This paper introduces Bitcoin-IPC, a protocol that scales Bitcoin through a network of permissionless, interconnected, programmable Proof-of-Stake (PoS) Layer-2 chains, called subnets, whose stake is denominated in L1 BTC. These subnets rely on Bitcoin L1 for the communication of critical information, settlement, and security. Subnets can communicate with each other and with Bitcoin: users deposit BTC from Bitcoin to a subnet and withdraw it back, and transfer wBTC directly between subnets. We provide formal definitions of these bridge protocols, incorporating a firewall property that limits the impact of malicious subnets on the security of the broader network. Our design, inspired by SWIFT messaging and embedded within Bitcoin's SegWit mechanism, enables seamless value transfer across L2 subnets. Uniquely, this mechanism reduces the virtual-byte cost per transaction (vB/tx) by up to 23x, compared to transacting natively on Bitcoin L1, effectively increasing monetary-transaction throughput from 7 tps to over 160 tps, without requiring any modifications to Bitcoin L1.
This research investigates the predictive power of news sentiment from Google News on Bitcoin price movements, leveraging a five-year dataset of news headlines (2019 to 2024). By correlating sentiment scores with historical Bitcoin prices, the study employs various machine learning algorithms to forecast price trends. The results indicate that while Decision Tree and Random Forest models offer balanced predictions, Logistic Regression and Support Vector Machines achieve high AUC scores but suffer from class imbalance. In contrast, Naïve Bayes and KNN models prove less effective. The findings suggest that sentiment analysis of news headlines can provide moderate short-term predictions for Bitcoin price fluctuations. This study introduces an innovative tool for investors and market analysts, offering insights into the influence of news sentiment on cryptocurrency prices.
Blockchain, originally devised for Bitcoin, has evolved beyond cryptocurrencies to become a transformative technology in banking and finance. Its decentralized, secure, and transparent characteristics promise improved efficiency, reduced fraud, and cost savings. However, challenges such as scalability, regulatory uncertainty, and cybersecurity risks persist. This paper explores the benefits, risks, and future prospects of blockchain adoption in the financial sector. The study includes a review of existing literature, real-world applications, and an analysis of ongoing challenges and potential future developments.
According to the advent of cryptocurrencies and Bitcoin, many investments and businesses are now conducted online through cryptocurrencies. Among them, Bitcoin uses blockchain technology to make transactions secure, transparent, traceable, and immutable. It also exhibits significant price fluctuations and performance, which has attracted substantial attention, especially in financial sectors. Consequently, a wide range of investors and individuals have turned to investing in the cryptocurrency market. One of the most important challenges in economics is price forecasting for future trades. Cryptocurrencies are no exception, and investors are looking for methods to predict prices; various theories and methods have been proposed in this field. This paper presents a new deep model, called \emph{Parallel Gated Recurrent Units} (PGRU), for cryptocurrency price prediction. In this model, recurrent neural networks forecast prices in a parallel and independent way. The parallel networks utilize different inputs, each representing distinct price-related features. Finally, the outputs of the parallel networks are combined by a neural network to forecast the future price of cryptocurrencies. The experimental results indicate that the proposed model achieves mean absolute percentage errors (MAPE) of 3.243% and 2.641% for window lengths 20 and 15, respectively. Our method therefore attains higher accuracy and efficiency with fewer input data and lower computational cost compared to existing methods.
ABSTRACT This study investigates the impact of environmental attention on cryptocurrency market volatility by introducing the Crypto Environmental Attention Index (CEAI), a new metric inspired by Wang et al. (2022) and constructed using daily web search data. Environmental concerns can significantly impact the popularity and volatility of cryptocurrencies, influencing risk perceptions, and shaping market dynamics. Using vector autoregression (VAR), vector error correction models (VECM), and Granger causality tests on data from 2014 to 2022, the study finds that Ethereum's volatility is strongly influenced by the CEAI in both the short and long‐term, whereas Bitcoin volatility has a short‐term unidirectional effect on environmental attention and a bidirectional relationship in the long term. This study is situated within a broader economic framework of sustainable finance, the transition to greener blockchain technologies, and regulatory responses to environmental issues. It offers actionable insights for risk management, policy formulation, and cryptocurrency valuation using environmental, social, and governance (ESG) criteria.
