Meng Qin, Tong Wu, Ran Tao, ChiâWei Su ¡ 5 authors
This paper clarifies the association between the Sino-U.S. bilateral relation (BR) and Bitcoin price (BCP) by applying the bootstrap full- and sub-sample Granger causality tests. It reveals that BR has positive and negative effects on BCP. The negative impact points out that Bitcoin is viewed as a tool to avoid uncertainties caused by the deterioration of BR, also proving that the strained relation between China and the U.S. can stimulate the Bitcoin market. However, this opinion is not held under a positive impact, the main explanation is that the burst of bubble weakens its ability to hedge risks. The above conclusion is not consistent with the theoretical model, underlining that the Bitcoin market is boosted by the deterioration of BR. Conversely, there is a negative influence from BCP to BR, meaning that the relationship between China and the U.S. can be reflected by the Bitcoin market. Under the complex and volatile international situation, investors can benefit from this investigation to compensate for the losses and keep their wealth. Also, it helps the related authorities to create a stable investment environment and promote friendly bilateral relations.
Marco Ortu, Stefano Vacca, Giuseppe Destefanis, Claudio Conversano
We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrenciesâ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkesâ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of âWallstreetbet and GameStopâ1 that took place in January 2021.
Youcef Maouchi, Lanouar Charfeddine, Ghassen El Montasser
This paper investigates digital financial bubbles amidst the COVID-19 pandemic. Using a sample of 9 DeFi tokens, 3 NFTs, Bitcoin, and Ethereum, we detect several bubbles overlapping the examined cryptoassets. We also uncover DeFi and NFT-specific bubbles in Summer 2020 suggesting distinct driving factors for this class of assets. We document that DeFi and NFTs bubbles are less recurrent but have higher magnitudes than cryptocurrencies' bubbles. We also find that COVID-19 and trading volume exacerbate bubble occurrences, while Total Value Locked (TVL) is negatively associated with cryptoassets' bubbles. Our results suggest that TVL can be used as a tool for market monitoring.
The multi-chain future is upon us. Modular architectures are coming to\nmaturity across the ecosystem to scale bandwidth and throughput of\ncryptocurrency. One example of such is the Ethereum modular architecture, with\nits beacon chain, its execution chain, its Layer 2s, and soon its shards. These\ncan all be thought as separate blockchains, heavily inter-connected with one\nanother, and together forming an ecosystem. In this work, we call each of these\ninterconnected blockchains "domains", and study the manifestation of Maximal\nExtractable Value (MEV, a generalization of "Miner Extractable Value") across\nthem. In other words, we investigate whether there exists extractable value\nthat depends on the ordering of transactions in two or more domains jointly. We\nfirst recall the definitions of Extractable and Maximal Extractable Value,\nbefore introducing a definition of Cross-Domain Maximal Extractable Value. We\nfind that Cross-Domain MEV can be used to measure the incentive for transaction\nsequencers in different domains to collude with one another, and study the\nscenarios in which there exists such an incentive. We end the work with a list\nof negative externalities that might arise from cross-domain MEV extraction and\nlay out several open questions. We note that the formalism in this work is a\nwork in progress, and we hope that it can serve as the basis for formal\nanalysis tools in the style of those presented in Clockwork Finance, as well as\nfor discussion on how to mitigate the upcoming negative externalities of\nsubstantial cross-domain MEV.\n
Decentralized Finance (DeFi) is a popular topic in the blockchain and cryptocurrency industry in the early 2020s. Still, cryptocurrencies have not yet become Decentralized Payment Systems (DPS) because of the high volatility of bitcoin and many of the altcoins. We investigated a proposed method to form a non-collateralized stablecoin called the Morini's Scheme of Inv&Sav wallets. We figured out two equations for the rebasement for the Inv wallet balances and then compared the results. We found the second rebasement method to be fairer to the agents, but we found the issue of negative balances with both methods. We proposed novel solutions to overcome these issues. One of the proposed solutions was to freeze some money in the Sav wallet if there is a negative balance in the Inv wallet. Another proposed solution was to introduce a two-money economy of money and antimoney to turn the current centralized token distribution model decentralized and make transactions more probable even if agents do not have enough money funds; this could be seen as a decentralized version of credit cards.
