In this chapter we have investigated the whole cryptocurrency market be- tween April 2013 and May 2019. We have shown that the total market capitalisation has entered a phase of exponential growth from January 2015 to January 2017 and continued to grow until January 2019. The market share of Bitcoin has been steadily decreasing until June 2017 to recover in 2018 however since then Bitcoin’s market share has been fluctuating. We have identified several observables that have been stable since the begin- ning of our time series, including the number of active cryptocurrencies, the market-share distribution and the rank turnover. By adopting an ecologi- cal perspective, we have pointed out that the neutral model of evolution captures several of the observed properties of the market.
The model is simple and does not capture the full complexity of the cryp- tocurrency ecology. However, the good match with at least part of the picture emerging from the data does suggest that some of the long-term properties of the cryptocurrency market can be accounted for based on simple hypothe- ses. In particular, since the model assumes no selective advantage of one cryptocurrency over the other, the fit with the data shows that there is no
detectable population-level consensus on what is the “best” currency or that different currencies are advantageous for different uses. Furthermore, the matching between the neutral model and the data implies that the observed patterns of the cryptocurrency market are compatible with a scenario where technological advancements have not been key so far (see Appendix B.3) and where users and/or investors allocate each packet of money independently. Future work will need to consider the role of an expanding overall market capitalisation and, more importantly, try to include the information about single transactions, where available, in the modelling picture.
Another possible direction for future work is to focus on competition in terms of price changes instead of overall market share distribution similar to [15]. Cryptocurrencies market analysis can benefit from adopting a time series analysis approach; firstly, by identifying competition regimes of different maturity level across time [189,190]; secondly, investigate markets efficiency through price predictability or other statistical properties of the time series and time irreversibility [191]. Another direction could be adopting a complex network approach similar to the work in [192].
In the immediate and mid-term future, legislative, technical and social ad- vancements will most likely impact the cryptocurrency market seriously and our approach, together with recent results in computational social sci- ence dealing with the quantification of financial trading and bubble forma- tion [193,194,195,196], could help make sense of the market evolution. In April 2017, for example, Japan started treating Bitcoin as a legal form of payment driving a sudden increase in the Bitcoin price in US dollars [197] while in February 2017 a change of regulation in China resulted to a $100 price drop [198]. Similarly, the exponential increase in the market capitalisa- tion (Figure 4.1) will likely attract further speculative attention towards this market while at the same time increasing the usability of cryptocurrencies as a payment method. While the use of cryptocurrencies as speculative assets should promote diversification [15], their adoption as payment method (i.e., the conventional use of a shared medium of payment) should promote a winner-take-all regime [199,200]. How the self-organized use of cryptocur- rencies will deal with this tension is an interesting question do be addressed
5 Wikipedia and cryptocurrencies:
interplay between collective
attention and market performance
As we have shown in the previous chapter, the cryptocurrency market grew super-exponentially for more than two years until January 2018, before suffering significant losses in the subsequent months. Consequence and driver of this growth is the attention it has progressively attracted from a larger and larger public. In this chapter, we quantify the evolution of the production and consumption of information concerning the cryptocurrency market as well as its interplay with the market behavior. Capitalizing on recent results showing that Wikipedia can be used as a proxy for the overall attention on the web [201], our analysis relies on data from the popular online encyclopedia.Social media platforms nowadays provide researchers with vast amount of data that can signal public opinions or interests. Since stock markets are highly influenced by the rationale of the investors and their interests, several studies investigated the link between online social signals and stock market prices. Pioneering studies showed how signals from Google Trends and Wikipedia [106,107] or Twitter sentiment [108,109] can help anticipate stock prices.
This approach has been recently extended to investigate the relationship between social digital traces and the price of Bitcoin [132,113,13,17,11,125,
121,202,110], or few top cryptocurrencies [132]. While these studies showed the importance of relying on different digital sources, a systematic investiga- tion of multiple cryptocurrencies has been lacking so far. Furthermore, only
in few cases [8,13,113], mostly centred on Bitcoin, the analysis incorporated social media signals into an investment strategy in the spirit of the work in [106]. Finally, an analysis of the community driving the discussions and the information on cryptocurrencies was limited to few cryptocurrencies and to discussion platforms such as Bitcointalk forum [140] and Reddit [203]. Here, we investigate the interplay between the consumption and production of information in Wikipedia and market indicators. Our analysis focuses on all cryptocurrencies with a page on Wikipedia, from July 2015 until January 2019. The chapter is organized as follows: In Section 5.1 we describe the data collection and preparation briefly. In the following sections, we present the results of our analysis. Namely, we study the interplay between cryptocurrencies’ Wikipedia pages and market properties in Section 5.2; we study in details the evolution of cryptocurrencies pages in Section 5.3; we investigate the role of cryptocurrency pages edits in Section 5.4, and, finally, we explore and investment strategy based on Wikipedia in Section 5.5. The work presented in this chapter is based on publication [II].