Chelsea M. Anderson, Vivian W. Fang, James Moon, Jonathan E. Shipman
ABSTRACT This paper explores U.S. public firms’ cryptocurrency holdings and accounting practices from 2013 to 2022 against the backdrop of the recently enacted crypto accounting rule, ASU 2023‐08. Descriptive analyses suggest exponential growth in corporate crypto holdings and significant variation in crypto accounting practices, underscoring the rule's necessity. Hypothesis tests using the pre‐rule data reveal three insights with direct relevance to the rule. First, firms appear to view crypto assets more akin to investments than intangible assets, consistent with the rule's mandate of the fair value model. Second, Big 4 auditors steer firms toward the impairment model and less detailed presentation choices. This conservative approach is unlikely to meet the new rule's goal of providing the most decision‐useful information. Third, increased liquidity of crypto markets prompts the use of the fair value model and a more detailed presentation, consistent with the rule's focus on more actively traded tokens. However, within our sample, we find some evidence consistent with fair value reporting increasing stock return volatility and no evidence that it enhances earnings informativeness.
Cryptocurrency is a cutting-edge Fintech innovation and currently a worldwide hotspot. However, the high-speed evolution of it has already caused a series of public security related events all around the world. Cryptocurrency was built initially as a possible implementation of digital currency, then various derivatives were created in a variety of fields such as financial transactions, capital management, and even nonmonetary applications. This paper aims to offer analytical insights to help understand cryptocurrency by treating it as a financial asset. We position cryptocurrency by comparing its dynamic characteristics with two traditional and massively adopted financial assets: foreign exchange and stock. Based on the daily close prices about four years, we first construct the correlation matrices and asset trees of all three markets, then conduct comparisons on five properties: volatility, centrality, clustering structure, robustness, and risk. Our investigation suggests that the dynamics of cryptocurrency are more similar to stock. As to the robustness and clustering structure, our analysis shows cryptocurrency market is more fragile than stock market, thus it is currently a high-risk financial market. Our work is the first to study cryptocurrency with the help of well-understood financial assets and may shed some light on investment decisions, regulation, and legislation.
We compare the ability of two measures of uncertainty, a newspaper-based measure and an internet search-based measure, to predict Bitcoin returns. Using monthly data from July 2010 to May 2019 and a predictive regression model characterized by a heteroskedastic error structure and, we show that Bitcoin is a hedge against both measures. However, the predictive content of the internet-derived uncertainty related queries measure is statistically stronger than the measure of uncertainty based on newspapers for predicting Bitcoin returns, which is possibly due to the fact that the measure of uncertainty is now directly obtained from individual investors via internet searches.
Public information arrivals and their immediate incorporation in asset price is a key component of semi-strong form of the Efficient Market Hypothesis. In this study, we explore the impact of public information arrivals on cryptocurrency market via Twitter posts. The empirical analysis was conducted through various methods including Kapetanios unit root test, Maki cointegration analysis and Markov regime switching regression analysis. Results indicate that while in bull market positive public information arrivals have a positive influence on Ripple’s value; in bear market, however, even if the company releases good news, it does not divert out the Ripple from downward trend.
Bitcoin is an online communication protocol, which facilitates electronic transactions. It has grabbed the attention of investors and researchers in the recent past. The non-regulatory feature of Bitcoin makes it riskier and the element of speculation in its trading is higher than any other financial asset. The study provides an insight into the price dynamics of Bitcoin by examining the day of the week and month of the year effect for the period 2013 to 2017. The findings of the study indicate the existence of seasonality in the return behaviour of Bitcoin as returns for Monday is higher than any other day of the week. Likewise, the returns earned during the month of November are significantly different from other months of the year. The results of the study assert a violation of the assumption of weak-form market efficiency and imply that the Bitcoin market provides an opportunity for the investors to exploit the market from its predictable behavior and fetch abnormal gains.