Abstract Cryptocurrencies are digital assets that can be stored and transferred electronically. Bitcoin (BTC) is one of the most popular cryptocurrencies that has attracted many attentions. The BTC price is considered as a high volatility time series with non-stationary and non-linear behavior. Therefore, the BTC price forecasting is a new, challenging, and open problem. In this research, we aim the predicting price using machine learning and statistical techniques. We deploy several robust approaches such as the Box-Jenkins, Autoregression (AR), Moving Average (MA), ARIMA, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and Grid Search algorithms to predict BTC price. To evaluate the performance of the proposed model, Forecast Error (FE), Mean Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.
Cryptoassets have experienced dramatic volatility in their prices, especially during the COVID-19 pandemic era. This pilot study explores the volatility asymmetry and correlations among three popular cryptoassets (Bitcoin, Ethereum, and Dogecoin) as well as Gold. Multiple Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are analyzed. We find that positive shocks have a greater impact on the volatility of these financial assets than negative shocks of the same magnitude, perhaps a manifestation of the fear of missing out (FOMO) effect. Our research is one of the first to use COVID-19-period volatility of financial assets (in-sample data) to forecast their later COVID-19-period volatility (out-of-sample data). This forecast accuracy is compared to that produced by forecasts using the same out-of-sample data and a longer in-sample data. Our results indicate that generally, the larger in-sample dataset gives a higher forecast accuracy though the smaller in-sample dataset is from the same regime as the out-of-sample data. We also evaluate the correlations among the assets using the Dynamic Conditional Correlation (DCC) framework and find that there is an elevated positive correlation between Gold and Bitcoin during the past two years. The Gold-Bitcoin correlation hit its peak during the peak of the COVID-19 pandemic and then fell back to around zero in July 2021 when the pandemic crisis eased. Unsurprisingly, there is a strong positive correlation among the cryptocurrencies. Pairwise correlation among all four assets was stronger during the COVID-19 pandemic. Such continuing analysis can inform portfolio asset allocation as well as general financial policy decisions.
I. S. Ivanchenko, Marina V. Charaeva, Alla A. Lysochenko, Ilya A. Nozhenkov
Since 2009, cryptocurrencies being a modern form of electronic means of payment have become widespread in the global financial market. In this regard, a study aimed to find an answer to the question: âAre cryptocurrencies a modern form of money?â was conducted. An analysis of the scientific works of leading economic schools has led to the conclusion that cryptocurrencies are a modern form of private money that performs the main monetary function being a means of payment, which corresponds to the idea of the Austrian economic school of full-fledged means of payment. The study attempts to predict the market rate of the three most popular cryptocurrencies at present being Bitcoin, Ethereum and Ripple due to the fact that modern cryptocurrencies demonstrate a high level of volatility in their market value, and reliable funds must maintain their purchasing power. The analysis of the cryptocurrency market with regard to the information efficiency has led to the conclusion that cryptocurrencies have been demonstrating instability of qualitative properties over the past five years. The authors proposed to improve the predictive characteristics of the HAR-RV model by additionally calculating the Shannon information entropy of the initial time series to level their insensitivity to unexpected information shocks in the cryptocurrency market being the main drawback of regression models. The study has proved that cryptocurrencies are a promising modern form of electronic money, their market rate is quite predictable, and the popularity of cryptocurrencies and their use in payment transactions will further increase.
Bitcoin is one of many crypto currencies used for peer - to - peer transactions accessible to anyone with internet access. It is a decentralized digital currency not backed by any government or other legal entity, making it an attractive alternative to the traditional fiat money system. There is no doubt that crypto currencies are the future of money. However, not all crypto projects will succeed in the long run and some might even turn out to be scams. It?s a jungle out there! How do you know which crypt currency project is going to survive? Our paper can help you identify which projects have a good chance of survival by analyzing their market capitalization trends over time using equicorrelation analysis. This paper examines whether or not Bitcoin returns are dependent on common factors, investigates whether or not Bitcoin returns are i. i. d., tests the efficiency of crypt currency markets, and provides an answer to the following question: are crypto currencies efficient? We'll be looking at crypto currencies and their impact on financial markets. We'll also discuss the challenges of using crypto currencies as a predictor for later price movements and look at equicorrelation and its effect on the crypt currency market. We'll also discuss some of the challenges of using equicorrelation as a predictor for future price movements. Finally, we'll explore some potential applications for equicorrelation within the business world.
