Bitcoin price volatility may be predicted by modeling on-chain transaction concentration to identify turning points. High transaction clustering often signals market shifts, as whale activity or liquidity changes impact prices. By analyzing metrics like transaction volume, wallet address concentration, and UTXO distribution, models can detect patterns preceding inflection points. Machine learning, such as LSTM or ensemble methods, enhances accuracy by capturing non-linear dynamics. Studies suggest on-chain data outperforms traditional indicators for short-term forecasts, with up to 82% accuracy in direction prediction. However, volatility’s complexity, driven by external factors like sentiment or macroeconomic trends, limits long-term precision. Integrating on-chain concentration with sentiment analysis or market indicators could improve robustness, but challenges remain due to Bitcoin’s non-stationary nature.