After a Big Company Reports Earnings, Which Other Stocks Should You Watch?
September 14, 2026
Altsets
Research by Altsets Research
Start with the economically material customers and suppliers, not every stock in the same sector. Rank direct relationships first, then filter them by whether the earnings surprise actually affects the business line connecting the companies.
Data used:Altsets Supply Chain Intelligence: 90k+ entities, 400k+ relationships, 20+ years of history.
Key findings
- The supplied 561M USD HPE-Microsoft relationship provides a direct candidate to research after relevant Microsoft enterprise-infrastructure news rather than treating every technology stock as equally exposed.
- The supplied Shin-Etsu customer percentages show how directional economic importance can rank a watchlist without being misused as a predicted stock-return magnitude.
Start with the company's economically material customers and suppliers, not every stock in the same sector. A useful post-earnings watchlist contains companies with a documented relationship to the reporting company, ranks those relationships by available economic importance, and then asks whether the earnings information actually affects the product, spending, or demand channel that connects the two firms.
Microsoft earnings can create a narrower watchlist than the entire technology sector
The supplied Altsets data contains a 561M USD relationship between HPE and Microsoft. That gives an investor a specific reason to watch HPE after Microsoft releases information about enterprise infrastructure, Azure Local, or other areas where the companies publicly collaborate. It is a stronger starting point than watching every hardware company simply because Microsoft is a large technology company.
The relationship still needs event relevance. Microsoft consumer software news may have little direct implication for HPE. An investor should read what changed, identify which Microsoft business generated the surprise, and then decide whether the HPE relationship belongs in the transmission path. Supply-chain data creates the candidate list before the event. The event details decide which candidates remain relevant afterward.
Customer importance can rank the watchlist
The supplied Shin-Etsu Chemical relationships provide a simple ranking example. TSMC represents 4.02% of Shin-Etsu revenue, Samsung Electronics 2.43%, and Intel 1.79% in the displayed data. If each company reports a similar demand surprise relevant to semiconductor materials, the TSMC relationship is the largest of those three displayed Shin-Etsu customer exposures.
That does not mean a TSMC earnings surprise should cause a mechanically larger Shin-Etsu stock move. The percentages have economic meaning, not return meaning. They can help prioritize which relationship deserves attention first, while product mix, guidance, expectations, and market pricing determine the actual investment conclusion.
Research supports watching economically linked firms after customer news
Cohen and Frazzini documented return predictability across economically linked customer-supplier firms and argued that investors can be slow to incorporate news about related companies. Separate analyst research finds that analysts who follow a supplier's major customers produce more accurate supplier earnings forecasts and benefit from customer earnings information.
Those findings do not guarantee that every modern customer event produces a trade. They support the research workflow: when one company reports, economically connected counterparties are a more defensible place to look for secondary effects than a random screen of sector names.
Build the watchlist in tiers
The first tier contains quantified direct relationships where the reporting company is economically meaningful to the counterparty. The second tier contains direct structural relationships whose existence is known but whose magnitude is unavailable. A third tier can contain second-order paths only when there is a clear business mechanism and the investor is careful not to multiply relationship percentages through the chain.
This tiered approach keeps the watchlist manageable. A major earnings report can affect dozens of companies thematically, but only a smaller number have a documented customer or supplier path that deserves immediate investigation. The graph turns a broad news event into a finite research queue.
The conclusion is to follow the money before following the sector
After a major company reports earnings, start with the direct customers and suppliers for which the reporting company is economically meaningful, then filter those names by the actual business segment affected by the news. Supply-chain data does not tell you which stock will move next. It tells you which stocks have a concrete economic reason to care.
The earnings lead-lag guide explains the broader information-transfer hypothesis. The customer analyst-revisions guide explains how customer information can improve supplier forecasting before it becomes a trading rule.
For relationship definitions and evidence limits, read the Altsets methodology.