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying components of predictability, memory, and synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models we argue that some results could relate to intra-market and inter-market regime shifts, and changes in direction of information flow between different market observables.
This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrenciesâ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of cryptocurrencies, trade them profitably, and in most cases outperform the buy and hold (B&H) simple strategy. Our systemâs best performance was achieved trading XRP, Binance Coin, Ethereum, and Bitcoin. Although our system has also generated a positive net profit (NP) for Cardano, it failed to outperform the B&H strategy. For all currencies, the system better predicted long trends than short trends.
The results of empirical analyses confirm that analysed unsystematic factors, the Stock-to-Flow index (S2F), and information on the Bitcoin (BTC) are directly correlated with BTC values. These results are expected and in line with the economic theory; however, this research paper aimed to investigate the impact of unsystematic factors on the value of decentralised virtual cryptocurrency BTC. Its aim was also to analyse the reasons for significant oscillations of market values in relation to the S2F and S2FX model and thus confirm the reliability of these models in the estimation of BTC value. The research further confirms the strong influence of non-technical information directly linked with the BTC. The limitations of this paper are the lack of possibilities for examining the impact of non-technical information affecting the Bitcoin price deviation regarding the S2F model. In addition to all mentioned limitations, the research results indicate the relevance of the S2F and S2FX models and show a strong impact of (half) the information on the value of cryptocurrencies.
Ana Todorovska, Eva Spirovska, Gorast Angelovski, Hristijan Peshov ¡ 9 authors
In a world where no country, market, or economy is an island, interconnectivity is becoming a fundamental feature of almost all social and economic systems. In the case of digital assets like cryptocurrencies, the impact of interconnectivity on their performance and price trajectory is amplified. Studying these phenomena is essential for understanding the processes driving the crypto-markets. In this paper, we propose seven different approaches to create a network of eighteen most important cryptocurrencies. The first three approaches discover correlations between cryptocurrencies based on their daily prices, daily returns, and sentiment extracted from Reddit data. The following two approaches offer insights from the frequency of joint appearance of cryptocurrencies in Google news and Reddit data. The remaining two approaches determine each cryptocurrencyâs impact over the others when forecasting prices and returns. Furthermore, we explore the networksâ interdependencies to explore the similarities of the cryptocurrency networks generated by different approaches. The proposed methodology allows us to understand the dynamics in the cryptocurrency markets and the different processes that influence their performance.
Using the asymmetric stochastic volatility model, this study investigates the day-of-the-week and holiday effects on the returns and volatility of Bitcoin from January 1, 2013 to August 31, 2019; in this context, we also discuss the characteristics of Bitcoin as a financial asset. The results of the estimation are threefold. First, the finding shows a small day-of-the week effect in volatility on Saturday and Sunday than in the rest of the week. Second, although the holiday effects are examined in active trading countries, namely Japan, China, Germany, and the United States, the positive post-holiday effect on the returns and weak positive pre-holiday effect on the volatility are only observed in the United States. Finally, the asymmetry effect is not observed. A comparison of Bitcoin to several assets such as stock, currency, and gold shows Bitcoin's positioning between stock, currency, and gold in relation to the week and holiday effects, its reaction to federal funds and medium of exchange characteristics, and the lack of asymmetry effect.
Fulvia Pennoni, Francesco Bartolucci, Gianfranco Forte, Ferdinando M. Ametrano
Abstract A hidden Markov model is proposed for the analysis of timeâseries of daily logâreturns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These logâreturns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and varianceâcovariance matrices of the logâreturns, so that they also differ in terms of volatility. Maximumâlikelihood estimation of the model parameters is carried out by the expectationâmaximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximumâaâposteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.
This paper initially presents a brief overview of the cryptocurrency and its history. We discuss the novel nature of literature attempting to create hybrid artificial neural network models to predict prices of cryptocurrency. For the remaining majority of the paper, we present the details of various hybrid artificial neural networks that have successfully been implemented to predict cryptocurrency prices in the form of a survey. Comparison of methods and results follow in the results section.
