The Bitcoin market is once again sparking widespread discussion about price cycles. Renowned analyst PlanB, based on his widely followed Stock-to-Flow (S2F) model, predicts that Bitcoin's average price could reach $500,000 during the current cycle from 2024 to 2028. This model assesses its value trend by measuring Bitcoin's scarcity – the ratio of existing supply to annual new production. The halving event, which occurs every four years, significantly reduces the output of new coins, creating a tightening effect on the supply side. Historically, this mechanism has often been accompanied by significant price increases.
A chart released by PlanB shows that Bitcoin's price trend is highly correlated with two key indicators in the long term: the 200-week moving average and the Realized Price. These two indicators are regarded by many market observers as important references for measuring market sentiment and actual holding costs. The chart clearly shows the phased characteristics of Bitcoin's price gradually climbing after each halving, presenting an almost regular evolutionary path.
However, it should be clarified that the S2F model is essentially a statistical extrapolation tool, not a price prediction. Bitcoin's actual price trend is still affected by multiple external variables. The macroeconomic environment, global liquidity conditions, the regulatory attitudes of major countries, and the pace of institutional capital inflows can all have a substantial impact on the market. In addition, Bitcoin's inherent high volatility can also cause short-term price deviations from model expectations.
Currently, market participants are closely monitoring on-chain data dynamics, including net exchange inflows, the movements of large wallets, and changes in miner holdings, to determine true demand and market sentiment. Although the model provides a valuable reference framework, this cycle is still in its early stages and far from complete. Investors should maintain a sense of awe for the complexity of the real market while understanding the logic of the model, and avoid relying solely on a single indicator for decision-making.