1. Introduction
Bitcoin is the most popular open-source digital currency. The Bitcoin network is decentralized, private, and anonymous (or pseudonymous), and eliminates the need for any financial intermediaries like banks. Bitcoin is built on a network known as the blockchain, a decentralized and open ledger that enables peer-to-peer and cryptographically secured transactions. Blockchain is one of the most interesting emerging technologies nowadays. Martinez et al. (2020) [
1] provide an excellent review of the cryptographic tools necessary to understand the fundamentals of blockchain technology. These mathematical tools include hash functions, digital signatures, elliptic curves, and Merkle trees.
Bitcoin has attracted great attention in recent years, and it has undeniably taken an important position in global financial markets. Bitcoin in its position as the virtual currency with the largest market capitalization has been found to have a weak correlation with other risky financial assets, making it a valuable financial instrument to hedge economic uncertainty and part of a suitably diversified portfolio [
2,
3,
4,
5,
6,
7]; however, one of its major criticisms is that trading bitcoins is used as a speculative investment similar to gambling [
8,
9,
10].
In stock markets, prior research suggests that some investors trade stocks as a fun and exciting gambling activity. Kumar (2009) [
11] shows that the propensity to gamble is correlated with investment decision-making and shows that state lotteries and lottery-type stocks attract similar socioeconomic clientele. Gao & Lin (2015) [
12] also postulate that individual investors use the stock market as a means of gambling. The authors show that individual investors trade less on large jackpot days or, equivalently, that there is a substitution effect between stock trading and lottery participation. Recent literature has also documented that some investors are only seeking risk and excitement when they trade cryptocurrencies [
13,
14].
The lottery-like features of Bitcoin returns together with the COVID-19 pandemic shock provide an interesting setting to examine the predictions from previous literature proposing that some people trade for fun and excitement. The financial literature defines lottery-like stocks as those with high return volatility and kurtosis. Bitcoin possesses some lottery-like features. For instance, Baek & Elbeck (2015) [
9] show that Bitcoin is 26 times more volatile than the S&P 500. The authors also show that Bitcoin returns are quite positively skewed and have positive excess kurtosis that causes fat tails, suggesting more opportunity for extreme values to occur.
During the year 2020, most countries imposed movement restrictions or mandatory lockdowns to contain the spread of the virus. Live sports and entertainment events were canceled, and gambling venues were temporarily shut down. An obvious result of this home confinement is that many people had an inordinate amount of free time available. Many of these individuals turned to trade as a replacement activity. One example can be found in the U.S. The trading app Robinhood and other online brokerages saw record trading volume and new accounts (see, for instance,
https://www.wsj.com/articles/everyones-a-day-trader-now-11595649609, accessed on 6 January 2021) resulting in greater retail stock market participation and trading activity throughout the lockdown period. Ozik, Sadka, and Shen (2021) [
15] find that the number of Robinhood trading accounts was significantly larger during lockdowns than during normal periods, especially among stocks with high COVID-19-related media coverage. Pagano et al. (2021) [
16] also examine the role of Robinhood investors during the pandemic and their impact on market quality. The authors show that retail investors were actively engaged in both momentum and contrarian trading strategies, resulting in Robinhood investors negatively impacting market quality during the early weeks of the pandemic in the U.S. Meanwhile, Chiah & Zhong (2020) [
17] document large spikes in trading volume during the COVID-19 pandemic in the international equity markets. Furthermore, Harjoto et al. (2021) [
18], using data across 53 emerging and 23 developed countries from 14 January to 20 August 2020, find that COVID-19 cases and deaths adversely affect stock returns and increase volatility and trading volume.
In this paper, we study Bitcoin trading activity during the pandemic, taking the above observations as its foundation. We address the following question: Did the movement restrictions and stay-at-home mandates put in place across most of the world increase the traded volume of Bitcoin? Using mobility indicators provided by Apple, we find that Bitcoin trading significantly increased during the pandemic lockdowns. In particular, we see that a driving mobility index is negatively correlated to Bitcoin trading in major cryptocurrency exchanges during 2020. These results are consistent with the hypothesis that individual investors traded bitcoins as a pandemic pastime and as a form of exciting gambling activity when they were confined at home.
