Akademik Veri Yönetim Sistemi
The International Conference on Modern Research in Management, Economics and Accounting, Berlin, Almanya, 17 - 19 Mart 2023, ss.1-12, (Tam Metin Bildiri)
This
paper determines the existence of “herd behavior” and the factors of herding in
cryptocurrency markets. We examine the causability between herding and sentiment
signals such as (FoMO, hopeful, negative, positive, and uncertain) as well as
between herding and volatility and valume as a measure of overconfidence for
Bitcoin. Our dataset covers January 1, 2019 – September 10, 2022, consisting of
COVID 19 pandemic. First, we use intraday aggregate trade data and construct
daily herding intensity statistics for negative, positive, and zero trades in
the sense of Patterson and Sharma (2006). Then, we compute realized volatility
series exploiting the Parkinson’s (1980) range-based measure. Following
Balcilar et al. (2017), the volatility series are log-detrended. The sentiment
signals are smoothed using the exponential smoothing (ETS) models in the statsmodule
Python module. Lastly, we estimate Fourier-type Granger causality tests
developed by Nazlioglu et al. (2019). Our results show the bi-directional
causality between sentiment signals from Twitter and the herding intensity
statistics at the conventional significance levels. The sentiment signals from
Reddit have a limited impact on the herding statistics, but most of the signals
significantly cause the volatility measures. Bitcointalk sentiment signals do
not cause any herding, volatility, and volume measures. Our results provide
important implications for investors and portfolio managers interested in cryptocurrency
investments.