Recently, cryptocurrency investment is one of the most interesting investment channels offered on the market. Investors are always trying to find an effective prediction model to increase profit as well as reduce loss in investment. In this paper, a combination of technical indicators and deep learning is applied to predict cryptocurrency price trends in the short term. The work also used the Multi-scale Residual Convolutional (MRC) module for feature extraction and a Long Short-Term Memory (LSTM) to predict price trends. Experimental results using Bitcoin and Ethereum time-series data in time frame as 1-hour and 30-minute show that our proposed method has better accuracy in a comparison to some other methods. Moreover, an automation trading bot using the proposed model was tested on the Binance Sandbox environments and got good results.
Samia Mohamed Hafez, Mustafa El Nainay, Mohamed S. Abougabal, Ahmed E. Kosba
The popularity of cryptocurrencies is increasing, as they have become an economy of their own, due to the observed returns of investments in cryptocurrencies and digital assets. This led to an increasing interest in the prediction of their prices over the past few years. Ethereum is one of the most popular cryptocurrencies that has witnessed an increase of prices since 2015 while having the second largest market cap. Ethereum is a decentralized platform that incorporates several interactions, not limited to asset trading only; it is also a platform of smart contracts execution and token trading. In this work, we aim at reflecting the interactions perceived in the Ethereum network on Ether prices using Topological Data Analysis (TDA). We introduce a method to extract the TDA features of the indicators of different interaction networks; traded volumes, smart contracts, and transactions between accounts. We conducted an analysis of the effect of using TDA features on Ether price prediction and extended our method to predict the prices of eight Ethereum tokens. Our method resulted in 0.75%, 4.9%, and 13.75% MAPE in hourly, daily, and weekly forecasts respectively, outperforming the previously reported results.
The collective behaviors of community members in dynamic bitcoin transaction network are significant to understand the evolutionary characteristics of communities for bitcoin transaction network. In this paper, we empirically investigate the behavior evolution of new nodes forming communities for the bitcoin transaction network. First, we divide the bitcoin transaction network into multiple time segments, and detect community on each time segment. Then, according to the set similarity method, we mark the community with maximal similarity [Formula: see text] at adjacent timestamps as the new community. Finally, we propose an evolution index to illustrate the evolution trend of new nodes forming communities, and introduce the reshuffle model to compare with it. The results show that there are obvious differences in the early stage, and new traders tend to join new communities. However, after August 2011, the trends of before and after reorganization are very similar, which indicates that in bitcoin trading, the behaviors of new traders forming communities become random. Our work may be helpful for the understanding of user behavior characteristics in bitcoin trading, and provide a new perspective for the research of bitcoin transaction network.
In this article, we provide an overview of ACM TWEB’s special issue, Financial Technology on the Web . This special issue covers diverse topics: (1) a new architecture for leveraging online news to investment and risk management, (2) a cross-platform analysis of the post quality and users’ behaviors, and (3) an empirical study on disentangling decentralized finance compositions. In addition to a guide for the special issue, we also share a brief opinion on the future of financial technology on the Web.
Cryptocurrency (e.g., Ethereum and its currency Ether) presents an opportunity to completely change the way money is transacted, which perhaps even redefines the money. On this basis, this paper will investigate and discuss the basic principles and corresponding applications of Ethereum. As a matter of fact, operating on a decentralized platform while also using the global reach of the internet, transactions can be made with little cost, without interference of intermediaries and government intervention. Besides, it will keep the personal information of the user private. In addition, according to the analysis, each user can define how they use ether as they please, e.g., using Ethereum to store assets like art and reselling them through Ethereum. However, Ethereum still has many issues to fix ranging from security risks, privacy risks, and perceived stability due to Ethereum’s notorious volatility and previous illegal activities from other platforms (e.g., Bitcoin). More research is required to distinguish whether Ether can be a viable alternative that could be added to the economy or even completely replacing traditional money altogether and address the current concerns. These results shed light on guiding further exploration of cryptocurrency.
