research team analyzing financial data in modern office

We Follow the Data

Our team started with a simple confession: most asset pricing models ignore the shop floor. We built a research practice that does not.

We study how labor intensity predicts returns. Not because it is fashionable. Because the evidence kept pointing there. Our work bridges academic rigor and practical application for investors who want to understand what balance sheets hide.
Meet our team Read our research

Evidence first

industrial facility with workers and machinery

Why Labor Intensity Matters More Than Most Admit

1

The Hidden Signal

Firms with high labor intensity often carry information the market misprices. We extract that signal through systematic cross-sectional analysis, treating payroll as a predictor, not an expense.

2

Productivity vs. Price

Labor productivity drives long-run profitability, yet standard models rarely weight it properly. Our framework connects workforce efficiency to future earnings dispersion.

3

Pricing Power Clue

Companies that can raise output without proportional hiring often possess durable advantages. We identify these patterns before they appear in headline margins.

4

Mechanism Over Correlation

We do not chase anomalies. We test economic mechanisms. If labor intensity predicts returns, we want to know why — and whether the effect persists.

The People Behind the Work

Researchers, economists, and analysts who believe the best ideas survive scrutiny.

Dr. Elena Voss

Dr. Elena Voss

Lead Researcher

Ph.D. in Economics Trinity College Dublin

Labor Market Analytics

Central Bank Research

Credentials:

Chartered Financial Analyst Certificate in Quantitative Methods

Methods:

Panel Data Analysis Event Study Design Factor Replication Cross-Sectional Regression Robustness Testing Out-of-Sample Validation

Expertise:

Stata & R Programming Academic Writing Peer Review Navigation Data Visualization

Former academic economist who left tenure to test whether labor market data could survive out-of-sample validation. It did.

Marcus O'Brien

Marcus O'Brien

Quantitative Analyst

M.Sc. in Financial Mathematics University College Dublin

Factor Construction

Dublin Asset Management

Credentials:

Certificate in Risk Management Advanced Python for Finance

Methods:

Factor Portfolio Construction Survivorship Bias Correction Transaction Cost Modeling Backtesting Frameworks Sensitivity Analysis

Expertise:

Python & SQL Development Database Architecture Statistical Modeling Automated Reporting

Spent six years building risk models for institutional desks. Now applies that discipline to labor-intensity factors most quant teams overlook.

Sarah Chen

Sarah Chen

Economic Strategist

M.A. in Labor Economics University of Limerick

Macro-Labor Linkages

Economic Advisory Group

Credentials:

Diploma in Policy Analysis Certificate in Data Journalism

Methods:

Macro Scenario Building Sectoral Labor Mapping Wage Pressure Modeling Earnings Forecast Integration Narrative Stress Testing

Expertise:

Economic Forecasting Policy Research Narrative Synthesis Stakeholder Communication

Connects macro labor trends to micro firm outcomes. Her insight: the same wage pressure that hurts margins can reveal which companies adapted early.

James Nolan

James Nolan

Research Advisor

Ph.D. in Finance University of Galway

Asset Pricing Theory

Academic Peer Review Board

Credentials:

Fellow of Economic Society Distinguished Research Award

Methods:

Theoretical Framework Design Peer Review Standards Replication Protocols Bias Detection Methods Longitudinal Study Design Causal Inference Testing

Expertise:

Critical Evaluation Research Governance Academic Publishing Mentorship & Guidance

Advisory role focused on ensuring our published findings meet the standards he enforced as a journal referee. Skepticism is not a bug here. It is the process.

Recognition and Citations

Our work has been referenced in academic journals, industry conferences, and research networks that value intellectual honesty over marketing.

2024

Best Working Paper Award

Recognized for rigorous methodology in cross-sectional labor intensity factor research at a leading European finance conference.

European Finance Association
2025

Research Transparency Honor

Acknowledged for publishing full replication code and datasets alongside peer-reviewed findings on labor market signals.

Open Science Finance Network
2025

Invited Symposium Speaker

Selected to present original research on workforce productivity as a return predictor at an international asset pricing forum.

Global Factor Research Summit
2026

Peer-Reviewed Publication

Published empirical study demonstrating the economic mechanism behind labor intensity premium in a respected finance journal.

Journal of Empirical Finance

How We Work

We operate under constraints most research shops ignore. No guaranteed outcomes. No hidden agendas. No pretending correlation is causation. Our values are not decorative. They determine what we publish and what we discard.

Intellectual Honesty

We report negative results. We disclose limitations. We revise when the data demands it. Our credibility depends on what we refuse to claim.

Empirical Rigor

Every hypothesis faces out-of-sample testing. Every factor survives robustness checks. We do not present preliminary findings as settled science.

Transparency First

Replication materials accompany every major publication. Readers should not trust our conclusions. They should be able to verify them independently.

No Conflicts

We do not manage money. We do not sell products. Our only incentive is to produce research that withstands scrutiny from peers we respect.

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Research Should Be Discussed, Not Just Consumed

We publish irregularly, but we publish carefully. If you want to receive our next working paper, event invitation, or commentary on a new labor market dataset, add your name to our list. No schedule promises. No content guarantees. Just access to work we believe merits attention.

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