We employ a repertoire of machine learning models to explore the cross-sectional return predictability in cryptocurrency markets. While all methods generate substantial economic gains, those that account for nonlinearities and interactions fare the best. The return predictability derives mainly...

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous...

This study examines the price explosiveness of stocks whose purchase Robinhood restricted during the GameStop episode. We find that those “meme stocks” comprise multiple periods of explosiveness, indicating that they are unlikely to be an epiphenomenon. We also document evidence of price...

We examine the role of geopolitical risk in the cross-sectional pricing of cryptocurrencies. We calculate cryptocurrency exposure to changes in the geopolitical risk index and document that coins with the lowest geopolitical beta outperform those with high geopolitical beta. Our findings suggest...