Bitcoin macro economy

Concurrent with a rapid price appreciation, the increase in financial market interest in digital currencies and in Bitcoin in particular, as well as global integration of virtual networks, have prompted the emergence of new academic studies related to economic behavior of this new asset that has been inserted in the world financial market.

Factors that make the asset extremely volatile to information and market variables include the absence of a centralized institution that controls and guarantees the value of Bitcoin and the understanding that its price is based on the belief that the virtual currency will continue its upward trajectory. It seems that there are yet opportunities to get benefits from Bitcoin volatilities and its market inefficiencies (Bouri et al. 2018). It is important to highlight that this inefficiency is getting weaker over time since liquidity seems to have a positive effect on the informational efficiency of Bitcoin prices (S Kumar and Ajaz 2019).

This study seeks to advance knowledge of how Bitcoin prices are set by the market, to identify the relevant variables affecting the Bitcoin market, and to provide a technical reference for the investors that believes in its appreciation over time and invests in this asset. The findings are also relevant for policymakers and monetary authorities in order to understand why people are seeing increasingly their interests to trade or hold Bitcoin. Understanding these interests is fundamental to create alternatives to avoid governments having their currencies depreciated against Bitcoin.

Some studies point to three major variables that influence the Bitcoin price: macroeconomic/financial, such as dollar quotation and stock exchange index; attractiveness, such as increased interest in the asset evidenced by its increasing appreciation over the years; and the dynamics between demand and supply.

The initial hypothesis of the research is that attractiveness factors influence the Bitcoin price at both global and local levels, updating previous studies of attractiveness pricing. These combined attractiveness factors define the interest of the world’s population in the asset, as measured by the number of Google searches for the terms bitcoin and bitcoin crash between December 2012 and February 2018.

This study adds to the analysis the crisis variable through a measurement of the number of Google searches using the term crisis. It seeks to verify if, in troubled periods of crisis with repercussions at the global level, Bitcoin tends to be more attractive as an alternative investment, as evidenced by an increase in its price.

The vector error correction model is the methodology chosen to test the hypothesis that the number of Google searches using the terms bitcoin and bitcoin crash affects the Bitcoin price and that, in times of increasing searches for news about crisis, there is an accompanying increase in the value of the digital currency. It is anticipated that the hypotheses and a feedback effect between endogenous variables will be confirmed.

Functions of money

Nakamoto (2008) described Bitcoin as an electronic currency embedded in a peer-to-peer system and capable of being transferred directly from one participant to another without the intermediation of a financial institution. A process called proof of work helps to assure that duplicate transfer expenses are avoided. Through this process, the Bitcoin network confirms each transfer as legitimate and unique by analyzing the digital signature and recording the chronological order in which the transaction took place.

The transactions recorded and confirmed are inserted into a block that becomes part of the blockchain, through a process known as mining. This chain of blocks, which contains all transaction history, is constantly sent to network participants to inform them of the new operations. In this sense, Nakamoto (2008) compared the digital currency to a stream of digital signatures.

This entire technological and cryptographic framework already makes Bitcoin different from sovereign currencies, primarily because of its ability to be cited as a representation of digital value and its virtual decentralization. In this sense, there is no consensus among scholars about using of the term currency when referring to Bitcoin. Some relevant aspects of Bitcoin differ from traditional fiduciary currencies that will be analyzed.

Initially, it is important to review some of a currency’s economic functions: as a medium of exchange, enabling the purchase (or sale) of products and services upon delivery (receipt) of the currency; as a unit of account, from which goods and services are priced; and as a store of value that ensures the maintenance of purchasing power and wealth over time, and sometimes allowing interest income through investment in financial assets. The question is whether Bitcoin has all these properties to be termed as currency. Yermack (2015) and Ciaian et al. (2016a) differ in this regard.