Transaction fee markets are essential components of blockchain economies, as they resolve the inherent scarcity in the number of transactions that can be added to each block. In early blockchain protocols, this scarcity was resolved through a first-price auction in which users were forced to guess appropriate bids from recent blockchain data. Ethereum's EIP-1559 fee market reform streamlines this process through the use of a base fee that is increased (or decreased) whenever a block exceeds (or fails to meet) a specified target block size. Previous work has found that the EIP-1559 mechanism may lead to a base fee process that is inherently chaotic, in which case the base fee does not converge to a fixed point even under ideal conditions. However, the impact of this chaotic behavior on the fee market's main design goal -- blocks whose long-term average size equals the target -- has not previously been explored. As our main contribution, we derive near-optimal upper and lower bounds for the time-average block size in the EIP-1559 mechanism despite its possibly chaotic evolution. Our lower bound is equal to the target utilization level whereas our upper bound is approximately 6% higher than optimal. Empirical evidence is shown in great agreement with these theoretical predictions. Specifically, the historical average was approximately 2.9% larger than the target rage under Proof-of-Work and decreased to approximately 2.0% after Ethereum's transition to Proof-of-Stake. We also find that an approximate version of EIP-1559 achieves optimality even in the absence of convergence.
This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.
With the rise of Blockchain technology, the cryptocurrency market has been gaining significant interest. In particular, the number of cryptocurrency traders and the market capitalization have grown tremendously. However, predicting cryptocurrency price is very challenging and difficult due to the high price volatility. In this paper, we propose a classification machine learning approach in order to predict the direction of the market (i.e., if the market is going up or down). We identify key features such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to feed the machine learning model. We illustrate our approach through the analysis of Bitcoin close price. We evaluate the proposed approach via different simulations. Particularly, we provide a backtesting strategy. The evaluation results show that the proposed machine learning approach provides buy and sell signals with more than 86% accuracy.
Economic systems play pivotal roles in the metaverse. However, we have not yet found an overview that systematically introduces economic systems for the metaverse. Therefore, we review the state-of-the-art solutions, architectures, and systems related to economic systems. When investigating those state-of-the-art studies, we keep two questions in our mind: (1) what is the framework of economic systems in the context of the metaverse, and (2) what activities would economic systems engage in the metaverse? This article aims to disclose insights into the economic systems that work for both the current and the future metaverse. To have a clear overview of the economic-system framework, we mainly discuss the connections among three fundamental elements in the metaverse, i.e., digital creation, digital assets, and the digital trading market. After that, we elaborate on each topic of the proposed economic-system framework. Those topics include incentive mechanisms, monetary systems, digital wallets, decentralized finance (DeFi) activities, and cross-platform interoperability for the metaverse. For each topic, we mainly discuss three questions: a) the rationale of this topic, b) why the metaverse needs this topic, and c) how this topic will evolve in the metaverse. Through this overview, we wish readers can better understand what economic systems the metaverse needs, and the insights behind the economic activities in the metaverse.
Luca Vincenzo Ballestra, Andrea Molent, Graziella Pacelli
Over the last years, the world of cryptocurrencies has undergone a tumultuous development, mostly characterized by speculative behaviors, and thus one might argue that it does not satisfy the Efficient Market Hypothesis. Since the efficiency of a financial market can be assessed by checking whether the times series of the assets traded on it are persistent/anti-persistent, we investigate the presence of memory in the price of seven among the most important cryptocurrencies. To this aim, we employ an original approach based on two fractional models, namely the geometric fractional Brownian motion and the geometric mixed fractional Brownian motion, which are tested against the Markovian geometric Brownian motion to detect the presence of memory. The above fractional models are estimated by employing an innovative maximum likelihood procedure that exploits the Toeplitz structure of the covariance matrix of log-returns. The null assumption of absence of memory in the time series is tested based on confidence ellipses for the estimated parameters. We validate the proposed procedure on artificial data and we apply it to the historical series of crypto-prices. Results suggest the presence of memory effects only for some of the considered cryptocurrencies. A possible explanation of such a persistence/anti-persistence phenomenon is provided.
This paper extends the study of Bourghelle et al. (2022) to check whether collective emotions could help to forecast bitcoin volatility over the period 2018-2021. To this end, we first assess whether consideration of investor sentiment and collective emotions can give us clearer insights into bitcoin dynamics over the period in question and whether they can help to explain the different price fluctuations. Formally, we ran causality tests and, as in Bourghelle et al. (2022), built a two-equation nonlinear vector autoregressive (VAR) model to assess for further lead-lag effects between bitcoin volatility and collective emotions. Second, we proposed in-sample forecasts of bitcoin volatility to test whether our forecasts could be improved by taking investors’emotions and sentiment into account. Our findings show that market sentiment and investors’ emotions provide useful information that can help to explainfluctuations, structural breaks, and changes in bitcoin volatility. Further, collective emotions improve bitcoin volatility forecasting as our nonlinear model, including emotions-related news, supplants the benchmark linear model.
