The relationship between trend and volume on the bitcoin market

Szetela, Beata; Mentel, Grzegorz; Bilan, Yuriy; Mentel, Urszula

doi:10.1007/s40822-021-00166-5

Cited by 11 publications

(5 citation statements)

References 73 publications

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“…• Both the K-means and Hist-DAWass partitions are associated with the market capitalization and the volume. The relation between price and volume for bitcoin has been documented in the literature (Balcilar et al 2017;Sahoo et al 2019;Szetela et al 2021) but our results hints that such a relationship could be extended to other cryptocurrencies in the market. Particularly, the association seems to be stronger in the case of Hist-DAWass, which means that the shape of the distribution plays a role in the association with the volume and market cap.…”

Section: Discussionsupporting

confidence: 52%

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Lorenzo

Arroyo

2022

Financ Innov

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Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

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Section: Discussionsupporting

confidence: 52%

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Lorenzo

Arroyo

2022

Financ Innov

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“…The price hype, however, could not be sustained, and the crypto price bubble exploded at the end of 2018. This extreme volatility in the crypto-currency highlights the dangers of investing in this type of asset and for this reason, many researchers examined the volatility of crypto-currencies and the co-movement between them (Chkili, 2021 ; Chuen et al, 2017 ; Dastgir et al, 2019 ; Demir et al, 2018 , 2020 ; Ghorbel & Jeribi, 2021 ; Gozgor et al, 2019 ; Omane-Adjepong et al, 2019 ; Ong et al, 2015 ; Peng et al, 2018 ; Szetela et al, 2021 ; Yi et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets

2022

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Using the NARDL model for the period of pandemic COVID19, we examined the asymmetric relationship between six crypto-currencies (Bitcoin, Litecoin, Bitcoin gold, Dash, Maker, and Ehereum) and seven stock market prices (S&P500, CAC40, DAX30, NIKKEI, FTSE, FTSEMIB, and SPTSX) accounting for the effects of Gold and WTI prices. In the long run, our results revealed, in most cases, a positive asymmetric relationship between digital and financial assets, suggesting a weak safe haven role for crypto-currencies. The oil price (WTI) was also found to act as a diversifier. However, for, the results revealed, in most cases, a negative asymmetric relationship between the yellow metal and the different stock prices, suggesting that gold can act as a good hedging instrument or a safe haven against stock prices in the long run. On the other hand, in the short run, the results indicate that only Bitcoin, Litecoin, and Maker have an asymmetric effect on the chosen stock prices but the effect is positive in most cases. Moreover, gold can act as a hedge/safe-haven asset in the short run. Finally, while examining the dynamic response of stock prices to the negative and positive shocks of crypto-currencies, we concluded that the majority of stock prices respond more to the negative shocks of crypto-currencies than to the positive ones.

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“…Once the series is stationary at all levels, the next step is to estimate the series through VAR. The use of an unrestricted VAR model in this study enables an examination of the bidirectional association between two variables when they interact with each other, as indicated by Szetela et al (2021). This means that the variables have an influence on each other.…”

Section: Vector Autoregression (Var)mentioning

confidence: 99%

Exploring the nexus between price and volume changes in the cryptocurrency market

2023

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The analysis of the price-volume relationship offers valuable insights into the underlying dynamics of financial markets, enabling investors and market participants to make informed inferences based on the data derived from price and volume. This concept has extensively been studied in the stock market, while it remained understudied in the cryptocurrency trading system. The study aims to contribute to the literature by examining the causal relationship between price changes and trading volume for the top three cryptocurrencies (Bitcoin, Ethereum and Tether) listed on CoinMarketCap. Previous research that examined causal relationship focused on Bitcoin and Ethereum, while neglecting Tether, which is often used by investors to avoid price volatility. The study employs correlation matrix, vector autoregression Model, and granger causality to conduct the analysis of the research. The data for this study were extracted from CoinMarketCap, which provides the daily closing price and 24 hours traded volume of the top three cryptocurrencies from January 1, 2016 to December 31, 2021. The outcome of the preliminary study suggested that there is a positive significant price-volume relationship for Bitcoin and Ethereum, while there is a negative relationship for Tether. Furthermore, the study exhibits that there is a unidirectional causality between price return and volume change of Bitcoin and Ethereum, while there is no evidence of causality for Tether. Therefore, the empirical results establish the reliability of variable forecasting, empowering market participants and investors to make informed investment decisions.

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The relationship between trend and volume on the bitcoin market

Cited by 11 publications

References 73 publications

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets

Exploring the nexus between price and volume changes in the cryptocurrency market

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