Now-a-days Crypto currencies are digital currency that can be used as long-term investments or for all kinds of transactions. Investor interest in it is high because of its fundamental values of security, transparency, and decentralization. Developing successful investing strategies requires an accurate price prediction of the crypto currency due to its unique qualities and price volatility. The price of Bitcoin, the most popular crypto currency, is predicted using this model. A multitude of factors will be considered in order to forecast the accurate prices. The change point detection technique is used to provide robust prediction performance in the unseen price range. Furthermore, unique records that exist in crypto currencies and are posted on the block chain, or “on-chain data,” are gathered and used as input variables to forecast values. Experiments with real world Bit coin price data and various methods have proven the proposed system’s effectiveness in Bit coin price prediction. Crypto currency price trading is a huge demand of exchange right now. It is a great advantage for investors and day-to-day traders. In this work they will use latest Machine Learning algorithms for finding out the Price Prediction of Crypto currency.These algorithms assist in identifying price prediction. These algorithms are all contrasted with one another to provide precise results.
Cet article explore les facteurs influençant les fluctuations du prix du Bitcoin et identifie les indicateurs clés pour prédire les mouvements du marché. Les tendances positives des prix sont associées à une activité accrue du réseau, à des blocs de plus grande taille et à des retours sur investissement plus élevés, offrant des opportunités potentielles aux investisseurs. À l’inverse, l’augmentation des valeurs du nombre d’actions d’adresse, de la valeur du marché des capitaux, de l’émission de Coinbase et du retour sur investissement, suggère un risque plus élevé de chocs de prix négatifs et de tendances potentielles à la baisse du marché. L’étude met également en évidence des facteurs atténuants tels que la capitalisation du Bitcoin, l’offre actuelle et la vitesse actuelle, offrant des informations précieuses pour améliorer la stabilité du marché. En outre, les résultats suggèrent qu’à mesure que l’ampleur absolue des chocs de prix augmente, la fréquence des chocs de prix négatifs dépasse celle des chocs positifs. Simultanément, on observe une diminution significative de la prévisibilité des chocs négatifs par rapport à la prévisibilité des chocs de prix positifs.
Cryptocurrency markets have emerged as a dynamic and intriguing domain, with Bitcoin at the forefront, captivating the attention of investors, researchers, and enthusiasts alike. The volatile nature of Bitcoin prices presents both opportunities and challenges for market participants seeking to understand and anticipate its movements. In this study, we delve into the realm of time series analysis to explore the feasibility of predicting The research journey begins with meticulous data preprocessing steps to ensure the quality and integrity of the input data. Leveraging Python libraries such as pandas and NumPy, we cleanse and format the historical Bitcoin price data, laying the foundation for subsequent analysis. Key preprocessing tasks include handling missing values, normalization, and addressing any anomalies or outliers that may distort the underlying patterns. With the data prepared, our attention turns to assessing the stationarity of the Bitcoin price time series—a fundamental prerequisite for applying classical time series models. Through visual inspection and statistical tests such as the Augmented Dickey-Fuller (ADF) test, we ascertain the presence of trends or seasonality that could influence the modelling process. To mitigate such effects, we employ techniques such as differencing and transformations, including the Box-Cox transformation, to stabilize the variance of the data. Armed with a stationary time series, we embark on the core of our analysis: modelling Bitcoin prices using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. These models, renowned for their versatility and effectiveness in capturing temporal dependencies, offer a sophisticated framework for forecasting time series data. Guided by the principles of parsimony and model selection criteria such as the Akaike Information Criterion (AIC), we systematically explore the parameter space to identify the optimal specifications for our models. The efficacy of the chosen models is rigorously evaluated through diagnostic checks, encompassing residual analysis, model fit statistics, and out-of-sample validation. Insights