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Promote with Leviathan NewsCryptoQuant CEO Ki Young Ju has walked back his earlier call that Bitcoin’s current bull cycle had ended, saying his “cycle theory” failed in the face of unprecedented institutional liquidity and ETF-driven demand. In a recent post on X, he acknowledged that his March prediction about the bull cycle’s end was wrong and apologized to followers, noting that the market structure today is fundamentally different from past cycles because of how much new capital is arriving from institutions, funds, and spot Bitcoin ETFs. He argued that this shift has broken the old four-year retail‑driven cycle playbook and makes traditional cycle timing frameworks unreliable. Ju explained that his earlier framework was built around two patterns: accumulating when large “whale” wallets buy and distributing when retail investors flood in, which historically mapped well onto Bitcoin’s four‑year halving cycles. According to him, on-chain data now shows a different dynamic: since early 2023, smaller retail holders have been net sellers while institutions, funds, large wallets, and ETF vehicles have been steadily accumulating Bitcoin, increasing the number of long-term holders relative to short-term traders. Other CryptoQuant analyses have similarly highlighted that ETFs and traditional finance channels are supplying significant new liquidity, while inventories on crypto exchanges are relatively tight, reinforcing the idea that institutional flows are now a dominant driver of the market rather than classic retail euphoria. This shift matters because it challenges widely used “Bitcoin cycle theory” models that many traders and analysts use to time tops and bottoms around halving dates and retail sentiment. Ju’s admission underscores that institutional adoption and ETF-driven flows can extend or reshape bull cycles beyond historical patterns, leaving old metrics and timing rules less predictive. For market participants, it highlights the need to track ETF flows, institutional behavior, and long-term holder dynamics alongside traditional on-chain indicators, rather than relying solely on past cycle analogies.
AI-generated background, compiled from web sources — not editorial content.
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