How Do You Build a Beta-Neutral Supply-Chain Trading Basket?
September 14, 2026
Altsets
Research by Altsets Research
Use customer and supplier relationships to choose an economically coherent candidate set, then separate that selection logic from the beta, sector, style, volatility, liquidity, and covariance controls used to size the actual trade.
Data used:Altsets Supply Chain Intelligence: 90k+ entities, 400k+ relationships, 20+ years of history.
Key findings
- The supplied network maps both Micron and SK Hynix to Nvidia, creating an economically interpretable shared-customer candidate set without implying that supplier revenue percentages should become hedge ratios.
- Neutralization is both a risk-control step and a falsification test: if a relationship effect disappears after reasonable market and industry controls, the original signal may have been a conventional semiconductor exposure rather than network-specific information.
A supply-chain basket can be economically coherent and still be a poor trading experiment if most of its profit and loss comes from market beta, sector exposure, or one dominant style factor. This matters because relationship data naturally clusters companies that already share industries and themes. A basket of semiconductor suppliers can look like a successful network trade when it is really a leveraged semiconductor trade. Quant portfolio construction should therefore separate the economic link used to choose the names from the systematic exposures used to size and neutralize the positions.
Start with an economically defined candidate set
The supplied Altsets network maps both Micron and SK Hynix to Nvidia as customers. The relationship inventory associates Nvidia with a substantial share of revenue for each supplier, although those directional percentages should not be converted mechanically into portfolio weights. The important research idea is that both companies have a documented path to the same external demand source. That gives the quant an economically interpretable candidate set for a relative-value or event basket before any return optimization takes place.
The basket can then be expanded to other companies with comparable network relationships if the historical dataset supports them. The selection rule should remain point in time and should not use future relationship discoveries. Once the candidates are fixed, the researcher can estimate ordinary market and sector exposures using return data. Network evidence chooses which companies belong in the experiment; traditional risk modeling determines how the trade avoids becoming an accidental bet on the whole semiconductor industry.
Neutrality should be defined against the hypothesis
Market neutrality usually means approximately zero broad-market beta, but that can be insufficient for a supply-chain strategy. A basket intended to isolate Nvidia-linked supplier behavior may also need to control semiconductor industry exposure, size, momentum, or other factors that systematically differ between the long and short sides. Otherwise a positive return can reflect a rally in memory stocks rather than the shared-customer mechanism the researcher set out to test.
The exact neutralization depends on the question. If the strategy is testing customer momentum spillover, the basket can compare highly exposed suppliers with similar less-exposed firms inside the same industry and then neutralize market beta. If the strategy is testing network concentration as a cross-sectional feature, broader sector and style controls may be necessary. Neutralizing everything indiscriminately can also remove part of the real economic signal, so the researcher should state which exposures are considered nuisance variables and why.
Relationship percentages are features, not hedge ratios
A common mistake is to interpret a supplier revenue percentage as the correct portfolio weight. A company that receives 20% of revenue from a customer should not automatically receive twice the position of a company receiving 10%. The percentages describe economic relationships, while portfolio weights need to account for expected return, volatility, covariance, liquidity, borrow conditions, and risk limits. The same distinction applies to customer cost percentages and relationship sizes.
A better workflow can use directional relationship metrics to rank or bucket exposure, then let a separate portfolio optimizer or simple risk-scaling rule determine position size. The model can test whether stronger economic exposure predicts a stronger event response without embedding that assumption directly into the trade size. This keeps the relationship interpretation clean and makes it easier to determine whether performance comes from stock selection or leverage.
Neutral baskets are useful for falsifying the network story
A market-neutral construction is not only a risk-control tool. It is also a research diagnostic. If a relationship-based effect disappears after controlling for market and industry exposures, the original result may have been a disguised conventional factor. If it survives reasonable neutralization, the network explanation becomes more interesting. That logic is consistent with the broader asset-pricing literature, where candidate anomalies are tested against known factor models rather than evaluated from raw long-short returns alone.
The researcher should still examine the residual exposures after optimization. A mathematically beta-neutral basket can remain economically concentrated in one country, customer, product cycle, or liquidity segment. Supply-chain data is particularly useful here because it can reveal the non-price dependencies that a covariance matrix does not label explicitly. A good basket can therefore be neutral in both conventional factor space and intentionally controlled in dependency space.
The conclusion is that economic selection and financial hedging solve different problems
Supply-chain data can define a set of companies that share a plausible economic driver. It should not be asked to perform every step of portfolio construction. Use the network to decide which securities belong in the hypothesis, then use beta, factor, liquidity, and covariance controls to decide how the hypothesis should be expressed as a trade. If the result survives those controls, the researcher has stronger evidence that the network relationship matters beyond a generic market or sector bet.
The stat-arb pair-selection guide explains how the network can narrow a relative-value search space. The incremental-alpha guide explains why supply-chain features should be tested after conventional predictors and exposures are already considered.
For relationship definitions and evidence limits, read the Altsets methodology.