gleaned from these assessments inform our confidence in the models' predictive capabilities and guide our interpretation of the forecasted outcomes. Finally, armed with a validated model, we turn our gaze to the future, employing it to generate forecasts of Bitcoin prices for forthcoming periods. Visualizations juxtaposing predicted prices against observed values provide a compelling narrative of the model's performance and offer stakeholders valuable insights into potential market trends and dynamics. In summary, this research contributes to the burgeoning field of cryptocurrency analytics by showcasing the application of time-tested statistical methodologies to forecast Bitcoin prices. By leveraging the power of ARIMA and SARIMAX models, we illuminate the intricate patterns underlying Bitcoin's price dynamics, empowering market participants with actionable intelligence for informed decision-making in an ever-evolving landscape. Keywords Cryptocurrency Markets, Bitcoin Price Prediction, Time Series Analysis, Python Programming, Data Preprocessing, Pandas, NumPy, Stationarity Testing, Augmented Dickey-Fuller (ADF) Test, Box-Cox Transformation, ARIMA Model, SARIMAX Model, Model Selection, Akaike Information Criterion (AIC), Diagnostic Checks, Residual Analysis, Out-of-Sample Validation, Forecasting, Visualization, Market Trends, Decision-Making, Cryptocurrency Analytics
• This systematic mapping examines the current state of cryptocurrency trading research. • This study observes a recent increase in high-quality research and international collaboration in cryptocurrency trading. • This study notes a shift towards practical applications in cryptocurrency trading research, particularly in AI-driven prediction and automated trading. • This study highlights the diverse data types and inputs employed in cryptocurrency trading systems, with emphasis on the prevalent use of neural networks and deep learning algorithms. Cryptocurrency's unique features – decentralisation, anonymity, and diversification – have propelled it into the spotlight, attracting both investors and researchers despite its relative youth compared to traditional markets. This study utilizes a systematic mapping approach to examine the current state of cryptocurrency trading research. We are particularly interested in influential variables and technologies involved in cryptocurrency trading systems. By summarizing key findings on data, technology compatibility, and future research directions, this study serves as a starting point for new research activities in the field of cryptocurrency trading.
Abstract This study examined the structural breakdowns and co-movements of Bitcoin (BTC) and Ethereum (ETH) cryptocurrencies from the onset of the COVID-19 pandemic. The Bai-Perron test was used to determine the change in the mean and variance of the two principal actors regarding market capitalization in the cryptocurrency market. Wavelet coherence analysis was also used to detect the co-movements between BTC and ETH. As a result of the study, several similar breaks were seen in each BTC and ETH series. Only one break could be directly associated with the pandemic process. This means that the pandemic is internalized and normalized in the process. The wavelet coherence results indicate a strong positive dependency (dark warm colours) between BTC and ETH and in phase (in the same direction) in the short and long bandgaps.
Modern finans teorisinin köşe taşlarından biri olan Etkin Piyasa Hipotezi, piyasada mevcut olan tüm bilginin kullanılması suretiyle piyasanın üzerinde getiri elde edilemeyeceğini öne sürmektedir. Bununla birlikte finansal piyasalarda yapılan çalışmaların birçoğu, yatırımcıların bazı dönemlerde normalin üzerinde getiri elde ettiğini gösteren bulgular ortaya koymaktadır. Etkin Piyasa Hipotezi ile çelişen ve bazı dönemlerde elde edilen getirilerin ve katlanılan riskin diğer dönemlere göre farklılaştığını ifade eden etkiler takvim anomalileri olarak tanımlanmaktadır. Takvim anomalileri içerisinde genellikle günlere, aylara ve yıllara göre farklılaşan etkiler incelenmektedir. Bu çalışmada Bitcoin ve Ethereum kripto para piyasasında takvim anomalilerinin incelenmesi amaçlanmıştır. Bu kapsamda haftanın günü, yılın ayı ve yıl dönümü anomalileri kukla değişken ile temsil edilerek Bitcoin ve Ethereum için belirlenen TGARCH(1,1) ve EGARCH(2,2) modeline ilave edilmiş ve Bitcoin için 18.07.2010 – 17.05.2023 dönemini, Ethereum için 10.03.2016 – 17.05.2023 dönemini kapsayan günlük veriler üzerinden analiz yapılmıştır. Çalışma sonucunda elde edilen bulgular, Bitcoin ve Ethereum piyasasında haftanın günü ve yılın ayı anomalilerinin bulunduğunu göstermektedir.