These results are built on a variety of time-series econometric models, ranging from ordinary least square (OLS) regressions to vector autoregressive (VAR) regressions. We partially base our model selection on the experience of previous literature. Other studies analyzing trading volume dynamics in equity markets during COVID-19 have mostly used OLS models (see, for example, [
15,
16,
17,
18]). Meanwhile, VAR models are commonly used in the financial literature to examine the dynamic relations among Bitcoin returns and trading activity (see, for instance, [
19,
20,
21]).
We believe our results will help cryptocurrency investors to better understand the financial effects of extreme events, such as a pandemic. We contribute to the literature by showing how the COVID-19 shock can be used to prove some of the theoretical predictions proposing that people trade for fun and excitement. We stand apart from previous studies examining trading volume in equity markets during the pandemic. Instead, we study the dynamics of trading volume in cryptocurrency markets. To our knowledge, this is the first study to show how reductions in mobility across the world increased traded volume of Bitcoin during the Coronavirus pandemic lockdowns.
3. Results
Table 2 presents the parameters estimated from OLS with HAC covariance estimator and ARMAX regressions for the total amount of daily Bitcoin trading volume taken from
coinmarketcap.com. Columns 1 to 3 report the results from using OLS models on the natural logarithm of total Bitcoin trading volume (in number of bitcoins). As we hypothesize, driving mobility has a negative correlation with Bitcoin trading volume across all models. Columns 4 to 6 present the results from ARMAX (1,1) models. We find that driving mobility has a statistically significant (at the 1% level) and negative effect on Bitcoin trading volume across all models. In general, there is little theoretical guidance on how large the number of lags in ARMAX models should be. To keep the models parsimonious, we decided to report results when using only one lag for both the autoregressive and moving-average components of the disturbances. We have tested several alternative specifications with up to seven lags, as suggested by correlograms and partial correlograms of the natural logarithm of total Bitcoin trading volume (unreported), and the results remain robust. Consequently, the hypothesis that movement restrictions and stay-at-home mandates across the world increased the volume traded of Bitcoin is supported.
Table 3 shows the estimated results for VARX models. Columns (1) and (3) of Panel A report results without exogenous control variables. Columns (2) and (4) of Panel A report results controlling for CBOE Volatility Index (VIX) returns, gold returns, and S&P 500 returns. Columns (2) and (4) of Panel A report results for the full model that incorporates Google search frequency and Twitter sentiment. Panel B of
Table 3 reports the AIC, HQIC, SBIC, FPE criteria we use to select the four lags used in the series of vector autoregressions presented in Panel A. Our results show that reduced mobility driven by Coronavirus is positively correlated with Bitcoin trading volume. These results further support the previous findings showing that reductions in mobility associated with lockdown mandates will lead to higher Bitcoin trading volume. Concerning the relationship between mobility and returns, we found no statistically significant results using vector autoregressive models. We also employ Granger causality tests to investigate the causal relationships between Bitcoin returns and trading volume. We present Granger causality tests for each VARX model at the bottom of Panel A. For this sample period, we cannot reject the null hypothesis that Bitcoin returns do not Granger-cause Bitcoin volume or vice versa.
Finally, we examine the relationship between global Bitcoin trading volume and mobility in some specific countries and regions. Given that the previously reported VARX models and Granger causality tests do not support the hypothesis of a dynamic relationship between Bitcoin volume and returns during this period, for this last test we prefer to use the relatively simpler ARMAX model.
Table 4 shows the results estimated from ARMAX regressions for the total amount of daily Bitcoin trading volume taken from
coinmarketcap.com on mobility in the U.S. (column 1), Russia (column 2), Europe, excluding Russia (column 3), Latin America (column 4), Africa (column 5), and Asia (column 6). We find that mobility trends in the U.S., Latin America, and Asia have statistically significant and negative effects on Bitcoin trading volume. Mobility clearly comes through as a significant factor in Bitcoin trading volume during pandemic lockdowns in these regions.
4. Discussion and Concluding Remarks
Despite the growing literature examining the effect of COVID-19 on the Bitcoin market [
21,
35,
36,
37,
38,
39,
40], the direct effects of movement restrictions have received scant attention in cryptocurrency research. The COVID-19 lockdowns and the Bitcoin market represent a proper setting to test the proposition concerning investors trading as a form of entertainment and a substitute for gambling activities.