Asset pricing models and investment styles have been researched intensively in equities, bonds, FX, and commodities. However, a new asset class has emerged since the end of 2008, namely, cryptocurrencies such as Bitcoin and Ethereum, among others. The author uses an extensive data set of over 1,500 cryptocurrencies and shows that almost none of the traditional investment styles such as momentum or defensive appear to be successful in this young asset class. Cryptocurrencies are also independent from the macroeconomic environment and cannot be explained by a standard asset pricing model. A cryptocurrency specific model yields clearly better results. In addition, the whole cryptocurrency space is dominated by only a few individual digital coins. Equally weighted mean monthly returns appear to be random with low or even no correlation with traditional asset classes such as US equities and global FX. <b>TOPICS:</b>Currency, portfolio construction, risk management, performance measurement
The erratic price behavior and inefficiency in the crypto markets offer possibility to examine the behavioral aspects in cryptocurrency prices. Further, the cryptocurrency market is dominated by the retail investor providing an interesting platform to examine the impact of attention-driven trading in this particular asset class. Thus, the authors investigate the influence of investor attention in the cryptocurrency prices using the quantile causality approach. The results indicate that investors pay attention to the frequent news-making and ranked cryptos (Bitcoin and Ethereum). For newer cryptocurrencies like Ripple, investor attention influences their prices only during superior performance. The study provides evidence of attention-induced price pressure hypothesis in the prices of cryptocurrencies during expansionary phases and fear selling during poor market performance.
Abstract\nThis paper applies cointegration tests to identify cryptocurrency pairs which can be used in pairs trading strategies. The aim of this research is twofold. First, I want to examine cointegration in a system of bitcoin, dashcoin, dogecoin and litecoin. In the second part, I create pairs trading strategies in order to determine whether excess return can be made, compared to a simple buy and hold approach. The results find evidence of cointegration between the cryptocurrencies and positive profitability using pairs trading. By creating a portfolio in which the funds are equally allocated to the strategies with an open position, excess return can be made.
This paper investigates if real investors other than rational investors could add value to their investment portfolios considering their mentality and psychology. The universe of assets constitutes 21 cryptocurrencies (37 international equities) and covers, respectively, the period from August 1, 2016, to March 31, 2018 (January 7, 2002, to March 23, 2018). The cumulative prospect theory and variant specifications were utilized to validate and compare the classification and selection of assets driven by some decision theories. The results of optimization analysis of all the formulated portfolios constituting assets from both markets showed that portfolios constituting assets with lower cumulative prospect theory values outperformed their counterpart with higher cumulative prospect theory values. The superiority of the cumulative prospect theory was established as an empirically corroborated theory of decision-making with rich psychological content. The findings of this paper are crucial for finance practitioners as they showcase an intuitive and coherent manner to guide fund managers, investors and other economic agents in their investment practices.
Ten years have passed since the emergence of Bitcoin and with it cryptocur- rencies as a new class of assets. Now, cryptocurrencies are not uncommon tool of investment and subject of academic research. This thesis focuses on investigating possible presence of weekly and monthly seasonal patterns in cryptocurrencies, namely Bitcoin, Litecoin, Ripple, Monero, Dash, Stellar and partly Ethereum, which are selected as representative sample. Insuffi- cient evidence is found for the day-of-the-week effect, the January effect is however revealed as significant by different methods in the whole sample, with cryptocurrencies generally exhibiting higher returns towards the end of the year and lowest from January to March. Examining probable causes of revealed seasonality, it is found that these are not likely to be caused by peculiar price development in 2017 and 2018, as well as the Chinese New Year or brought to the market by proposed price drivers of Bitcoin. How- ever, significant evidence for correlation of patterns followed by Bitcoin and other examined cryptocurrencies is found.
We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging feedback effects that cause illiquidity during crises and exacerbate collateral drawdown, and characterize stable dynamics of the system under particular conditions. The possibility of such `deleveraging spirals' was first predicted in the initial release of our paper in 2019 and later directly observed during the `Black Thursday' crisis in Dai in 2020. From these insights, we suggest design improvements that aim to improve long-term stability. We also introduce new attacks that exploit arbitrage-like opportunities around stablecoin liquidations. Using our model, we demonstrate that these can be profitable. These attacks may induce volatility in the `stable' asset and cause perverse incentives for miners, posing risks to blockchain consensus. A variant of such attacks also later occurred during Black Thursday, taking the form of mempool manipulation to clear Dai liquidation auctions at near zero prices, costing $8m.
Abstract In this paper, we first estimate the monthly realised correlation, based on daily data, between stock returns of the United States (US) and Bitcoin returns. Then, we relate the realised correlation over the period October 2011 to May 2019 with a news‐based measure of the growth of trade uncertainty of the US. Our results show that the realised correlation is negatively impacted by increases in trade uncertainty, which continues to hold under alternative robustness checks, suggesting that Bitcoin can act as a hedge relative to the conventional stock market in the wake of heightened trade policy‐related uncertainties, and provide diversification benefits for investors.
