Policy-to-Profits Screening Workflow
Trace regulatory change from policy signal to measurable company profits
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 7
- Confidence
- 95%
The workflow begins with a policy signal, such as an executive order, and asks which operational bottleneck it removes. Researchers first identify highly regulated industries where approvals, compliance costs, permitting, or government contracting slow activity. AI-assisted screening then searches earnings transcripts, valuations, and SG&A line items to find companies discussing or bearing those costs. Each candidate is evaluated for a credible causal chain from regulatory relief to faster time to market, lower expenses, improved margins, or higher growth. Human reviewers challenge the model output because no universal deregulation score captures every situation. The resulting portfolio remains active so it can respond when new policies shift the likely winners and losers.
Origin
Michael Gayed described the workflow used by the Free Market ETF after an adviser proposed deregulation as an investible theme. Extracted from Masters in Business.
Core principles
- 01Treat policy as an economic input rather than a political statement
- 02Look for regulation that creates costly operational bottlenecks
- 03Trace relief from sector effects down to individual companies
- 04Validate management narratives against financial line items
- 05Keep the process active because policy targets change
How to run it
- 1
Capture the policy signal
Monitor executive orders, agency decisions, and other regulatory changes. Record the specific rule, approval process, or restriction that may change.
Pro tip Prioritize enacted actions over campaign language or media speculation.
Watch out Do not treat general political rhetoric as an investible catalyst.
- 2
Find the bottleneck
Identify sectors where regulation materially slows approvals, raises compliance expenses, or limits capacity. Define exactly how the current friction affects operations.
Pro tip Look closely at financials, energy, nuclear, aerospace, cannabis, and government contracting when relevant.
Watch out A regulated industry is not automatically a beneficiary of every deregulatory action.
- 3
Generate company candidates
Use AI and structured searches to analyze earnings transcripts, valuations, and regulatory-cost indicators. Find companies whose executives connect policy changes to their operations.
Pro tip Search both direct references to deregulation and indirect references to permitting, approvals, compliance, and contract timing.
Watch out Management mentions can signal relevance without proving financial benefit.
- 4
Trace the causal chain
Map the policy change to a concrete mechanism such as lower SG&A, faster contracting, quicker permitting, or improved market access. Reject candidates where the chain remains vague.
Pro tip Write the chain as policy change, operational effect, financial effect, and expected timing.
Watch out Political access or ideological alignment alone does not establish a profit mechanism.
- 5
Validate the fundamentals
Check whether margins, growth rates, expenses, contract awards, or management guidance begin reflecting the expected benefit. Account for the lag between policy action and reported earnings.
Pro tip Compare reported results with the original investment thesis each quarter.
Watch out Deregulatory effects may take several reporting periods to appear.
- 6
Apply team judgment
Have multiple portfolio managers review the evidence and AI output. Decide whether the causal argument is strong enough to warrant exposure.
Pro tip Document dissenting views and the evidence that would invalidate the thesis.
Watch out The process cannot be reduced to a single quantitative deregulation score.
- 7
Refresh continuously
Re-run the workflow as new policies emerge and reported evidence changes. Rotate away from candidates whose expected benefits weaken or fail to materialize.
Pro tip Tie every holding to a current, reviewable policy-and-profit justification.
Watch out A static portfolio can retain yesterday's beneficiaries after policy priorities move elsewhere.
In the wild
The episode links Oracle to changes in the Federal Risk and Authorization Management Program. Removing approval friction could shorten the process of qualifying for government contracts, creating a direct path from administrative change to faster commercial activity.
→ Oracle becomes a candidate because the thesis rests on contract speed rather than political proximity alone.
AI data centers increase electricity demand, but lengthy approvals constrain new nuclear capacity. A policy that shortens plant development timelines could improve time to market for companies positioned to supply that demand.
→ The screen identifies potential beneficiaries at the intersection of regulatory bottlenecks, energy demand, and project delivery.
Common mistakes
Buying the political narrative
A company may be politically prominent without receiving a measurable operational or financial benefit. Require a specific policy-to-profit causal chain.
Trusting transcript mentions alone
Management commentary is a screening signal, not final proof. Validate it against expenses, margins, growth, contracts, and guidance.
Ignoring implementation lag
Policy changes may not reach earnings immediately. Prematurely abandoning or celebrating a thesis can obscure the true result.
Is it for you?
Best for
It is best for active thematic investors evaluating how policy changes could affect sectors, margins, contracts, and growth.
Not ideal for
It is not ideal for passive investors who cannot continually monitor policy, management commentary, and company fundamentals.
From the transcript
“So we've got multiple kind of filters.”
“So it's all the stuff that our bottlenecks, right, is kind of the area the way to think about it.”
“politics goes into the policy, policy goes into profits, right”
From the episode
At The Money: Deregulation Will Free Your Portfolio