We develop an analysis of the cryptocurrency market borrowing methods and concepts from ecology. This approach makes it possible to identify specific diversity patterns and their variation, in close analogy with ecological systems, and to characterize the cryptocurrency market in an effective way. At the same time, it shows how non-biological systems can have an important role in contrasting different ecological theories and in testing the use of neutral models. The study of the cryptocurrencies abundance distribution and the evolution of the community structure strongly indicates that these statistical patterns are not consistent with neutrality. In particular, the necessity to increase the temporal change in community composition when the number of cryptocurrencies grows, suggests that their interactions are not necessarily weak. The analysis of the intraspecific and interspecific interdependency supports this fact and demonstrates the presence of a market sector influenced by mutualistic relations. These latest findings challenge the hypothesis of weakly interacting symmetric species, the postulate at the heart of neutral models.
Stefan Kitzler, Friedhelm Victor, Pietro Saggese, Bernhard Haslhofer
We present a measurement study on compositions of Decentralized Finance (DeFi) protocols, which aim to disrupt traditional finance and offer services on top of distributed ledgers, such as Ethereum. Understanding DeFi compositions is of great importance, as they may impact the development of ecosystem interoperability, are increasingly integrated with web technologies, and may introduce risks through complexity. Starting from a dataset of 23 labeled DeFi protocols and 10,663,881 associated Ethereum accounts, we study the interactions of protocols and associated smart contracts. From a network perspective, we find that decentralized exchange (DEX) and lending protocol account nodes have high degree and centrality values, that interactions among protocol nodes primarily occur in a strongly connected component, and that known community detection methods cannot disentangle DeFi protocols. Therefore, we propose an algorithm to decompose a protocol call into a nested set of building blocks that may be part of other DeFi protocols. This allows us to untangle and study protocol compositions. With a ground truth dataset that we have collected, we can demonstrate the algorithmâs capability by finding that swaps are the most frequently used building blocks. As building blocks can be nested, that is, contained in each other, we provide visualizations of composition trees for deeper inspections. We also present a broad picture of DeFi compositions by extracting and flattening the entire nested building block structure across multiple DeFi protocols. Finally, to demonstrate the practicality of our approach, we present a case study that is inspired by the recent collapse of the UST stablecoin in the Terra ecosystem. Under the hypothetical assumption that the stablecoin USD Tether would experience a similar fate, we study which building blocks â and, thereby, DeFi protocols â would be affected. Overall, our results and methods contribute to a better understanding of a new family of financial products.
The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoinâs pricing at various cryptocurrency market development stages â before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) â the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets â stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.
M. Eren Akbiyik, Mert Erkul, Killian Kaempf, Vaiva VasiliauskaitÄ Âˇ 5 authors
Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding stable and accurate volatility predictors for a relatively new asset class of cryptocurrencies, in particular Bitcoin, using deep learning representations of public social media data obtained from Twitter. For our experiments, we extracted semantic information and user statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute frequency price data over a horizon of 144 days. Using this data, we built several deep learning architectures that utilized different combinations of the gathered information. For each model, we conducted ablation studies to assess the influence of different components and feature sets over the prediction accuracy. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both classical autoregressive models and other deep learning-based architectures in the literature, and (ii) tweet author meta-information, even detached from the tweet itself, is a better predictor of volatility than the semantic content and tweet volume statistics. We demonstrate how different information sets gathered from social media can be utilized in different architectures and how they affect the prediction results. As an additional contribution, we make our dataset public for future research.
Abstract This study examines intraday time series momentum in Bitcoin. Unlike stock markets, Bitcoin trades 24 h a day and therefore has not got a clear opening and closing period. Therefore, we use trading volume as a proxy for the market trading time and show that the first halfâhour positively predicts the last halfâhour return. We find that the first trading sessions with the highest volume or volatility are associated with the greatest predictability for intraday time series momentum. We also show that intraday momentumâbased trading yields substantial economic gains in terms of market timing and asset allocation, especially in periods of a market downturn in Bitcoin. Consistent with the finding in foreign exchange markets, our results also show that the Bitcoin intraday momentum is driven by liquidity provision rather than lateâinformed trading.