Beyond the Map
Understanding Where Revenue Actually Lives Across Borders
We built this platform because the old question — where is a company headquartered? — answers almost nothing about where it earns its money. A firm domiciled in Dublin might draw seventy percent of its sales from North America. Another based in Frankfurt could depend on emerging markets. If you are trying to understand exposure, the mailing address is a distraction. We examine cross-sectional foreign sales data so you can see which firms carry genuine geographic diversification and which ones merely look global on paper.
Why We Started This Work
A small team of researchers grew tired of proxy variables that misled more than they clarified
Traditional geographic allocation analysis treats headquarters location as a reliable
indicator of revenue origin. We found this assumption produced systematic errors in risk
assessment and return predictability models. Our platform was born from a simple
observation: cross-sectional foreign sales exposure data reveals patterns that
domicile-based classifications obscure entirely. What began as an internal research tool
evolved into a shared resource for analysts who refuse to let administrative boundaries
dictate their understanding of corporate revenue streams.
Domicile-Agnostic Classification
We classify firms by where sales occur, not where incorporation paperwork was filed.
Cross-Sectional Comparisons
Compare exposure profiles across hundreds of firms within the same reporting period.
Predictability Research
Investigate how foreign revenue concentration relates to subsequent return patterns.
Transparent Methodology
Every classification rule and data source is documented and open to scrutiny.
What We Believe About Global Exposure Analysis
Transparency Builds Better Models Than Secrecy Ever Could
When classification methodologies are hidden inside proprietary black boxes, nobody can verify whether the output is sound. We publish our assumptions, our data sources, and our limitations. If a classification is uncertain, we say so. If a data point is estimated rather than reported, we flag it.
Investors Deserve Data That Reflects Reality, Not Convention
The investment industry has long accepted headquarters-based geographic labels
because they were easy to obtain. We reject convenience as a justification for
inaccuracy. Our work is slower, more painstaking, and more useful because it
starts from the actual geography of sales rather than the mythology of corporate
nationality.
The People Behind the Data
Dr. Ciara Brennan
Lead Research Analyst
Marcus O'Donnell
Data Integrity Director
Dr. Aisha Patel
Senior Quantitative Researcher
Finn Gallagher
Geographic Classification Specialist
Dr. Yuki Tanaka
Cross-Sectional Methods Lead
Saoirse Murphy
Research Operations Manager
Recognition and Citations
Excellence in Research
2023Open Research Prize
2025How We Work
Our daily practice is shaped by commitments to intellectual honesty, methodological care, and respect for the people who use our research.
Source Verification
We trace every data point to its original filing, survey, or regulatory
disclosure. Estimates are labeled as such and never presented as reported fact.
Replication Standards
Our analytical steps are documented so that independent researchers can reproduce our classifications and test our conclusions.
Conflict Disclosure
We publish our affiliations, funding sources, and any relationships that might
influence our analytical judgments.
Error Correction
When we discover mistakes, we correct them promptly and notify users who may have relied on the affected data.
Plain Language
We explain our methods in terms that do not require a doctoral degree to
understand. Complexity is in the analysis, not the explanation.
Visual Honesty
Our charts and maps do not distort scale, omit inconvenient data, or use
misleading color schemes to suggest conclusions the numbers do not support.
Practical Application
Every research product we publish must answer a question that an analyst or researcher actually faces in their work.
Regular Updates
Our datasets are refreshed on schedules that match the reporting cycles of the
underlying sources, not arbitrary calendar dates.
Open Documentation
Our methodology documents are available without subscription or registration
barriers.
Responsive Support
We answer questions about our data and methods from anyone who asks, regardless
of whether they pay for our services.
No Commission Bias
We do not accept compensation from companies to alter their geographic classifications or improve their exposure profiles.
Editorial Control
Our research agenda is set by our analysts, not by marketing departments or external sponsors.