Virtual money use has increased as a medium of exchange in the e-commerce environment where major brands such as Microsoft and Subway have offered it as a payment method in online purchases. The speed and low cost of transferring Bitcoin, the anonymity of the transference, and the transparency of transactions recorded in the blockchain are positive aspects that promote adoption of Bitcoin as cash.

However, legal issues may compromise Bitcoin’s role as a medium of exchange since sovereign governments has authority to prohibit its adoption by their populations and emphasize negative aspects such as cyberattacks and virtual crimes—all characteristics that are cited by an analysis by Ciaian et al. (2016a). While investigating fraudulent activities at the MtGox brokerage firm aimed at leveraging the Bitcoin price, Gandal et al. (2018) highlighted threats to the Bitcoin network, such as Ponzi schemes, theft of Bitcoin brain wallets, and malware. Also, cryptocurrencies could be illegally used to facilitate Trade-based Money Laundering (TBML) schemes and it can be justified by the easy way the digital coins are transferred. Chao et al. (2019) say that TBML is seriously concerned by emerging markets and developing economies in a way that regulations and methods to monitor and fight against it have been created.

The lack of regulation is also an unfavorable criterion, since it eliminates judicial settlements of disputes and makes it difficult to obtain reimbursement from operations prejudiced against cryptocoins. In November 2017, the Central Bank of Brazil - Bacen (2017) said that does not regulate or supervise virtual currencies even though it monitors related discussions in international forums. In addition, the bank emphasized the imponderable risks of this type of investment to the market, including the loss of all invested capital.

Concerning the unit of account function, Ciaian et al. (2016a) highlighted the high volatility of Bitcoin pricing as costly from the point of view of the virtual re-mark of goods and services prices denominated in Bitcoin monetary units. This function is the main differentiating factor between Bitcoin and sovereign currencies. Another striking difference concerns to divisibility since the coin can be denominated beyond two decimal places (the smallest fraction of Bitcoin is called satoshi and corresponds to one hundredth of millionth of Bitcoin). Yermack (2015) stated that the market can be disconcerted about the use of multiple decimal places, hindering price comparisons by the consumer.

Regarding the store of value function, Ciaian et al. (2016a) also stated that Bitcoin has two important advantages over other currencies: the fact that the offer is predetermined by the platform and is protected by cybersecurity since all registrations made in the blockchain are unchangeable. Volatility and cyberattacks are negative factors in this regard.

In addition, investment in virtual currencies can generate interest income, including through available platforms, such as BitPass, that offer interest payments to customers who leave their bitcoins stored for a certain period of time.

A brief literature review

Bitcoin pricing has been the subject of research by scholars who seek to infer the variables that affect Bitcoin value. In the literature, basically, three groups of these variables are found: macroeconomic and financial; attractiveness; and the dynamics between demand and supply. There are studies that focus on just one of these groups and others that seek to conduct a more holistic analysis by covering all of them.

Some authors have verified in their research that macro-financial variables do not have a statistically significant influence on Bitcoin pricing in the long term (Bouri et al. 2017; Chao et al. 2019; Ciaian et al. 2016a; Polasik et al. 2015). The price of gold, much compared to Bitcoin, also does not seem to be related to Bitcoin pricing (Bouoiyour and Selmi 2015; Kristoufek 2015). However, in the short term, economic factors seem to have a significant impact, as in the U.S. dollar quotation (Dyhrberg 2016; Zhu et al. 2017) and in the Chinese market represented by the Shanghai index (Bouoiyour and Selmi 2015; Kristoufek 2015).

It is interesting to note that most published studies give important prominence in their analyses to attractiveness factors, such as the variable number of searches over time using the term bitcoin in Google Web Search. In the early years of Bitcoin consolidation, tests based on vector autoregressive and vector error correction methodologies indicated that the amount of searches on Google and Wikipedia had a strong temporal association with the price curve, i.e., that public interest in increasing knowledge about the asset’s operation was followed by the increase in its price (Buchholz et al. 2012; Kristoufek 2013; Kristoufek 2015). However, with the subsequent consolidation of the currency and the population’s greater knowledge concerning Bitcoin’s operation, the attractiveness factor has increasingly failed to have the same relevance as before (Ciaian et al. 2016a; Hayes 2017) even though attractiveness is still a valuable variable for pricing analysis.

