Evidence first. Mechanism second.

How We Measure, Validate, and Apply Labor Intensity Signals

Most asset pricing models treat labor as a cost to minimize. Our methodology treats it as a signal to measure. We built a five-step quantitative process that sources raw workforce data, constructs economically meaningful metrics, generates testable predictions, adjusts for risk and implementation friction, and delivers signals that survive replication. No black boxes. No proprietary magic. Just a transparent pipeline from data to decision.

Data

1 Source Raw Data

We collect firm-level workforce disclosures, payroll expenditures, and asset base figures from regulatory filings and standardized databases.

Metrics

Construct Metrics

Labor intensity is defined as total labor cost divided by total assets, adjusted for industry composition and temporal variation.

Signal

3 Generate Signals

Cross-sectional rankings identify firms with high labor intensity relative to peers. These rankings form the basis of portfolio sorts.

Risk

Adjust for Risk

We model transaction costs, liquidity constraints, and factor overlap to ensure reported premiums are implementable in practice.

Output

Deliver Insights

Validated signals are packaged with full replication materials so practitioners can test independently before adoption.

Our Approach Versus Common Alternatives

Full Replication Materials Provided

Many research platforms publish conclusions without sharing code or data. We release complete replication packages so every result can be independently verified. Transparency is not a feature. It is the foundation.

Economic Mechanism Tested, Not Just Correlation

Standard factor research often stops at statistical significance. We decompose labor intensity into operating leverage, adjustment costs, and productivity channels to explain why the effect exists.

Transaction Costs Modeled Explicitly

Academic studies frequently ignore implementation friction. Our backtests include explicit estimates for trading costs, slippage, and liquidity constraints so reported premiums reflect real-world feasibility.

Out-of-Sample Validation Required

Some platforms present in-sample results as final proof. We require out-of-sample and post-publication testing before presenting any signal as robust. If it breaks, we say so.

Factor Orthogonalization and Interaction Effects

Labor intensity does not operate in isolation. It correlates with value, profitability, and investment factors. Our methodology orthogonalizes the signal to isolate its marginal contribution. We also model how labor intensity interacts with momentum and quality metrics, revealing conditions under which the premium strengthens or weakens. Transaction cost modeling is integrated at every stage, not added as an afterthought.

Technical Questions

What data frequency?

Data

We use annual and quarterly filings as primary sources. High-frequency labor market data is incorporated where available but does not replace the structural information contained in regulatory disclosures.

See the Methodology in Action

We do not expect trust. We expect curiosity. Request a live walkthrough of our research pipeline, or ask us to run a custom backtest on parameters you specify. Our team will demonstrate how the methodology handles your specific analytical requirements.

Read research

How We Measure, Validate, and Apply Labor Intensity Signals

Our Research Pipeline

Our Approach Versus Common Alternatives

Technical Questions

What data frequency?

How is look-ahead bias controlled?

How is survivorship bias handled?

What out-of-sample protocols?

Are transaction costs included?

How do you handle factor overlap?

What robustness checks apply?

How are signals implemented?

See the Methodology in Action