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<p>This study investigates the lagged price transmission from Bitcoin (BTC) to altcoins (ALTs) using high-frequency data and empirically validates trading strategies that leverage these market inefficiencies. Our comprehensive analysis across multiple market regimes reveals that small-cap cryptocurrencies exhibit significant delayed responses to BTC price movements. To examine this behavior, we introduce a simple indicator of immediate price responsiveness and find that lower liquidity tends to be associated with slower reactions. Granger causality tests further demonstrate unidirectional Granger-causal relationships from BTC to ALTs. Based on these findings, we develop a lag trading strategy using BTC's preceding returns as a leading indicator. Through machine learning-based trading decisions, our strategy consistently outperforms traditional buy-and-hold approaches across diverse market conditions. This research provides evidence of information transmission frictions in cryptocurrency markets and demonstrates the viability of practical investment strategies that leverage short-term anomalies. Our findings offer value to high-frequency and arbitrage traders while contributing new academic insights to the literature on cryptocurrency market microstructure.</p>

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Price Transmission from Bitcoin to Altcoins: High-Frequency Evidence and Implications for Trading Strategy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

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  • Tomoki Kurihara,
  • Takuji Matsumoto

摘要

This study investigates the lagged price transmission from Bitcoin (BTC) to altcoins (ALTs) using high-frequency data and empirically validates trading strategies that leverage these market inefficiencies. Our comprehensive analysis across multiple market regimes reveals that small-cap cryptocurrencies exhibit significant delayed responses to BTC price movements. To examine this behavior, we introduce a simple indicator of immediate price responsiveness and find that lower liquidity tends to be associated with slower reactions. Granger causality tests further demonstrate unidirectional Granger-causal relationships from BTC to ALTs. Based on these findings, we develop a lag trading strategy using BTC's preceding returns as a leading indicator. Through machine learning-based trading decisions, our strategy consistently outperforms traditional buy-and-hold approaches across diverse market conditions. This research provides evidence of information transmission frictions in cryptocurrency markets and demonstrates the viability of practical investment strategies that leverage short-term anomalies. Our findings offer value to high-frequency and arbitrage traders while contributing new academic insights to the literature on cryptocurrency market microstructure.