Wajid Shakeel Ahmed, Ahsan Mehmood, Talha Sheikh, Allah Bachaya
This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to analyse bivariate connectedness. Our findings unveil the absence of mean reversion in majority cases which indicates high persistence in series. Furthermore, bivariate analysis reveals disconnection between cryptocurrencies prices and stock indices. Surprisingly, a different picture emerges on using conditional volatility instead of prices. Like, conditional volatility-based estimation uncovers evidence of mean reversion in univariate analysis as expected. There is some evidence of cointegration on volatility grounds between cryptocurrencies and emerging stock market indices. Our findings implies that investment decision regarding digital currencies should be taken cautiously. As cryptocurrencies are extremely volatile with high degree of persistence which can make them counterproductive.
Japjeet Singh, Ruppa K. Thulasiram, A. Thavaneswaran
Cryptocurrencies have emerged as an alternative financial asset in the last decade, with their market growing exponentially in recent years. The price of cryptocurrencies is highly volatile and is prone to rapid swings within short periods of time. This behaviour makes them a high-risk and high-return financial asset. The efficacy of neural networks in forecasting the high frequency financial time series has become widely accepted in the research community. This work explored the use of Long Short Term Memory (LSTM), a neural network based non-linear sequence model, to propose a novel algorithmic trading strategy for cryptocurrencies. The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. This simple, yet effective trading algorithm uses the network's price forecasts to make buy and short selling decisions for cryptocurrency based on certain set criteria. The proposed trading strategy gives positive returns when backtested on Bitcoin hourly prices taken from yahoo! finance. We also verified the effectiveness of the trading strategy for Ethereum, the second largest cryptocurrency, based on the positive backtesting returns. As an extension to the study, the proposed strategy is applied on an even higher frequency (minute by minute) Bitcoin price data, and the strategy gives positive backtesting returns in this extended study. We also provide fuzzy intervals for the algorithmic return of our strategy and compare those with corresponding intervals on a simple buy and hold strategy.
The global economy receives a catastrophic blow due to the COVID-19 epidemic, with long-term pessimism shown towards the global market and exponential increasing expectation for looking for a reliable safe-haven asset. Now, it seems the possible alternatives to traditional currencies issued and backed by governments have primarily emerged - in the form of Bitcoin and Ethereum, as well as several other cryptocurrencies. Cryptocurrencies have been on the market for a long time and have been controversial in the international financial markets for their unique properties. Since the outbreak of COVID-19, the price of cryptocurrencies has seen an unprecedented increase. Whether the price increase of cryptocurrencies is linked to the COVID-19 outbreak is a mystery. This paper will focus on exploring this question through a linear regression machine learning model. The data in the U.S.A are used here. Our results show that the price of bitcoin is significantly related to the price of Ethereum. There is some correlation between covid-19 new cases incensement and the price of Bitcoin and Ethereum, indicating the legitimacy of predicting cryptocurrencies’ price using covid-19 new cases incensement as a factor.
The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at http://stpbtc-ii.up.krakow.pl/. Source codes of our research are available to download in order to make our experiment reproducible.
Cryptocurrency markets have experienced large growth in recent years, with an increase in the number and diversity of traded assets. Previous work has addressed the economic properties of Bitcoin with regards to its hedging or diversification properties. However, the surge of many alternatives, applications, and decentralized finance services on a variety of blockchain networks requires a re-examination of those properties, including indexes from outside the big economies and the inclusion of a variety of cryptocurrencies. In this paper, we report the results of studying the most representative cryptocurrency of each consensus mechanism by trading volume, forming a list of twenty-four cryptocurrencies from the 1st of January 2018 to the 30th of September 2022. Using the Baur and McDermott model, we examine hedge, safe haven, and diversifier properties of all assets for all G7 country’s major indexes as well as all BRICS major indexes breaking it down by two attributes: kind of blockchain technology and pre/during COVID health crisis. Results show that both attributes play an important role in the hedge, safe haven, and diversifier properties associated with the asset. Concretely: stablecoins appear to be the only ones to maintain hedge property in most analyzed markets pre- and during-COVID; Bitcoin investment properties shifted after the COVID crisis started; China and Russia stopped being correlated with the cryptocurrency after the COVID crisis hit.