Bitcoin price forecasting is characterized by extreme volatility and non-stationarity, often defying traditional univariate time-series models over long horizons. This paper addresses a critical gap by integrating Global M2 Liquidity, aggregated from 18 major economies, as a leading exogenous variable with a 12-week lag structure. Using the TimeXer architecture, we compare a liquidity-conditioned forecasting model (TimeXer-Exog) against state-of-the-art benchmarks including LSTM, N-BEATS, PatchTST, and a standard univariate TimeXer. Experiments conducted on daily Bitcoin price data from January 2020 to August 2025 demonstrate that explicit macroeconomic conditioning significantly stabilizes long-horizon forecasts. At a 70-day forecast horizon, the proposed TimeXer-Exog model achieves a mean squared error (MSE) 1.08e8, outperforming the univariate TimeXer baseline by over 89 percent. These results highlight that conditioning deep learning models on global liquidity provides substantial improvements in long-horizon Bitcoin price forecasting.
Transaction fee plays an important role in determining the priority of transaction processing in public blockchain systems. Owing to the observability of unconfirmed transactions, a strategic user can postpone his transaction broadcasting time and set a fee as low as possible by prying into his mempool that stores them. However, the stochastic mining interval may cause the delayed transaction to miss the next valid block. Meanwhile, a new feature (i.e. fee bumping) emerges that allows each user to increase his transaction fee before confirmation, making the fee setting more challenging. In this paper, we investigate a novel transaction policy from the perspective of a single strategic user that determines the broadcasting time and the transaction fee simultaneously. Two representative scenarios are considered, in which a number of coexisting ordinary users are mempool-oblivious that set their fees according to certain distribution, and are semi-strategic that check their mempools at a Poisson rate and update their fees. In the former, we compute the optimal broadcasting time and transaction fee that adapts to the arbitrary distribution of mining interval. When the block interval is exponentially distributed in Bitcoin-like PoW systems, the strategic user needs to broadcast his transaction immediately after its creation. And when the block interval is fixed in Ethereum-like PoS systems, he finds it profitable to wait until the last moment before block generation. In the latter, we formulate a continuous-time Markov chain to characterize the dynamics of mempool states, and derive the optimal fee adjusting frequency of the strategic user when the block interval is exponentially distributed. In both theory and simulations, we show that this strategic user should immediately increase his fee whenever it falls behind the minimum fee of being included.
ABSTRACT This paper examines the impact of regulatory controls on Bitcoin's excess returns and volatility. The paper innovates by proxying changes in the regulatory environment using global Google search volume intensity data. The generated regulatory indices accurately identify episodes of regulatory tightening within cryptocurrency markets. A three‐factor model—incorporating market, momentum, and size factors—is employed to evaluate the effects of regulation on Bitcoin returns. The study also assesses the influence of changes in the regulatory environment on volatility using additional controls. Findings reveal that increased regulation significantly reduces monthly Bitcoin returns and increases return volatility. These effects are both statistically and economically significant, robust across multiple proxies for regulatory activity, and persist even when accounting for the effects of the COVID‐19 pandemic. The results highlight the real regulatory risks associated with Bitcoin investments, particularly for risk‐averse investors, and underscore the importance of policy developments in shaping cryptocurrency market dynamics.
Este boletim quinzenal gratuito visa analisar o comportamento do Bitcoin, um ativo financeiro digital, oferecendo notícias, análises gráficas e informações sobre as mais recentes novidades, softwares e aplicativos relacionados a essa criptomoeda. Nosso objetivo é enriquecer as discussões em torno da cultura do Bitcoin, colaborando com a Amauta, uma instituição de economia criativa que busca disseminar conhecimento sobre inovação, educação e finanças na comunidade acadêmica e empresarial. Esperamos que este trabalho represente uma contribuição valiosa para o debate. Reconhecemos a importância do Bitcoin e seu impacto na economia global, motivo pelo qual nos dedicamos a fornecer informações atualizadas aos nossos leitores. Acreditamos que ao promover discussões e compreensão sobre o Bitcoin, podemos incentivar a adoção e o uso responsável dessa tecnologia disruptiva. Para além das análises e informações sobre o Bitcoin, incentivamos ativamente nossos leitores a se educarem sobre finanças pessoais e investimentos. Acreditamos que, munidos do conhecimento adequado, todos podem tomar decisões financeiras inteligentes e bem informadas. Comprometemo-nos a fornecer informações de alta qualidade e precisas, esforçando-nos para manter nossos leitores atualizados sobre as últimas tendências e desenvolvimentos no mundo do Bitcoin. Esperamos que este relatório seja do seu agrado e contribua para uma compreensão mais aprofundada do Bitcoin e das finanças pessoais em geral.
