Screening Pure-Play Comparable Companies

Why Comparable Screening Matters

A project-specific discount rate should reflect the risk of the project’s operating cash flows, not simply the average risk of the company funding it. When a firm’s other businesses have materially different customers, demand patterns, or cost exposures, a publicly traded company focused on the project’s type of business may offer more relevant evidence. Earlier material on WACC establishes why a risk mismatch can make a hurdle rate inappropriate; the first task here is to decide which companies are genuinely comparable.

A company’s industry label is only a starting point. Two firms may both describe themselves as “technology” businesses while one sells long-term software subscriptions to hospitals and the other sells custom hardware to manufacturers. Their operating exposures may differ more than their labels suggest. Conversely, firms in differently named industries can share important demand drivers and business economics.

Definition: A pure-play comparable is a publicly traded company whose principal operating business closely resembles the project or division being assessed. Comparability is judged by the underlying activity and its economic exposures—not merely by the company’s name, broad industry classification, or stock-market listing.

“Pure play” is a matter of degree. A company can have a small amount of unrelated activity and still be informative. A diversified company may also disclose enough information about a relevant segment to help with a screen, but its share price reflects the company as a whole. If the relevant segment is small or poorly described, the company-wide evidence is less clean.

A Structured Screening Framework

Screen candidates across several operating dimensions before treating their market data as evidence about the project. A useful screen asks what the company sells, who buys it, what drives customer demand, and which costs or constraints affect its operating cash flows.

Dimension	Questions to investigate	Potential mismatch
Business activity	Are the products, services, and value-chain roles alike?	A component supplier compared with a finished-system operator
Customers	Are customer types, purchasing processes, and concentration similar?	Consumer sales compared with long-term contracts to utilities
Cyclicality	Do demand and investment respond similarly to economic conditions?	Discretionary purchases compared with essential, stable services
Operating exposure	Are key inputs, regulations, technologies, and contract structures alike?	Commodity-sensitive production compared with fee-based services
Business mix and disclosure	How much of the company is relevant, and can it be identified reliably?	A relevant activity buried in a diversified company with limited segment detail

A simple revenue-share calculation helps identify diversified candidates. If a company reports revenue from distinct business segments, define its target-like revenue share as $s = \frac{\text{revenue from target-like activities}}{\text{total company revenue}}$. This ratio describes how much of the company’s reported sales come from activities resembling the project; it is not a risk measure or a sufficient selection rule.

R_{\text{total}} = R_{\text{target-like}} + R_{\text{other}}, \qquad s = \frac{R_{\text{target-like}}}{R_{\text{total}}}

The identity follows by dividing the relevant segment’s revenue by the sum of all segment revenues. If the target-like business is a larger share, the company’s overall operations are less diluted by unrelated activities. But revenue shares do not reveal segment profitability, fixed-cost intensity, or the risks of other assets and obligations. They are a screening aid, not proof that the company is a close comparable.

Screening principle: Look for agreement across multiple operating dimensions. A high target-like revenue share is useful, but a candidate may still be a poor match if its customers, demand cycle, or cost and regulatory exposures differ materially.

Worked Screening Process

A disciplined screen separates basic fit from possible reasons for exclusion. First describe the project’s actual operating economics. Then compare the candidates using business and segment disclosures, customer information, and evidence about demand and costs. Finally, record which candidates are strong matches, secondary evidence, or unsuitable—and why.

Specify the target.
Describe its products or services, customers, demand drivers, and material operating exposures.

Establish what each candidate actually does.
Use segment descriptions and operating disclosures rather than relying on a broad industry label.

Test the match and document limits.
Compare customers, cyclicality, and operating exposures; note any diversification or disclosure gaps.

Worked Example: Screening Cold-Chain Equipment Companies

A hypothetical manufacturer is considering a commercial cold-chain refrigeration division. Its customers are mainly warehouse operators and grocery distribution centers. Demand depends in part on investment in cold-storage capacity. Three publicly traded candidates report the following hypothetical revenue and operating information:

PolarTech reports $920 million in cold-chain equipment revenue and $80 million in general HVAC revenue. Its cold-chain customers are 60% warehouse operators, 25% grocery distribution centers, and 15% others.
FrostLine reports $850 million in cold-storage equipment revenue and $150 million in consumer appliances. Its customers are 70% logistics operators, 20% food processors, and 10% others.
ClimateWorks reports $700 million in broad HVAC revenue and $300 million in residential heating equipment. Its main customers are office-building owners and households.

