Documented model codeInputs, transformations, tests, and what the model is not designed to do.
Review-ready figuresUnits, source or derivation, and a table behind every chart.
Methodology notesMethod before results. Definitions before performance.
TransferOwnership, update path, and reproducibility so your team can run it.
48 simulated paths · σ 18% · seed 2026 · median in signal blue

Every chart shows how it was made.

We do not publish pictures for decoration. Each chart says where the numbers came from, what was assumed, how it was checked, and what it cannot tell you. You can read every value in a table.

Model
GBM, drift 0, σ 18%, 72 steps
Reproducibility
Seed 2026, Python random
Status
Illustrative Example
Not designed for
Forecasting any instrument

Recent notes

All notes →
Credit · Methods note

Monte Carlo methods in cash flow modeling

How probabilistic simulation techniques can improve credit risk assessment and underwriting decisions.

Macro · Methods note

Understanding nowcast models

A deep dive into nowcasting methodologies that enable real-time assessment of economic conditions using high-frequency data.

Equity · Research standard

Factor investing: a quantitative framework

An introduction to factor-based investing strategies and their implementation in systematic portfolio construction.

What are you trying to decide?

Bring us a modelling question, a valuation problem, or a dataset you need to understand. We will scope the work around the decision rather than around a product.

Start a conversation

How an engagement can be scoped

  • Focused analytical sprintA bounded question, a few weeks, a review pack.
  • Custom model buildDocumented code, assumption register, validation.
  • Research projectMethod note, figures, and the data behind them.
  • RetainerStanding capacity for recurring questions.
  • Research and data partnershipShared datasets and joint method work.