Deep learning has emerged as a widely applied approach across various fields, with finance and forecasting being among its most prominent areas of use. Within this domain, different deep learning architectures have been developed to address specific prediction problems. This study compares the performance of ARIMAX and several deep learning models—including LSTM, BILSTM, CNN-LSTM, GRU, and TFT—in forecasting Bitcoin prices. The dataset consists of daily values from January 2014 to January 2025. The dependent variable is the daily Bitcoin closing price ($), while the independent variables include oil price (USD/barrel), gold price (USD/ounce), platinum price ($/XPT), and the USD/TRY exchange rate. All analyses were conducted in Python using Google Colab, with the Keras library employed for model implementation. Root Mean Square Error (RMSE) was selected as the evaluation metric for predictive accuracy. The results indicate that the TFT model achieved the highest predictive performance, followed closely by the GRU model. LSTM, BILSTM, and ARIMAX models showed similar yet weaker performance, while the CNN-LSTM model produced the least accurate forecasts, with significantly higher RMSE values compared to the other models.
Virtual currency has become one of the most sought-after alternative assets in the past decade with bitcoin being a leading example. value leapt from its starting price of $0.0025 to increase by more than 40 million times that amount, creating one of the greatest rises in value in the entire history of finance. In the past few years, many academic studies show that even though Bitcoin runs independently from traditional finance, but still there is a high correlation between Bitcoin and stock market. In particular, following the introduction of Bitcoin options back in 2017, Bitcoin now appears more predictive of stock return movements than before. Research by Afees A. Salisu and his coworkers display that a solitary Bitcoin price prediction model using an optimized predictive regression framework notably surpasses older ones. but don’t say how long this goes on Therefore this research will go to try and determine the time frame when Bitcoin is better at predicting the future of the stock market as opposed to stock options. Also, we’ll use machine learning techniques to train machine learning models to predict the movements of the stock market and see if they work.
This paper investigates the Granger causality relationship in Bitcoin mining from environmental, sustainable, and miner’s financial perspectives for the period of February 2017 to January 2025. Using a time-varying Granger causality approach of Shi et al. (2018,2020), we explore how the hashrate, a measure of computational power in the Bitcoin mining process, affects energy consumption, electronic waste, and miners’ revenues. Our findings reveal that an increase in hashrate leads to a significant rise in energy use and e-waste and affects miners’ revenues. In addition, we show that mining revenue Granger causes the hashrate, suggesting economic incentives drive the network security through the hashrate. These results offer new insights for investors, policymakers, and environmental economists. • A time-varying Granger causality approach is adopted. • Higher computational power directly increases electricity demand and electronic waste. • The intensity of competition, as measured by hashrate, has a significant impact on mining profitability. • Higher mining revenues incentivise the use of greater hash power.
Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior.
ABSTRACT Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018–January 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVID‐19 pandemic and Russia–Ukraine conflict, and Bitcoin and Ethereum emerge as net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant risk–return relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the return–volatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management.
Niccolò Scatena, Pericle Perazzo, Giovanni Nardini
This paper proposes iblock, a comprehensive C++ library for Bitcoin simulation, designed for OMNeT++. iblock offers superior efficiency and scalability with respect to state-of-the-art simulators, which are typically written in high-level languages. Moreover, the possible integration with other OMNeT++ libraries allows highly detailed simulations. We measure iblock's performance against a state-of-the-art blockchain simulator, proving that it is more efficient at the same level of simulation detail. We also validate iblock by using it to simulate different scenarios such as the normal Bitcoin operation and the selfish mine attack, showing that simulation results are coherent with theoretical expectations.
This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.
