Abstract This research presents a regime-aware hybrid forecasting framework for the Bitcoin market’s nonlinear, nonstationary and regime-switching behavior. The architecture integrates econometric models, neural forecasting and meta-learning, unified under a regime-detection mechanism using probabilistic inference. Central to the approach is a Hidden Markov Model (HMM) trained on log returns, which infers latent market regimes, bull, bear and sideways, based on statistical characteristics rather than arbitrary thresholds. Each detected regime triggers a specialized forecasting model: ARIMAX for volatile bear markets, SARIMAX for cyclical sideways periods and NeuralProphet for nonlinear bullish dynamics. These models leverage historical returns (Jan. 2012-Jun. 2025) and external signals, including technical indicators (RSI, MACD, Bollinger bands) and volatility metrics. A meta-learning layer, implemented via XGBoost, dynamically selects the optimal model at each time step based on the regime. This enables real-time adaptation to evolving market conditions. Predictions are made on log returns and translated into price forecasts through exponentiation. The framework’s performance is evaluated using R 2 , Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The regime-aware model outperforms the no-regime model significantly across all metrics, especially in error reduction (MAE cut by ~ 56%) and higher explanatory power (R² increased from 0.82 to 0.91). Ablation results confirm the structural validity of the proposed framework, with the regime–model assignment (ARIMAX for bear, SARIMAX for sideways, NeuralProphet for bull) achieving the lowest forecasting error (MAE = 736, R 2 = 0.93) at the yearly level and outperforming alternative configurations. The inferred regimes exhibit economically meaningful persistence (average durations 14.8–22.4 days) and transition stability (diagonal probabilities 0.91–0.94). The meta-learning component shows coherent and interpretable behavior, with regime labels and recent model errors explaining nearly 70% of decision weight and regime-consistent model selection exceeding 80%. These forecasting gains translate into tangible economic benefits: in a six-month backtest, the proposed strategy delivers the highest return (19%), lowest drawdown (19%) and highest Sharpe ratio (1.01), outperforming all benchmarks.
Bu çalışma, Bitcoin fiyat tahmininde mevsimsel ARIMA (SARIMA) modelinin öngörü performansını, basit bir Naive kıyas modeliyle açık biçimde karşılaştırarak yeniden değerlendirmeyi amaçlamaktadır. Analiz, 12 Mart 2021 ile 12 Mart 2026 dönemini kapsayan günlük Bitcoin kapanış fiyatlarına dayanmaktadır. Seri logaritmik forma dönüştürülmüş ve durağanlık özellikleri fark alma işlemleriyle incelenmiştir. İlk aşamada mevsimsel olmayan ARIMA modelleri tahmin edilmiş, ardından mevsimsel dinamikleri içeren alternatif SARIMA modelleri değerlendirilmiştir. Model seçiminde parametre anlamlılığı ile Ljung-Box tanı istatistikleri dikkate alınmış ve mevsimsel hareketli ortalama bileşeninin kısmen anlamlı olduğu görülmüştür. Bu çerçevede SARIMA(0,1,1)(0,1,1)[30] nihai mevsimsel aday model olarak belirlenmiştir. Tahmin performansı değerlendirmesi, yalnızca model uyumuna değil, örneklem dışı tahmin doğruluğuna odaklanmaktadır. Bu amaçla SARIMA modelinin performansı, ortalama mutlak hata (MAE) ve hata kareler ortalamasının karekökü (RMSE) ölçütleri kullanılarak Naive model ile karşılaştırılmıştır. Bulgular, örneklem dışı dönemde Naive modelin SARIMA modeline göre belirgin biçimde daha düşük tahmin hataları ürettiğini göstermektedir. Naive model için MAE 0.016011 ve RMSE 0.023173 iken, SARIMA modeli için bu değerler sırasıyla 0.23172 ve 0.28931’dir. Sonuçlar, Bitcoin gibi yüksek oynaklığa sahip finansal zaman serilerinde daha karmaşık mevsimsel yapıların her zaman daha üstün tahmin performansı sağlamadığını göstermektedir.
In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM.In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one.The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.
