Our story

We started by asking who really moves prices

Most market commentary chases headlines. We chase filings. Our team built this space to map how concentrated ownership by large institutions shapes the way individual securities trade. No slogans. Just filings, code, and stubborn curiosity.
Filings-first research
Collective team lens
Ownership dynamics mapped
See our methods Get in touch
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Team analyzing ownership data together

We exist because ownership concentration is under-studied

Academic finance talks about liquidity. Activist campaigns grab headlines. But the quiet accumulation and dispersion of institutional positions across the cross section of equities is where a lot of price formation actually happens. We built this platform to study that gap. Not to sell predictions. To document patterns and share what we find.
Our work begins with regulatory filings and ends with open, reproducible analysis. We do not hide behind proprietary models or black-box scores. Every conclusion we publish can be traced back to a primary source.
We are not advisors. We do not manage capital. We are researchers who believe that understanding who owns what and how those positions shift is a public good worth building tools around.
The platform is maintained by a small team of data scientists and market analysts. We update our datasets continuously and publish insights as we verify them.

What guides us

Five principles keep our research honest and our community useful.
$ Source first
We never quote a number we cannot trace. Every statistic links to a filing, a dataset, or a documented methodology. If the source is weak, we say so.
$ No predictions sold

We map what has happened and what is happening now. We do not forecast prices or promise outcomes. Our value is clarity, not clairvoyance.

$ Open methodology

Our analytical steps are published alongside our results. If you disagree with a filter or a weighting scheme, you can replicate our work and improve it.

$ Community review
We publish drafts and invite correction. The best research survives scrutiny, and we build that scrutiny into our process from day one.
$ Minimal jargon

Complex ownership dynamics deserve plain language. We write for curious readers, not for tenure committees.

Researchers and builders behind the ownership maps

Mara O'Donnell portrait

Mara O'Donnell

Lead Researcher

1
Institutional ownership dynamics

PhD in Financial Economics Trinity College Dublin

Central Statistics Office Ireland

Core skills

Stata
Python
SEC parsing

Methods

Cross-sectional regression
Filing text mining
Panel data analysis

Credentials

Chartered Financial Analyst
Certificate in Data Ethics

Mara oversees the ownership dataset and leads the analytical review process for all published findings.

Mara has spent her career studying how large asset managers cluster into the same securities and what that means for price discovery. She designed the core ownership scoring framework used across the platform.

Sean Byrne portrait

Sean Byrne

Data Architect

2
Large-scale data engineering

MSc in Data Science University College Dublin

Irish Revenue Commissioners

Core skills

SQL
Spark
Airflow

Methods

ETL pipeline design
Schema normalization
API integration

Credentials

AWS Certified Solutions Architect
Professional Scrum Master

Sean builds the data pipelines that ingest, clean, and structure millions of regulatory filings into usable research datasets.

Sean ensures that every ownership position we report is traceable to its original filing and that our infrastructure can handle real-time updates without compromising accuracy.

Niamh Kelleher portrait

Niamh Kelleher

Market Analyst

3
Equity market structure

BA in Economics Dublin City University

Irish Stock Exchange

Core skills

Excel
Tableau
Financial writing

Methods

Comparative sector analysis
Trend decomposition
Narrative stress testing

Credentials

Certificate in Securities Regulation
Bloomberg Market Concepts

Niamh translates raw ownership data into narrative insights and coordinates the platform's publication schedule.

Niamh focuses on making ownership concentration visible to readers who do not have a quantitative background. She tests every insight against real market events before publication.

Rory Gallagher portrait

Rory Gallagher

Quantitative Developer

4
Statistical modeling

MSc in Computational Finance University of Limerick

Enterprise Ireland

Core skills

R
Shiny
D3.js

Methods

Monte Carlo simulation
Time-series decomposition
Interactive dashboard design

Credentials

Certificate in Machine Learning
Financial Risk Manager

Rory develops the statistical models and interactive tools that let users explore ownership patterns across sectors and time periods.

Rory bridges the gap between raw data and usable tools. He builds the interactive charts and models that let users test their own hypotheses about ownership dynamics.

Key moments in building a research platform focused on institutional ownership concentration

2019

First ownership map built

Mara compiled the initial cross-sectional dataset linking institutional positions to price volatility metrics. The project began as a personal research repository with no public interface.

Dataset Research
2021

Platform launched publicly

After two years of internal validation, the team released the first public version of the ownership mapping tool. Users could now explore concentration metrics across sectors and compare historical trends.

Launch Public tool
2023

Real-time filing integration

Sean led the rebuild of the data pipeline to process regulatory filings within hours of publication. This reduced the lag between filing date and analytical availability from weeks to under a day.

Infrastructure Speed
2025

Methodology published openly

The team released its full analytical framework under an open documentation standard. Other researchers could now replicate, critique, and extend the ownership concentration scores.

Transparency Open science
2026

Community review program introduced

Niamh launched a structured feedback system where registered users could flag data anomalies and suggest methodological improvements. Corrections are now published monthly.

Community Quality
From a single dataset to a growing research community.

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