Purpose This paper aims to attempt to examine some of the unique features of cryptocurrency and the reasons for its growing market acceptability. Given the expanding size of cryptocurrency markets, the present study strives to identify whether it can be used as an alternative financial asset in place of traditional financial assets to meet firms' financial constraints. It also provides issues for future research in the area of cryptocurrency markets. Design/methodology/approach This paper analysed 94 research papers from databases such as ScienceDirect, Proquest, EBSCO, Emerald Insight and Web of Science. Articles connected to cryptocurrency, financial assets and corporate financial constraints research were explored. VOSviewer software has been used to visualise the specified body of literature and identify eight clusters in previous literature using keyword and abstract analysis. Findings Studies reveal that cryptocurrency markets are independent of traditional financial markets and cryptocurrency returns have less correlation with traditional financial asset classes. This can be an advantage to firms, especially during times of crisis when traditional financial assets are impacted by significantly lower returns, while cryptocurrencies can serve as an alternative. Realtime data reveals that during the pandemic, cryptocurrencies had the maximum growth in returns which also happened to be a time when firms faced severe cash constraints. While accepting cryptocurrency as a means of exchange is still under review by regulatory authorities, it can be considered an alternative asset for investment purposes. Firms can take advantage of it to overcome financial constraints and thus reap the gains from holding crypto assets for precautionary reasons. Originality/value The present study investigates using cryptocurrency as an alternative financial asset to solve the financial constraint problem in corporates. The issues regarding volatility, cyber securities, gold returns, long-term and short-term returns have been some of the most prominent studies in the area of cryptocurrency. The present study uses eight theme-based clusters to identify the role of cryptocurrency as an alternative investment class and examines evidence-based research regarding the financial returns from holding cryptocurrency over certain traditional asset classes such as gold, currency or stocks. In recent years, it has been found that investors' growing interest in holding cryptocurrency as part of their financial portfolio has led to the substantial appreciation of cryptocurrency prices. To the best of the authorsâ knowledge, the study will be a novel attempt to identify the role of cryptocurrency as an antidote to the companiesâ financial constraints and liquidity issues.
Kate Murray, Andrea Rossi, Diego Carraro, Andrea Visentin
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.
Juan PiĂąeiro Chousa, Aleksandar Ĺ eviÄ, Isaac GonzĂĄlez-LĂłpez
In our study, we have evaluated the impact of tweets, social indicators, uncertainty, and attention indices on the selected variables calculated from a pool of 51 decentralised finance entities. In so doing, we have identified some evidence that returns are impacted by tweets, but not by social indicators that appear to be more relevant for volatility. We have further confirmed that the S&P500 Index negatively influences cryptocurrency returns, which means that these two asset classes are substitutes. Uncertainty and attention indices are relevant in determining returns and the alternative measurement of volatility. However, they remain insignificant for illiquidity and our initial volatility choice.
We investigate the multifractal properties of daily price returns and trading volume variations in 35 cryptocurrencies by using the method of wavelet leaders prior and during the COVID-19 pandemic. The obtained results from the analysis of scaling exponent functions and multifractal spectrums show that, in general, price returns and trading volume variations exhibit multifractal properties prior to the COVID-19 pandemic and that they tend to exhibit monofractal behavior during the pandemic. As a result, the level of multifractality diminished during the COVID-19 for both price returns and trading volume variations. Since complexity in price returns and trading volume variations decreased during the pandemic, cryptocurrencies may offer an interesting investment during times of serious world economic downturns.
Abstract There has been a tremendous growth in cryptocurrencies, which has challenged policy makers around the globe. We obtain millisecond data of some of the most frequently traded cryptocurrencies â bitcoin, ethereum, ripple, litecoin and dash â and two cryptocurrency indices â CRIX and CCI30 â to examine their profitability. Our profitability findings suggest that cryptocurrency traders generate significant profits after considering reasonable transaction costs. We also observe that cryptocurrency market participants can expand and sustain the levels of profitability levels in the subsequent trading activity. Our robustness checks with more recent postâCovid data are consistent with the initial profitability findings, although we observe lower levels of profits for the two indices and weaker profit persistency for all digital assets.
Minhyuk Lee, Younghwan Cho, Seung Eun Ock, Jae Wook Song
This research analyzes asymmetric volatility and multifractality in four representative cryptocurrencies using index-based asymmetric multifractal detrended fluctuation analysis. We suggest investigating an idiosyncratic risk premium, which can be obtained by removing the market influence in the cryptocurrency return series. We call the process a capital asset pricing model filter. The analyses on the original return series showed no significant sign of asymmetric volatility. However, the filter revealed a distinct asymmetric volatility, distinguishing the uptrend and downtrend fluctuations. Furthermore, the analyses on the idiosyncratic risk premium detected some cases of asymmetry in the degree and source of multifractality, whereas that on the original return series failed to detect the asymmetry. In conclusion, in a highly volatile market, the capital asset pricing model filter can improve an investigation of the asymmetric multifractality in cryptocurrencies.