Bitcoin already faces a quantum threat through Shor attacks on elliptic-curve signatures. This paper isolates the other component that public discussion often conflates with it: mining. Grover's algorithm halves the exponent of brute-force search, promising a quadratic edge to any quantum miner of Bitcoin. Exactly how large that edge grows depends on fault-tolerant hardware. No prior study has costed that hardware end to end. We build an open-source estimator that sweeps the full attack surface: reversible oracles for double-SHA-256 mining and RIPEMD-based address preimages, surface-code factory sizing, fleet logistics under Nakamoto-consensus timing, and Kardashev-scale energy accounting. A parametric sweep over difficulty bits b, runtime caps, and target success probabilities reveals a sharp transition. At the most favourable partial-preimage setting (b = 32, 2^224 marked states), a superconducting surface-code fleet still requires about 10^8 physical qubits and about 10^4 MW. That load is comparable to a large national grid. Tightening to Bitcoin's January 2025 mainnet difficulty (b about 79) explodes the bill to about 10^23 qubits and about 10^25 W, approaching the Kardashev Type II threshold. These numbers settle a narrower question than "Is Bitcoin quantum-secure?" Once Grover mining is lifted from asymptotic query counts to fault-tolerant physical cost, practical quantum mining collapses under oracle, distillation, and fleet overhead. To push mining into non-trivial consensus effects, one must invoke astronomical quantum fleets operating at energy scales that lie far above present-day civilization.
Version: v1.6.4 (June 2026) Major additions in this version: phased migration protocol with cryptographic quarantine (Section 6.4.4), sensitivity boundaries delineating the statistical decoupling threshold up to mu = 1.9% (Section 6.7), and integration of recent empirical MEV findings (Mancino & Rezzoli, 2025). Abstract Contemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem encompassing decentralization, security, scalability, and thermodynamic sustainability. Legacy Proof-of-Work networks confront diminishing security budgets due to the exhaustion of block subsidies, while Proof-of-Stake systems inherently risk oligarchic centralization. This paper establishes a Unified Monetary-Supply Framework that resolves these structural conflicts by synthesizing the deterministic Customized Halving schedule with the probabilistic regeneration logic of the Proof of Rinne (PoR). We demonstrate that by enforcing a "Thermodynamic Statute of Limitations" on dormant assets, the protocol functions as a Non-Equilibrium Thermodynamic Engine. This architecture transforms entropic asset attrition—traditionally viewed as systemic loss—into a regenerative security budget. The remainder of the abstract, covering the SDE and Fokker-Planck validation, the ZKP owner recovery model, and the resulting equilibrium, is in the manuscript. Data & Code AvailabilityThe mathematical models and high-precision stochastic simulations (e.g., Monte Carlo paths, SDE convergence, and Fokker-Planck distributions) presented in this manuscript are fully reproducible. The corresponding Python simulation suite and open-source models are made available at the author's GitHub repository (rincoin-regenerative-simulations) to ensure scientific transparency. Integrity & Provenance This document is anchored to the Bitcoin blockchain via OpenTimestamps. The proof file verification_data_v1.6.4.ots, included in the files below, covers the SHA-256 digest of Tokino_Rincoin_v1.6.4.pdf: 5269207ea7e363e8df312ed50c00afc119b43e6fa5d3c717e6a7d8fc9863147b The archived proof is in its as-submitted form: it commits the digest to the public OpenTimestamps calendars and does not itself embed the Bitcoin attestations. Completing it against those calendars — which both verification paths below do automatically — yields three Bitcoin attestations, the earliest in block 952366. An OpenTimestamps proof carries no wall-clock time of its own — any date reported for it is read from a Bitcoin block header. To verify, upload the PDF and the .ots file to opentimestamps.org, or with a Bitcoin node: ots verify -f Tokino_Rincoin_v1.6.4.pdf verification_data_v1.6.4.ots — the -f flag is required because the proof's filename differs from the document's. The provenance of this document is recorded in a separate signed artifact, the Rincoin Provenance Certificate (10.5281/zenodo.21415730), which binds this whitepaper to the digest above and is the reference for the full anchoring detail. That certificate carries its own OpenPGP signature, Bitcoin anchor, and PAdES signature; this whitepaper itself carries the OpenTimestamps proof only. Zenodo archival gives this record a persistent identifier and an independent retrieval path; it is not itself a cryptographic control. Validation_Scientific_Provenance_v1.6.4.pdf in the files below is an earlier certificate edition, retained as evidence. It is superseded by the record cited above. Correspondence & AffiliationPrimary Author: Tokino, Michiru (時乃 満)Affiliation: Rincoin Core Research Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram) Keywords: Rincoin, Proof of Rinne (PoR), regenerative crypto-economics, non-equilibrium thermodynamics, non-equilibrium steady state (NESS), stochastic differential equations (SDE), Fokker-Planck equation, recirculation incentive mechanism, macroeconomic homeostasis, Nash equilibrium, cryptographic vault, zero-knowledge proofs (ZKP), modular blockchain architecture, account abstraction, blockchain tetra-lemma, MEV mitigation, sandwich attack resistance, sensitivity analysis, statistical decoupling threshold, phased migration protocol
The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities.
Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulated in 1986. This paper maps the conceptual lineage from Gray's requestor/server model to modern blockchain architectures, showing how his emphasis on modularity, autonomy, data integrity, and standardized communication anticipated the design of systems like Bitcoin and Ethereum, and, more recently, the Web3 movement and Layer-2 scaling architectures. We examine consensus mechanisms, cryptographic foundations, rollup-based Layer-2 protocols, and cross-chain interoperability through this historical lens, identify persistent challenges in scalability and modularity, and outline future directions toward Web4: an intelligent, decentralized internet integrating blockchain, artificial intelligence, and the Internet of Things.
Multi-chain ecosystems suffer from fragmented identity, siloed liquidity, and bridge-dependent token transfers. We present n-VM, a Layer-1 architecture that hosts n heterogeneous virtual machines as co-equal execution environments over shared consensus and shared state. The design combines three components: a dispatcher that routes transactions by opcode prefix, a unified identity layer in which one 32-byte commitment anchors VM-specifific addresses, and a unified token ledger that exposes VM-native interfaces such as ERC-20 and SPL over a common balance store. We formalize routing, identity derivation, and token transfer semantics, and prove cross-VM transfer atomicity and identity isolation under standard cryptographic assumptions. We describe a concrete instantiation with five VMs: a native runtime, EVM, SVM, Bitcoin Script, and TVM. We also present context-based sharding and a write-set scheduler for parallel execution. Under an analytical throughput model, the architecture admits a projected range of about 16,000 to 66,000 transactions per second on commodity hardware.
Since World War II, the US dollar (USD) has substantially increased its prominence in international financial systems, culminating in its position as the predominant currency, facilitating approximately 90% of global foreign exchange transactions. The reliance of most nations on the USD for international trade - particularly for oil, commodities, and other goods - has cemented its critical role in global finance and geopolitics. Hence, the usage of the USD supported and forged an economic and geopolitical function for the emitting country, the United States of America. The geopolitical implications and risks related to the USD hegemonic power in trade and financial transactions have become increasingly more striking, especially in recent decades and years. The sanctions imposed on Venezuela, Iran and more recently on Russia via the US dollar-dominated SWIFT payment system highlighted the potential threat posed by the USD hegemonic power in the global monetary system. However, in the new millennium, alternative digital currencies have begun to exert influence and have implicitly and explicitly posed a threat to that hegemony. Bitcoin and other cryptocurrencies, for instance, have enabled international transactions without reliance on USD use. Additionally, the emergence of several multi-currency Central Bank Digital Currencies (CBDCs) would allow nations to conduct cross-border payments using various currencies without passing through the USD as an intermediary. Our paper explores the geopolitical implications of USD use on the international stage and examines the potential opportunities and threats posed by these new digital currencies for countries.
Distributed systems in which concurrent proposals are mutually exclusive face a fundamental stability constraint under network delay. In open systems where global state progression is event-driven rather than round-driven, propagation delay creates a conflict window within which overlapping proposals may generate competing branches. This paper derives a density-delay law for such exclusive state progression processes. Under independent proposal arrivals and bounded propagation delay, overlap is approximated by a Poisson model and fork depth is represented by a birth-death process. The analysis shows that maintaining bounded fork depth as the number of participants grows requires the density-delay product $λΔ$ to remain $O(1)$, implying that aggregate proposal intensity must stay bounded and yielding an inverse-scaling law $g(N)=O(1/N)$ at the unit level. Simulation experiments across varying network sizes and propagation delays align with a common density-delay curve, supporting the predicted scaling behavior. The result provides a compact law for stable event-driven state progression in open distributed systems and offers a scaling-based interpretation of Bitcoin-style difficulty adjustment as a decentralized way to regulate effective event density.