M. S. F. Nasrifa, R. P. D. M. Amarasinghe, W. M. P. K. Weerasinghe
The purpose of this research is to explore how investor attention, measured by GSVI, influences cryptocurrency market behavior under varying conditions. For this the study examines the impact of Google Search Volume Index (GSVI) on cryptocurrency returns, considering market uncertainty, news sentiment, and the COVID-19 pandemic. A regression analysis was conducted using datasets covering BNB, Bitcoin, Dogecoin, Solana, and Tether from 2015 to 2022. Stata was used to estimate the relationships between cryptocurrency returns and key variables, ensuring accurate and reliable results to quantify the relationships. Our findings indicate that abnormal increases in GSVI positively affect cryptocurrency returns, particularly during high uncertainty periods and when news sentiment is favorable. Moreover, the effect of investor attention on returns was significantly amplified during the COVID-19 pandemic, suggesting that global crises has heightened the role of behavioral factors in cryptocurrency markets. This research contributes to the literature by integrating investor attention with uncertainty and sentiment measures, offering a comprehensive view of cryptocurrency price dynamics. Unlike previous studies that examine these factors in isolation, our study highlights their combined effect, providing valuable insights for investors, policymakers, and analysts in understanding market trends and decision-making strategies.
The article is devoted to virtual assets (cryptocurrency) as a subject of a criminal offense from the point of view of its definition and proof. It is noted that cryptocurrency is characterized by a high level of anonymity, since the personal data of the owners of electronic wallets (in particular, the user’s name or address) are not disclosed, and identification is carried out only using a unique set of characters, which does not allow to identify the person. At the same time, information about transactions is open, and all operations with cryptocurrency are anonymous and irreversible. That is, in a broad sense, cryptocurrency is a digital intangible asset that functions as a settlement system with a dynamically changing value, has a predominantly anonymous nature and is not under the control of central banks. The most famous cryptocurrencies include Bitcoin, Ethereum, Dash, Ripple, Monero, Litecoin, Augur, MaidSafeCoin, etc. At the same time, the most famous and expensive of them is Bitcoin – the first decentralized digital currency, created in 2008, which is used to exchange for goods or services using blockchain technology. And the exchange of cryptocurrencies for real money is carried out through online platforms, electronic payment systems or specialized exchange services. It was established that there are no other norms in the current criminal legislation that would directly mention virtual assets or cryptocurrency. In practice, this leads to the fact that investigators and prosecutors in criminal proceedings related to cryptocurrencies act in fact at their own discretion, making procedural decisions, carrying out the seizure of digital assets and organizing their storage without a clearly defined legal mechanism. As a result, the issue of the further legal fate of the seized crypto-assets remains unregulated and is resolved situationally. It is concluded that virtual assets (cryptocurrency) can be the subject of many criminal offenses. For example, fraud (when deception is used when selling or exchanging it for real currency), theft (secret theft of property using a certain computer program), extortion or illicit enrichment (since cryptocurrency can be easily converted into fiat currency, which is quite difficult to track later), etc.
Open access
Legal, Health, Environmental and COVID-19 Challenges
The phenomenon of increasing public interest in investing in crypto assets, especially Bitcoin, has raised major questions about its feasibility and profitability compared to conventional investment instruments such as mutual funds. This study aims to compare the profitability and risk levels between Bitcoin as a cryptocurrency asset and mutual funds as traditional financial instruments. Although Bitcoin has gained increasing popularity as an alternative investment, there remains a lack of empirical research directly comparing its performance with mutual funds over an equivalent time horizon. Using a quantitative approach, this study analyzes historical monthly data from 2015 to 2024. Metrics such as cumulative return, average monthly return, CAGR, Sharpe Ratio, and maximum drawdown were employed to evaluate the performance of both instruments. Positioned within the existing literature on asset comparison, this study offers a novel empirical contribution by directly contrasting Bitcoin and mutual funds through risk-return analysis. The findings reveal that while Bitcoin offers significantly higher returns, it also carries much greater volatility and drawdown risk. These insights serve as a practical foundation for designing investment strategies aligned with different investor risk profiles. The research contributes to the body of knowledge in portfolio management and data-driven investment decision-making. Keywords: Bitcoin, mutual funds, risk-return, volatility, investment performance, portfolio management
Con el pasar de los años, los individuos han perdido la capacidad de ver la realidad por sí mismos. Se ha vuelto costumbre en la humanidad, necesitar de una institución, por perversa que sea, para que diga que es verdad o mentira, que existe y que no. La institución que más relevancia ha tomado para expresarse sobre lo anterior es el Estado. A través de él se ha negado la verdad por años. Ahora, el presente trabajo busca establecer si el Estado, a través del ordenamiento jurídico, intenta desconocer otra realidad. A lo largo de este texto, estudiaremos, en primer lugar, la cadena de bloques o Blockchain, aquel invento innovador y tecnológico que es utilizado para realizar las transacciones en criptomonedas toda vez que permite que su funcionamiento sea descentralizado, seguro y casi imposible de falsificar. En segundo lugar, estudiaremos el Bitcoin, un revolucionario desarrollo tecnológico que se convirtió en un medio de intercambio y que se constituye en una alternativa de libre elección a las monedas de curso legal. Estudiaremos su origen, qué es y cómo funciona. La problemática radica en que, a pesar de que esta criptomoneda se utiliza como dinero, no cuenta con el reconocimiento de la mayoría de los Estados, consecuencia de ello, no se le da tratamiento de dinero. En tercer lugar, exploraremos la teoría evolutiva del dinero. Esta teoría es de vital importancia, pues nos permite entender cómo determinado bien que tiene ciertas cualidades se convierte en un medio universal de intercambio y, con ello, en dinero. En este punto, también revisaremos las funciones del dinero que se derivan de su principal función: ser un medio de intercambio. Esto, con la intención de establecer si el Bitcoin, a la luz de la teoría evolutiva del dinero, puede o no ser considerado como tal así el Estado se niegue a reconocerlo.
