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This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

Regulation of digital currency is still at its infancy as authorities around the world grapple with its mechanics, and study its impact and the best method to regulate it. Significant increase in the use of digital cryptocurrency based on Blockchain technology post-Bitcoin phenomenon had challenged the conventional idea of central bank monopoly in currency issuance. This had also raised concern that digital currency being used as an instrumentality of crime given its anonymity feature that allows for the flow of funds without tracing and the fact that it is built on trustless system that provides security of transaction. This concern, plus other consideration including the prospect of issuing central bank digital currency, had driven some authorities around the world to adopt countermeasures either via an outright ban or a regulatory regime that suits the nature of digital currency, which is purely virtual and anonymous. However, in coming out with an appropriate legal regime, authorities faced multiple difficulties especially when the pace of legal development does not sync congruently with the rapid progress of technology. In addition, given the growing prominence of Islamic finance around the world, questions also arise pertaining to the legality of digital cryptocurrency from the Islamic perspective. Through a qualitative study of relevant literatures as well as legislations in different countries, this chapter discusses the various categories of digital currency, its position from the Islamic perspective, regulatory regimes of digital cryptocurrency in selected jurisdictions and challenges faced by authorities around the world in regulating this new medium of exchange.

This paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design.

Against this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies.

The empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness.

Further investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation.

In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities.

As the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.

This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic.

This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021.

The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash.

The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.

The purpose of the article is to analyse the use of cryptocurrencies in general and Bitcoin specifically. The majority of academics are aware of the benefits of using cryptocurrencies for trade facilitation, cost reduction, and similar purposes. Peer-to-peer and remittance transactions without compliance requirements have the potential to be transformed and revolutionised by cryptocurrencies; nevertheless, end users must overcome several obstacles relating to security, privacy, and control in order to take use of Bitcoin. The study elaborates on several facets of cryptocurrencies, beginning with their early development, difficulties and dangers, chances, benefits and drawbacks, and prospects. Secondary data has been used for this study like as from government sources, Scopus indexed journal, famous print media. The study find the addressed challenges pertaining to the operational and technological aspects of cryptocurrencies. And how to resolve the modern problem faced while using cryptocurrency. So we conclude that it is difficult to predict the future of cryptocurrencies, as there is still a lot of work to be done, especially in the area of formal rules.

Implications: In this digital era there is a need for cryptocurrency while the whole world is turning into a cashless economy, this will be useful for our common society and have the best use for implication in the business sector, this will stop the paperwork and sustainability, and, even there is threat cybercrime while the use of cryptocurrency will be increased so by data protection and strong security and protection bill or regulation will give the usual and systematic direction for uses and one line development.

This paper aims to empirically investigate the volatility of Bitcoin, Litecoin and the Euro.

The authors use quantitative methodologies to assess the annualized volatility of two cryptocurrencies and one international fiat currency. The exchange rate of the currencies is monitored on a daily basis using 1,460 observations from January 1, 2014 to December 31, 2017. The models used include the augmented Dickey–Fuller test, Akaike Information Criteria, autocorrelation function and exchange rate changes determining which currency is the most volatile.

The findings indicate, based on the statistical measures used, including the standard deviation of selected currencies and annualized volatility, that Litecoin is more volatile than Bitcoin and the Euro and that Bitcoin is more volatile than the Euro. This furthers previous research on cryptocurrency volatility.

The paper provides compelling evidence about the volatility of Litecoin and Bitcoin. The volatility of cryptocurrencies is furthered with data that are more current. The findings are important for investors, financial markets and central banks.

The purpose of the study is to show that divergent perceptions among regulators, the regulated and the associated regulatory bodies across multiple jurisdictions regarding the nature and functionality of cryptocurrencies hamper the development of a more comprehensive and coherent regulatory framework in curbing crimes and other related risks associated with cryptocurrencies.

The study has used a descriptive doctrinal legal research method to investigate and understand the insights of existing laws and regulations in four selected jurisdictions concerning cryptocurrencies and how these laws could be further improved and developed to reduce crypto-related crimes. Furthermore, the study has also used a comparative research method to conceptualize the contours of the new legal discourse emerging from cryptocurrencies to adopt and implement a sound regulatory framework.

The study illustrated that divergent regulatory treatment among different jurisdictions might suffocate novel digital innovations such as cryptocurrency. These fragmented regulatory approaches by various jurisdictions question the sustainability of the present national legislation adopted to regulate cryptocurrencies. Looking into other jurisdictional developments in regulating cryptocurrencies, it is apparent that a concerted regulatory approach is needed to minimize the abuse of this innovation.

The study has implications for regulators and policymakers to review the current regulatory framework for regulating cryptocurrencies to prevent regulatory arbitrage. The divergent legislative measures concerning cryptocurrency among different jurisdictions question the sustainability of these legislative initiatives, considering the evolving and borderless nature of cryptocurrency. Therefore, this paper will help regulators to consider the present legislative gaps in establishing a common global regulatory approach in the crypto sphere.

The study contributes to the existing body of literature by examining the regulatory frameworks of four jurisdictions, namely, the USA, Canada, China and the EU, related to cryptocurrencies, with a discussion on the development of cryptocurrencies-related laws among these four jurisdictions and their sustainability in curbing crimes in the Darknet.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market.

The paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares.

The authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies.

The authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates.

To date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).

The innovation of cryptography technique and blockchain has made cryptocurrency an alternative medium of exchange due to its safety, transparency and cost effectiveness. But its main feature cannot be separated from the users who use cryptocurrency for their illegal transactions. There are several arguments related to the legality of cryptocurrency. The purpose of this paper is to analyze the nature of cryptocurrency based on characteristics of money, legal perspective, economic perspective and Sharia perspective.

In this study, the methodology used is descriptive with a qualitative approach. The object of this research is cryptocurrency. The data are secondary data obtained from peer-reviewed journal articles, conference papers review, working paper and Sharia consultant reports addressing the legality of cryptocurrency. The literature review analysis includes the following steps: material collection, descriptive analysis, discussion with people in Sharia competency and intuitive-subjective material evaluation.

Regarding the characteristic of money, cryptocurrency is acceptable. But in terms of the legal perspectives, cryptocurrency does not meet the criteria as currency. From the economic perspective, cryptocurrency does not fully meet the characteristic currency due to high price volatility, and from the Sharia perspective, cryptocurrency can be considered property (mal) but not as a monetary value (thamanniyah).

The research findings are based on the journal articles, working paper and Sharia consultant report, and it may lack Sharia’s opinion. Any further discussion related to Sharia perspectives will be a great input to enrich the study.

This study also includes the implications related to the opportunities and the risks of cryptocurrency that can be discussed for the development of the cryptocurrency in the future.

This study includes the implication cryptocurrency is using as nature of money and not as speculative instrument.

This study argued the legality of cryptocurrency in four perspectives such as the nature of money, legal, economy and Sharia perspective.