In this research, the energy consumption models of Bitcoin, Ethereum, and Dogecoin are analyzed using Explainable Artificial Intelligence (XAI) models aided by the three stages of analysis involving Digiconomist data from 2022 to 2025: (1) exploratory data analysis for the nature of energy consumption, (2) model identification of influential variables using Random Forest models enhanced with SHAP values, and (3) an LSTM transfer learning method for predicting the energy consumption of Ethereum and Dogecoin using a model developed with Bitcoin data. The initial results show that while both assets vary largely when it comes to their normal usage level, Ethereum sees a sharp drop after the changeover from Proof-of-Work to Proof-of-Stake as a mechanism. The XAI analysis indicates that energy use is largely a consequence of past use, seasonality, and annual patterns. In addition to this, the models show a high level of accuracy for Dogecoin (R²: 88.4%, MAPE: 13.45%) and Ethereum (R²: 86.2%, MAPE: 11.47%) when it comes to predicting energy usage using the concepts of transfer learning.
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.
Durante 15 años la misma pregunta ha dividido al mundo: ¿cuánto vale realmente un Bitcoin? Los creyentes responden con narrativas — escasez, adopción, reserva de valor. Los escépticos responden con escepticismo — aire, especulación, nada. Ninguno ha podido probarlo con ciencia. Hasta ahora. Cualquier sistema complejo que procesa energía e información obedece la termodinámica. Bitcoin consume energía física real para procesar información real. Por tanto tiene un estado de equilibrio termodinámico calculable. Ese estado tiene un nombre: MAXIMUSS. MAXIMUSS no se calcula mirando el historial de precios. No usa medias móviles ni indicadores técnicos. Se calcula desde el estado energético actual del sistema usando física pura derivada de los trabajos de Einstein, Landauer y Prigogine. El precio de mercado de Bitcoin es el que se desvía de MAXIMUSS — no al revés. Cuando los especuladores se retiran, BTC regresa a MAXIMUSS. Siempre. La evidencia empírica: 53,559 predicciones en BTC/USD durante enero–marzo 2026 con 91.6% de contención dentro de zonas calculadas desde física pura. Dirección macro correcta tres meses consecutivos. Señal de mínimo de febrero detectada antes de confirmación de precio. Indicadores técnicos utilizados: cero. Bitcoin siempre tuvo un precio físico real. Nadie sabía calcularlo. Este documento presenta el argumento y la evidencia de manera accesible para cualquier persona de la comunidad Bitcoin, independientemente de su formación científica.
This study examines short-term return forecasting for Bitcoin, Ethereum, and Litecoin over 2020–2024, comparing autoregressive benchmarks with Kitchen Sink and VARX-type models using point and density accuracy measures supported by Diebold–Mariano and Model Confidence Set inference. The results demonstrate that the AR(1) benchmark and parsimonious specifications incorporating cryptocurrency-specific variables consistently outperform the more elaborate linear frameworks considered, while the inclusion of macro-financial predictors offers limited benefits. Findings highlight the robustness of autoregressive dynamics for short-term cryptocurrency forecasting and underscore the importance of parsimony over model complexity. These results are consistent with a market environment characterised by high structural uncertainty, sentiment-driven trading and rapidly shifting regimes, in which additional macro-financial information contributes little to forecastability beyond short-run return momentum and crypto-specific volatility.
This paper develops a deep reinforcement learning (DRL) framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation (PPO) agent is trained on a reward function derived from non-equilibrium thermodynamics: the free-energy Bellman equation, in which (i) transaction costs are the geodesic slippage S∗ on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and (ii) regime-transition costs are the Wasserstein-2 distance Wt between the calm and turbulent return distributions. The agent is embedded in the WOW-E-W quadrilogy, a four-paper research programme that integrates statistical mechanics, fluid dynamics, Riemannian information geometry, and thermodynamic control into a unified cryptocurrency risk architecture. The PPO agent observes an 11-dimensional state vector ot that combines turbulent-regime probabilities \( \hat{\xi}_t(2) \) and parameter estimates \( \hat{\theta}_t \) from a maximum-entropy Markov-switching GARCH model, a viscosity-filtered velocity signal ht and gate states zt, rt from a GRU viscosity filter, and the Fisher curvature Gt, Ricci scalar κt, Betti numbers β0,t, β1,t,Wasserstein dissipation Wt, and topological alarm dI(t) from the Riemannian execution geometry layer. The framework establishes a thermodynamic Carnot bound on portfolio efficiency: η ≤ 1 − Hturb/Hcalm, where Hturb and Hcalm are the maximum-entropy values of the turbulent and calm regime distributions. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026: the geometric-cost PPO agent achieves higher Sharpe ratio than Buy-and-Hold, Greedy signal-following, and flat-fee PPO baselines (bootstrap p < 0.05 for four of five assets); portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ranges from 0.6 percent (Bitcoin) to 1.8 percent (Ethereum), ordered by turbulent half-life (Spearman ρ = 0.94, p = 0.017); a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of ot contributes a statistically significant performance gain (Diebold-Mariano p < 0.05 for at least four of five assets per component). The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration and is an explicitly bounded limitation.
