The hidden predictive power of cryptocurrencies and QE: Evidence from US stock market

Abstract

Motivated by the increasing evidence of digital assets as hedge against traditional financial assets, this study examines the predictive power of cryptocurrencies (Bitcoin) on the US stock returns. We also hypothesize that the unconventional monetary policy namely, Quantitative Easing (QE), is an underlying factor that has sustained the evolution of cryptocurrencies. We advance the literature by accounting for the role QE in the Bitcoin predictability of stock returns. Essentially, we extend the bivariate single factor Bitcoin-based predictive model propose by Salisu et al. (2018) to a multi-factor cryptocurrency-based predictive model. Our findings are as follow: (i) when QE is measured directly, the single predictive model seems to be the preferred model; (ii) when QE is measured indirectly, via some transmission channels, the multi-factor based predictive model tend to outperform the single-factor model and (iii) relative to the historical average, the multi-factor based predictive model is the more accurate model to forecast stock returns. These results are robust to different methods of forecast performance evaluation measures and different sub-sample periods. These results have important policy implications for the investors and policymakers.