The emergence of decentralized finance has transformed asset trading on the blockchain, making traditional financial instruments more accessible while also introducing a series of exploitative economic practices known as Maximal Extractable Value (MEV). Concurrently, decentralized finance has embraced rollup-based Layer-2 solutions to facilitate asset trading at reduced transaction costs compared to Layer-1 solutions such as Ethereum. However, rollups lack a public mempool like Ethereum, making the extraction of MEV more challenging. In this paper, we investigate the prevalence and impact of MEV on Ethereum and prominent rollups such as Arbitrum, Optimism, and zkSync over a nearly three-year period. Our analysis encompasses various metrics including volume, profits, costs, competition, and response time to MEV opportunities. We discover that MEV is widespread on rollups, with trading volume comparable to Ethereum. We also find that, although MEV costs are lower on rollups, profits are also significantly lower compared to Ethereum. Additionally, we examine the prevalence of sandwich attacks on rollups. While our findings did not detect any sandwiching activity on popular rollups, we did identify the potential for cross-layer sandwich attacks facilitated by transactions that are sent across rollups and Ethereum. Consequently, we propose and evaluate the feasibility of three novel attacks that exploit cross-layer transactions, revealing that attackers could have already earned approximately 2 million USD through cross-layer sandwich attacks.
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the Fama–MacBeth regression method and portfolio regressions to assess the predictive capabilities of market, size, value, and momentum factors, adjusted for the unique characteristics of the cryptocurrency market. These characteristics include high volatility and continuous trading, which differ markedly from those of traditional financial markets. To address the challenges posed by the perpetual operation of cryptocurrency trading, this study introduces an innovative rebalancing strategy that involves weekly adjustments to accommodate the market’s constant fluctuations. Additionally, to mitigate issues like autocorrelation and heteroskedasticity in financial time series data, this research applies the Newey–West standard error approach, enhancing the robustness of regression analyses. The empirical results highlight the significant predictive power of momentum and value factors in forecasting cryptocurrency returns, underscoring the importance of tailoring conventional investment frameworks to the cryptocurrency context. This study not only investigates the applicability of factor investing in the rapidly evolving cryptocurrency market, but also enriches the financial literature by demonstrating the effectiveness of combining Fama–MacBeth cross-sectional analysis with portfolio regressions, supported by Newey–West standard errors, in mastering the complexities of digital asset investments.
Bitcoin, the first cryptocurrency, has attracted much attention in the digital currency market, and its price volatility is affected by a variety of factors, posing a challenge to investors. For the sake of analytical feasibility, many studies have adopted simplified modeling assumptions. This may overlook certain key nonlinear features of the market, such as the complexity of investor behavior and the volatility of market sentiment. This study explores the application of Long Short-Term Memory (LSTM) networks and their derivative models in predicting Bitcoin prices. Recognizing the complex nature of Bitcoin’s market dynamics, the research delves into the effectiveness of LSTM in capturing the nonlinear patterns of cryptocurrency prices. Furthermore, it extends the analysis to derivative models like MSM-LSTM and Empirical Mode Decomposition (EMD) LSTM, evaluating their ability to enhance prediction accuracy by addressing the limitations of the standard LSTM. This study provides an in-depth analysis of the effectiveness of LSTM and its derived models in the practical application of Bitcoin price prediction, with a special focus on their ability to capture non-linear patterns in the market. In Bitcoin price prediction, LSTM models have limitations but also great potential in capturing nonlinear patterns in the market. Extended models based on their own can perform well in Bitcoin price prediction. This study contributes to the field by suggesting strategies to improve the accuracy of the model and by providing ideas for developing trading strategies based on the results of the analysis.