This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.
Essais sur la dynamique du prix des crypto-monnaies Cet essai, qui comprend trois recherches empiriques originales, met en lumière les caractéristiques du comportement du marché des crypto-monnaies en fonction des effets macroéconomiques exogènes, du réseau interne et de leurs “matières premières" - les marchés de l'énergie. Le premier chapitre étudie les réponses des rendements et de la volatilité des crypto-monnaies aux annonces de nouvelles macroéconomiques américaines. En utilisant les données \textit{intraday} de 5 minutes sur les prix des crypto-monnaies, nous trouvons des preuves de la réaction des rendements et de la volatilité des crypto-monnaies aux nouvelles macroéconomiques et les possibilités d'utiliser les crypto-monnaies comme un outil de refuge en raison de la différence de réponse aux nouvelles macroéconomiques américaines entre les crypto-monnaies et les autres actifs financiers conventionnels. Dans le deuxième chapitre, nous étudions la causalité dynamique sur le marché des crypto-monnaies du point de vue du rendement et de la liquidité en utilisant une méthode de réseau de causalité stable. Les résultats mettent en évidence la spéculation du marché en montrant que les principales crypto-monnaies ne sont pas les acteurs les plus influents du réseau. Le chapitre 3 traite du sujet controversé de la relation entre les crypto-monnaies et les marchés de l'énergie. En outre, la consommation d'énergie des crypto-monnaies augmente considérablement suite à la difficulté croissante du minage et à une gamme plus complète d'application de la technologie blockchain telle que NFT et DeFi. En utilisant le modèle VAR à paramètres variables dans le temps, ce chapitre complète la littérature existante sur la liaison entre les crypto-monnaies et les marchés de l'énergie. Contrairement à la plupart des chercheurs existants, notre étude aborde non seulement les principales crypto-monnaies consommatrices d'énergie, le Bitcoin et l'Ethereum, mais aussi d'autres actifs basés sur la blockchain.
In this research, the U.S. investor sentiment effect on cryptocurrency returns and volatility is examined by separating it into irrational and rational parts. According to the data, an unforeseen rise in the rational part of U.S. individual investor attitude influences cryptocurrency returns statistically and positively. In other words, rational sentiment can result in rising cryptocurrency returns. Additionally, a positive significant association exists between cryptocurrency volatility and the rational part of the individual U.S. investor sentiment. The findings confirm the hypothesis that the behavior of rational investors who utilize and study the impact of economic factors on asset prices reduces cryptocurrency volatility.
Purpose This study aims to investigate the safe-haven and hedging properties of Bitcoin against a wide variety of conventional assets before and during the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach This paper uses a smooth transition regression (STR) to jointly test the hedging properties of Bitcoin in normal conditions and Bitcoin's safe-haven properties in extreme stock market conditions. Findings Highlighting the results, the authors show that Bitcoin is able to provide safe-haven feature during the COVID-19 pandemic period while Bitcoin serves as a hedge tool in the pre-COVID-19 pandemic period. The findings also show that the prowess of the safe-haven/hedge nature is sensitive to the type of the asset market and the time horizon when switching from daily to weekly frequency data. Originality/value This is one of the first studies that conduct a combined analysis of the safe-haven and hedging capabilities of Bitcoin against several asset classes using an STR method. This study uses the longest sample period to yet, allowing researchers to examine Bitcoin's safe-haven and hedging features both before and after the COVID-19 pandemic.
We applied the SVAR-LiNGAM to illustrate the causal relationships between the spot exchange rate, and three crypto-asset exchange rates, Bitcoin, Ethereum, and Ripple. It was notable that the causal order, the EUR_USD spot rate->Bitcoin->Ethereum->Ripple, was obtained by this approach. All the instantaneous effects were strongly positive. Moreover, it was notable that Bitcoin can influence the EUR_USD spot rate positively with a one-day time lag.
This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.
Miriam Sosa, Edgar Ortiz, Alejandra Cabello Rosales
Purpose The purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility. Design/methodology/approach The empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t -distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021). Findings Findings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC. Originality/value Findings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.