Cheap energy, absence of regulations on mining, low taxes, free industrial zones made Georgia an attractive place for Bitcoin mining and home to such big companies as Bitfury and Binance. This paper asks how and why Georgia become a crypto mining hub and examines crypto mining in relation to the neoliberal state and its economic development mode. This study frames crypto currency mining as a state facilitated development project, which is embedded in Washington Consensus (WC) liberalization and deregulation policies and is enabled by Wall Street Consensus (WSC) derisking policies. The paper argues that crypto currencies - once emerged on allegedly nonpolitical economic grounds to challenge the state and existing financial order - need the state and its sovereign space. The study also unfolds continuities between WC and WSC and demonstrates the destructive character of crypto mining. The paper thus challenges the claims of the crypto industry of being against the state and traditional financial system, provides insights into the political economy of Bitcoin from a peripheral country perspective and enriches ongoing debates on neoliberal derisking states.
Since the introduction of Bitcoin in 2008, the blockchain technology as its underlying architecture, has attracted attention from various sides due to its decentralized and distributed computing characteristics. AS the core advantage of blockchain technology, the consensus mechanism determines various characteristics of blockchain, such as security, scalability, and decentralization. Currently, there are many consensus mechanisms suitable for different scenarios. This paper studies the existing consensus mechanisms from the perspectives of algorithm principles, performance, etc. Firstly, this article divides the existing consensus mechanisms into Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT). Secondly, for each type of consensus mechanisms, the study analyzes their algorithmic principles, understands typical solutions and latest ones, clarifies the advantages, disadvantages, and the possible attack methods of various consensus mechanisms. Finally, the paper defines the basic requirements for new consensus mechanisms. It aims to help break through the application bottlenecks of blockchain technology and promote the development of blockchain technology in various scenarios.
Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial fraud detection, graph Transformer models such as Graph-BERT are gaining prominence due to their Transformer-based architecture, which mitigates issues such as over-smoothing. Graph-BERT is designed for static graphs and primarily evaluated on citation networks with undirected edges. However, financial transaction networks are inherently dynamic, with evolving structures and directed edges representing the flow of money. To address these challenges, we introduce DynBERG, a novel architecture that integrates Graph-BERT with a Gated Recurrent Unit (GRU) layer to capture temporal evolution over multiple time steps. Additionally, we modify the underlying algorithm to support directed edges, making DynBERG well-suited for dynamic financial transaction analysis. We evaluate our model on the Elliptic dataset, which includes Bitcoin transactions, including all transactions during a major cryptocurrency market event, the Dark Market Shutdown. By assessing DynBERG's resilience before and after this event, we analyse its ability to adapt to significant market shifts that impact transaction behaviours. Our model is benchmarked against state-of-the-art dynamic graph classification approaches, such as EvolveGCN and GCN, demonstrating superior performance, outperforming EvolveGCN before the market shutdown and surpassing GCN after the event. Additionally, an ablation study highlights the critical role of incorporating a time-series deep learning component, showcasing the effectiveness of GRU in modelling the temporal dynamics of financial transactions.
ABSTRACT Research Question/Issue Blockchain technology promises to revolutionize governance through strong commitments, trustlessness, and transparency. This paper examines how these promises have failed to materialize in practice. Research Findings/Insights Drawing on case evidence from major blockchains, including Bitcoin and Ethereum, I argue that blockchains have evolved into technocracies where developers, foundations, and companies exercise disproportionate control. Rather than being exceptional, blockchain governance suffers from the same coordination problems, collective action failures, and centralization tendencies that plague traditional governance systems. Theoretical/Academic Implications The paper concludes that while blockchains offer valuable experiments in governance design, their alleged advantages over traditional institutions remain largely mythical. Practitioner/Policy Implications Blockchain organizations should acknowledge their reliance on off‐chain coordination and informal authority. Investors must understand that blockchain governance depends on trusting technical elites, while regulators should recognize that decentralization claims often mask concentrated power structures requiring traditional oversight.
