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EMPIRICALPaper #7
When System Harm Reaches Asset Prices
A public-data asset-pricing study tests four predictions about system-harm exposure. Normal-period returns show no detected premium, restoration events show a strong exposure loading, and the proposed skewness and shared-system covariance predictions fail in the current data.
THEOREM TYPE
Empirical
classification from the paper record
SOURCE STATUS
Summary + deck generated
verified by paper record
OPEN HTML DECK ↗Deck mode is an on-site reading view, not a PowerPoint download.
Theorem status: evidence-traced claim under the cited paper's assumptionsFalsification: show the same game preserving system welfare without changing the payoff structure
KEY FINDINGS
THEOREM
P1 tests the factor-adjusted normal-period high-minus-low exposure portfolio. P2 estimates the exposure loading in the restoration-event panel with the stated controls. P3 tests the high-minus-low skew difference. P4 tests shared-system residual event covariance. The frozen v1.1 package reproduces all four reported estimates.
PLAIN ENGLISH
The study asks whether a firm's measured system-harm exposure appears in ordinary returns or becomes visible only when an event forces that harm into investors' information. Two predictions survive and two fail.
EVIDENCE & LIMITATIONS
- Theorem status: evidence-traced claim under the cited paper's assumptions
- Falsification: show the same game preserving system welfare without changing the payoff structure
REFERENCES / CITATION STATUS
Reference counts for this manuscript have not been published yet. Treat its citations as unverified until a source list is available.
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EXECUTIVE SUMMARY
The paper connects a domain-level measure of system-harm exposure to public equity returns. It pre-specifies four return signatures and reports the full mixed result: no detected normal-period premium, a strong restoration-event loading, wrong-sign skewness, and no supported within-system covariance. The design treats the failed predictions as evidence against the broader proposed factor story. The public-data boundary is explicit; a journal-grade extension requires point-in-time audited exposures, issuer and event reconstruction, delisting-complete returns, and redesigned inference for the fourth prediction.
METHODOLOGY
The frozen package combines public issuer metadata and adjusted returns with Fama-French and momentum controls, disclosed industry controls, a restoration-event panel, missing-history stress tests, and an independent single-domain audit. The package reproduces nine derived panels and the four headline results. The current version is a public working paper rather than the final journal data build.
LITERATURE CONTEXT
The paper positions its tests relative to factor models, sin-stock returns, climate-finance and green-minus-brown research, litigation and regulatory-event studies, and recent evidence on environmental and social exposures. Its contribution is the four-prediction test and the disciplined mixed result, including the two failed predictions.
SOURCE QUESTIONS
WHY THIS MATTERS
For the economist
A structural claim about when bilateral optimization degrades the shared system. Read the formal statement and its stated axioms.
For the regulator
The constraint is physical or biological, so disclosure alone will not internalize it. The policy lever is to bound exposure, not to price it away.
For the executive
This is where a privately efficient decision can degrade the system the business depends on. The governance question is which decision records would make that system cost visible before it is normalized.
For the teacher
An on-site HTML deck and the expanded curriculum cover the argument, the evidence, and the measurement. Use the deck as a self-contained class session, then route deeper through the 45-50h core course or 100+h full curriculum.
For the affected community
In plain terms: who gains from the current arrangement, who pays for it, and what rule change would alter that split. The summary states each without jargon.
© 2026 Erik Postnieks · Independent Researcher · Salt Lake City