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PurposeThis study examines the prediction power of investor sentiment on Bitcoin return.Design/methodology/approachWe construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.).FindingsUsing a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions.Research limitations/implicationsThe findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency.Originality/valueThis study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.

Understanding the connectedness of financial markets and hence possible sources of systematic risk is central to the debate on the process of financialisation and its consequences for financial stability. In this study, we examine the connectedness between commodity spot and futures prices by applying a novel frequency connectedness framework on data from January 1979 to December 2019 to measure the connectedness among financial variables. Focusing on the seven most widely traded commodities, including gold, silver, crude oil (WTI and BRENT), corn, soya and iron, we find that (i) volatility of the commodity derivatives (futures) contribute to the spot volatility and hence influence spot prices of the underlying commodities in international markets (ii) volatility spillover effects are stronger in the first four days of the shock, suggesting that shocks to the underlying asset volatility caused by its own fundamental are more prevalent and persistent in the long-term (iii) commodities futures volatility transmission is higher than spot price volatility transmission to the futures prices. Our findings shed new light on the relationship between the actual spot price of commodities and their derivatives and have crucial socio-economic implications in terms of financialisation of important commodities.

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