Quantum computers have the potential to break classical cryptographic systems by efficiently solving problems such as the elliptic curve discrete logarithm problem using Shor's algorithm. While resource estimates for factoring-based cryptanalysis are well established, comparable evaluations for Shor's elliptic curve algorithm under realistic architectural constraints remain limited. In this work, we propose a carry-lookahead quantum adder that achieves Toffoli depth $\log n + \log\log n + O(1)$ with only $O(n)$ ancillas, matching state-of-the-art performance in depth while avoiding the prohibitive $O(n\log n)$ space overhead of existing approaches. Importantly, our design is naturally compatible with the two-dimensional nearest-neighbor architectures and introduce only a constant-factor overhead. Further, we perform a comprehensive resource analysis of Shor's elliptic curve algorithm on two-dimensional lattices using the improved adder. By leveraging dynamic circuit techniques with mid-circuit measurements and classically controlled operations, our construction incorporates the windowed method, Montgomery representation, and quantum tables, and substantially reduces the overhead of long-range gates. For cryptographically relevant parameters, we provide precise resource estimates. In particular, breaking the NIST P-256 curve, which underlies most modern public-key infrastructures and the security of Bitcoin, requires about $4300$ logical qubits and logical Toffoli fidelity about $10^{-9}$. These results establish new benchmarks for efficient quantum arithmetic and provide concrete guidance toward the experimental realization of Shor's elliptic curve algorithm.
Blockchain is a distributed ledger technology that provides pseudo-anonymity among participants to maintain privacy. However, malicious actors utilise this property to hide their illegal rewards received through cyber attacks, dark market trades, money laundering and Ponzi schemes. The recent confiscation by the FBI of more than $4 million USD worth of bitcoin from the ‘Silk Road’ dark marketplace indicates the scale of the problem faced by financial regulators and law enforcement authorities. Analysing and identifying harmful actors is, therefore, necessary to regulate the transactions of digital assets. Machine learning models can assist in detecting patterns and correlations between the actors in blockchain networks that may not be apparent through traditional methods. In blockchain networks, the number of actors linked to illegal activities is significantly smaller than that of regular activities. Also, only very limited labelled transaction data is available about these malicious actors. These limitations make it harder to train supervised learning models to provide real-time proactive responses. This article represents a pioneering effort in thoroughly examining the different unsupervised learning methods for clustering suspicious behaviour of actors within blockchain networks. The proposed unsupervised learning-based analysis considers metadata and interconnectivity information of blockchain transactions. The metadata contains time-based and amount-based information. Interconnectivity data represents centrality measures and embedding vectors of the blockchain network. The quality of the identified clusters is validated using internal and external cluster validation measures. The validation results were used to identify influential features using the eXplainable AI technique Shapley (ShAP) values. The results reveal that the features related to the spending and receiving transactions strongly influenced cluster identification. Overall, the centroid-based and connectivity-based approaches identified well-separated clusters for metadata and centrality-based features of blockchain transactions.
I Made Ardita, Ni Made Suci, Fridayana Yudiaatmaja
Penelitian ini dilatarbelakangi oleh dinamika harga Bitcoin yang dipengaruhi oleh tiga peristiwa utama, yaitu halving, persetujuan Exchange-Traded Fund (ETF) Bitcoin, dan adopsi institusional. Ketiga faktor tersebut dianggap membentuk mekanisme penawaran permintaan serta memengaruhi stabilitas pasar aset digital. Penelitian ini bertujuan menganalisis pengaruh halving, ETF Bitcoin, dan adopsi institusional terhadap tren harga Bitcoin. Jenis penelitian yang digunakan adalah deskriptif kuantitatif, dengan subjek berupa data historis harga Bitcoin, termasuk periode halving, peristiwa ETF, dan momen adopsi institusional. Data dikumpulkan melalui dokumentasi, studi pustaka, dan pemanfaatan data sekunder dari platform keuangan dan laporan institusi. Instrumen penelitian berupa data log return harian yang kemudian diuji melalui serangkaian uji asumsi klasik (normalitas, homoskedastisitas, heteroskedastisitas). Analisis data menggunakan Analysis of Variance (ANOVA) dan uji pengaruh parsial simultan. Hasil penelitian menunjukkan bahwa halving berpengaruh signifikan terhadap perubahan harga Bitcoin, mencerminkan efek kelangkaan pasokan. Sebaliknya, adopsi institusional dan ETF Bitcoin tidak menunjukkan pengaruh signifikan secara statistik, namun tetap memberikan kontribusi struktural dalam meningkatkan stabilitas dan legitimasi pasar. Secara simultan, ketiga faktor tersebut membentuk pola yang saling melengkapi dalam memengaruhi dinamika harga Bitcoin. Penelitian ini menyimpulkan bahwa analisis siklus halving, tren adopsi institusional, dan perkembangan ETF penting digunakan sebagai dasar pengambilan keputusan investasi serta penyusunan kebijakan yang mendukung ekosistem aset digital yang berkelanjutan.