Juraj Fabuš, Iveta Kremeňová, Natália Stalmašeková, Terézia Kvasnicová-Galovičová
This article explores the significance of Bitcoin halving events within the cryptocurrency ecosystem and their impact on market dynamics. While the existing literature addresses the periods before and after Bitcoin halving, as well as financial bubbles, there is an absence of forecasting regarding Bitcoin price in the time after halving. To address this gap and provide predictions of Bitcoin price development, we conducted a rigorous analysis of past halving events in 2012, 2016, and 2020, focusing on Bitcoin price behaviour before and after each occurrence. What interests us is not only the change in the price level of Bitcoins (top and bottom), but also when this turn occurs. Through synthesizing data and trends from previous events, this article aims to uncover patterns and insights that illuminate the impact of Bitcoin halving on market dynamics and sustainability, movement of the price level, the peaks reached, and price troughs. Our approach involved employing methods such as RSI, MACD, and regression analysis. We looked for the relationship between the price of Bitcoin (top and bottom) and the number of days after the halving. We have uncovered a mathematical model, according to which the next peak will be reached 19 months (in November 2025) and the trough 31 months after Bitcoin halving 2024 (in November 2026). Looking towards the future, this study estimates predictions and expectations for the upcoming Bitcoin halving. These discoveries significantly enhance our understanding of Bitcoin’s trajectory and its implications for the finance cryptocurrency market. By offering novel insights into cryptocurrency market dynamics, this study contributes to advancing knowledge in the field and provides valuable information for cryptocurrency markets, investors, and stakeholders.
Total value locked (TVL) is widely used to measure the size and popularity of decentralized finance (DeFi). However, TVL can be easily manipulated and inflated through "double counting" activities such as wrapping and leveraging. As existing methodologies addressing double counting are inconsistent and flawed, we propose a new framework, termed "total value redeemable (TVR)", to assess the true underlying value of DeFi. Our formal analysis reveals how DeFi's complex network spreads financial contagion via derivative tokens, increasing TVL's sensitivity to external shocks. To quantify double counting, we construct the DeFi multiplier, which mirrors the money multiplier in traditional finance (TradFi). This measurement reveals substantial double counting in DeFi, finding that the gap between TVL and TVR reached \$139.87 billion during the peak of DeFi activity on December 2, 2021, with a TVL-to-TVR ratio of approximately 2. We conduct sensitivity tests to evaluate the stability of TVL compared to TVR, demonstrating the former's significantly higher level of instability than the latter, especially during market downturns: A 25% decline in the price of Ether (ETH) leads to a \$1 billion greater non-linear decrease in TVL compared to TVR via the liquidations triggered by derivative tokens. We also document that the DeFi money multiplier is positively correlated with crypto market indicators and negatively correlated with macroeconomic indicators. Overall, our findings suggest that TVR is more reliable and stable than TVL.
Rui Dias, Rosa Galvão, Mohammad Iran, Paulo Alexandre · 5 authors
Background: Islamic cryptocurrencies are different from conventional ones in that they are backed by physical assets and are based on religious principles. After the COVID-19 pandemic, cryptocurrencies showed different behavior. However, there are not many studies on the efficiency, in its weak form, of these three typical families of cryptocurrencies (Islamic, green, and traditional). Purpose: This study compares the efficiency levels of Islamic cryptocurrencies (HelloGold), green cryptocurrencies (Cardano, NANO, Stellar, IOTA), and traditional cryptocurrencies (BTC and ETH) in the preceding period and during the geopolitical conflict between Russia and Ukraine in 2022. Methods: This research will use Lo and Mackinlay's (1988) variance ratio methodology, and the Detrended Fluctuation Analysis (DFA) model will be used. Results: The results indicate that the Islamic currency HGT and the green currency XNO display significant information asymmetries, rejecting the random walk hypothesis for various time intervals. Similarly, other green currencies such as XLM, ADA, and MIOTA, as well as ETH and BTC, reject the hypothesis to varying degrees and time intervals. Furthermore, the Islamic cryptocurrency (HelloGold) was anti-persistent before and during the conflict. The digital currencies ADA and BTC are persistent in both periods. ETH is in equilibrium in the pre-conflict period and becomes persistent during the conflict (0.50 - 0.56), while MIOTA and XLM are persistent during the pre-conflict period and shift to equilibrium during the Russian invasion of Ukraine in 2022. Finally, the XNO eco-currency shows the same anti-persistence characteristics during the two sub-periods. Conclusion: These results highlight the complexity and dynamics of cryptocurrency markets, indicating that different digital currencies can exhibit different temporal behaviors regarding information efficiency and persistence or anti-persistence patterns.
