Investor sentiment and Bitcoin relationship: A quantile-based analysis

This paper applies a quantile-based analysis to investigate the causal relationships between Bitcoin and investor sentiment by considering the possible effects of the ongoing COVID-19 pandemic. Such an analysis allows investigating the predictive power of investor sentiment (Bitcoin) on Bitcoin (investor sentiment) at different levels of the distributions. Results emphasize that only Bitcoin returns/volatility have significant predictive power on the investor sentiment whether investors are fear or greed before and over the COVID-19 period. Moreover, the COVID-19 crisis has no effect on the causal relationship between the two variables. Further analysis shows an asymmetric causality observed only during the pandemic period. Furthermore, the quantile autoregressive regression model shows a significant positive relationship between investor sentiment and Bitcoin returns.     

How well do investor sentiment and ensemble learning predict Bitcoin prices?

Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency prices, it has not yet generated cohesive empirical findings on an important question: How effective is investor sentiment in predicting cryptocurrency prices? To address this gap, we propose a novel prediction model based on the Bitcoin Misery Index, using trading data for cryptocurrency rather than judgments from individuals who are not Bitcoin investors, as well as bagged support vector regression (BSVR), to forecast Bitcoin prices. The empirical analysis is performed for the period between March 2018 and May 2022. The results of this study suggest that the addition of the sentiment index enhances the predictive performance of BSVR significantly. Moreover, the proposed prediction system, enhanced with an automatic feature selection component, outperforms state-of-the-art methods for predicting cryptocurrency for the next 30 days.     

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

Which cryptocurrency data sources should scholars use?

Inspired by Alexander and Dakos (2020), we shed more light on the adequacy of data in the cryptocurrency literature by analysing the scaling properties and underlying processes of the main cryptocurrency databases (Coinmarketcap, Coingecko, BraveNewCoin and Cryptocompare) and exchange platforms (Coinbase, Bitstamp, Bittrex, Cexio and Exmo). Our results show that coin-ranking sites, such as Coinmarketcap, Coingecko and BraveNewCoin (i) include most of the cryptocurrency trading activity and (ii) are essentially characterised by the same underlying processes as the main exchange platforms (Coinbase and Bitstamp) and alternative coin-ranking sites (Cryptocompare), regardless of the possible issues arising from the aggregation of different exchanges to compute a unique cryptocurrency price. Therefore, we state that these databases are appropriate to conduct research. At any rate, we observe that all the databases analysed in this paper show the same underlying process for most liquid cryptocurrencies; consequently, scholars could use any of them for their studies, as long as they consider the different trading activity included by each database. This result is supported by an empirical analysis focused on weak-form market efficiency, since we report the same degree of efficiency regardless of the database and exchange platform. Nevertheless, we recognise the need for further research, given the gap in the literature and the black-box method used by coin-ranking sites to compute a unique cryptocurrency price.     

Taxing bitcoin: Incentivizing the difficulty adjustment mechanism to reduce electricity usage

This paper develops a model of the bitcoin market that views the bitcoin as a tradeable commodity whose supply is managed by the Bitcoin protocol. Miners utilize equipment and electricity to solve complex computational problems and the first miner to solve a problem is rewarded with bitcoins. The protocol adjusts the difficulty of the problem to target a constant growth rate in the supply of bitcoins over time. The model demonstrates that an increase (decrease) in the difficulty works in effect like a government’s placing an ad valorem tax (subsidy) on the price of a commodity. The rents that would have arisen from limiting supply, however, are wasted as electricity costs. It is shown that an actual tax on the price of the bitcoin can be used to displace the electricity costs. Using data from March 2014 to January 2019, it is estimated that the difficulty adjustment mechanism resulted in net welfare losses to the miners and buyers of bitcoins of 373.8 million USD. Average initial tax rates of 35% and 347.5% would have fully displaced the electricity costs and maximized their reduction, respectively.     

Macroeconomic fundamentals and cryptocurrency prices: A common trend approach

Based on asset pricing theory, we posit and find that equity markets and cryptocurrency markets share a common fundamental. Our cointegration tests show that the most important asset pricing primitive, consumption, can serve as the common fundamental. We further show that additional macroeconomic factors, as well as uncertainty and sentiment, all play a role in explaining the deviation from fundamentals. To understand the linkage between equity markets, cryptocurrency markets, and the macroeconomy, we suggest the following three channels: (i) portfolio allocation decisions, (ii) intermarket order flows, and (iii) technological adaption expectations.     

Network analysis on Bitcoin arbitrage opportunities

Many studies have shown that investment in Bitcoin could serve to diversify the stock market. The existing literature offers insight into how arbitrage trading may diminish the risks associated with cryptocurrencies. Using arbitrage data and a novel methodology, this paper uncovers pertinent insights for investors interested in hedging against losses. Evidence points to the dsx, kraken, and bitstamp exchanges as the most attractive for buying Bitcoin and the cexio, bitmarketlt, and coindeal exchanges as the most attractive for selling Bitcoin. We employ network analysis to explore the interrelationships in thirteen cryptocurrency exchanges, providing evidence that kraken and bitstamp are leaders in market-forming trends, while coindeal and dsx serve as intermediary exchanges. Overall, these findings show that investors can mitigate their trading risks by knowing precisely where to buy and sell Bitcoin and which exchanges offer arbitrage opportunities. Using these results, investors can develop long-term and algorithmic trading strategies.     

Economic uncertainty and national bitcoin trading activity

This study analyzes the dynamics between bitcoin trading, price activities, and economic surprise shocks from a broad and novel perspective on a national level. We start by estimating the response of bitcoin trading in terms of volume and volatility to economic surprises. Following this, we extend our framework by applying a Generalized AutoRegressive Conditional Heteroskedasticity model to get an indication of the volatility reaction of national bitcoin activities to economic surprise shocks. Our results show that local and global shocks affect local bitcoin activities and trading volatilities, confirming that economic events affect bitcoin markets. We argue that increased trading activity, coupled with a price reaction, indicates that bitcoin might be considered a hedge or safe haven asset against economic uncertainty. We find evidence that bitcoin is treated as a speculative asset against negative economic policy uncertainty shocks in Canada pre-Covid-19. These results change during the Covid-19 pandemic, leading to a significant structural break. Here, we find indications that bitcoin might be treated as a safe-haven asset in New Zealand and Australia. This shows that bitcoin behaves differently depending on the studied country, underlining the importance of country-level studies. It also shows that bitcoin is a new asset that is evolving rapidly and that the period in which it is studied is important.     

Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

Distrust or speculation? The socioeconomic drivers of U.S. cryptocurrency investments

Employing representative data from the U.S. Survey of Consumer Payment Choice, we find no evidence that cryptocurrency investors are motivated by distrust in fiat currencies or regulated finance. Compared with the general population, investors show no differences in their level of security concerns with either cash or commercial banking services. We find that cryptocurrency investors tend to be educated, young and digital natives. In recent years, a gap in ownership of cryptocurrencies across genders has emerged. We examine how investor characteristics vary across cryptocurrencies and show that owners of cryptocurrencies increasingly tend to hold their investment for longer periods.