The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.
This paper presents an option pricing model that incorporates clustered jumps using a bivariate Hawkes process. The process captures both self- and cross-excitation of positive and negative jumps, enabling the model to generate return dynamics with asymmetric, time-varying skewness and to produce positive or negative implied volatility skews. This feature is especially relevant for assets such as cryptocurrencies, so-called ``meme'' stocks, G-7 currencies, and certain commodities, where implied volatility skews may change sign depending on prevailing sentiment. We introduce two additional parameters, namely the positive and negative jump premia, to model the market risk preferences for positive and negative jumps, inferred from options data. This enables the model to flexibly match observed skew dynamics. Using Bitcoin (BTC) options, we empirically demonstrate how inferred jump risk premia exhibit predictive power for both the cost of carry in BTC futures and the performance of delta-hedged option strategies.
Despite the popularity of Hashed Time-Locked Contracts (HTLCs) because of their use in wide areas of applications such as payment channels, atomic swaps, etc, their use in exchange is still questionable. This is because of its incentive incompatibility and susceptibility to bribery attacks. State-of-the-art solutions such as MAD-HTLC (Oakland'21) and He-HTLC (NDSS'23) address this by leveraging miners' profit-driven behaviour to mitigate such attacks. The former is the mitigation against passive miners; however, the latter works against both active and passive miners. However, they consider only two bribing scenarios where either of the parties involved in the transfer collude with the miner. In this paper, we expose vulnerabilities in state-of-the-art solutions by presenting a miner-collusion bribery attack with implementation and game-theoretic analysis. Additionally, we propose a stronger attack on MAD-HTLC than He-HTLC, allowing the attacker to earn profits equivalent to attacking naive HTLC. Leveraging our insights, we propose \prot, a game-theoretically secure HTLC protocol resistant to all bribery scenarios. \prot\ employs a two-phase approach, preventing unauthorized token confiscation by third parties, such as miners. In Phase 1, parties commit to the transfer; in Phase 2, the transfer is executed without manipulation. We demonstrate \prot's efficiency in transaction cost and latency via implementations on Bitcoin and Ethereum.