Method. Compare business focus first, then customer fit and the stated source of demand. The revenue-share calculation is a screening indicator, not an automatic selection rule.

Step 1: Calculate PolarTech’s relevant share. Total revenue is $920 million + $80 million = $1,000 million. Its cold-chain share is $920 million / $1,000 million = 0.92, or 92%.

Step 2: Calculate FrostLine’s relevant share. Total revenue is $850 million + $150 million = $1,000 million. Its cold-storage share is $850 million / $1,000 million = 0.85, or 85%.

Step 3: Calculate ClimateWorks’ relevant share. If broad HVAC is the closest disclosed activity, its share is $700 million / $1,000 million = 0.70, or 70%. However, broad HVAC and residential heating are not the same activity as commercial cold-chain equipment.

Step 4: Assess operating fit. PolarTech has the highest relevant share and its two leading customer groups match the division’s main customers. FrostLine also has a substantial cold-storage business. Its logistics-operator customers are relevant, although food processors and other customers do not map perfectly to the target. ClimateWorks’ broad HVAC description and customer base provide a weaker match, and its disclosed business does not establish a close cold-chain activity.

Conclusion. PolarTech is the strongest candidate for further analysis; FrostLine is a reasonable secondary candidate whose customer mix should be reviewed. ClimateWorks should not be treated as equally comparable based only on its HVAC label. The common mistake avoided is selecting every firm in a broad “climate equipment” category without testing actual products and customers.

Worked Example: Distinguishing Recurring Claims Software from Adjacent Services

A hypothetical division sells claims-administration software to regional health insurers. Most of its revenue comes from recurring subscriptions; it also earns fees for implementation projects. The division operates under healthcare regulation and sells primarily to insurers. Consider two hypothetical public companies:

CareLogic derives 60% of company revenue from health-claims software. Within that segment, 75% of revenue is recurring subscriptions and 25% is implementation. Its principal customers are health insurers.
WorkSpan derives 90% of company revenue from workflow software, of which 90% is recurring. Its main customers are manufacturers, and its software is not primarily used for regulated health claims.

Method. Check the actual activity and customer base before treating a high recurring-revenue proportion as evidence of fit.

Step 1: Calculate CareLogic’s recurring claims-software revenue as a share of company revenue. The relevant calculation is 60% × 75% = 45%. Thus, recurring health-claims software represents 45% of its total company revenue.

Step 2: Calculate WorkSpan’s recurring workflow revenue as a share of company revenue. The calculation is 90% × 90% = 81%. This indicates a large recurring-software business, but does not establish a match to regulated insurer claims processing.

Step 3: Compare the operating characteristics. CareLogic’s activity, customers, and healthcare regulatory context align more closely with the division. WorkSpan’s recurring model is relevant to one feature, but its customer base and application differ.

Conclusion. CareLogic is the stronger comparable on the information provided. WorkSpan may provide limited context about recurring software operations, but its 81% figure is not a reason to treat it as a direct match. A common mistake is to compare CareLogic’s 45% with WorkSpan’s 81% and select the larger number without checking what the revenue represents. The percentages are calculated consistently, but the businesses are not equally comparable.

Worked Example: Screening Grid-Scale Battery Businesses

A hypothetical project will develop grid-scale battery storage for electric utilities. Its operating economics are exposed to utility investment plans, power-market conditions, project timing, technology choices, and relevant public policy. A screen identifies three hypothetical listed companies:

GridCell earns 80% of revenue from utility-scale battery systems and 20% from electric-vehicle chargers. Utilities account for 75% of its customers; its projects are often contracted with utilities.
VoltMaterials manufactures battery cells. Its customers are primarily automakers, and its costs are materially exposed to battery-material input prices.
RenewBuild reports 70% of revenue from wind and solar engineering and construction and 30% from storage projects. Utilities are 65% of its customers, and most work is project-based.

Method. Test activity and customer alignment, then consider whether other operating exposures make the candidate’s overall business misleading.

Step 1: Identify the closest operating activity. GridCell’s 80% utility-scale battery-system revenue directly resembles the project. The remaining 20% comes from chargers, so the company is not a completely undiversified pure play, but most of its business is closely related.

Step 2: Test customer and contract fit. GridCell’s utility customers and utility contracts align with the project. VoltMaterials sells cells mainly to automakers, which is a different customer group and a different place in the supply chain. RenewBuild has utility customers, but only 30% of its reported revenue is storage work.

