Intraday patterns of price clustering in Bitcoin

Abstract

In this study, an investigation is conducted into the phenomenon of price clustering in Bitcoin (BTC) denominated in the Japanese yen (JPY). It answers two questions using tick-by-tick data. The first is whether price clustering exists in BTC/JPY transactions, and the other is how the scale of price clustering varies throughout a trading day. With the assistance of statistical measures, the last two digits of BTC price were discovered to cluster at the numbers that end with ’00’. In addition, the scales of BTC/JPY clustering at ’00’ tended to decline at the specific hour intervals. This study contributes to the emerging literature on price clustering and investor behavior.

Price Clustering in International Financial Markets During the Covid-19 Pandemic and Its Implications

“…The significance is evident in the p-values that are less than 5% from the chi-square and Kolmogorov-Smirnov test, hence inferring the presence of price resolution in these markets. These findings agree with the findings of Ascioglu et al (2007), Lobão et al (2019), and Ma and Tanizaki (2022) but contradict the findings of Hu et al (2017), although they were not explicitly conducted during the pandemic era. Results also support the findings of Enow (2021), stating that the JPX-Nikkei Index 400 is informationally inefficient, and investors can use fundamental analysis to profit.…”

“…The presence of price clustering in a market has both positive and negative effects. Where empirical evidence supports price clustering, investors, analysts, and investment practitioners can take advantage of market price aggregation around certain intervals and trade with the information (Ma and Tanizaki, 2022). That is to say, investors can purchase stock below the grouping price and sell when the prices move to the group.…”

Asymmetric interactions among cutting-edge technologies and pioneering conventional and Islamic cryptocurrencies: fresh evidence from intra-day-based good and bad volatilities

“…In this study, the raw data are based on a one-hour frequency and the volatilities are based on realized absolute returns over that period. Based on the studies of Wen et al (2022), Hu et al (2019), Ma andTanizaki (2022), andSifat et al (2019) and the limited time coverage of intra-day observations with high and low frequencies for technology stocks and cryptocurrencies in different databases, we selected hourly data to provide the best possible coverage. Furthermore, using an hourly frequency to determine liquidity for Bitcoin, Urquhart and Zhang (2019) concluded that higher frequencies do not provide liquidity, and Zhang et al (2019) demonstrated that the prices of selected cryptocurrencies-Bitcoin, Ethereum, Ripple, and Litecoin-are relatively efficient on an hourly basis.…”

Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers

“…In this study, we use the hourly closing prices of Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Stellar (XLM). In accordance with Wen et al (2022), Hu et al (2019), Ma and Tanizaki (2022) and Sifat et al (2019), and taking into account the limitations of time coverage of observations made with high and low frequency in different databases, as well as the limitations of time coverage of intraday observations, our analysis is carried out based on hourly data collected between 2018.06.01, 00:00:00, and 2022.07.22, 05:00:00, comprising more than 36,000 data records.…”