Berikut ringkasan akademik dari tulisan “Bitcoin dalam Ekonomi Syariah: Tinjauan di Pasar Muslim”: Artikel ini mengkaji keamanan dan kepatuhan Bitcoin terhadap prinsip ekonomi syariah dalam konteks pasar Muslim, ditengah tren global kripto yang berkembang pesat. Kajian berangkat dari kebutuhan akan penilaian mendalam terkait kesesuaian Bitcoin dengan nilai maqasid al-shariah, khususnya keadilan, transparansi, dan kemaslahatan. Tujuan utama penelitian adalah mengevaluasi apakah Bitcoin dapat diadopsi dalam sistem keuangan Islam, dengan menyoroti aspek keamanan transaksi dan kepatuhan terhadap larangan riba, gharar, serta maysir. Penelitian menggunakan pendekatan mixed methods, menggabungkan survei kuantitatif dari pengguna Bitcoin di pasar Muslim serta kajian kualitatif atas literatur, fatwa, dan pendapat ulama. Hasil survei menunjukkan bahwa sebagian besar responden mengakui keunggulan teknologi blockchain dalam aspek keamanan dan transparansi, namun mengkhawatirkan volatilitas harga dan potensi spekulasi yang belum sesuai prinsip syariah. Analisis empiris dan wawancara ahli menemukan bahwa penerimaan Bitcoin secara syariah masih tergantung pada penguatan regulasi, pengawasan lembaga keuangan Islam, dan inovasi digital yang dapat mengeliminasi unsur spekulatif. Secara teoretis, penelitian berkontribusi dengan integrasi antara perspektif maqasid al-shariah dan analisis keamanan digital—memperluas pemahaman tentang potensi dan tantangan kripto dalam ekonomi Islam modern. Rekomendasi diberikan kepada regulator dan pelaku industri untuk mengembangkan instrumen kripto halal melalui smart contract, audit syariah, serta peningkatan literasi digital di kalangan masyarakat Muslim. Dengan landasan evidence-based dan pendekatan interdisipliner, artikel ini memperkuat wacana integrasi teknologi blockchain ke dalam prinsip keuangan syariah sebagai strategi inklusi dan inovasi di pasar global Muslim.
Distributed peer-to-peer (P2P) networking delivers the new blocks and transactions and is critical for the cryptocurrency blockchain system operations. Having poor P2P connectivity reduces the financial rewards from the mining consensus protocol. Previous research defines beneficalness of each Bitcoin peer connection and estimates the beneficialness based on the observations of the blocks and transactions delivery, which are after they are delivered. However, due to the infrequent block arrivals and the sporadic and unstable peer connections, the peers do not stay connected long enough to have the beneficialness score to converge to its expected beneficialness. We design and build Dynamic Peer Beneficialness Prediction (DyPBP) which predicts a peer's beneficialness by using networking behavior observations beyond just the block and transaction arrivals. DyPBP advances the previous research by estimating the beneficialness of a peer connection before it delivers new blocks and transactions. To achieve such goal, DyPBP introduces a new feature for remembrance to address the dynamic connectivity issue, as Bitcoin's peers using distributed networking often disconnect and re-connect. We implement DyPBP on an active Bitcoin node connected to the Mainnet and use machine learning for the beneficialness prediction. Our experimental results validate and evaluate the effectiveness of DyPBP; for example, the error performance improves by 2 to 13 orders of magnitude depending on the machine-learning model selection. DyPBP's use of the remembrance feature also informs our model selection. DyPBP enables the P2P connection's beneficialness estimation from the connection start before a new block arrives.
Covariance matrices estimated from short, noisy, and non-Gaussian financial time series are notoriously unstable. Empirical evidence suggests that such covariance structures often exhibit power-law scaling, reflecting complex, hierarchical interactions among assets. Motivated by this observation, we introduce a power-law covariance model to characterize collective market dynamics and propose a hybrid estimator that integrates Random Matrix Theory (RMT) with deep Residual Neural Networks (ResNets). The RMT component regularizes the eigenvalue spectrum in high-dimensional noisy settings, while the ResNet learns data-driven corrections that recover latent structural dependencies encoded in the eigenvectors. Monte Carlo simulations show that the proposed ResNet-based estimators consistently minimize both Frobenius and minimum-variance losses across a range of population covariance models. Empirical experiments on 89 cryptocurrencies over the period 2020-2025, using a training window ending at the local Bitcoin peak in November 2021 and testing through the subsequent bear market, demonstrate that a two-step estimator combining hierarchical filtering with ResNet corrections produces the most profitable and well-balanced portfolios, remaining robust across market regime shifts. Beyond finance, the proposed hybrid framework applies broadly to high-dimensional systems described by low-rank deformations of Wishart ensembles, where incorporating eigenvector information enables the detection of multiscale and hierarchical structure that is inaccessible to purely eigenvalue-based methods.
