Cryptocurrency Market Size, Share And Analysis

Since the rise of Bitcoin, cryptocurrencies have grown significantly not only in terms of capitalization, but also in number. Consequently, the cryptocurrency market can be a favorable scenario for investors, as it offers many opportunities. This study aims to describe, summarize and segment the key trends of the entire cryptocurrency market in 2018, using data analysis tools. Since each representation Coinpaper offers a different perspective on the market, we also examine the integration of the three grouping results, to get a detailed analysis of the most important market trends. In conclusion, we analyzed the association of clustering results with other descriptive characteristics of cryptocurrencies, including age, technological characteristics, and the financial ratios derived from them.

As the pandemic unfolded, all assets, including cryptocurrencies, were actually sold. Overall, investors may believe that small-cap market currencies have greater growth potential, although high market caps may also indicate stronger infrastructure and lasting strength. While forgotten currency, lost wallets, and irreparable keys mean we’ll never know exactly how many coins are in circulation, market capitalization provides an approximation to a coin’s network value.

Being able to separate the market price from the “real” value of a network is an excellent skill to have when trading. The study of stylized facts has been expanded by increasing the number of digital currencies to 222 (Hu et al. 2019). Similarly, we find it important to include as many cryptocurrencies as possible in our research, in order to fully characterize the market. The combination of clustering methods makes it possible to profile each cryptocurrency, taking into account the different clusters to which it belongs. In addition, the most populated clustering crossings will help us detect the most important market trends. Basically, the clustering analyst can detect the intersection that a particular financial behavior has by choosing the prototype of interest for each cluster method.

Obviously, distribution aggregation does not take into account the evolution of time, but this problem can be partially solved by considering a series of distributions that aggregate data, for example, quarterly, rather than annually. Therefore, we consider Hist-DAWass to be a suitable and promising profiling tool for investors and we believe that it can be used in the financial markets in general. If done correctly, fundamental analysis can provide valuable insights into cryptocurrencies in a way that technical analysis cannot.

Increasing visibility, growing investor interest and supportive regulations further increase the growth of the market. The maturation of the value of bitcoin cash and the ease of offering rewards for transactions also increase the market value of digital cash. Developing countries such as Japan, the United States, European countries and many more indicate that people are leaning towards digital currencies, which is expected to facilitate the growth of the cryptocurrency market in the coming years. By region, the cryptocurrency market was dominated by Asia-Pacific in 2020 and is expected to maintain its position during the forecast period.

Primary trends are the most important market movements and tend to last for months or years. Primary trends can be a bull market, meaning asset prices rise over time or a bear market, meaning they fall over time. Fundamental analysis is the study of financial information that influences the price of an asset to predict its potential growth. For a company’s stock, fundamental analysis can include analyzing earnings, industry performance, and brand equity. Sometimes we will be wrong, but we would like to think that we are not wrong for long, because the indicators do not allow us to. We’re not trying to be ultra-predictive, but just keep people on the right side of trends, give them some risk statistics to observe and some goals, maybe based on current action, talking about catalysts, etc.

The same conclusion can be drawn from other cluster analyses using cryptocurrencies. Stosic et al. represent the correlations of 119 cryptocurrency markets as a complex network and discover different community structures in their minimal tension tree. Song et al. analyze 76 cryptocurrencies using correlation-based clustering and filter the linear influences of Bitcoin and Ethereum, and detect 6 clusters, but which remain unstable after the announcement of regulations from different countries. The time dimension also plays an important role (Sigaki et al. 2019) grouping 437 time series of cryptocurrencies, using hierarchical techniques that detect four different groups with behaviors evolving differently, in terms of efficiency for information.