Abstract
This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors. Implications for future research are discussed.
Highlights
Herding in financial markets has been a heated debate in the scholarly world over the past two decades
The findings show that there is a strong tendency to herd on nonfundamental information that explains cross-sectional absolute deviation of returns under normal and up-trending market conditions
The results show that the coefficient γ3, which captures the herd behaviour of investors in the cryptocurrency market, is positive and statistically insignificant at 5 percent significance level
Summary
Herding in financial markets has been a heated debate in the scholarly world over the past two decades. If investors trade without information about the trading securities, price movements will reflect the information variables related to common premiums in the market, while price changes may co-move closely with this premium on average because investors sometimes trade for the sake of trading with speculative profit motives (Senarathne and Jayasinghe, 2017) In such a case, the benchmark for expected payoffs would be the common market expectation (or premium), because there is no security (i.e., firm) specific information 1. Scholars show that herding on fundamental information (i.e., mimicking fundamental factors) results in inefficient outcomes, whereas herding on non-fundamental information (i.e., mimicking firm-specific factors) leads to efficient market conditions (See Bikhchandani and Sharma 2000) These findings can be effectively observed in markets with instruments (i.e., assets) that have underlying assets.
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