Jiri Gavenda, Petr Svenda, Stanislav Bobon, Vladimir Sedlacek
A coinjoin protocol aims to increase transactional privacy for Bitcoin and Bitcoin-like blockchains via collaborative transactions, by violating assumptions behind common analysis heuristics. Estimating the resulting privacy gain is a crucial yet unsolved problem due to a range of influencing factors and large computational complexity. We adapt the BlockSci on-chain analysis software to coinjoin transactions, demonstrating a significant (10-50%) average post-mix anonymity set size decrease for all three major designs with a central coordinator: Whirlpool, Wasabi 1.x, and Wasabi 2.x. The decrease is highest during the first day and negligible after one year from a coinjoin creation. Moreover, we design a precise, parallelizable privacy estimation method, which takes into account coinjoin fees, implementation-specific limitations and users' post-mix behavior. We evaluate our method in detail on a set of emulated and real-world Wasabi 2.x coinjoins and extrapolate to its largest real-world coinjoins with hundreds of inputs and outputs. We conclude that despite the users' undesirable post-mix behavior, correctly attributing the coins to their owners is still very difficult, even with our improved analysis algorithm.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
• Wavelet coherence reveals Bitcoin’s persistent link with climate policy uncertainty • Green cryptos show context-dependent coherence with climate policy uncertainty • Partial decoupling of green cryptos from Bitcoin emerges at medium-term scales • Twofold framework uncovers time-scale responses of crypto to policy uncertainty • Emphasizes need for stable climate regulations to curb crypto market volatility As global climate policy uncertainty (CPU) intensifies, understanding its intersection with emerging financial technologies becomes increasingly urgent. This study, therefore, investigates the dynamic relationship between CPU and the cryptocurrency market, focusing on Bitcoin and eight leading green cryptocurrencies (Algorand, Cardano, EOS, Hedera, IOTA, Nano, Stellar, and Tezos) using a wavelet coherence analysis. Specifically, the study employs a twofold framework: first, assessing the responsiveness of Bitcoin and green cryptocurrencies to climate policy uncertainty across time scales; second, examining Bitcoin's interaction with green cryptocurrencies to determine their potential stabilizing or decoupling effects amid regulatory uncertainty. The analysis spans from November 2017 to March 2025, capturing multiple phases of regulatory evolution and market transformation. The findings reveal that Bitcoin exhibits a structurally embedded and persistent coherence with CPU, especially over longer investment horizons. This persistent linkage highlights Bitcoin’s role in exacerbating regulatory volatility due to its significant environmental footprint. Conversely, green cryptocurrencies demonstrate more sporadic and context-dependent coherence, often aligning with major climate policy announcements or periods of regulatory scrutiny. While positioned as sustainable alternatives, these assets remain influenced by Bitcoin’s dominance and broader market sentiment, particularly at medium-term investment scales. The partial synchronization observed across key periods suggests an incomplete decoupling from both CPU and Bitcoin. These results highlight the importance of clear and stable climate regulations to reduce market uncertainty and support innovation in sustainable blockchain technologies.
Background The medical device sector, valued at $569 billion, faces persistent financing challenges. Around 78% of startups fail because of capital shortages, not due to lacking technical quality. Blockchain-based tokenization emerges as a way to broaden access, yet success relies on economic factors of platforms and clear regulations. Methods Transaction cost data from Bitcoin, Ethereum, and XRP Ledger covered 540 days from January 2024 to June 2025, providing 3,240 observations per network. Experts, numbering 12, participated in a modified Delphi method to form a framework tailored to healthcare. Project outcomes came from Monte Carlo simulations running 10,000 iterations, checked by a triple control-loop system, and compared against two real-world examples. Volumes of transactions drew from stochastic models involving monthly, quarterly, and annual elements, mixing fixed regulatory needs with variable market influences. Results Layer-1 (L1) fees differ by orders of magnitude; representative 2025 snapshots show BTC and ETH L1 far above XRPL and major ETH L2s. XRPL fees are typically a tiny fraction of a cent; the base cost is 10 drops (0.00001 XRP) and is dynamically adjusted by network load. Probabilities of success varied from 10.1% to 12.3% on Bitcoin, 31.4%–48.3% on Ethereum based on Layer-2 adoption, and 71.6%–73.2% on XRP Ledger. Investor involvement correlated negatively with logarithms of costs, showing Spearman <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow></mml:math> of −0.91. Differences in success exceeded 60 percentage points across platforms. Examples illustrated how elevated expenses reduce engagement in VitaDAO on Ethereum, whereas low-cost systems like XRP Healthcare support ongoing involvement. Conclusion Choosing a blockchain platform critically influences viability in tokenizing medical devices. Layer-2 options reduce cost gaps but add complexities in bridging and use. Platforms offering stability, minimal fees, and regulatory alignment promote wider inclusion and reliable funding. Technical features, steady costs, and readiness for compliance together shape whether tokenization boosts innovation in healthcare or maintains barriers.