Jim Kyung-Soo Liew, Richard Li, Tamás Budavári, Avinash Sharma
In this work we examine the largest 100 cryptocurrency return series ranging from 2015 to early 2018. We concentrate our analysis on daily returns and find several interesting stylized facts. First, principal components analysis reveals a complex return generating process. As we examine our data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation in returns. Second, similar to hedge fund returns, cryptocurrency returns suffer from the “beta-in-the-tails” hidden risk. Third, we find that predicting cryptocurrency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per cryptocurrency. Fourth, lower volatile cryptocurrencies are slightly more predictable than more volatile ones. Fifth, evidence exists that efficacy of distinct information sets varies across machine learning algorithms, showing that predictability may be much more complex given a set of machine learning algorithms. Finally, short-term predictability is very tenuous, which suggests that near-term cryptocurrency markets are semi-strong form efficient and therefore, day trading cryptocurrencies may be very challenging. Keywords: cryptocurrency, blockchain, machine learning, bitcoin, beta-in-the-tails, risks
Bitcoin futures were launched by the Chicago Board of Options Exchange and the Chicago Mercantile Exchange group on December 18th, 2017. This study stands as a first attempt to explore the reactions of Bitcoin spot market to the launch of futures contracts. Using an event-study methodology and an adjusted asset pricing model, we show that Futures trading drove up the price of Bitcoin immediately after the announcement day. This reaction started to decrease noticeably following the launch of the futures contracts. Such outcome seems in line with the trading behavior that typically accompanies the launch of futures markets for an asset.
Daniel Traian Pele, Miruna Mazurencu-Marinescu-Pele
Abstract In this paper the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's law (the value of a network is proportional to the square of the number of connected users of the system) for the evaluation of cryptocurrencies; however, in the short-run, the validity of Metcalfe’s law for Bitcoin is questionable. According to the bidirectional causality between the price and the network size, the expected price increase is a driver for more investors to join the Bitcoin network, which may lead in the end to a super-exponential price growth, possibly due to a herding behaviour of investors. The authors then used LPPL models to capture the behaviour of cryptocurrencies exchange rates during an endogenous bubble and to predict the most probable time of the regime switching. The main conclusion of this paper is that Metcalfe’s law may be valid in the long-run, however in the short-run, on various data regimes, its validity is highly debatable.
We investigate the extent to which Bitcoin price fluctuations are associated with investors’ sentiment disagreement. We employ three textual sentiment analysis techniques: 1) a Python library offered by the Computational Linguistics and Psycholinguistics Research Center; 2) Loughran and McDonald’s (2011) dictionary; and 3) semantic orientation by the point-wise mutual information method. The results show that investors’ attention and sentiment disagreement induce extremely high volatility and jumps in Bitcoin prices. These findings complement existing studies on how investors’ sentiment manifests in asset prices.
Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce, but the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stability and associated risks. For investors, it is crucial to uncover the dependence relationships between cryptocurrencies for a more resilient portfolio diversification. Moreover, the stochastic behavior in both tails is important, as long positions are sensitive to a decrease in prices (lower tail), while short positions are sensitive to an increase in prices (upper tail). In order to assess both risk types, we develop in this paper a flexible copula model which is able to distinctively capture asymptotic dependence or independence in its lower and upper tails simultaneously. Our proposed model is parsimonious and smoothly bridges (in each tail) both extremal dependence classes in the interior of the parameter space. Inference is performed using a full or censored likelihood approach, and we investigate by simulation the estimators' efficiency under three different censoring schemes which reduce the impact of non-extreme observations. We also develop a local likelihood approach to capture the temporal dynamics of extremal dependence among two leading cryptocurrencies. We here apply our model to historical closing prices of five leading cryotocurrencies, which share most of the cryptocurrency market capitalizations. The results show that our proposed copula model outperforms alternative copula models and that the lower tail dependence level between most pairs of leading cryptocurrencies -- and in particular Bitcoin and Ethereum -- has become stronger over time, smoothly transitioning from an asymptotic independence regime to an asymptotic dependence regime in recent years, whilst the upper tail has been relatively more stable overall at a weaker dependence level.
Khalid Abouloula, Ali Ou-yassine, Salah-ddine Krit
In automatic trading, the interfaces to use in fulfilment are predefined to launch the platform for trade, which provides computers with the ability to make decisions and learn without explicit programming where all monitors are automated. The main goal of this chapter is to set a trading algorithm for a quick action in the market of smart money, using data science in the analysis of the integration, the distributed ledger technology to securities network and managing the trading account of a token ecosystem, which they are the basics of the crypto-currencies to enhance momentum trading strategies during the time frame of automated broker, and the trading with a large portfolio strategy in the market that needs big data.