Lennart Ante, Ingo Fiedler, Jan Marius Willruth, Fred Steinmetz
This study reviews the current state of empirical literature on stablecoins. Based on a sample of 22 peer-reviewed articles, we analyze statistical approaches, data sources, variables, and metrics, as well as stablecoin types investigated and future research avenues. The analysis reveals three major clusters: (1) studies on the stability or volatility of different stablecoins, their designs, and safe-haven-properties, (2) the interrelations of stablecoins with other crypto assets and markets, specifically Bitcoin, and (3) the relationship of stablecoins with (non-crypto) macroeconomic factors. Based on our analysis, we note future research should explore diverse methodological approaches, data sources, different stablecoins, or more granular datasets and identify five topics we consider most significant and promising: (1) the use of stablecoins in emerging markets, (2) the effect of stablecoins on the stability of currencies, (3) analyses of stablecoin users, (4) adoption and use cases of stablecoins outside of crypto markets, and (5) algorithmic stablecoins.
Abstract Cryptocurrencies are notoriously difficult to value from a fundamental perspective. This valuation challenge is rooted in various debated issues in academia and the investments industry. For example, do cryptocurrencies and other cryptoassets have intrinsic value in the conventional sense? Can one appropriately regard cryptocurrencies as digital fiat currencies? What distinguishes cryptocurrencies such as bitcoin and ether from precious metals like gold from a financial perspective? How do cryptocurrencies compare to other cryptoassets in terms of pricing and valuation? This chapter aims to provide responses to these questions, discuss approaches to cryptoasset valuation, and identify areas for future research.
This review aims to analyze and synthesize the literature produced so far on investor behavior in the cryptocurrency market. We use VOSviewer 1.6.17 software to perform a bibliometric analysis and elaborate a systematic literature review on investor behavior in the cryptocurrency market on a sample of 166 papers published in journals ranked in the ABS 2021 journal list, considering the different fields of knowledge. We found a growing body of literature on the presence of herding behavior in the cryptocurrency market, where there are indications that the main intentions behind crypto investment are mostly affected by social influence or public sentiment; the crypto market is dominated by irrational investors who base their investment decisions on market sentiment; the uncertainty of the fundamentals leads to investorsâ dispersed beliefs, which in turn leads to high trading and speculative bubbles. Additionally, we demonstrate some sociodemographic characteristics of crypto investors and some characteristics of the crypto market that affect investorsâ behavior, such as market inefficiency. Our study helps researchers and academics, investors, and regulators by providing a structured network analysis for literature strands, with relevant information for future studies on crypto investor behavior. In addition, it shows the most relevant factors that influence the behavior of the crypto market and its investors, providing the basis for better regulation and protection of investors in the cryptocurrency market.
This study examines the value of System and Organization Controls 2 (SOC 2) audits to customers. A SOC 2 audit is a voluntary assurance service provided by an independent CPA over a firm's internal controls relevant to information system security. I use cryptocurrency exchanges, a setting where the lack of customer trust can be particularly acute, to examine whether SOC 2 audits increase customer demand. I find a substantial increase in liquidity following the disclosure of initial SOC 2 audit completion: the trading volume of cryptocurrencies listed on audited exchanges increases by more than 60 percent, and the price impact decreases by approximately 40 percent in the three months after SOC 2 audit disclosure. Exploring the channels through which SOC 2 audits provide value to customers, I find that exchanges with high-quality security measures are more likely to initiate SOC 2 audits, and that continued audits ensure that the quality of security measures remains high. Overall, this study provides novel evidence that SOC 2 audits provide value to customers by sending a credible and positive signal of exchange security, and thus significantly increase customer demand.
Cryptocurrencies and blockchain technologies have been among the most widely discussed topics in academic research and practical applications in recent years. The adoption of these technologies has seen tremendous growth and is becoming increasingly relevant as a potential disrupter of many economic industries. Thus, this thesis aims to investigate the relevance of cryptocurrencies to global financial markets. In the second chapter of this thesis, we explore the impact of Bitcoinâs risks on traditional asset classes. Our cross-asset analysis reveals that Bitcoin has positive spillover effects on risky assets but negative spillover effects on defensive assets. By examining the source of these risk transmissions, we demonstrate that U.S. companiesâ increased economic exposures to blockchain and cryptocurrency technologies have exacerbated these spillovers. Our empirical findings highlight that the price fluctuations of an unregulated asset such as Bitcoin can influence the price dynamics of regulated assets. Motivated by the findings of the second chapter, our third chapter investigates risk exposures associated with Bitcoin in equity portfolios. We show that the Bitcoin-equity dynamics intensified post-COVID-19 and provide investment practitioners with practical guidance on managing unregulated asset risks. Finally, we turn our attention to the behavioural biases of cryptocurrency investors, drawing on the low volatility anomaly. This study investigates the differentiated pricing of jump and diffusive risks in the cross-section of cryptocurrency returns. We show that a hedged portfolio sorted on idiosyncratic diffusive risk yields a weekly return of -1.11%, suggesting the existence of a low idiosyncratic risk anomaly. Subsequently, we examine explanations for this anomaly and show that limits to arbitrage prevent arbitrageurs from correcting the mispricing. In doing so, we demonstrate that cryptocurrency investors exhibit similar biases to equity investors.