Open access
Quality and Safety in Healthcare
Neuroethics, Human Enhancement, Biomedical Innovations
Betas from spot regressions are central to asset pricing and risk management, as measures of systematic risk. This paper develops a new estimation and inference framework for spot regressions by leveraging high-frequency candlesticks, extending conventional (open-to-close) returns with intra-period high/low prices. Specifically, I construct candlestick-based estimators of regression parameters, including spot beta, by minimizing a quadratic risk under a fixed-k asymptotic framework. I then develop a feasible hypothesis testing procedure for spot betas with correct asymptotic size. Simulation results show that the proposed estimator reduces estimation risk relative to return-based estimators, especially in small samples, and the test achieves notably higher power. I apply the framework to assess the market neutrality of Bitcoin using 1-minute data on IBIT and SPY, finding deviations from neutrality, particularly in high-volatility periods.
Cryptocurrency investment is a rapidly growing financial sector, marked by high volatility, decentralized technologies, and significant profit potential. Investors use strategies like long-term holding (“HODLing”), portfolio diversification, and short-term trading. “HODLing” relies on long-term value appreciation but requires resilience to price fluctuations. Diversifying with assets like Bitcoin and Ethereum reduces risk due to their low correlation with traditional investments. The crypto market is highly sensitive to geopolitical, economic, and technological factors, attracting investors during economic instability. Advanced models like LASSO and AutoEncoder aid in price prediction and strategy optimization. Despite high return potential, careful risk management is essential due to volatility and regulatory uncertainty. This study experimentally applies identical cryptocurrency portfolios to different investment strategies, identifying the most profitable approach.
Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies, may improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, in the present contribution a recently proposed method of disentangling sources of multifractality in time series was applied to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality are temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series.
El estudio analizó el comportamiento de las criptomonedas Bitcoin y Ethereum durante el año 2024 mediante la construcción de un modelo estadístico ARIMA (AutoRegressive Integrated Moving Average). La investigación utilizó un enfoque cuantitativo que se dividió en cuatro etapas: recopilación y limpieza de datos históricos, verificación de la estabilidad de los datos, identificación y estimación de los mejores parámetros usando criterios de información, y validación mediante medidas de precisión y análisis de residuos. Los resultados demostraron que el modelo ARIMA fue útil en el pronóstico de valores en mercados estables, destacando su trayectoria en el análisis de datos financieros. Además, los valores bajos de RMSE y MAPE validaron que el modelo tiene la capacidad de realizar pronósticos precisos en escenarios con alta frecuencia. En particular, el MAPE de Bitcoin fue 2,25 % y el de Ethereum 2,85 % durante la etapa de prueba, demostrando que los valores pronosticados tuvieron una ligera desviación con respecto a los reales. No obstante, el modelo puede verse afectado en periodos de alta volatilidad, como en las burbujas especulativas o en los desplomes bursátiles, ya que no tiene la capacidad de adaptarse dinámicamente a cambios súbitos en los parámetros; sin embargo, su utilidad puede mejorar al combinar modelos híbridos con ARIMA.
