What Other Data Should You Combine With Supply-Chain Data First?
September 14, 2026
Altsets
Research by Altsets Research
For most beginners, start with price and fundamentals. Add analyst revisions for earnings questions, options for uncertainty, and news or event timestamps when the goal is to study how information travels through the relationship graph.
Data used:Altsets Supply Chain Intelligence: 90k+ entities, 400k+ relationships, 20+ years of history.
Key findings
- The supplied HPE-Microsoft relationship can already be combined with ordinary earnings dates, fundamentals, and returns before the researcher adds more expensive alternative datasets.
- Academic evidence supports analyst revisions for customer-to-supplier earnings information and options data for customer-to-supplier volatility information, giving beginners distinct data pairings for distinct research jobs.
For most beginners, pair supply-chain data with ordinary price and fundamental data first. Add analyst revisions when the question is about earnings and demand, options data when the question is about event uncertainty, and news or event timestamps when the question is about how information travels through the network. The graph is most useful when another dataset supplies the changing signal and the relationship data tells you where that signal may matter.
Price and fundamentals create the baseline
Supply-chain data tells the investor who depends on whom. Price history tells the investor how the market has reacted, while financial statements tell the investor what actually happened to revenue, margins, cash flow, and capital spending. Those basic datasets are enough to test many useful questions before adding more expensive alternative data.
For example, the supplied Altsets data shows Microsoft connected to HPE through a 561M USD relationship. A beginner can combine that relationship with HPE and Microsoft earnings dates, revenue growth, and stock returns to test whether Microsoft information historically coincided with changes in HPE fundamentals or price behavior. There is no need to start with five exotic feeds at once.
Analyst revisions pair naturally with customer and supplier relationships
If the goal is forecasting earnings, analyst revisions are one of the most natural additions. Research finds that analysts who follow a supplier's major customer produce more accurate supplier earnings forecasts and benefit from information along the supply chain.
The relationship graph determines which outside company's revisions deserve attention. A supplier model can monitor estimate changes at its important customers rather than aggregating revisions across the entire industry. This keeps the alternative-data stack economically targeted instead of simply adding more variables.
Options data is useful when the question is uncertainty
Options answer a different question from analyst revisions. Implied volatility, skew, and term structure describe how the market prices uncertainty around future outcomes. Research on customer-supplier volatility transmission finds that customer disclosures can affect expected supplier volatility and that stronger economic links can produce stronger effects.
That makes options a useful partner for event trading. The graph identifies the connected suppliers or customers, while the options market shows whether uncertainty is already being priced into those securities. An investor can study whether the relationship is known but underpriced, fully anticipated, or irrelevant to the options market.
News and event data provide the catalyst timestamp
Relationship data can remain stable for months while the market changes minute by minute. News, earnings releases, product announcements, regulatory decisions, and guidance updates provide the timestamp that activates the network.
This pairing is conceptually simple. The graph is the map. The event is the shock. Market and fundamental data measure the response. A beginner who keeps those three jobs separate will usually build a clearer research process than someone who throws dozens of unrelated alternative datasets into one model.
The conclusion is to add data according to the question
For most beginners, start with supply-chain relationships plus price and fundamentals, then add one dataset that matches the next question you are trying to answer. Use analyst revisions for changing earnings expectations, options for uncertainty, and event data for timing. The goal is not to collect the most alternative data. The goal is to combine one changing signal with the economic network that tells you where to look.
The alternative-data comparison explains what different alternative datasets measure. The customer analyst-revisions guide and supplier-options guide show two concrete combinations in more detail.
For relationship definitions and evidence limits, read the Altsets methodology.