In this article, we show that reductions in mobility across the world increased the traded volume of Bitcoin during the pandemic. High Bitcoin volatility provides great gambling opportunities for individual investors. Thus, the results of this study suggest that some investors resort to the Bitcoin market as a substitute for gambling. The results also confirm prior literature documenting that some investors are seeking risk and excitement when they trade cryptocurrencies [
13,
14].
Moreover, Bitcoin has been historically characterized by explosive behavior due to its multiple bubbles since its inception in 2009 as an open-source digital currency. For instance, Cheung et al. (2015) [
41], documented three Bitcoin bubbles during 2011–2013. Fry (2018) [
42] showed the existence of bubbles in Bitcoin prices during 2015–2018. Further, Cheah and Fry (2015) [
43], in addition to finding evidence to suggest that Bitcoin prices are prone to substantial bubbles, empirically estimate the value of a Bitcoin to be zero. Meanwhile, Corbet et al. (2018) [
44] and Bouri et al. (2019) [
30] provide evidence of bubbles in Ethereum and other large cryptocurrencies. In
Appendix B, we show that Bitcoin price bubbles can be predicted by an increase in trading volume. This result is consistent with the predictions from Barberis et al. (2018) [
45] and Scheinkman and Xiong (2003) [
46] that financial bubbles in equity markets will be accompanied by high trading volume. Considering the documented history of Bitcoin bubbles and their relation to trading activity, our results contribute to the literature that examines the occurrence of speculative bubbles in the Bitcoin market.
Our results also have implications for the literature that examines investors herding behavior and overconfidence leading to systematic noise trader risk in cryptocurrency markets. Regarding investor herd behavior, Lux (1995) [
47] describes the formation of expectations by those who are not fully informed about fundamentals. Abundant empirical evidence has challenged the hypothesis that financial markets are efficient, and several studies have shown that prices are not entirely driven by news, but also by irrational or uninformed trading [
48,
49,
50,
51,
52]. The expectations of uninformed traders depend mainly on the behavior and expectations of others leading to a process of mutual mimetic contagion among speculators. According to Lux, this contagion may also lead to the existence of bubbles, that is, stationary states where actual prices exceed fundamental values or are below them [
47]. Regarding the relation between COVID-19 and investors herding in cryptocurrency markets, recent literature offers contrasting evidence. Yarovaya et al. (2021) [
53] show that COVID-19 does not amplify herding in leading cryptocurrencies. Meanwhile, Vidal-Tomás (2021) [
54], using network analysis, shows that from 12 March, 2020 to 1 April 2020 there was a remarkable increase in market synchronization.
Overconfidence could also lead investors to overreact to private information, underreact to public information, trade more aggressively in subsequent periods, underestimate risk, and contribute to excessive volatility [
55]. Scheinkman and Xiong (2003) [
46] posit that the existence of bubbles is positively associated with the degree of the agents’ overconfidence and the fundamental volatility of the asset. In this sense, overconfidence investors with ample free time on their hands and the presence of the “fear of missing out” (FOMO) psychological effect in the highly volatile Bitcoin market could create the conditions needed for speculative bubbles typically associated with high trading volume.
Considering that Bitcoin has taken an important position in global financial markets, the above observations together with our findings and empirical implications should be of interest to all investors, but in particular, institutional investors that are increasingly incorporating Bitcoin in their portfolio allocations. Professional investors should design investment strategies to tackle the volatility of Bitcoin. These strategies should be designed to properly manage risks and avoid potentially large financial losses when bubbles created by the speculative behavior of individual investors burst.
Several important questions still need to be addressed. Future research expanding on similarities and differences between retail and institutional trading volumes would further elucidate which kind of investor is behind the meaningful increase in overall Bitcoin trading volume. Such research should also provide international evidence regarding the effects of lockdowns and the trading volume of cryptocurrencies other than Bitcoin. Further research should also model possible nonlinearity and accounting for tail behaviors when analyzing dynamic relationships among Bitcoin trading volume, returns, investors’ free time, and level of attention to cryptocurrency markets as forms of entertainment.