For the Researcher Who Cares About Data Provenance
The analytical instrument for academic finance work where the question is as important as the answer.
Academic finance research occupies an unusual analytical position. The data requirements are strict (provenance, time-stamping, completeness); the budgets are typically modest; the tooling available within the university is uneven. Drusus is positioned to provide the working researcher with the cross-market data and analytical depth that a serious paper requires, at a subscription cost an academic budget can absorb.
What You Will Use Most
- The AI analyst chat, as a sparring partner on the analytical structure of a project rather than as a substitute for the researcher's own thinking.
- Data export, in formats suitable for analysis in Python, R, or Stata.
- The Data Sources documentation, which records every primary source the platform draws on for every figure it publishes.
- Drusus Insights, as a methodology reference for the analytical apparatus used in the platform.
On Citation
For academic work that draws on Drusus as a data source, we ask that the platform be cited as the source of the figures, and that the specific date of data retrieval be recorded. Where the platform's methodology contributes to your paper, we ask that the methodology note be cited specifically. The full citation guidance is on the Methodology page.
How They Use Drusus
The shape of a typical working pattern for this audience.
Project scoping
When framing a new project, use the AI analyst to interrogate the analytical structure. Ask not for an answer but for the structural objections to your proposed methodology.
Data acquisition
Export the data you require with provenance metadata attached. The export records the primary source, the time-stamp, and the platform retrieval date.
Methodology cross-check
Where the platform's methodology overlaps with the analytical work of your project, examine the methodology notes. They are written in academic register, not marketing register.
Pre-submission review
Before submission, ask the AI analyst for the critique a referee might raise. The exercise is most useful when the analyst is constrained to argue against your conclusion.
An Illustrative Query
The form a typical interaction with the Drusus AI analyst takes for this audience.
I am examining the FX-adjusted price relationship between Hong Kong listings and New York ADRs of dual-listed Chinese equities. I want to test whether the deviation has widened systematically since 2020. Frame the principal methodological questions I should address before estimating.
Six methodological questions are worth addressing before estimation. First, the definition of the deviation: are you using the simple percentage difference, the log-difference, or a normalised metric that accounts for trading-hours non-overlap between Hong Kong and New York. Second, the FX adjustment: are you using the closing spot rate at the time of each listing's close, an interpolated mid-rate, or a synchronised reference rate. Third, the sample selection: are you restricting to names with continuous dual listing throughout the sample, or admitting names that listed or delisted during the sample period (with appropriate survivorship-bias correction). Fourth, the regime control: 2020 saw a substantial shift in the policy environment, a sustained dollar strength, and a series of regulatory interventions specific to Chinese ADRs. Disentangling the post-2020 widening from these confounders may require explicit regime indicators. Fifth, the liquidity question: is the deviation primarily a function of name-level liquidity, in which case the headline result will be driven by a small set of less-liquid names. Sixth, the holiday and corporate action treatment: dual-listed names exhibit large transient deviations around holiday non-overlap and corporate actions that must be filtered to isolate the structural relationship. These six concerns do not predetermine your conclusion, but the work will benefit from each being addressed explicitly in your methodology section.
Recommended Tier
Drusus Analyst at £29 per month covers most academic use cases, with full data export, the AI analyst at expanded quota, and the methodology archive. For research groups making intensive use of the platform, the Strategist or Institution tier is appropriate. Educational pricing arrangements are available for graduate programmes; please write to contact@gravenos.com.