The final group concerns the dynamics between demand and supply. The equilibrium point of the supply and demand curve determines the Bitcoin price in a brokerage firm. However, what is peculiar about this digital currency is that the supply curve is known and pre-determined since there is a definitive limit on the quantity of virtual money offered in the market. Therefore, variations in the factors that determine and directly impact the demand curve enable the high volatility of this currency over time. In this sense, research seeks to use the variables that directly influence demand to predict currency pricing.

Macroeconomic drivers

Zhu et al. (2017) is one of the most recent studies about the impact of macroeconomic-financial factors on Bitcoin pricing. The author used some of the variables that affect gold pricing to identify those that have the same effect on Bitcoin pricing. The study defined Bitcoin as an investment asset rather than as a currency, because of its sensitivity to variations in macroeconomic indices. The study also noted that there was evidence of Granger causality in relation to gold price (GP) and dollar index (USDI) factors as applied to the dependent variable Bitcoin price.

According to Zhu et al. (2017), the influence of the USDI was negative, possibly because a valuation of the U.S. dollar currency against other currencies is also applicable to the virtual currency Bitcoin. Therefore, it was inferred that at the moment of U.S. dollar appreciation, there would be a devaluation of the Bitcoin price denominated in dollars. In the second half of 2014, for example, there was a continuous increase in the USDI caused by the resumption of the U.S. economy and, at the same time, there was a significant drop in the Bitcoin price.

Based on this behavior, Dyhrberg (2016) said that bitcoin could be used as a hedging product for the dollar exposure in the short term and as an additional instrument for market analysts to protect against specific risks. It should be noted that the dollar quotation against other currencies was negatively correlated with the Bitcoin price, not only in the short term but also in the long run, according to Van Wijk (2013) and Zhu et al. (2017).

Zhu et al. (2017) also stated that changes in the federal funds rate (FFR), established by the Federal Reserve System, had a negative impact on the Bitcoin price in the short term. The study cited two main reasons for this conclusion: an increase in the dollar’s exchange rate on the foreign exchange market due to migration of financial capital to the U.S.; and a reduction in the attractiveness of speculative investments that entail high risk because of an increase in the U.S. fixed income market.

The Dow Jones index, according to Van Wijk (2013), seemed to be positively correlated in the short and long term with the Bitcoin price. The study suggested an improvement in the performance of the U.S. economy could generate positive effects on Bitcoin pricing. Bouoiyour and Selmi (2015) saw the Shanghai index as a positive and short-term influence because of their perception that the Shanghai market was one of the big players in transactions with the virtual currency. Kristoufek (2015) also highlighted the impact of the Chinese economy on the Bitcoin price. In contrast, Dyhrberg (2016) said Bitcoin might be a possible hedging instrument against FTSE index variations, having no correlation with the 100 largest listed companies on the London Stock Exchange.

There are authors who report that they find no consistent evidence regarding the causal relationship between macroeconomic variables and the Bitcoin price. When including demand and attractiveness variables in their model, Ciaian et al. (2016b) concluded that there was no significant statistical relevance of macroeconomic factors such as the Dow Jones index and oil prices and suggested speculation was the primary driver of price. Polasik et al. (2015) concluded that the correlation between Bitcoin returns and the fluctuations of sovereign currencies was weak and statistically insignificant. Al-Khazali et al. (2018) argued via a GARCH model that Bitcoin is weakly related to macro-developments due to low predictability for Bitcoin return and volatility after macroeconomic news surprises. According to Al-Khazali et al., the cryptocurrency acts more like a risky asset than a safe haven instrument.

