Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models

This paper addresses market efficiency of cryptocurrencies. We investigate predictability of daily returns and strive to uncover the underlying dynamics. Four major cryptocurrencies are considered for their representativeness of the market: Bitcoin, Ethereum, Binance Coin and Litecoin. A Gaussian Mixture Modeling (GMM) is applied as framework in a two-step process. The first step targets the clustering of returns while the second focuses on regime-specific dynamics of returns. The ensemble aims to capture nonlinearity and to assess asymmetric behavior. On purpose we use macro-financial variables, coin-specific and global market sentiment indicators. We find significant predictability in terms of conditional mean prediction, trend prediction and market regime prediction. Moreover, economic value of forecasts for these four coins shows evidence of counterarguments to the Efficient Market Hypothesis (EMH). Our findings provide insights for profitable investment strategies and enable a better understanding of returns dynamics. The results are robust enough to motivate active strategies and replication on larger panel of cryptocurrencies. Simultaneously, evidence highlights new issues that necessitate further investigation into the observed asymmetrie