Step 3: Identify material exposure differences. VoltMaterials’ material-price exposure and automotive demand drivers could dominate its operating experience. RenewBuild’s wind and solar construction activities may respond to utility investment and policy, but its larger non-storage business and project-based construction model limit how directly it represents a storage project.

Conclusion. GridCell is the strongest candidate to investigate further. RenewBuild may be a secondary reference if its storage segment is described in sufficient detail; otherwise its company-wide evidence is diluted by other construction activities. VoltMaterials should generally be excluded as a direct comparable: sharing battery technology does not outweigh its different product, customer, and input-cost exposures. The common mistake avoided is selecting companies because they use related technology without checking how their operating businesses earn revenue.

Interpreting the Evidence and Avoiding Traps

A screening conclusion should explain both the matches and the exclusions. “Comparable because it is in the same sector” is weak. A more useful record states, for example, that a candidate sells similar products to similar customers, but has a different demand cycle or substantial unrelated revenue. That reasoning makes later analysis easier to review and update.

Several limitations matter in practice:

Revenue is not operating risk. Segment revenue share does not show how much profit or cash flow a segment contributes. A smaller business can have a large economic effect, and a large segment can have different margins or cost structures from the target.
Customer categories can conceal differences. Two companies may both sell to businesses, yet face different purchasing cycles, contract lengths, customer concentration, and bargaining power.
Cyclicality should be tied to demand drivers. Do not label a company “cyclical” or “defensive” solely from its sector name. Ask whether customers can defer purchases and whether demand depends on economic growth, capital budgets, or essential ongoing services.
Related products are not necessarily comparable businesses. A supplier, installer, and service provider can serve the same end market while facing different operating exposures.
Limited disclosure is a reason for caution. If company-wide results combine several businesses and the relevant segment cannot be isolated, state that limitation rather than implying the candidate is a clean pure play.

This screen identifies companies worth considering; it does not itself determine a project discount rate. In particular, operating similarity should not be confused with similarity in financing. Capital-structure adjustments and the estimation and use of beta are separate steps in the broader project discount-rate analysis. The screen’s purpose is to make sure that the operating businesses entering that later analysis are relevant in the first place.

Key takeaway: Select comparables by testing the business activity, customer base, cyclicality, and operating exposures against the project. Use segment revenue shares to understand diversification, but do not let a single percentage or industry label substitute for a reasoned operating comparison.

Check Your Understanding

For each original screening problem, identify the most relevant conclusion from the information provided.

A project sells cooling equipment for data centers. Candidate A earns $840 million of its $1,200 million revenue from data-center cooling and sells mainly to cloud operators. Candidate B earns $960 million of $1,200 million from cooling products but sells mainly household air conditioners. Which is the stronger operating match, and what is each candidate’s relevant revenue share? (Answer: A is stronger on the stated customer and activity fit; A’s share is 70%, and B’s is 80%.)
A listed company earns 60% of its revenue from a relevant medical-records software segment. Three-quarters of that segment’s revenue is subscription revenue. What share of total company revenue is subscription revenue from the relevant segment? (Answer: 45%.)
A project serves regulated water utilities. A candidate serves utilities but earns most revenue from construction of large, one-time infrastructure projects; the project earns recurring service fees. Name one important mismatch that should be recorded before retaining the candidate. (Answer: Its project-based construction revenue model differs from the target’s recurring service-fee model; utility customers alone do not establish a close match.)
A company reports $360 million of target-like revenue and $240 million of unrelated revenue. A second company reports $700 million of target-like revenue and $300 million of unrelated revenue, but serves a different customer group and faces a different demand cycle. Calculate both target-like shares and explain why the second company is not automatically the better comparable. (Answer: The first share is 360/600 = 60%; the second is 700/1,000 = 70%. The second has the higher share, but its customer and cyclicality mismatches still require evaluation.)
A project supplies monitoring systems to hospitals under multi-year service contracts. Candidate X sells hospital monitoring systems and earns 65% of revenue from maintenance contracts; Candidate Y earns 95% of revenue from hospital beds sold in one-time transactions. Which candidate is the stronger initial match, and what dimensions support the choice? (Answer: X is stronger because its monitoring activity and substantial recurring maintenance exposure better resemble the project; Y’s hospital customer base does not offset its different product and transaction model.)

What You'll Learn