Return and Volatility Forecasting Using On-Chain Flows in Cryptocurrency Markets

Yeguang Chi, Qionghua, Chu and Wenyan Hao
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Yeguang Chi: Ruihua
Qionghua: Ruihua

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Abstract: We empirically examine the intraday return- and volatility-forecasting power of on-chain flow data for Bitcoin(BTC), Ethereum(ETH), and Tether(USDT). We find ETH net inflows to strongly predict ETH returns and volatility in the 2017-2023 period. Our intraday frequencies are 1-6 hours. We find that differing significantly from forecasting patterns for BTC, ETH net inflows negatively predict ETH returns and volatility. First, we find that USDT flowing out of investors wallets and into cryptocurrency exchanges, namely, USDT net inflows into the exchanges, positively predicts BTC and ETH returns at multiple intervals and negatively predicts ETH volatility at various intervals and BTC volatility at the 6-hour interval. Second, we find that ETH net inflows negatively predict ETH returns and volatility for all intraday intervals. Third, BTC net inflows generally lack predictive power for BTC returns(except at 4 hours) but are negatively associated with volatility across all intraday intervals. We illustrate our findings on return forecasting via case studies. Moreover, we develop option strategies to assess profits and losses on ETH investments based on ETH net inflows. Our findings contribute to the growing literature on on-chain activity and its asset pricing implications, offering economically relevant insights for intraday portfolio management in cryptocurrency markets.

Date: 2024-11, Revised 2025-09
New Economics Papers: this item is included in nep-fmk, nep-mst and nep-pay
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