Within the decision-making process, investors are interested in finding the most effective solutions that will allow them to obtain short-term benefits. Current economic environment is characterized by the emergence of new financial instruments that can assist investors to diversify their investment portfolio. Crypto-currencies represents a category of financial assets that can be used by investors to reduce risk and achieve significant returns. Therefore, the study intends to analyze the financial behavior of investors in the moment of publishing the financial statements. Financial statements could have a positive or negative influence on the investment portfolio and structure. The issue analyzed by this study is represented by the ability of the cryptocurrency Bitcoin to be considered as an alternative investment asset. The study is divided into two parts. In the first part, the study presents the review of literature about value-relevance, cryptocurrency term and speculative bubble. The second part presents the research methodology and results. The results of the study validate the hypothesis of this study, cryptocurrency Bitcoin being a financial asset that can be used as an alternative investment asset for diversification of investment portfolio.
Abstract This paper provides a comprehensive overview of cryptocurrencies, including the origin of cryptocurrencies, how cryptocurrencies operate, and the current situation of cryptocurrencies. In addition, we also provide the performance comparison of major cryptocurrencies with the performance of the stock market indexes. All the cryptocurrencies exhibit higher average returns and volatility than the stock market indexes, which appeals to risk-taking investors. We then perform additional analysis on the determinants of cryptocurrencies returns. We show that major fundamental variables are less likely to affect the returns of cryptocurrencies except for the S&P 500 index returns and the exchange rates between U.S. dollars and Euros.
Stephanie F. Cheng, Gus De Franco, Haibo Jiang, Pengkai Lin
This paper provides evidence on public firms’ initial 8-K disclosures that mention Blockchain and investors’ response to these disclosures. We categorize the description of Blockchain activities in firms’ 8-Ks as Speculative (e.g., a vague future plan that involves Blockchain) or Existing (e.g., a description of Blockchain product). We document a sharp increase in the number of initial 8-K disclosures of Blockchain, particularly by Speculative firms, coinciding with the rise of Bitcoin prices and excitement in Blockchain technology in the last quarter of 2017. Investors react positively to the Blockchain 8-Ks issued by Speculative firms in the initial seven-day event window although the reaction is mostly reversed over the 30 days following the disclosure. The reaction is stronger when Bitcoin returns are more positive. Overall, our results are consistent with a situation that troubles the SEC and the financial press: investors overreact to a firm’s first 8-K disclosure of a potential foray into Blockchain technology and that overreaction is a function of the Bitcoin price bubble. This paper was accepted by Brian Bushee, accounting.
Robiyanto Robiyanto, Yosua Arif Susanto, Rihfenti Ernayani
Cryptocurrency market is an attractive field for researchers in finance nowadays. One topic that can be studied is related to the existence of anomalies in the cryptocurrency market. This research was conducted to examine whether the cryptocurrency market, especially on Bitcoin and Litecoin, has day-of-the-week and month-of-the-year effects. The Bitcoin and Litecoin were used as objects because they were a cryptocurrency with a large market capitalization. The data used were monthly cryptocurrency returns for examining the month-of-the-year-effect and daily returns for examining the day-of-the-week-effect from 2014-2018. GARCH (1,1) analysis was done to see these effects on the cryptocurrency market. The results indicate that the phenomena of day-of-the-week and month-of-the-year effect existed in the cryptocurrency market. Therefore, the cryptocurrency market was not an efficient market. The pattern in the Bitcoin and Litecoin could later be utilized by investors. The investors should buy Bitcoin at the end of January and they should sell them at the end of February. While, for the investors who traded daily, can trade Bitcoin in Monday, Wednesday and Thursday because in these days, the Bitcoin have the potential to generate daily profits. JEL Classification: G14, G19 DOI: https://doi.org/10.26905/jkdp.v23i3.3005
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.
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.
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.
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.
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.
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.
This study proposes to investigate the dynamic relationships between the three weather factors (temperature, humidity, and wind speed) in New York City of USA and Coinbase Index from Federal Reserve Bank of St. Louis, in the USA. Statistical tools like Descriptive Statistics, Unit Root, Granger Causality Test and Johansen Co-Integration test were employed. This study clearly found that the temperature influenced the investors’ mood and their investment decision in respect of Cryptocurrency index (Coinbase Index) and also found that there was long run equilibrium between the sample variables during the study period. The results of study provided strong evidence against the Efficient Market Hypothesis (EMH).
Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, Angel Hernando Veciana
This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book from a liquidity pool in a decentralized setting for comparison with centralized exchanges. Chapter 2 describes the methodological tools used: Hasbrouck's Information Share, Gonzalo and Granger's Permanent-Transitory decomposition, and the Hayashi-Yoshida estimator. These are applied to explore lead-lag dynamics, cointegration, and price discovery across market types. Chapter 3 presents the empirical analysis. For ETH, it compares price dynamics on Binance and Uniswap v2 over a one-year period, focusing on five key events in 2024. For BTC, it analyzes the relationship between spot and futures prices on the CME. The study estimates lead-lag effects and cointegration in both cases. Results show that centralized markets typically lead in ETH price discovery. In futures markets, while they tend to lead overall, high-volatility periods produce mixed outcomes. The findings have key implications for traders and institutions regarding liquidity, arbitrage, and market efficiency. Various metrics are used to benchmark the performance of modified AMMs and to understand the interaction between decentralized and centralized structures.
Sosyal medya, insanları alış veriş alışkanlıklarından yatırım kararlarına kadar birçok ticari niyetleri üzerinde yüksek etki düzeyi olduğu güncel birçok çalışmada araştırılmaya başlanmıştır ve bu ilişki ortaya konmuştur. Bu ilişki üzerine inşa edilerek geliştirilen güncel analiz yöntemleri yatırım araçlarının gelecek değerlerini tahmin ederek yatırım kararları almada bir destek mekanizması olarak kullanılması çok cazip bir konudur. Bu sebeple bu ilişki yatırımcı ve analistlerden akademisyenlere kadar güncel bir ilgi konusu olmuştur. Bu çalışmanın amacı da sosyal medya ile yatırım kararları arasındaki ilişkiyi metinsel ve finansal analiz aracılığı ile görmeye çalışmaktır. Bu çalışmada Twitter üzerinden metin madenciliği ile veri çekilmiş ve sentiment(duygu) analizi ile yorumların olumlu ya da olumsuz olma durumu incelenmiştir. Sentiment analizinden elde edilen sayısal değerler ile güncel ve küresel bir yatırım aracı olan Bitcoin fiyatları arasındaki ilişkinin varlığını sorgulamak adına Granger Nedensellik analizi gibi finansal analizler kullanılmıştır.
Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than $1/2$, which indicates log-volatility increments that are rough. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly comes from the distributional property.
This study examines whether the efficiency of cryptocurrency markets (Bitcoin and Ethereum) evolve over time based on Lo's (2004) adaptive market hypothesis (AMH). In particular, we measure the degree of market efficiency using a generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods. The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) Bitcoin's market efficiency level is higher than that of Ethereum over most periods, and (3) a market with high market liquidity has been evolving. We conclude that the results support the AMH for the most established cryptocurrency market.
This study conducts a systematic survey on whether the pricing behavior of cryptocurrencies is predictable. Thus, the Efficient Market Hypothesis is rejected and speculation is feasible via trading. We center interest on the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) as well as other relevant methodologies of testing long memory in returns and volatility. It is found that the majority of academic papers provides evidence for inefficiency of Bitcoin and other digital currencies of primary importance. Nevertheless, large steps towards efficiency in cryptocurrencies have been traced during the last years. This can lead to less profitable trading strategies for speculators.