How to Trace Earnings Surprises Through a Supply-Chain Network
September 1, 2026
Altsets
Research by Altsets Research
Use direct and second-order relationship exposure plus reporting sequence to decide where an earnings or guidance surprise deserves follow-up research.
Data used:Altsets Supply Chain Intelligence: 90k+ entities, 400k+ relationships, 20+ years of history.
Key findings
- Supplier revenue percentage can prioritize direct earnings read-through while economically meaningful upstream relationships create a second research queue.
- Relationship percentages should rank propagation paths rather than be multiplied into unsupported multi-hop earnings forecasts.
An earnings surprise at one company can create research questions for suppliers, customers, and companies one step further through the network. The useful task is not to assume that the surprise will propagate. It is to rank where the same economic signal is most plausible to investigate next. Nvidia provides a worked example because the displayed network contains material supplier relationships into Nvidia and additional relationships upstream from those suppliers. Two direct supplier relationships are:
| Supplier to Nvidia | Supplier revenue percentage | Relationship size | Nvidia cost percentage |
|---|---|---|---|
| SK Hynix | 27.88% | 21B USD | 27.33% |
| Micron Technology | 17.62% | 9.8B USD | 14.00% |
Upstream from those suppliers, the network also includes SK ecoplant to SK Hynix at 36.49% of SK ecoplant revenue, 3.1B USD, and 18.81% of SK Hynix COGS. It also includes ASML, Lam Research, and KLA into Micron with material displayed metrics.
These values create an earnings-propagation research queue. They do not create a forecast.
First hop: ask who is economically exposed to the reporting company
Suppose Nvidia reports a material change in demand. The first step is to rank suppliers by supplier revenue percentage.
SK Hynix is associated with 27.88% of revenue from Nvidia in the displayed relationship. Micron is associated with 17.62%.
Those percentages make both companies reasonable direct read-through candidates. The event itself still has to be relevant to the products represented by the relationships. A change in one Nvidia segment may not affect every supplier relationship equally.
Second hop: ask which upstream companies depend on those suppliers
If the first-hop supplier becomes a serious research candidate, the network can be expanded again. For SK Hynix, the displayed upstream SK ecoplant relationship is large on both directional measures. For Micron, several equipment and materials relationships are visible.
This creates second-order questions such as whether a Micron demand change could affect equipment purchasing, whether a SK Hynix production change could matter to an upstream infrastructure supplier, and which upstream companies report after the first-hop supplier. The network helps prioritize those questions.
Earnings propagation is not percentage multiplication
The direct and second-order percentages should not be multiplied into one forecast. A 17.62% Micron revenue relationship with Nvidia and an 11.91% ASML share of Micron COGS do not imply a simple numeric Nvidia-to-ASML earnings exposure. The denominators are different.
The relationships can also reflect different products, time periods, contract structures, and inventory cycles. The percentages should be used to rank paths, not create unsupported transmission coefficients.
Timing is what makes the method useful
The strongest use case comes from reporting sequence. If Company A reports before Company B, and Company B has material revenue exposure to A, Company A's update becomes an input before B reports. If Company C is economically connected upstream from B and reports later still, the same signal can become a second research question. This is why an earnings-propagation screen should include reporting dates, relationship direction, supplier revenue concentration, customer cost concentration, product relevance, and whether guidance has already changed.
The network supplies the relationship structure. The calendar supplies the sequence.
Positive and negative surprises should be treated symmetrically
The method is not only for bad news. A customer raising guidance, accelerating capital spending, or reporting strong unit demand can create positive read-through questions for suppliers. A supplier reporting stronger demand from one customer can create questions for its own upstream vendors. The correct output is a prioritized research list, not an automatic bullish or bearish signal.
When a signal should stop propagating
A network can always be expanded further. That does not mean it should be. Stop when the next relationship is economically small, metrics are missing, product relevance becomes weak, the path crosses unrelated business segments, the event has already been reflected in guidance, or too many alternative explanations dominate the signal. This prevents a two-hop method from turning into storytelling.
A repeatable earnings-propagation workflow
- Record the earnings or guidance surprise.
- Identify direct suppliers and customers.
- Rank direct relationships by the metric that matches the direction of the signal.
- Verify product relevance.
- Compare reporting dates.
- Expand only the strongest first-hop nodes.
- Rank second-order relationships.
- Stop when economic or business relevance weakens.
- Keep every downstream conclusion as a hypothesis until company-specific evidence supports it.
The customer read-through guide covers the first-hop method. The second-order exposure guide covers the structural two-hop method. This guide combines those ideas around the information-timing problem created by reporting order.
For metric definitions and limitations, read the Altsets supply-chain data methodology. Browse Supply-Chain Data Use Cases for other methods.