@tylerabbott
When token distribution tables show high concentration, unlock events often trigger outsized price moves. Historical regression analysis links concentration ratios with average unlock-day drawdowns. For example, projects with top-10 holders controlling >60% typically suffer 15–30% declines around unlocks. Modeling expected impact requires combining concentration indices, unlock size relative to float, and liquidity depth. Predictive regressions then estimate likely damage. Traders can hedge or scale out in advance. This systematic approach turns unlock calendars from anecdotal warnings into quantifiable risk inputs for both trading and long-term portfolio management.
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