Privacy protection mechanisms are a fundamental aspect of security in cryptocurrency systems, particularly in decentralized networks such as Bitcoin. Although Bitcoin addresses are not directly associated with real-world identities, this does not fully guarantee user privacy. Various deanonymization solutions have been proposed, with network layer deanonymization attacks being especially prominent. However, existing approaches often exhibit limitations such as low precision. In this paper, we propose \textit{NTSSL}, a novel and efficient transaction deanonymization method that integrates network traffic analysis with semi-supervised learning. We use unsupervised learning algorithms to generate pseudo-labels to achieve comparable performance with lower costs. Then, we introduce \textit{NTSSL+}, a cross-layer collaborative analysis integrating transaction clustering results to further improve accuracy. Experimental results demonstrate a substantial performance improvement, 1.6 times better than the existing approach using machining learning.
Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy, Maria‐Dolores Cano
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
This study aims to comparatively examine the relationships between Bitcoin and Ethereum's energy consumption and price dynamics. Using daily frequency data, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), ARDL cointegration tests, and Toda–Yamamoto causality analysis were applied to evaluate the effects of cryptocurrency markets on energy demand from both short-term and long-term perspectives. The analysis results indicate that there is a long-term cointegration relationship between energy consumption and prices for Bitcoin and a unidirectional causality from prices to energy consumption. In contrast, ARDL boundary test results for Ethereum revealed no long-term relationship, and causality analysis also failed to detect any directional causality between price and energy consumption. This indicates that with Ethereum's transition to a Proof-of-Stake mechanism, energy consumption has become independent of price movements. The findings reveal that the effects of cryptocurrency markets on the energy economy vary according to technology-specific structural characteristics.
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification. Large-scale datasets like Elliptic have allowed for a rise in the analysis of these systems and in tasks such as fraud detection. In these settings, the amount of transactional context available to each node is determined by the neighborhood aggregation and sampling strategies, yet the interaction between these receptive fields and embedding geometry has received limited attention. In this work, we conduct a controlled comparison of Euclidean and tangent-space hyperbolic GNNs for node classification on a large Bitcoin transaction graph. By explicitly varying the neighborhood while keeping the model architecture and dimensionality fixed, we analyze the differences in two embedding spaces. We further examine optimization behavior and observe that joint selection of learning rate and curvature plays a critical role in stabilizing high-dimensional hyperbolic embeddings. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of hyperbolic GNNs for computational social systems.
Ifran Khan, Huangbao Gui, BiJia Li, Chin Man Chui · 5 authors
The Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) are two complementary models used in this study to examine the transmission of volatility spillover among the five precious metals (gold, silver, platinum, palladium, and rhodium); the top five cryptocurrencies (bitcoin, ethereum, tether, ripple, and binance coin); two green equities (NASDAQ OMX green energy and S&P global clean energy indexes); and two physical and transition climate risk indexes (PRI and TRI). The analysis spans daily data from January 2018 to December 2023, covering multiple crises. One key contribution is offering new insights into asset interactions with transition and physical climate risks based on textual analysis established by Bua et al. (2024). We conclude that volatility spillovers explain 40.3% of market uncertainty. The largest transmitters include ethereum (72.17%), bitcoin (64.65%), silver (52.42%), and XRP (49.18%), while TRI and PRI also play considerable roles. Ethereum, bitcoin, silver, XRP, rhodium, and clean energy emerged as net transmitters, while palladium, TRI, PRI, USDT, gold, BNB, the green economy, and platinum act as net receivers. Short-term spillovers (39.15%) dominate medium-term (18.27%) and long-term (20.88%), implying that short-term shocks pose greater risks to investors. The climate-related risks demonstrate distinct transmission mechanisms, with transition risks (TRI) responding to broad market movements while physical risks (PRI) propagate through more specialized channels. Our study suggests that investors should closely monitor cryptocurrencies and green assets in the short term, approach gold and stablecoins with caution in the medium term, and consider long-term allocations to rhodium and clean energy assets.