A novel approach of gravity strength centrality (GSC) model is proposed to identify the influential risk spreaders in cryptocurrency networks. We also validate the performance of GSC model in terms of discrimination and accuracy using methods such as individuation rate, imprecision function, and Kendall coefficient. Our findings show that (i) the novel GSC model can serve as an excellent indicator for measuring risk spreaders in the cryptocurrency market, (ii) Ethereum has a stronger influence compared to Bitcoin,(iii) the role of cryptocurrency EOS is increased after the COVID-19 pandemic.
Laeeq Razzak Janjua, Iza Gigauri, Agnieszka Wójcik-Czerniawska, Elżbieta Pohulak-Żołędowska
This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January 2011 to November 2023 reveals that volatile US economic policy indicators, such as high economic policy uncertainty, volatile inflation, and rising interest rates, have recently exerted a negative impact on Bitcoin returns. This study shows that these results are true not only for traditional money but also for cryptocurrencies such as Bitcoin, despite their cardinal features. Its decentralized nature, indicating that it has no physical representation, is not tied to any authority or national economy and relies on a complex algorithm to track transactions. Further, it yields volatile returns that depend on macroeconomic indicators.
Abstract The stated aim of cryptocurrencies is to free the monetary system from the need to trust financial intermediaries, by relying on incentive design and technology. Many descriptive studies, however, have questioned cryptocurrencies’ delivery on the promise of trustlessness. This paper promotes a normative analysis of trust in cryptocurrencies by discussing (i) whether trust is in principle eliminable, and (ii) whether trustlessness is in itself a desirable goal. These issues are closely related, we argue, to the further issue of what kind of institutions cryptocurrencies represent. We discuss the cognitive functions played by cryptocurrencies through the lens of the “extended mind” hypothesis in the philosophy of mind and hence conceive of cryptocurrencies as mind-extending institutions. As the models of institutional mind extension differ in the fiduciary bond they assume exists between individuals and institutional resources, we compare the reliance-based model of “scaffolding institutions” with the trust-based model of “cognitive institutions,” showing that the ineliminability and desirability of trust lead to seeing cryptocurrencies as instances of the latter. In the end, our discussion suggests that trust is a necessary component of cryptocurrencies’ cognitive functions and its promotion helps to perform such cognitive functions more effectively and sustainably
Abstract This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies. Using the high-frequency trading data of exchanges, the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains. We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges. Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets. Finally, we identify the determinants of total connectedness in two types of volatility spillover, which can explain cryptocurrency or exchange interlinkage.
