15 Apr PlanB Explains Why Bitcoin Won’t Drop to $24.5K Again
PlanB, the creator of the bitcoin Stock-to-Flow (S2F) model, implied that only a black-swan incident could drag the price of Bitcoin down to $24,500. The anonymous Twitter pseudonym in the past has missed Bitcoin’s price predictions on some occasions, but his bullishness on the asset still remains intact, asserting that it could reach $100K at the end of the year.
The bitcoin analyst with over 1.7 million followers on Twitter said in a tweet that he doesn’t see a reason that would bring bitcoin’s price down to its current realized price (average cost price) at $24.5K. He also noted that there were only three times that such a capitulation occurred in history.
Bitcoin SV (BSV) forking in 2018, Bitstamp, and Mt. Gox being respectively hacked in 2014 and 2011 were incidents – in PlanB’s view – that pushed BTC down below its realized price level.
Some people are afraid BTC might drop to Realized Price level of $24.5K. However, last 3 times such a capitulation happened, the cause was (yellow circles):
– 2018: BSV FORK
– 2014: Bitstamp exchange HACK
– 2011: MtGox exchange HACK
Not seeing that now. pic.twitter.com/pdrkChrUOE
— PlanB (@100trillionUSD) April 15, 2022
Willy Woo, another famed Bitcoin analyst, disagreed that BSV forking was attributed to the sudden drop of Bitcoin’s price below its major support levels back in 2018; instead, he believes it was the ”impact of whale control of BitMEX perps at full scale” that caused the precipitous price drop.
Founded back in 2014, BitMEX was one of the most well-known cryptocurrency exchanges in the world. Its introduction of “perpetual futures” in 2016, aiming to attract traders interested in leverage trading, made it the most popular derivatives product at one point.
Though PlanB has missed its prior prediction of Bitcoin reaching nearly $100K in 2021, according to his “floor model,” the analyst suggested back in February that BTC will reach a six-digit price tag by the end of 2023 after tapping into both the S2F and the logarithmic regression.
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