Attractiveness drivers

Bitcoin emerged at a time of massive expansion of the Internet, search engines, and social networks. Because it is a virtually mined coin and with peculiar characteristics, there is a certain unfamiliarity with its modus operandi, even to those who use in their day-to-day interactions with the Internet. Bitcoin it is not simple to understand since this is a new technology based on encryption and codifications that are more technically familiar to information technology professionals.

Searches on electronic media for information about what Bitcoin is and how it works may be a variable that explains demand increases for the coin and, consequently, its price. Some authors sought to estimate a relationship between the search history of the term Bitcoin on platforms such as Google (Kristoufek 2013; Buchholz et al. 2012; Bouoiyour and Selmi et al. 2015; Polasik et al. 2015; Nasir et al. 2019), Wikipedia (Kristoufek 2013), Twitter (Davies 2014) and online forums (Kim et al. 2017).

Polasik et al. (2015) described popularity as a strong factor for Bitcoin price returns. The authors further stated that a 1 % increase in the number of articles mentioning the term bitcoin generated an approximate return of 31 to 36 basis points in its price. This percentage is even higher when an analysis is based on Google’s database, where the return can be from 53 to 62 basis points. Buchholz et al. (2012) concluded that Google searches had a causal effect on Bitcoin transactions; however, the opposite did not seem to be applicable. Nasir et al. (2019), by using copulas and a nonparametric approach, confirmed that Google searches have a direct relationship with Bitcoin performance, particularly in the short run: the more often the investors look for information about the cryptocurrency, the higher the returns and trading volume that follow.

Although the attractiveness variable, represented by quantification of searches and use of the term bitcoin in certain relevant sites, was of great value for predicting the price of the currency for some authors, it is limited by the horizon of long-term analysis. Ciaian et al. (2016b), when analyzing a database with a higher data history between 2009 and 2015, indicated that online searches were better predictors of punctual returns in the early years of bitcoin. With the consolidation of the currency, we can see a reduction in the relevance of this prediction. Hayes (2017) believed that searches for the term bitcoin would lessen with the spread of knowledge about the currency and make the variable unsatisfactory for inclusion in predictive models. Bouoiyour and Selmi’s analysis also did not find evidence of the impact of Google searches on price in the long run.

Demand versus supply

Ciaian (2016a) demonstrated that the increase in the number of available bitcoins (inventory) was related to a decrease in its price, while the increase in the number of addresses (virtual portfolios) accompanied an increase in price. Civitarese (2018) analyzed the value of Bitcoin based on the growth of network users using the Metcalfe law, and verified the existence of a consistent relationship between the number of portfolios and the short-term Bitcoin price, although the study rejected the hypothesis of cointegration between the real price and prices calculated by law. Considering that the amount of currency offered by the Bitcoin platform is finite and known, Buchholz et al. (2012) stated that fluctuations in the Bitcoin price occurred mostly because of shocks in the demand curve. In addition to the factors highlighted above, there are others that measure the size of the Bitcoin market and cause a direct shock to the curve. Such examples include the volume variables of daily transactions and transfers by network users.

The volume variable, according to Bouoiyour and Selmi (2015), impacts Bitcoin pricing in the short term. Balcilar et al. (2017) emphasized that the variable can predict returns, except in up- or down-market periods. Therefore, under normal market conditions, investors have transacted volume as a prediction tool; in contrast, during stress scenarios, an association between the variable and price returns is not identified.

The increasing realization of Bitcoin transactions tends to stimulate its adoption by other economic agents, boosting the demand for bitcoins. Ciaian et al. (2016b) noted that the size of the bitcoin economy’s impact on demand tends to grow over time. The expectation is that the more frequent the use of money, the greater the demand and, consequently, the higher the price for bitcoins (Kristoufek 2015). Polasik et al. (2015) cited e-commerce as a major driver of payment systems that do not involve banking institutions and, in this sense, payment service providers aid in the development and adoption of virtual currencies.