Turan Kocabıyık, Meltem KARAATLI, Mehmet Özsoy, Muhammet Fatih Özer
The aim of this study is to identify crypto assets with similar characteristics and to explore the similar responses of these assets to market-priced events. This process is carried out in two stages. Cluster analysis and association analysis were applied in the research. First of all, cluster analysis was performed using the variables; the total number of active unique addresses, USD value of the current supply, fixed closing price of the asset, return on investment of the asset, total of the current supply, number of transactions, USD value of the sum of native units and 30 days volatility criteria. HK-Means algorithm and R Program were used for clustering. Then, the co-movement of crypto assets was analyzed using the FP-Growth algorithm and the WEKA program. 71 crypto assets with the highest market capitalization and meeting the research criteria were included in the research. The data used in the research covers the period of May 2021-May 2022. According to the main findings obtained from the research; within the framework of the criteria used in the research, 4 clusters were formed. Most important association rules found to be between; btc (bitcoin) & aave (nominex), eth (ethereum) & aave (nominex), dot (polkadot) & aave (nominex), neo & aave (nominex), uni (uniswap) & aave (nominex) , btg (bitcoin gold) & etc (ethereum classic), xrp (riple) & algo (algorand) & doge (dogecoin), xrp (riple) & doge (dogecoin), cro (cronos) & xrp (riple) & algo ( algorand) & trx (tron) & doge (dogecoin).
Financial markets, particularly cryptocurrency markets, are characterized by high volatility and sudden price jumps, making it essential to develop models that can capture these dynamics effectively. In this paper, our focus lies on the Merton’s jump diffusion model, employing jump processes characterized by the compound Poisson process. Our primary objective is to forecast the drift and volatility of the model using a variety of methodologies. We adopt an approach that involves implementing different drift, volatility, and jump terms within the model through various machine learning techniques, traditional methods, and statistical methods on price-volume data. Additionally, we introduce a path-dependent Monte Carlo simulation to model cryptocurrency prices, taking into account the volatility and unexpected jumps in prices. The results indicate that incorporating jump processes significantly improves forecasting accuracy, especially in volatile markets. Our findings highlight the effectiveness of combining machine learning and traditional methods for more robust predictions.
Yiming Zhao, Sultan Salem, Areej M. AL-Zaydi, Jin-Taek Seong · 6 authors
Among the different financial sectors, the modeling and forecasting of log-returns of cryptocurrency have received considerable attention. Numerous statistical models have been put forward to analyze the log returns of the cryptocurrency. However, as per our knowingness and immense literature search, we did not find published shreds of evidence about modeling cryptocurrency's log-returns while manipulating trigonometric-based statistical models. This paper provides a worthwhile endeavor to fill out this amusing research gap by manipulating a new trigonometric-based statistical methodology called the generalized sine-G family. Utilizing the generalized sine-G, a statistical model called the generalized sine-Logistic distribution is introduced. The generalized sine-Logistic distribution is applied for modeling the log-returns of two cryptocurrencies. Using certain decisive tools, it is observed that the generalized sine-Logistic is the best-suited distribution for modeling the given log-returns data sets. Additionally, this study uses various sophisticated and robust econometric techniques, such as the Least Absolute Shrinkage and Subset Selection, Markov Switching Generalized Autoregressive Conditional Heteroscedasticity (MSGARCH), and Step Indicator Saturation (SIS) model with different distributions, to predict (in-sample) the log-returns data sets. The effectiveness of each method is assessed through a popular loss function known as the root-mean-square error (RMSE).
Rohit Sharma, Sahiba Khan, Ranjit Singh, Abhijeet Birari
Cryptocurrency markets are inclined towards speculative usage due to the inherent high risk of financial loss and the potential for substantial gains during transaction completion. In response to this phenomenon, this study represents the inaugural effort to explore the influence of variables such as subjective norms, domain knowledge, impulsive investment tendencies, and self-control on decisions related to speculative investments. Utilising structural equation modelling with a dataset of 367 responses in India, the study is the first of its kind. The research reveals that subjective norms and domain knowledge play a significant role in influencing impulsive investment and self-control. Additionally, impulsive investment exhibits significant associations with decisions involving speculative investments. This insight underscores the complexity wherein individuals, despite exercising self-control, may still engage in speculative decisions that lead to adverse consequences. The findings have practical implications for investors and regulators, offering valuable insights into investment behaviours within the cryptocurrency realm.
Abstract The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.