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Want to figure out how much those perpetual contract products without tokens are really worth? Here's an interesting approach:

**Core idea**: The valuation of perpetual contract products should be linked to the fees they generate. In other words, the higher the fee revenue, the more reasonable the product's market cap(FDV).

**Analytical approach**: My method is to first take existing perpetual contract products with governance tokens as references, and examine the ratio between their FDV and daily/monthly fee income. Then, apply this ratio to those perpetual contract platforms that haven't issued tokens yet but are already generating fees. Theoretically, this can help derive a plausible valuation range for them.

**Underlying assumption**: The intrinsic value of a product comes from how much fee revenue it can collect from traders—higher fees indicate active trading volume, which in turn suggests user stickiness.

Based on this logic, reverse-engineering the potential FDV of tokenless products from fee data can help assess the early investment potential of such projects.
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MerkleTreeHuggervip
· 3h ago
Cost model valuation? Sounds reasonable but also feels too simple. Are those platforms that haven't issued tokens really that valuable?
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blocksnarkvip
· 4h ago
This logic is actually the multiple-of-fee valuation method, which has been around for a while. However, applying it to non-currency projects can indeed reveal some insights.
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ArbitrageBotvip
· 4h ago
Cost is the truth; everything else is just a story.
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TokenomicsTinfoilHatvip
· 4h ago
Cost model valuation, sounds good but depends on whether the trading volume can support it.
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GasFeeSobbervip
· 4h ago
Fee data reverse estimation of valuation, this set of logic is quite interesting... but it still depends on trading depth and slippage to be accurate.
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FOMOmonstervip
· 4h ago
Hmm, this logic seems reliable. Trying the method of reverse estimating valuation based on costs might be worth a shot.
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RadioShackKnightvip
· 4h ago
Cost model valuation? Sounds reliable, but it also depends on the data; otherwise, it's just armchair analysis.
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