The bitcoin market has exhibited highly volatile return movements, experiencing a sharp surge starting from in November 2022 to 2024. This significant fluctuation underscores the importance of analyzing the factors influencing bitcoin’s return dynamics. This study utilizes daily data with a final sample of 590 observations. All time-series variables must be stationary before being processed in the statistical model. The analysis was conducted using Stata 16 software. To ensure the absence of unit roots, the stationarity of the research variables was tested using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The findings indicate that market capitalization, gold, and litecoin have no significant impact on bitcoin returns. In contrast, miners’ revenue has a significant negative effect, while hashrate, mining difficulty, and the S&P 500 exhibit a significant positive influence on bitcoin returns. This study highlights bitcoin’s role as a store of value and investment asset, emphasizing the impact of hashrate and mining difficulty on its returns and integration into financial markets, particularly the S&P 500. The findings provide insights for investors on portfolio diversification and assets like a gold and equities. Additionally, the study underscores the importance of sustainable mining practices and regulatory policies to balance cryptocurrency’s economic potential with environmental sustainability. Keywords: Market capitalization; Mines’s Revenue; Hashrate; Mining difficulty; Commodity Asset, Cryptocurrency
The cryptocurrency market, characterized by high volatility, has evolved into a significant financial asset class, attracting both retail and institutional investors. Understanding its interconnectedness with macroeconomic factors is crucial for risk management and financial stability. This study empirically analyzes the dynamic relationships between two primary crypto assets, Bitcoin (BTC) and Ethereum (ETH), and the monetary policy shifts of the U.S. Federal Reserve (The Fed). Using a Vector Autoregression (VAR) model on daily time-series data from January 1, 2022, to June 16, 2025, this research investigates the short-term dynamics, Granger causality, and shock transmissions within this system. The findings reveal a significant one-way causal relationship from The Fed's interest rate changes to both Bitcoin and Ethereum returns, challenging the weak-form Efficient Market Hypothesis. Furthermore, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses provide robust evidence of Bitcoin's market leadership, with shocks in Bitcoin explaining nearly 70% of the variance in Ethereum's movements. These results highlight a clear hierarchical structure: The Fed influences broad market sentiment, while Bitcoin leads internal market dynamics, offering critical insights for investors and policymakers navigating the digital asset ecosystem.
Nyoman Sri Subawa, Caren Angellina Mimaki, I Made Oka Mahendra, Made Srinitha Millinia Utami
Bitcoin halving is a quadrennial event that halves mining rewards and is believed to influence cryptocurrency prices and cryptocurrency market dynamics. This study examines the effect of Bitcoin halving on Cryptocurrency Prices, with Government Regulations, Market Sentiment, and Cryptocurrency Performance as mediating variables. A quantitative research approach was employed, gathering original data via survey instruments from 294 participants within the cryptocurrency community in Bali, which were analyzed using PLS-SEM. The findings indicate that Bitcoin halving exerts a favorable and statistically meaningful influence on Government Regulations, Market Sentiment, Cryptocurrency Performance, and Cryptocurrency Prices. Market Sentiment fully mediates the influence of Government Regulations and Cryptocurrency Performance on Cryptocurrency Prices, while Government Regulations and Cryptocurrency Performance partially mediate the effect of Bitcoin halving. These findings highlight that Cryptocurrency Prices are shaped by the interplay of technical, policy, and psychological factors, with strategic implications for investors, regulators, and developers.