Abstrak - Tingginya volatilitas Bitcoin mendorong kebutuhan model prediktif presisi untuk landasan keputusan investasi optimal. Studi ini mengimplementasikan algoritma Random Forest guna memprediksi pergerakan harga berdasarkan 1.766 data historis harian (Januari 2020-Oktober 2024). Pra-pemodelan diawali analisis korelasi Pearson dengan ambang batas 0.5, yang menyeleksi fitur High (0,999), Open (0,998), dan Low (0,997) sebagai prediktor akibat asosiasi kuat, sementara Volume dan Change% dieliminasi karena kontribusi minimal. Pengujian membandingkan dua strategi pembagian data: partisi acak dan tidak acak (rasio 80:20), menggunakan metrik Mean Absolute Percentage Error (MAPE) dan Akurasi. Hasil empiris menunjukkan partisi acak unggul (MAPE 1,34%; Akurasi 98,66%) dibanding partisi tidak acak (MAPE 1,7%; Akurasi 98,3%). Konklusi menegaskan efektivitas signifikan algoritma Random Forest, dengan keberhasilan bergantung pada ketepatan seleksi fitur dan adaptasi strategi pembagian data terhadap karakteristik dataset.Kata kunci: Bitcoin; Random Forest; Prediksi Harga; Korelasi Pearson; MAPE; Abstract - The high volatility of Bitcoin necessitates precise predictive models for optimal investment decision-making. This study implements the Random Forest algorithm to predict price movements based on 1,766 daily historical data points (January 2020 - October 2024). Pre-modeling began with Pearson correlation analysis with a threshold 0.5, which selected the High (0.999), Open (0.998), and Low (0.997) features as predictors due to their strong association, while Volume and Change% were eliminated due to their minimal contribution. The testing compared two data splitting strategies: random and non-random (80:20 ratio), using the Mean Absolute Percentage Error (MAPE) and Accuracy metrics. Empirical results showed that random partitioning outperformed non-random partitioning (MAPE 1.34%; Accuracy 98.66% compared to MAPE 1.7%; Accuracy 98.3%). The conclusion confirms the significant effectiveness of the Random Forest algorithm, with success depending on the accuracy of feature selection and adapting the data splitting strategy to the characteristics of the dataset. Keywords: Bitcoin; Random Forest; Price Prediction; Pearson Correlation; MAPE;
Lingling Xia, Tao Zhu, Zhengjun Jing, Qun Wang · 7 authors
Digital currencies, led by Bitcoin and USDT, are characterized by decentralization and anonymity, which obscure the identities of traders and create a conducive environment for illicit activities such as drug trafficking, money laundering, cyber fraud, and terrorism financing. Focusing on the USDT-TRC20 token on the Tron blockchain, we propose a two-layer transaction network-based approach for virtual currency address identity recognition for digging out hidden relationships and encrypted assets. Specifically, a two-layer transaction network is constructed: Layer A describes the flow of USDT-TRC20 between on-chain addresses over time, while Layer B represents the flow of TRX between on-chain addresses over time. Subsequently, an identity metric is proposed to determine whether a pair of addresses belongs to the same user or group. Furthermore, transaction records are systematically acquired through blockchain explorers, and the efficacy of the proposed recognition method is empirically validated using dataset from the Key Laboratory of Digital Forensics. Finally, the transaction topology is visualized using Neo4j, providing a comprehensive and intuitive representation of the traced transaction pathways.
This study investigates the dynamic relationship between order flow toxicity, measured by the volume-synchronized probability of informed trading (VPIN), and price jumps in the Bitcoin market using high-frequency data and vector autoregressive model (VAR) modelling. By integrating behavioral finance theory to market microstructure framework, we explore how informed trading activity influences jumps in price, and how traders respond to such volatility. Our findings reveal that VPIN significantly predicts future price jumps, with positive serial correlation observed in both VPIN and jump size, suggesting persistent asymmetric information and momentum effects. On the contrary, price jumps occasionally affect VPIN. This study also identifies time-zone and day-of-the-week effects in VPIN, highlighting the role of global trading patterns. The results are robust among the choices of jump tests including Jiang and Oomen (2008) test which is empirically robust against market microstructure noise. These results contribute to a deeper understanding of intraday volatility in cryptocurrency markets and offer practical implications for risk management, trading strategy design, and regulatory oversight.