Blockchain technology has evolved from its initial application in cryptocurrencies such as Bitcoin to a versatile decentralized infrastructure supporting decentralized finance (DeFi), digital identity systems, smart contracts, and Web3 ecosystems. Despite its transformative potential, the rapid expansion of blockchain platforms has significantly increased the security attack surface, exposing networks to threats such as double-spending, Sybil attacks, smart contract vulnerabilities, transaction laundering, and large-scale financial fraud. At the same time, the emergence of quantum computing introduces a fundamental challenge to classical cryptographic mechanisms particularly Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA that form the backbone of blockchain authentication and transaction verification. This paper presents a comprehensive study of Machine Learning (ML) techniques and Post-Quantum Cryptographic (PQC) frameworks for strengthening blockchain security and threat detection. The study reviews supervised, unsupervised, and deep learning models used for fraud detection, anomaly identification, smart contract vulnerability analysis, and blockchain transaction monitoring. In parallel, it examines quantum-resistant cryptographic algorithms emerging from the NIST post-quantum standardization process, including lattice-based, hash-based, and code-based schemes, and evaluates their suitability for blockchain environments. Furthermore, the paper analyzes the limitations of ML-based security mechanisms and the practical challenges of integrating PQC into decentralized infrastructures, including scalability, key size overhead, and performance trade-offs. A comparative analysis highlights that ML enhances adaptive behavioral threat detection, while PQC ensures long-term cryptographic resilience against quantum attacks. Therefore, the study emphasizes the importance of a hybrid ML–PQC security model that combines intelligent anomaly detection with quantum-resistant cryptographic protection. Finally, the paper identifies key research challenges and outlines future directions toward building scalable, adaptive, and quantum-secure blockchain ecosystems capable of supporting next-generation decentralized applications.
This study determines whether Bitcoin enhances portfolio diversification and serves as a valuable investment asset during the COVID-19 crisis. In particular, we evaluate the significance and magnitude of the risk price associated with Bitcoin’s returns based on the ICAPM and NARDL models. Three methodological approaches were employed. First, we use the Intertemporal Capital Asset Pricing Model (ICAPM) to assess the effect of Bitcoin on a portfolio comprising 25 Fama-French portfolios. Second, a Nonlinear Autoregressive Distributed lag (NARDL) model explores Bitcoin’s impact on cross-sectional variation within the Fama-French portfolios, capturing potential asymmetric responses to price changes. Finally, we determine Bitcoin’s risk premium using the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model (FF3), and the Fama-French five-factor model (FF5). Bitcoin fails to provide significant diversification benefits for profitability factor (RMW), and exhibit insensitivity to value (HML) and investment (CMA). The NARDL model indicates a potential hedging role only during crypto market downturns. The factor models reveal that Bitcoin behaves differently than traditional assets, exhibiting low sensitivity to market risk and a negative relationship with the size premium, further supporting its potential for diversification within specific portfolio contexts. Our finding shows that Bitcoin can protect the 25 Fama-French portfolio when Bitcoin loses value.
Ceyda Yerdelen Kaygın, Musa Gün, Osman Nuri Akarsu, Haşim Bağcı · 5 authors
Forecasting cryptocurrency prices is challenging due to extreme volatility, nonlinear dynamics, and frequent structural shifts in digital asset markets. While recent research increasingly applies deep learning architectures, the predictive advantage of highly complex models in noisy financial environments remains uncertain. This study evaluates the forecasting performance of shallow and deep learning approaches by comparing Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, along with hybrid configurations (GRU + SVM, LSTM + SVM, and GRU + LSTM). Using daily data spanning from 1 October 2020 to 23 September 2025 for five major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Solana, and Ripple—the models are estimated within a consistent framework and assessed using out-of-sample performance metrics, including MAE, MAPE, MSE, and R2. The results indicate that greater algorithmic complexity does not necessarily improve forecasting accuracy. In several cases, the parsimonious SVM model outperforms deep neural network architectures, particularly for highly volatile assets, while hybrid models fail to provide systematic improvements and sometimes amplify prediction errors. SHapley Additive exPlanations analysis further shows that immediate price-based variables dominate predictive power, whereas many lagged technical indicators contribute relatively limited explanatory value. Overall, the findings underscore the importance of algorithmic parsimony, suggesting that simpler machine learning models may deliver more robust forecasts in highly volatile cryptocurrency markets.